From 6977b9d0444a9fcb22dba1e82252a9a4d25f76ff Mon Sep 17 00:00:00 2001 From: Barbie Date: Sat, 6 Jan 2024 17:30:24 -0500 Subject: [PATCH 1/4] birdy: project proposal written, assests included --- _posts/2023-11-11-plankton-representation.md | 126 + .../2023-11-11-plankton-representation.bib | 159 ++ assets/bibliography/My Library.bib | 2462 +++++++++++++++++ .../bibliography/PlanktonRepresentation.bib | 2462 +++++++++++++++++ .../fig-IFCB-examples.png | Bin 0 -> 1015316 bytes .../fig-dataset-class-distribution.png | Bin 0 -> 215407 bytes .../fig-simCLR-diagram.png | Bin 0 -> 168108 bytes .../vandermaaten08a.pdf | Bin 0 -> 3716712 bytes 8 files changed, 5209 insertions(+) create mode 100644 _posts/2023-11-11-plankton-representation.md create mode 100644 assets/bibliography/2023-11-11-plankton-representation.bib create mode 100644 assets/bibliography/My Library.bib create mode 100644 assets/bibliography/PlanktonRepresentation.bib create mode 100644 assets/img/2023-11-11-plankton-representation/fig-IFCB-examples.png create mode 100644 assets/img/2023-11-11-plankton-representation/fig-dataset-class-distribution.png create mode 100644 assets/img/2023-11-11-plankton-representation/fig-simCLR-diagram.png create mode 100644 assets/img/2023-11-11-plankton-representation/vandermaaten08a.pdf diff --git a/_posts/2023-11-11-plankton-representation.md b/_posts/2023-11-11-plankton-representation.md new file mode 100644 index 00000000..8780972c --- /dev/null +++ b/_posts/2023-11-11-plankton-representation.md @@ -0,0 +1,126 @@ +--- +layout: distill +title: Comparing Clustering in Representation Space for Plankton Images +description: Plankton imaging devices are becoming a key method for gathering in-situ data about plankton communities. These instruments can produce millions of images so automatic processes are necessary for extracting information from the images, making the application of machine learning to plankton data a key step in advancing the study of ocean biogeochemistry. In this project I will explore how the representation of plankton images in the latent space differs between classic supervised learning, the unsupervised contrastive learning method descriped in SimCLR, and a modified supervised contrastive learning method. +date: 2023-11-11 +htmlwidgets: true + +# Anonymize when submitting +# authors: +# - name: Anonymous + +authors: + - name: Barbara Duckworth + url: "https://github.com/barbara42" + affiliations: + name: MIT + + +# must be the exact same name as your blogpost +bibliography: 2023-11-11-plankton-representation.bib + +# Add a table of contents to your post. +# - make sure that TOC names match the actual section names +# for hyperlinks within the post to work correctly. +toc: + - name: Equations + - name: Images and Figures + subsections: + - name: Interactive Figures + - name: Citations + - name: Footnotes + - name: Code Blocks + - name: Layouts + - name: Other Typography? + +# Below is an example of injecting additional post-specific styles. +# This is used in the 'Layouts' section of this post. +# If you use this post as a template, delete this _styles block. +_styles: > + .fake-img { + background: #bbb; + border: 1px solid rgba(0, 0, 0, 0.1); + box-shadow: 0 0px 4px rgba(0, 0, 0, 0.1); + margin-bottom: 12px; + } + .fake-img p { + font-family: monospace; + color: white; + text-align: left; + margin: 12px 0; + text-align: center; + font-size: 16px; + } +--- +# Introduction + +Plankton are fundamental to ocean ecosystems, serving as the primary producers in marine food webs and playing a critical role in our planet's carbon cycle . Understanding their distribution and behavior is vital for assessing ocean health and predicting environmental changes . + +Marine imaging devices, such as the Imaging Flow Cytobot, produce millions of datapoints during expeditions, making machine learning methods necessary for analyzing their output . This project aims to advance this analysis by exploring representation learning for plankton images. + +Representation learning, where the system learns to automatically identify and capture the most relevant features of the data, can provide insight into morphological patterns that are not immediately obvious, and improve classification of the organisms. + +Using a new dataset from a 2017 North Pacific research cruise, I will compare the effectiveness of classic supervised learning, unsupervised contrastive learning as described in SimCLR, and a modified supervised contrastive learning method. + +This exploration will potentially offer insights into the latent space representation of plankton imagery, enhancing our understanding of these crucial organisms and improving the efficiency of ecological data processing. + +# Dataset + +{% include figure.html path="assets/img/2023-11-11-plankton-representation/fig-IFCB-examples.png" class="img-fluid" %} + +Data for this project was gathered using the Imaging FlowCytobot (IFCB), an in-situ automated submersible imaging flow cytometer. It generates images of particles within aquatic samples between the size of 10 to 200 microns. These particles might be detritus, organisms, or beads used for calibration . In this dataset, gathered during the Gradients research cruise in the North Pacific in 2017, there are 168,406 images. Each of the images has been classified by a taxonomic expert, Fernanda Freitas, as a part of the Angelique White's research group at the University of Hawaii. There are 170 unique classes, with a handful of classes dominating the data. The distribution of samples per class can be seen in the figure below. + +{% include figure.html path="assets/img/2023-11-11-plankton-representation/fig-dataset-class-distribution.png" class="img-fluid" %} + +# Methodologies + +## Baseline classifier + +To lighten the workload of the project, I will use a codebase developed by a team at WHOI that is designed to work with IFCB data - [WHOI IFCB classifier](https://github.com/WHOIGit/ifcb_classifier). This CNN classifier provides the option to use a number of namebrand models as the backbone, and I will be using ResNet . The WHOI IFCB classifier first resizes all images, and I will be using the standard class balancing and augmentation techniques it provides. + +## SimCLR + +SimCLR is an unsupervised contrastive learning method for visual data. The labels of the dataset are ignored, and each individual image will have a "postive" pair generated from a set of augmentations. It is then assumed that all other images in a batch are "negative". Positive pairs are pulled together and pushed apart from the negatives in representation space using the NT-Xent (normalized temperature-scaled cross entropy) loss function. For the project, the base encoder will be ResNet, and subsequent projection head will be a 2-layer MLP, with all other settings based on the defualts descriped in the SimCLR paper. + +{% include figure.html path="assets/img/2023-11-11-plankton-representation/fig-simCLR-diagram.png" class="img-fluid" %} + +The NT-Xent loss function is defined as follows: + +$$ +\ell_{i,j} = -\log \left( \frac{\exp(\text{sim}(z_i, z_j)/T)}{\sum_{k=1}^{2N} \mathbb{1}_{[k \neq j]} \exp(\text{sim}(z_i, z_k)/T)} \right) +$$ + + +where $z_i$ and $z_j$ are the positive pairs, $z_k$ is a negative pair, and $\tau$ is the temerature parameter. + + +## Supervised Constrastive Learning + +Instead of just using data augmentation to create a positive pair $x_j$ for image $x_i$, the positive match for image $x_i$ will be chosen from the pool of images with the same class label as $x_i$. Similarly, the negative matches will not be the full set of data, but rather only from classes that $x_i$ does not belong to. + +The resulting subervised normalized temperature-scaled cross entropy loss function (SNT-Xent) is defined as follows: + +$$ +\ell_{i,j} = -\log \left( \frac{\exp(\text{sim}(z_i, z_j)/\tau)}{\sum_{k=1}^{2N} \mathbb{C}_{[i, k]} \exp(\text{sim}(z_i, z_k)/\tau)} \right) +$$ + +where $\mathbb{C}_{[i, k]}$ is a function that evaluates to 1 if the class $C_j$ of the image $z_j$ is not the same as the class $C_i$ of the image $x_i$. + +$$ +\mathbb{C}_{[i, k]} = + +\begin{cases} +1 & \text{if } C_i \neq C_j \\ +0 & \text{if } C_i = C_j \\ +\end{cases} +$$ + +I am choosing not to pull all images from the same class together do to computational constraints. Instead, $z_j$ is randomly chosen from the pool of images with the same class as $z_i$. + +## Representation Space Analysis + +Latent space representations will be extracted from each of the models at the layer preceeding the last fully connected layer. + +I will use t-SNE (t-Distributed Stochastic Neighbor Embedding) to visualize the feature vectors. + +I will then use K-means clustering and compare the emergent groups to the labels in the dataset. \ No newline at end of file diff --git a/assets/bibliography/2023-11-11-plankton-representation.bib b/assets/bibliography/2023-11-11-plankton-representation.bib new file mode 100644 index 00000000..cdcbf5e6 --- /dev/null +++ b/assets/bibliography/2023-11-11-plankton-representation.bib @@ -0,0 +1,159 @@ +@article{falkowski_role_1994, + title = {The role of phytoplankton photosynthesis in global biogeochemical cycles}, + volume = {39}, + issn = {1573-5079}, + url = {https://doi.org/10.1007/BF00014586}, + doi = {10.1007/BF00014586}, + abstract = {Phytoplankton biomass in the world's oceans amounts to only ∽1–2\% of the total global plant carbon, yet these organisms fix between 30 and 50 billion metric tons of carbon annually, which is about 40\% of the total. On geological time scales there is profound evidence of the importance of phytoplankton photosynthesis in biogeochemical cycles. It is generally assumed that present phytoplankton productivity is in a quasi steady-state (on the time scale of decades). However, in a global context, the stability of oceanic photosynthetic processes is dependent on the physical circulation of the upper ocean and is therefore strongly influenced by the atmosphere. The net flux of atmospheric radiation is critical to determining the depth of the upper mixed layer and the vertical fluxes of nutrients. These latter two parameters are keys to determining the intensity, and spatial and temporal distributions of phytoplankton blooms. Atmospheric radiation budgets are not in steady-state. Driven largely by anthropogenic activities in the 20th century, increased levels of IR- absorbing gases such as CO2, CH4 and CFC's and NOx will potentially increase atmospheric temperatures on a global scale. The atmospheric radiation budget can affect phytoplankton photosynthesis directly and indirectly. Increased temperature differences between the continents and oceans have been implicated in higher wind stresses at the ocean margins. Increased wind speeds can lead to higher nutrient fluxes. Throughout most of the central oceans, nitrate concentrations are sub-micromolar and there is strong evidence that the quantum efficiency of Photosystem II is impaired by nutrient stress. Higher nutrient fluxes would lead to both an increase in phytoplankton biomass and higher biomass-specific rates of carbon fixation. However, in the center of the ocean gyres, increased radiative heating could reduce the vertical flux of nutrients to the euphotic zone, and hence lead to a reduction in phytoplankton carbon fixation. Increased desertification in terrestrial ecosystems can lead to increased aeolean loadings of essential micronutrients, such as iron. An increased flux of aeolean micronutrients could fertilize nutrient-replete areas of the open ocean with limiting trace elements, thereby stimulating photosynthetic rates. The factors which limit phytoplankton biomass and photosynthesis are discussed and examined with regard to potential changes in the Earth climate system which can lead the oceans away from steady-state. While it is difficult to confidently deduce changes in either phytoplankton biomass or photosynthetic rates on decadal time scales, time-series analysis of ocean transparency data suggest long-term trends have occurred in the North Pacific Ocean in the 20th century. However, calculations of net carbon uptake by the oceans resulting from phytoplankton photosynthesis suggest that without a supply of nutrients external to the ocean, carbon fixation in the open ocean is not presently a significant sink for excess atmospheric CO2.}, + language = {en}, + number = {3}, + urldate = {2024-01-06}, + journal = {Photosynthesis Research}, + author = {Falkowski, Paul G.}, + month = mar, + year = {1994}, + keywords = {biogeochemical cycles, oceans, photoacclimation, Photosystem II nutrient limitation, phytoplankton quantum efficiency of photosynthesis}, + pages = {235--258}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/6ECKTMBR/Falkowski - 1994 - The role of phytoplankton photosynthesis in global.pdf:application/pdf}, +} + +@article{treguer_influence_2018, + title = {Influence of diatom diversity on the ocean biological carbon pump}, + volume = {11}, + copyright = {2017 Springer Nature Limited}, + issn = {1752-0908}, + url = {https://www.nature.com/articles/s41561-017-0028-x}, + doi = {10.1038/s41561-017-0028-x}, + abstract = {Diatoms sustain the marine food web and contribute to the export of carbon from the surface ocean to depth. They account for about 40\% of marine primary productivity and particulate carbon exported to depth as part of the biological pump. Diatoms have long been known to be abundant in turbulent, nutrient-rich waters, but observations and simulations indicate that they are dominant also in meso- and submesoscale structures such as fronts and filaments, and in the deep chlorophyll maximum. Diatoms vary widely in size, morphology and elemental composition, all of which control the quality, quantity and sinking speed of biogenic matter to depth. In particular, their silica shells provide ballast to marine snow and faecal pellets, and can help transport carbon to both the mesopelagic layer and deep ocean. Herein we show that the extent to which diatoms contribute to the export of carbon varies by diatom type, with carbon transfer modulated by the Si/C ratio of diatom cells, the thickness of the shells and their life strategies; for instance, the tendency to form aggregates or resting spores. Model simulations project a decline in the contribution of diatoms to primary production everywhere outside of the Southern Ocean. We argue that we need to understand changes in diatom diversity, life cycle and plankton interactions in a warmer and more acidic ocean in much more detail to fully assess any changes in their contribution to the biological pump.}, + language = {en}, + number = {1}, + urldate = {2024-01-06}, + journal = {Nature Geoscience}, + author = {Tréguer, Paul and Bowler, Chris and Moriceau, Brivaela and Dutkiewicz, Stephanie and Gehlen, Marion and Aumont, Olivier and Bittner, Lucie and Dugdale, Richard and Finkel, Zoe and Iudicone, Daniele and Jahn, Oliver and Guidi, Lionel and Lasbleiz, Marine and Leblanc, Karine and Levy, Marina and Pondaven, Philippe}, + month = jan, + year = {2018}, + note = {Number: 1 +Publisher: Nature Publishing Group}, + keywords = {Biogeochemistry, Carbon cycle, Marine biology, Ocean sciences}, + pages = {27--37}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/VLLSAC7H/Tréguer et al. - 2018 - Influence of diatom diversity on the ocean biologi.pdf:application/pdf}, +} + +@article{olson_submersible_2007, + title = {A submersible imaging-in-flow instrument to analyze nano-and microplankton: {Imaging} {FlowCytobot}}, + volume = {5}, + issn = {1541-5856}, + shorttitle = {A submersible imaging-in-flow instrument to analyze nano-and microplankton}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lom.2007.5.195}, + doi = {10.4319/lom.2007.5.195}, + abstract = {A fundamental understanding of the interaction between physical and biological factors that regulate plankton species composition requires, first of all, detailed and sustained observations. Only now is it becoming possible to acquire these types of observations, as we develop and deploy instruments that can continuously monitor individual organisms in the ocean. Our research group can measure and count the smallest phytoplankton cells using a submersible flow cytometer (FlowCytobot), in which optical properties of individual suspended cells are recorded as they pass through a focused laser beam. However, FlowCytobot cannot efficiently sample or identify the much larger cells (10 to {\textgreater}100 µm) that often dominate the plankton in coastal waters. Because these larger cells often have recognizable morphologies, we have developed a second submersible flow cytometer, with imaging capability and increased water sampling rate (typically, 5 mL seawater analyzed every 20 min), to characterize these nano- and microplankton. Like the original, Imaging FlowCytobot can operate unattended for months at a time; it obtains power from and communicates with a shore laboratory, so we can monitor results and modify sampling procedures when needed. Imaging FlowCytobot was successfully tested for 2 months in Woods Hole Harbor and is presently deployed alongside FlowCytobot at the Martha's Vineyard Coastal Observatory. These combined approaches will allow continuous long-term observations of plankton community structure over a wide range of cell sizes and types, and help to elucidate the processes and interactions that control the life cycles of individual species.}, + language = {en}, + number = {6}, + urldate = {2022-10-20}, + journal = {Limnology and Oceanography: Methods}, + author = {Olson, Robert J. and Sosik, Heidi M.}, + year = {2007}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lom.2007.5.195}, + pages = {195--203}, + file = {Full Text:/Users/birdy/Zotero/storage/JP4F24FI/Olson and Sosik - 2007 - A submersible imaging-in-flow instrument to analyz.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/9E2FX4K7/lom.2007.5.html:text/html}, +} + +@misc{sosik_automated_2007, + title = {Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry}, + volume = {5}, + issn = {1541-5856}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lom.2007.5.204}, + doi = {10.4319/lom.2007.5.204}, + abstract = {High-resolution photomicrographs of phytoplankton cells and chains can now be acquired with imaging-in-flow systems at rates that make manual identification impractical for many applications. To address the challenge for automated taxonomic identification of images generated by our custom-built submersible Imaging FlowCytobot, we developed an approach that relies on extraction of image features, which are then presented to a machine learning algorithm for classification. Our approach uses a combination of image feature types including size, shape, symmetry, and texture characteristics, plus orientation invariant moments, diffraction pattern sampling, and co-occurrence matrix statistics. Some of these features required preprocessing with image analysis techniques including edge detection after phase congruency calculations, morphological operations, boundary representation and simplification, and rotation. For the machine learning strategy, we developed an approach that combines a feature selection algorithm and use of a support vector machine specified with a rigorous parameter selection and training approach. After training, a 22-category classifier provides 88\% overall accuracy for an independent test set, with individual category accuracies ranging from 68\% to 99\%. We demonstrate application of this classifier to a nearly uninterrupted 2-month time series of images acquired in Woods Hole Harbor, including use of statistical error correction to derive quantitative concentration estimates, which are shown to be unbiased with respect to manual estimates for random subsamples. Our approach, which provides taxonomically resolved estimates of phytoplankton abundance with fine temporal resolution (hours for many species), permits access to scales of variability from tidal to seasonal and longer.}, + language = {en}, + number = {6}, + urldate = {2022-10-20}, + journal = {Limnology and Oceanography: Methods}, + author = {Sosik, Heidi M. and Olson, Robert J.}, + year = {2007}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lom.2007.5.204}, + pages = {204--216}, + file = {Full Text:/Users/birdy/Zotero/storage/6E2WBCLM/Sosik and Olson - 2007 - Automated taxonomic classification of phytoplankto.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/UIZ3PRI8/lom.2007.5.html:text/html}, +} + +@misc{bengio_representation_2014, + title = {Representation {Learning}: {A} {Review} and {New} {Perspectives}}, + shorttitle = {Representation {Learning}}, + url = {http://arxiv.org/abs/1206.5538}, + abstract = {The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning.}, + urldate = {2024-01-06}, + publisher = {arXiv}, + author = {Bengio, Yoshua and Courville, Aaron and Vincent, Pascal}, + month = apr, + year = {2014}, + note = {arXiv:1206.5538 [cs]}, + keywords = {Computer Science - Machine Learning}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/EYA2B52H/1206.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/2CFSH6MT/Bengio et al. - 2014 - Representation Learning A Review and New Perspect.pdf:application/pdf}, +} + +@misc{white_gradients2-mgl1704-ifcb-abundance_2020-04-01_v10_2020, + title = {Gradients2-{MGL1704}-{IFCB}-{Abundance}\_2020-04-01\_v1.0}, + url = {https://zenodo.org/records/4267140}, + doi = {10.5281/zenodo.4267140}, + abstract = {Cruise: Gradients 2, MGL1704 Project Name: Simons Foundation, Gradient NPSG Dataset Description: The Imaging FlowCytoBot (IFCB) is an in situ automated imaging flow cytometer that generates images of particles suspended in seawater, in this case from the underway uncontaminated seawater system aboard the R/V Langseth (intake 5m).   The IFCB uses a recycled sheath fluid (0.2 µm filtered seawater) to align and drive particles individually towards a light source (red laser, 4.5 mW) in order to detect and identify single or colonial cells using a combination of optical properties (red fluorescence and light scattering intensities) and high resolution images (3.2 pixels per micron) by a mounted camera. Both optical properties are used to trigger targeted image acquisition of suspended particles in the size range {\textless}4 to 100 μm. The instrument continuously samples (few seconds) from  {\textasciitilde}5 ml aliquots from the intake, and processes all particles contained in that volume for the next 20 mins.  Images corresponding to the "Sample" variable are available on the cruise's dashboard (http://ifcb-data.soest.hawaii.edu/IFCB\_NPTZ).  This dataset is for the abundance of imaged cells by genus.  For each sample, the total number of cells classified to the genus-level by a random forest algorithm (Sosik and Olson, 2007 doi:10.4319/lom.2007.5.204) is counted and divided by the corresponding volume analyzed ({\textasciitilde}5 mL). Note that we used all the images collected during Gradients 2.0 to train the random forest algorithm and that classification is therefore highly accurate for this dataset. Using 7 µm calibration beads, we estimated that the error of cell concentration due to cell detection during sample acquisition averages 11 ± 10 \%, independently of concentrations in the range 1-10000 cell/mL.}, + urldate = {2024-01-06}, + publisher = {Zenodo}, + author = {White, Angelicque}, + month = apr, + year = {2020}, + file = {Zenodo Snapshot:/Users/birdy/Zotero/storage/456EVCSQ/4267140.html:text/html}, +} + +@article{he_deep_2016, + title = {Deep {Residual} {Learning} for {Image} {Recognition}}, + url = {https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html}, + urldate = {2024-01-06}, + author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, + year = {2016}, + pages = {770--778}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/RYXX2LD8/He et al. - 2016 - Deep Residual Learning for Image Recognition.pdf:application/pdf}, +} + +@article{chen_simple_2020, + title = {A {Simple} {Framework} for {Contrastive} {Learning} of {Visual} {Representations}}, + url = {http://arxiv.org/abs/2002.05709}, + abstract = {This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5\% top-1 accuracy, which is a 7\% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1\% of the labels, we achieve 85.8\% top-5 accuracy, outperforming AlexNet with 100X fewer labels.}, + urldate = {2024-01-04}, + publisher = {arXiv}, + author = {Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey}, + month = jun, + year = {2020}, + note = {arXiv:2002.05709 [cs, stat]}, + keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/QSFZN5RI/2002.html:text/html;Chen et al. - 2020 - A Simple Framework for Contrastive Learning of Vis.pdf:/Users/birdy/Zotero/storage/UA8HJXR5/Chen et al. - 2020 - A Simple Framework for Contrastive Learning of Vis.pdf:application/pdf}, +} + +@article{vanderMaaten2008tsne, + title={Visualizing Data using t-SNE}, + author={van der Maaten, Laurens and Hinton, Geoffrey}, + journal={Journal of Machine Learning Research}, + volume={9}, + pages={2579--2605}, + year={2008} +} + + +@article{kanungo_efficient_2002, + title = {An efficient k-means clustering algorithm: analysis and implementation}, + volume = {24}, + issn = {1939-3539}, + shorttitle = {An efficient k-means clustering algorithm}, + url = {https://ieeexplore.ieee.org/document/1017616}, + doi = {10.1109/TPAMI.2002.1017616}, + abstract = {In k-means clustering, we are given a set of n data points in d-dimensional space R/sup d/ and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd's (1982) algorithm. We present a simple and efficient implementation of Lloyd's k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm's running time, which shows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in color quantization, data compression, and image segmentation.}, + number = {7}, + urldate = {2024-01-06}, + journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, + author = {Kanungo, T. and Mount, D.M. and Netanyahu, N.S. and Piatko, C.D. and Silverman, R. and Wu, A.Y.}, + month = jul, + year = {2002}, + note = {Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence}, + pages = {881--892}, + file = {IEEE Xplore Abstract Record:/Users/birdy/Zotero/storage/RC5LGQ6V/1017616.html:text/html;IEEE Xplore Full Text PDF:/Users/birdy/Zotero/storage/NUTPDLG4/Kanungo et al. - 2002 - An efficient k-means clustering algorithm analysi.pdf:application/pdf}, +} + diff --git a/assets/bibliography/My Library.bib b/assets/bibliography/My Library.bib new file mode 100644 index 00000000..b201fa10 --- /dev/null +++ b/assets/bibliography/My Library.bib @@ -0,0 +1,2462 @@ + +@article{araujo_validation_2005, + title = {Validation of species–climate impact models under climate change}, + volume = {11}, + issn = {1365-2486}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-2486.2005.01000.x}, + doi = {10.1111/j.1365-2486.2005.01000.x}, + abstract = {Increasing concern over the implications of climate change for biodiversity has led to the use of species–climate envelope models to project species extinction risk under climate-change scenarios. However, recent studies have demonstrated significant variability in model predictions and there remains a pressing need to validate models and to reduce uncertainties. Model validation is problematic as predictions are made for events that have not yet occurred. Resubstituition and data partitioning of present-day data sets are, therefore, commonly used to test the predictive performance of models. However, these approaches suffer from the problems of spatial and temporal autocorrelation in the calibration and validation sets. Using observed distribution shifts among 116 British breeding-bird species over the past ∼20 years, we are able to provide a first independent validation of four envelope modelling techniques under climate change. Results showed good to fair predictive performance on independent validation, although rules used to assess model performance are difficult to interpret in a decision-planning context. We also showed that measures of performance on nonindependent data provided optimistic estimates of models' predictive ability on independent data. Artificial neural networks and generalized additive models provided generally more accurate predictions of species range shifts than generalized linear models or classification tree analysis. Data for independent model validation and replication of this study are rare and we argue that perfect validation may not in fact be conceptually possible. We also note that usefulness of models is contingent on both the questions being asked and the techniques used. Implementations of species–climate envelope models for testing hypotheses and predicting future events may prove wrong, while being potentially useful if put into appropriate context.}, + language = {en}, + number = {9}, + urldate = {2021-12-15}, + journal = {Global Change Biology}, + author = {Araújo, Miguel B. and Pearson, Richard G. and Thuiller, Wilfried and Erhard, Markus}, + year = {2005}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2486.2005.01000.x}, + keywords = {bioclimatic-envelope models, breeding birds, Britain, climate change, model accuracy, uncertainty, validation, unread, not printed}, + pages = {1504--1513}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/6EVK36UH/Araújo et al. - 2005 - Validation of species–climate impact models under .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/C68Y6MBK/j.1365-2486.2005.01000.html:text/html}, +} + +@incollection{kolter_unveiling_2012, + title = {Unveiling {Prochlorococcus}}, + isbn = {978-1-55581-540-0 978-1-55581-847-0}, + url = {http://www.asmscience.org/content/book/10.1128/9781555818470.chap23}, + language = {en}, + urldate = {2021-12-15}, + booktitle = {Microbes and {Evolution}}, + publisher = {American Society of Microbiology}, + author = {Chisholm, Sallie W.}, + editor = {{Kolter} and {Maloy}}, + month = jan, + year = {2012}, + doi = {10.1128/9781555818470.ch23}, + keywords = {read, printed}, + pages = {165--171}, + file = {Chisholm - 2012 - Unveiling Prochlorococcus.pdf:/Users/birdy/Zotero/storage/D2MZS4I5/Chisholm - 2012 - Unveiling Prochlorococcus.pdf:application/pdf}, +} + +@article{follett_moving_2021, + title = {Moving ecological and biogeochemical transitions across the {North} {Pacific}}, + volume = {66}, + issn = {1939-5590}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lno.11763}, + doi = {10.1002/lno.11763}, + abstract = {In the North Pacific Ocean, nutrient rich surface waters flow south from the subpolar gyre through a transitional region and into the subtropics. Along the way, nutrients are used, recycled, and exported, leading to lower biomass and a commensurate change in ecosystem structure moving southward. We focus on the region between the two gyres (the Transition Zone) using a coupled biophysical ocean model, remote sensing, floats, and cruise data to explore the nature of the physical, biogeochemical, and ecological fields in this region. Nonlinear interactions between biological processes and the meridional gradient in nutrient supply lead to sharp shifts across this zone. These transitions between a southern region with more uniform biological and biogeochemical properties and steep meridional gradients to the north are diagnosed from extrema in the first derivative of the properties with latitude. Some transitions like that for chlorophyll a (the transition zone chlorophyll front [TZCF]) experience large seasonal excursions while the location of the transitions in other properties moves very little. The seasonal shifts are not caused by changes in the horizontal flow field, but rather by the interaction of seasonal, depth related, forcing with the mean latitudinal gradients. Focusing on the TZCF as a case study, we express its phase velocity in terms of vertical nutrient flux and internal ecosystem processes, demonstrating their nearly equal influence on its motion. This framework of propagating biogeochemical transitions can be systematically expanded to better understand the processes that structure ecosystems and biogeochemistry in the North Pacific and beyond.}, + language = {en}, + number = {6}, + urldate = {2021-12-15}, + journal = {Limnology and Oceanography}, + author = {Follett, Christopher L. and Dutkiewicz, Stephanie and Forget, Gael and Cael, B. B. and Follows, Michael J.}, + year = {2021}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lno.11763}, + keywords = {read, darwin, printed}, + pages = {2442--2454}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/XUKLUSS4/Follett et al. - 2021 - Moving ecological and biogeochemical transitions a.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/MASWCC2S/lno.html:text/html}, +} + +@article{guisan_predictive_2000, + title = {Predictive habitat distribution models in ecology}, + volume = {135}, + issn = {03043800}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0304380000003549}, + doi = {10.1016/S0304-3800(00)00354-9}, + abstract = {With the rise of new powerful statistical techniques and GIS tools, the development of predictive habitat distribution models has rapidly increased in ecology. Such models are static and probabilistic in nature, since they statistically relate the geographical distribution of species or communities to their present environment. A wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate change research, and habitat or species management. In this paper, we present a review of predictive habitat distribution modeling. The variety of statistical techniques used is growing. Ordinary multiple regression and its generalized form (GLM) are very popular and are often used for modeling species distributions. Other methods include neural networks, ordination and classification methods, Bayesian models, locally weighted approaches (e.g. GAM), environmental envelopes or even combinations of these models. The selection of an appropriate method should not depend solely on statistical considerations. Some models are better suited to reflect theoretical findings on the shape and nature of the species’ response (or realized niche). Conceptual considerations include e.g. the trade-off between optimizing accuracy versus optimizing generality. In the field of static distribution modeling, the latter is mostly related to selecting appropriate predictor variables and to designing an appropriate procedure for model selection. New methods, including threshold-independent measures (e.g. receiver operating characteristic (ROC)-plots) and resampling techniques (e.g. bootstrap, cross-validation) have been introduced in ecology for testing the accuracy of predictive models. The choice of an evaluation measure should be driven primarily by the goals of the study. This may possibly lead to the attribution of different weights to the various types of prediction errors (e.g. omission, commission or confusion). Testing the model in a wider range of situations (in space and time) will permit one to define the range of applications for which the model predictions are suitable. In turn, the qualification of the model depends primarily on the goals of the study that define the qualification criteria and on the usability of the model, rather than on statistics alone. © 2000 Elsevier Science B.V. All rights reserved.}, + language = {en}, + number = {2-3}, + urldate = {2021-12-15}, + journal = {Ecological Modelling}, + author = {Guisan, Antoine and Zimmermann, Niklaus E.}, + month = dec, + year = {2000}, + keywords = {unread, sdm, printed}, + pages = {147--186}, + file = {Guisan and Zimmermann - 2000 - Predictive habitat distribution models in ecology.pdf:/Users/birdy/Zotero/storage/SGKKM6TL/Guisan and Zimmermann - 2000 - Predictive habitat distribution models in ecology.pdf:application/pdf}, +} + +@article{bardon_testing_2021, + title = {Testing the {Skill} of a {Species} {Distribution} {Model} {Using} a 21st {Century} {Virtual} {Ecosystem}}, + volume = {48}, + issn = {1944-8007}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2021GL093455}, + doi = {10.1029/2021GL093455}, + abstract = {Plankton communities play an important role in marine food webs, in biogeochemical cycling, and in Earth's climate; yet observations are sparse, and predictions of how they might respond to climate change vary. Correlative species distribution models (SDM's) have been applied to predicting biogeography based on relationships to observed environmental variables. To investigate sources of uncertainty, we use a correlative SDM to predict the plankton biogeography of a 21st century marine ecosystem model (Darwin). Darwin output is sampled to mimic historical ocean observations, and the SDM is trained using generalized additive models. We find that predictive skill varies across test cases, and between functional groups, with errors that are more attributable to spatiotemporal sampling bias than sample size. End-of-century predictions are poor, limited by changes in target-predictor relationships over time. Our findings illustrate the fundamental challenges faced by empirical models in using limited observational data to predict complex, dynamic systems.}, + language = {en}, + number = {22}, + urldate = {2021-12-15}, + journal = {Geophysical Research Letters}, + author = {Bardon, L. R. and Ward, B. A. and Dutkiewicz, S. and Cael, B. B.}, + year = {2021}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021GL093455}, + keywords = {climate change, unread, darwin, sdm, complex systems, numerical models, oceanography, plankton ecology, statistical learning, printed}, + pages = {e2021GL093455}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/RKQ6X8MD/Bardon et al. - 2021 - Testing the Skill of a Species Distribution Model .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/53EFUGZ7/2021GL093455.html:text/html}, +} + +@article{elith_species_2009, + title = {Species {Distribution} {Models}: {Ecological} {Explanation} and {Prediction} {Across} {Space} and {Time}}, + volume = {40}, + shorttitle = {Species {Distribution} {Models}}, + url = {https://doi.org/10.1146/annurev.ecolsys.110308.120159}, + doi = {10.1146/annurev.ecolsys.110308.120159}, + abstract = {Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in “established use.” Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.}, + number = {1}, + urldate = {2021-12-15}, + journal = {Annual Review of Ecology, Evolution, and Systematics}, + author = {Elith, Jane and Leathwick, John R.}, + year = {2009}, + note = {\_eprint: https://doi.org/10.1146/annurev.ecolsys.110308.120159}, + keywords = {read, sdm, printed}, + pages = {677--697}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/94HVWZRY/Elith and Leathwick - 2009 - Species Distribution Models Ecological Explanatio.pdf:application/pdf}, +} + +@article{henson_future_2021, + title = {Future phytoplankton diversity in a changing climate}, + volume = {12}, + issn = {2041-1723}, + url = {https://www.nature.com/articles/s41467-021-25699-w}, + doi = {10.1038/s41467-021-25699-w}, + abstract = {Abstract + The future response of marine ecosystem diversity to continued anthropogenic forcing is poorly constrained. Phytoplankton are a diverse set of organisms that form the base of the marine ecosystem. Currently, ocean biogeochemistry and ecosystem models used for climate change projections typically include only 2−3 phytoplankton types and are, therefore, too simple to adequately assess the potential for changes in plankton community structure. Here, we analyse a complex ecosystem model with 35 phytoplankton types to evaluate the changes in phytoplankton community composition, turnover and size structure over the 21st century. We find that the rate of turnover in the phytoplankton community becomes faster during this century, that is, the community structure becomes increasingly unstable in response to climate change. Combined with alterations to phytoplankton diversity, our results imply a loss of ecological resilience with likely knock-on effects on the productivity and functioning of the marine environment.}, + language = {en}, + number = {1}, + urldate = {2021-12-15}, + journal = {Nature Communications}, + author = {Henson, Stephanie A. and Cael, B. B. and Allen, Stephanie R. and Dutkiewicz, Stephanie}, + month = dec, + year = {2021}, + keywords = {unread, not printed}, + pages = {5372}, + file = {Henson et al. - 2021 - Future phytoplankton diversity in a changing clima.pdf:/Users/birdy/Zotero/storage/GLZDZK9S/Henson et al. - 2021 - Future phytoplankton diversity in a changing clima.pdf:application/pdf}, +} + +@article{wu_modeling_2021, + title = {Modeling {Photosynthesis} and {Exudation} in {Subtropical} {Oceans}}, + volume = {35}, + issn = {1944-9224}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2021GB006941}, + doi = {10.1029/2021GB006941}, + abstract = {Marine phytoplankton contributes nearly half of the total primary production on Earth through photosynthesis. Parameterizations of algal photosynthesis commonly employed in global biogeochemical simulations generally fail to capture the observed vertical structure of primary production. Here we examined the consequences of decoupling photosynthesis (carbon fixation) and biosynthesis (biomass building) with accumulation or exudation of excess photosynthate under energy rich conditions in both regional and global models. The results show that the decoupling of these two processes improved the simulated vertical profile of primary production, increased modeled primary production over 30\% globally and over 40\% in subtropical oceans, improved simulated meridional patterns of particulate C:N:P and increased modeled surface pool of labile/semi-labile dissolved organic carbon. More generally, these results highlight the importance of exudation, which results from the decoupling of photosynthesis and biosynthesis, as a major physiological process affecting ocean biogeochemistry.}, + language = {en}, + number = {9}, + urldate = {2021-12-15}, + journal = {Global Biogeochemical Cycles}, + author = {Wu, Zhen and Dutkiewicz, Stephanie and Jahn, Oliver and Sher, Daniel and White, Angelicque and Follows, Michael J.}, + year = {2021}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021GB006941}, + keywords = {unread, not printed, biogeochemical model, exudation, photosynthesis, phytoplankton, primary production, subtropical gyres, EAPS}, + pages = {e2021GB006941}, +} + +@article{ser-giacomi_lagrangian_2021, + title = {Lagrangian betweenness as a measure of bottlenecks in dynamical systems with oceanographic examples}, + volume = {12}, + issn = {2041-1723}, + url = {https://www.nature.com/articles/s41467-021-25155-9}, + doi = {10.1038/s41467-021-25155-9}, + abstract = {Abstract + The study of connectivity patterns in networks has brought novel insights across diverse fields ranging from neurosciences to epidemic spreading or climate. In this context, betweenness centrality has demonstrated to be a very effective measure to identify nodes that act as focus of congestion, or bottlenecks, in the network. However, there is not a way to define betweenness outside the network framework. By analytically linking dynamical systems and network theory, we provide a trajectory-based formulation of betweenness, called Lagrangian betweenness, as a function of Lyapunov exponents. This extends the concept of betweenness beyond the context of network theory relating hyperbolic points and heteroclinic connections in any dynamical system to the structural bottlenecks of the network associated with it. Using modeled and observational velocity fields, we show that such bottlenecks are present and surprisingly persistent in the oceanic circulation across different spatio-temporal scales and we illustrate the role of these areas in driving fluid transport over vast oceanic regions. Analyzing plankton abundance data from the Kuroshio region of the Pacific Ocean, we find significant spatial correlations between measures of diversity and betweenness, suggesting promise for ecological applications.}, + language = {en}, + number = {1}, + urldate = {2021-12-15}, + journal = {Nature Communications}, + author = {Ser-Giacomi, Enrico and Baudena, Alberto and Rossi, Vincent and Follows, Mick and Clayton, Sophie and Vasile, Ruggero and López, Cristóbal and Hernández-García, Emilio}, + month = dec, + year = {2021}, + keywords = {unread, not printed, EAPS}, + pages = {4935}, + file = {Ser-Giacomi et al. - 2021 - Lagrangian betweenness as a measure of bottlenecks.pdf:/Users/birdy/Zotero/storage/ENVD6A4Z/Ser-Giacomi et al. - 2021 - Lagrangian betweenness as a measure of bottlenecks.pdf:application/pdf}, +} + +@article{juranek_importance_2020, + title = {The {Importance} of the {Phytoplankton} “{Middle} {Class}” to {Ocean} {Net} {Community} {Production}}, + volume = {34}, + issn = {1944-9224}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2020GB006702}, + doi = {10.1029/2020GB006702}, + abstract = {The net balance between photosynthesis and respiration in the surface ocean is a key regulator of ocean-atmosphere carbon dioxide (CO2) partitioning, and by extension, Earth's climate. The slight excess of photosynthesis over community respiration in sunlit waters, known as net community production (NCP), sets the upper bound on the sequestration of carbon via biologically mediated export. Prevailing paradigms suggest a high/low binary where net primary production (NPP), NCP, and export are highest in ecosystems characterized by microplankton ({\textgreater}20 μm) and lowest in ecosystems dominated by picoplankton ({\textless}2 μm). This bifurcation model neglects the potential importance of nanoplankton (2–20 μm)—i.e., the “middle” size class—toward global biological pump functioning. Here, we show a relationship between the biomass of nanoplankton and oxygen-based estimates of NCP across natural ecological gradients in the North Pacific Ocean. Using a suite of high-resolution optical imaging approaches including SeaFlow, Imaging FlowCytobot, and laser-based scattering, nanoplankton dynamics are observed to dominate the particle size distribution throughout an 1,000 km transition between the subtropical and subpolar North Pacific, where NCP rates are threefold to fivefold higher than subtropical values. Based on ecological theory applied to the Darwin size-based ecosystem model, we hypothesize that intermediate size class organisms are capable of high rates of production via an optimization of bottom-up and top-down control inherent to the “middle class.” More broadly, the model indicates the global importance of nanoplankton for ocean biological production.}, + language = {en}, + number = {12}, + urldate = {2021-12-15}, + journal = {Global Biogeochemical Cycles}, + author = {Juranek, Lauren W. and White, Angelicque E. and Dugenne, Mathilde and Henderikx Freitas, Fernanda and Dutkiewicz, Stephanie and Ribalet, Francois and Ferrón, Sara and Armbrust, E. Virginia and Karl, David M.}, + year = {2020}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020GB006702}, + keywords = {read, not printed, EAPS}, + pages = {e2020GB006702}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/FI7EY7HA/Juranek et al. - 2020 - The Importance of the Phytoplankton “Middle Class”.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/48CTG827/2020GB006702.html:text/html}, +} + +@article{kempes_growth_2012, + title = {Growth, metabolic partitioning, and the size of microorganisms}, + volume = {109}, + copyright = {© . Freely available online through the PNAS open access option.}, + issn = {0027-8424, 1091-6490}, + url = {https://www.pnas.org/content/109/2/495}, + doi = {10.1073/pnas.1115585109}, + abstract = {Population growth rate is a fundamental ecological and evolutionary characteristic of living organisms, but individuals must balance the metabolism devoted to biosynthesis and reproduction against the maintenance of existing structure and other functionality. Here we present a mathematical model that relates metabolic partitioning to the form of growth. The model captures the observed growth trajectory of single cells and individuals for a variety of species and taxa spanning prokaryotes, unicellular eukaryotes, and small multicellular eukaryotes. Our analysis suggests that the per-unit costs of biosynthesis and maintenance are conserved across prokaryotes and eukaryotes. However, the relative metabolic expenditure on growth and maintenance of whole organisms clearly differentiates taxa: prokaryotes spend an increasing fraction of their entire metabolism on growth with increasing cell size, whereas eukaryotes devote a diminishing fraction. These differences allow us to predict the minimum and maximum size for each taxonomic group, anticipating observed evolutionary life-history transitions. The framework provides energetic insights into taxonomic tradeoffs related to growth and metabolism and constrains traits that are important for size-structured modeling of microbial communities and their ecological and biogeochemical effects.}, + language = {en}, + number = {2}, + urldate = {2022-01-28}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Kempes, Christopher P. and Dutkiewicz, Stephanie and Follows, Michael J.}, + month = jan, + year = {2012}, + pmid = {22203990}, + note = {Publisher: National Academy of Sciences +Section: Biological Sciences}, + keywords = {unread, evolutionary transitions, maintenance metabolism, metabolic ecology, ontogenetic growth, single cell growth}, + pages = {495--500}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/72H8W27A/Kempes et al. - 2012 - Growth, metabolic partitioning, and the size of mi.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/4LRVBPN7/495.html:text/html}, +} + +@article{dutkiewicz_dimensions_2020, + title = {Dimensions of marine phytoplankton diversity}, + volume = {17}, + issn = {1726-4170}, + url = {https://bg.copernicus.org/articles/17/609/2020/}, + doi = {10.5194/bg-17-609-2020}, + abstract = {{\textless}p{\textgreater}{\textless}strong class="journal-contentHeaderColor"{\textgreater}Abstract.{\textless}/strong{\textgreater} Biodiversity of phytoplankton is important for ecosystem stability and marine biogeochemistry. However, the large-scale patterns of diversity are not well understood and are often poorly characterized in terms of statistical relationships with factors such as latitude, temperature and productivity. Here we use ecological theory and a global trait-based ecosystem model to provide mechanistic understanding of patterns of phytoplankton diversity. Our study suggests that phytoplankton diversity across three dimensions of trait space (size, biogeochemical function and thermal tolerance) is controlled by disparate combinations of drivers: the supply rate of the limiting resource, the imbalance in different resource supplies relative to competing phytoplankton demands, size-selective grazing and transport by the moving ocean. Using sensitivity studies we show that each dimension of diversity is controlled by different drivers. Models including only one (or two) of the trait dimensions will have different patterns of diversity than one which incorporates another trait dimension. We use the results of our model exploration to infer the controls on the diversity patterns derived from field observations along meridional transects in the Atlantic and to explain why different taxa and size classes have differing patterns.{\textless}/p{\textgreater}}, + language = {English}, + number = {3}, + urldate = {2022-02-02}, + journal = {Biogeosciences}, + author = {Dutkiewicz, Stephanie and Cermeno, Pedro and Jahn, Oliver and Follows, Michael J. and Hickman, Anna E. and Taniguchi, Darcy A. A. and Ward, Ben A.}, + month = feb, + year = {2020}, + note = {Publisher: Copernicus GmbH}, + keywords = {read}, + pages = {609--634}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/GDPB2N66/Dutkiewicz et al. - 2020 - Dimensions of marine phytoplankton diversity.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/6J7BBRZ4/2020.html:text/html}, +} + +@article{armstrong_grazing_1994, + title = {Grazing limitation and nutrient limitation in marine ecosystems: {Steady} state solutions of an ecosystem model with multiple food chains}, + volume = {39}, + issn = {1939-5590}, + shorttitle = {Grazing limitation and nutrient limitation in marine ecosystems}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lo.1994.39.3.0597}, + doi = {10.4319/lo.1994.39.3.0597}, + abstract = {Here I develop a model with multiple phytoplankton-zooplankton food chains where each food chain is based on a different size class of algae. Model parameters were chosen to allow food chains based on smaller algal size classes to dominate under oligotrophic conditions, with larger size classes being added sequentially with increased nutrient loading. The model makes use of allometric relations among size classes to minimize the number of free parameters, facilitating numerical exploration of its steady state behavior. The model is simple, yet it is complex enough to allow simultaneous predator limitation of algal size classes (or species) and nutrient limitation of total phytoplankton biomass. This perspective, not obtainable from models with single phytoplankton and zooplankton size classes, is applied to the particular case of elevated nitrate levels in the equatorial Pacific. The model shows clearly that it is possible for each phytoplankton size class to be limited by its herbivores, while at the same time micronutrient (notably iron) deficiency may limit the number of size classes that can exist in the community and hence the total phytoplankton biomass that can be supported.}, + language = {en}, + number = {3}, + urldate = {2022-02-07}, + journal = {Limnology and Oceanography}, + author = {Armstrong, Robert A.}, + year = {1994}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lo.1994.39.3.0597}, + keywords = {unread}, + pages = {597--608}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/62GDW2AR/Armstrong - 1994 - Grazing limitation and nutrient limitation in mari.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/FJKQ3LLF/lo.1994.39.3.html:text/html}, +} + +@article{dutkiewicz_modeling_2009, + title = {Modeling the coupling of ocean ecology and biogeochemistry}, + volume = {23}, + issn = {1944-9224}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2008GB003405}, + doi = {10.1029/2008GB003405}, + abstract = {We examine the interplay between ecology and biogeochemical cycles in the context of a global three-dimensional ocean model where self-assembling phytoplankton communities emerge from a wide set of potentially viable cell types. We consider the complex model solutions in the light of resource competition theory. The emergent community structures and ecological regimes vary across different physical environments in the model ocean: Strongly seasonal, high-nutrient regions are dominated by fast growing bloom specialists, while stable, low-seasonality regions are dominated by organisms that can grow at low nutrient concentrations and are suited to oligotrophic conditions. In the latter regions, the framework of resource competition theory provides a useful qualitative and quantitative diagnostic tool with which to interpret the outcome of competition between model organisms, their regulation of the resource environment, and the sensitivity of the system to changes in key physiological characteristics of the cells.}, + language = {en}, + number = {4}, + urldate = {2022-02-14}, + journal = {Global Biogeochemical Cycles}, + author = {Dutkiewicz, S. and Follows, M. J. and Bragg, J. G.}, + year = {2009}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2008GB003405}, + keywords = {unread, ecology, ocean biogeochemistry, resource competition}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/ME3K4FVT/Dutkiewicz et al. - 2009 - Modeling the coupling of ocean ecology and biogeoc.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/JWID8TMI/2008GB003405.html:text/html}, +} + +@article{maranon_resource_2015, + title = {Resource supply alone explains the variability of marine phytoplankton size structure}, + volume = {60}, + issn = {1939-5590}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lno.10138}, + doi = {10.1002/lno.10138}, + abstract = {Due to the covariation between temperature and resource availability in the surface ocean, a correct assessment of resource supply is crucial to determine if temperature has a direct effect on phytoplankton size structure. To remove the effect of resources, López-Urrutia and Morán analyzed data subsets with narrow ranges of variation in Chlorophyll a (Chl a) concentration and found that temperature is correlated with Chl a partitioning among size classes, from which they concluded that temperature is an important variable to explain the variability of phytoplankton size structure. Our analysis, however, shows that resource supply varies widely also within these subsets and, importantly, that it is inversely correlated with temperature. Therefore, the relationship between temperature and size structure reflects instead the effect of resources. When groups of samples with similar resource supply conditions are considered, no correlation between temperature and phytoplankton size structure is observed, which invalidates the conclusion of López-Urrutia and Morán. Even within restricted ranges of variation for phytoplankton biomass and production, changes in resource supply alone are sufficient to explain the variability of phytoplankton size structure in the sea.}, + language = {en}, + number = {5}, + urldate = {2022-02-16}, + journal = {Limnology and Oceanography}, + author = {Marañón, Emilio and Cermeño, Pedro and Latasa, Mikel and Tadonléké, Rémy D.}, + year = {2015}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lno.10138}, + keywords = {unread, prosynref}, + pages = {1848--1854}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/PFJ4GLDA/Marañón et al. - 2015 - Resource supply alone explains the variability of .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/HSLF3VCJ/lno.html:text/html}, +} + +@article{follett_trophic_2022, + title = {Trophic interactions with heterotrophic bacteria limit the range of \textit{{Prochlorococcus}}}, + volume = {119}, + issn = {0027-8424, 1091-6490}, + url = {http://www.pnas.org/lookup/doi/10.1073/pnas.2110993118}, + doi = {10.1073/pnas.2110993118}, + abstract = {Prochlorococcus + is both the smallest and numerically most abundant photosynthesizing organism on the planet. While thriving in the warm oligotrophic gyres, + Prochlorococcus + concentrations drop rapidly in higher-latitude regions. Transect data from the North Pacific show the collapse occurring at a wide range of temperatures and latitudes (temperature is often hypothesized to cause this shift), suggesting an ecological mechanism may be at play. An often used size-based theory of phytoplankton community structure that has been incorporated into computational models correctly predicts the dominance of + Prochlorococcus + in the gyres, and the relative dominance of larger cells at high latitudes. However, both theory and computational models fail to explain the poleward collapse. When heterotrophic bacteria and predators that prey nonspecifically on both + Prochlorococcus + and bacteria are included in the theoretical framework, the collapse of + Prochlorococcus + occurs with increasing nutrient supplies. The poleward collapse of + Prochlorococcus + populations then naturally emerges when this mechanism of “shared predation” is implemented in a complex global ecosystem model. Additionally, the theory correctly predicts trends in both the abundance and mean size of the heterotrophic bacteria. These results suggest that ecological controls need to be considered to understand the biogeography of + Prochlorococcus + and predict its changes under future ocean conditions. Indirect interactions within a microbial network can be essential in setting community structure.}, + language = {en}, + number = {2}, + urldate = {2022-02-23}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Follett, Christopher L. and Dutkiewicz, Stephanie and Ribalet, François and Zakem, Emily and Caron, David and Armbrust, E. Virginia and Follows, Michael J.}, + month = jan, + year = {2022}, + pages = {e2110993118}, + file = {Follett et al. - 2022 - Trophic interactions with heterotrophic bacteria l.pdf:/Users/birdy/Zotero/storage/PJMSAXIG/Follett et al. - 2022 - Trophic interactions with heterotrophic bacteria l.pdf:application/pdf}, +} + +@article{muratore_complex_2022, + title = {Complex marine microbial communities partition metabolism of scarce resources over the diel cycle}, + volume = {6}, + copyright = {2022 The Author(s), under exclusive licence to Springer Nature Limited}, + issn = {2397-334X}, + url = {https://www.nature.com/articles/s41559-021-01606-w}, + doi = {10.1038/s41559-021-01606-w}, + abstract = {Complex assemblages of microbes in the surface ocean are responsible for approximately half of global carbon fixation. The persistence of high taxonomic diversity despite competition for a small suite of relatively homogeneously distributed nutrients, that is, ‘the paradox of the plankton’, represents a long-standing challenge for ecological theory. Here we find evidence consistent with temporal niche partitioning of nitrogen assimilation processes over a diel cycle in the North Pacific Subtropical Gyre. We jointly analysed transcript abundances, lipids and metabolites and discovered that a small number of diel archetypes can explain pervasive periodic dynamics. Metabolic pathway analysis of identified diel signals revealed asynchronous timing in the transcription of nitrogen uptake and assimilation genes among different microbial groups—cyanobacteria, heterotrophic bacteria and eukaryotes. This temporal niche partitioning of nitrogen uptake emerged despite synchronous transcription of photosynthesis and central carbon metabolism genes and associated macromolecular abundances. Temporal niche partitioning may be a mechanism by which microorganisms in the open ocean mitigate competition for scarce resources, supporting community coexistence.}, + language = {en}, + number = {2}, + urldate = {2022-02-25}, + journal = {Nature Ecology \& Evolution}, + author = {Muratore, Daniel and Boysen, Angela K. and Harke, Matthew J. and Becker, Kevin W. and Casey, John R. and Coesel, Sacha N. and Mende, Daniel R. and Wilson, Samuel T. and Aylward, Frank O. and Eppley, John M. and Vislova, Alice and Peng, Shengyun and Rodriguez-Gonzalez, Rogelio A. and Beckett, Stephen J. and Virginia Armbrust, E. and DeLong, Edward F. and Karl, David M. and White, Angelicque E. and Zehr, Jonathan P. and Van Mooy, Benjamin A. S. and Dyhrman, Sonya T. and Ingalls, Anitra E. and Weitz, Joshua S.}, + month = feb, + year = {2022}, + note = {Number: 2 +Publisher: Nature Publishing Group}, + keywords = {unread, Microbial biooceanography, Microbial ecology}, + pages = {218--229}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/T58JCF6Q/Muratore et al. - 2022 - Complex marine microbial communities partition met.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/HC4GW85C/s41559-021-01606-w.html:text/html}, +} + +@article{mangolte_plankton_2022, + title = {Plankton community response to fronts : winners and losers}, + shorttitle = {Plankton community response to fronts}, + url = {https://hal.archives-ouvertes.fr/hal-03607937}, + doi = {10.1093/plankt/fbac010}, + abstract = {Fronts are particularly productive regions of the ocean, and biodiversity hotspots for many marine species. Here we use an ocean-ecosystem model to investigate the effect of fronts on plankton ecology. We focus on energetic fronts in Western Boundary Current systems that efficiently inject nutrients into the euphotic layer and which are physical boundaries between productive and oligotrophic provinces. We found that the fronts form an environment distinct from both provinces, favorable to some plankton groups (diatoms, dinoflagellates and large carnivorous zooplankton) and less favorable to others (pico-phytoplankton, coccolithophores, small grazers), and with an overall larger diversity. In agreement with previous understanding, we find that bottom-up abiotic processes (nutrient enrichment) explain the prevalence of groups with fast growing rates (the "winners"). Importantly, our results also show that biotic interactions within the ecosystem may play a larger role than previously thought. We show that the winners can have a negative impact on other plankton species (the "losers") through two indirect competitive processes: community shading (modification of the light environment by the plankton community leading to light-limitation of some plankton groups) and shared predation (where an increase in one functional group leads to increased grazing by a shared predator on another functional group).}, + urldate = {2022-03-23}, + journal = {Journal of Plankton Research}, + author = {Mangolte, Inès and Lévy, Marina and Dutkiewicz, Stephanie and Clayton, Sophie and Jahn, Oliver}, + month = mar, + year = {2022}, + note = {Publisher: Oxford University Press (OUP)}, + keywords = {unread, phytoplankton, biodiversity, fronts, marine ecology, modelling}, + file = {HAL PDF Full Text:/Users/birdy/Zotero/storage/65HVTJEW/Mangolte et al. - 2022 - Plankton community response to fronts winners an.pdf:application/pdf}, +} + +@article{shang_oxidative_2022, + title = {Oxidative metabolisms catalyzed {Earth}’s oxygenation}, + volume = {13}, + copyright = {2022 The Author(s)}, + issn = {2041-1723}, + url = {https://www.nature.com/articles/s41467-022-28996-0}, + doi = {10.1038/s41467-022-28996-0}, + abstract = {The burial of organic carbon, which prevents its remineralization via oxygen-consuming processes, is considered one of the causes of Earth’s oxygenation. Yet, higher levels of oxygen are thought to inhibit burial. Here we propose a resolution of this conundrum, wherein Earth’s initial oxygenation is favored by oxidative metabolisms generating partially oxidized organic matter (POOM), increasing burial via interaction with minerals in sediments. First, we introduce the POOM hypothesis via a mathematical argument. Second, we reconstruct the evolutionary history of one key enzyme family, flavin-dependent Baeyer–Villiger monooxygenases, that generates POOM, and show the temporal consistency of its diversification with the Proterozoic and Phanerozoic atmospheric oxygenation. Finally, we propose that the expansion of oxidative metabolisms instigated a positive feedback, which was amplified by the chemical changes to minerals on Earth’s surface. Collectively, these results suggest that Earth’s oxygenation is an autocatalytic transition induced by a combination of biological innovations and geological changes.}, + language = {en}, + number = {1}, + urldate = {2022-04-29}, + journal = {Nature Communications}, + author = {Shang, Haitao and Rothman, Daniel H. and Fournier, Gregory P.}, + month = mar, + year = {2022}, + note = {Number: 1 +Publisher: Nature Publishing Group}, + keywords = {unread, Coevolution, Element cycles}, + pages = {1328}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/JAG35KDC/Shang et al. - 2022 - Oxidative metabolisms catalyzed Earth’s oxygenatio.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/28GUMIIB/s41467-022-28996-0.html:text/html}, +} + +@article{ganopolski_climber-2_2001, + title = {{CLIMBER}-2: a climate system model of intermediate complexity. {Part} {II}: model sensitivity}, + volume = {17}, + issn = {0930-7575, 1432-0894}, + shorttitle = {{CLIMBER}-2}, + url = {http://link.springer.com/10.1007/s003820000144}, + doi = {10.1007/s003820000144}, + language = {en}, + number = {10}, + urldate = {2022-05-02}, + journal = {Climate Dynamics}, + author = {Ganopolski, A. and Petoukhov, V. and Rahmstorf, S. and Brovkin, V. and Claussen, M. and Eliseev, A. and Kubatzki, C.}, + month = jul, + year = {2001}, + keywords = {read}, + pages = {735--751}, + file = {Ganopolski et al. - 2001 - CLIMBER-2 a climate system model of intermediate .pdf:/Users/birdy/Zotero/storage/X4G6F5HW/Ganopolski et al. - 2001 - CLIMBER-2 a climate system model of intermediate .pdf:application/pdf}, +} + +@article{bhatia_its_2022, + chapter = {The Upshot}, + title = {‘{It}’s {Super} {Spectacular}.’ {See} {How} the {Tonga} {Volcano} {Unleashed} a {Once}-in-a-{Century} {Shockwave}.}, + issn = {0362-4331}, + url = {https://www.nytimes.com/interactive/2022/04/14/upshot/tonga-pressure-wave.html}, + abstract = {A new simulation gives a detailed look at a shockwave that circled the planet for days.}, + language = {en-US}, + urldate = {2022-05-06}, + journal = {The New York Times}, + author = {Bhatia, Aatish and Catangui, Sean}, + month = apr, + year = {2022}, + keywords = {Hunga Tonga-Hunga Ha'apai (Tonga), Tidal Waves and Tsunamis, Tonga, Volcanoes}, + file = {Snapshot:/Users/birdy/Zotero/storage/K9I8BQD2/tonga-pressure-wave.html:text/html}, +} + +@article{lee_improved_2018, + title = {Improved {Prediction} of {Harmful} {Algal} {Blooms} in {Four} {Major} {South} {Korea}’s {Rivers} {Using} {Deep} {Learning} {Models}}, + volume = {15}, + issn = {1661-7827}, + url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6069434/}, + doi = {10.3390/ijerph15071322}, + abstract = {Harmful algal blooms are an annual phenomenon that cause environmental damage, economic losses, and disease outbreaks. A fundamental solution to this problem is still lacking, thus, the best option for counteracting the effects of algal blooms is to improve advance warnings (predictions). However, existing physical prediction models have difficulties setting a clear coefficient indicating the relationship between each factor when predicting algal blooms, and many variable data sources are required for the analysis. These limitations are accompanied by high time and economic costs. Meanwhile, artificial intelligence and deep learning methods have become increasingly common in scientific research; attempts to apply the long short-term memory (LSTM) model to environmental research problems are increasing because the LSTM model exhibits good performance for time-series data prediction. However, few studies have applied deep learning models or LSTM to algal bloom prediction, especially in South Korea, where algal blooms occur annually. Therefore, we employed the LSTM model for algal bloom prediction in four major rivers of South Korea. We conducted short-term (one week) predictions by employing regression analysis and deep learning techniques on a newly constructed water quality and quantity dataset drawn from 16 dammed pools on the rivers. Three deep learning models (multilayer perceptron, MLP; recurrent neural network, RNN; and long short-term memory, LSTM) were used to predict chlorophyll-a, a recognized proxy for algal activity. The results were compared to those from OLS (ordinary least square) regression analysis and actual data based on the root mean square error (RSME). The LSTM model showed the highest prediction rate for harmful algal blooms and all deep learning models out-performed the OLS regression analysis. Our results reveal the potential for predicting algal blooms using LSTM and deep learning.}, + number = {7}, + urldate = {2022-05-19}, + journal = {International Journal of Environmental Research and Public Health}, + author = {Lee, Sangmok and Lee, Donghyun}, + month = jul, + year = {2018}, + pmid = {29937531}, + pmcid = {PMC6069434}, + pages = {1322}, + file = {Full Text:/Users/birdy/Zotero/storage/AA2K29UM/Lee and Lee - 2018 - Improved Prediction of Harmful Algal Blooms in Fou.pdf:application/pdf}, +} + +@article{yu_predicting_2021, + title = {Predicting coastal algal blooms with environmental factors by machine learning methods}, + volume = {123}, + issn = {1470-160X}, + url = {https://www.sciencedirect.com/science/article/pii/S1470160X20312760}, + doi = {10.1016/j.ecolind.2020.107334}, + abstract = {Harmful algal blooms are a major type of marine disaster that endangers the marine ecological environment and human health. Since the algal bloom is a complex nonlinear process with time characteristics, traditional statistical methods often cannot provide good predictions. In this paper, we propose a method based on machine learning with the aim to predict the occurrence of algal blooms by environmental parameters. The features related to algal bloom growth have been experimented for achieving a good prediction of algal concentrations by a combination strategy. We validate the prediction performance on two real datasets from two locations in US and China, i.e., Scripps Pier, California and Sishili Bay, Shandong, respectively. The models and feature subsets have been selected to complete the missing data and predict the phytoplankton concentration. The results demonstrate the efficiency of our method in the short-term prediction of concentrations by selecting appropriate features. The comparison studies prove the advantage of our developed machine learning method. The importance of every features for the prediction performance reveals crucial factors for the outbreak of harmful algal blooms.}, + language = {en}, + urldate = {2022-05-24}, + journal = {Ecological Indicators}, + author = {Yu, Peixuan and Gao, Rui and Zhang, Dezhen and Liu, Zhi-Ping}, + month = apr, + year = {2021}, + keywords = {unread, Feature importance, Feature selection, GBDT, Harmful algal bloom, Machine learning}, + pages = {107334}, + file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/XICJL7KH/S1470160X20312760.html:text/html}, +} + +@article{dutkiewicz_capturing_2015, + title = {Capturing optically important constituents and properties in a marine biogeochemical and ecosystem model}, + volume = {12}, + issn = {1726-4189}, + url = {https://bg.copernicus.org/articles/12/4447/2015/}, + doi = {10.5194/bg-12-4447-2015}, + abstract = {Abstract. We present a numerical model of the ocean that couples a three-stream radiative transfer component with a marine biogeochemical–ecosystem component in a dynamic three-dimensional physical framework. The radiative transfer component resolves the penetration of spectral irradiance as it is absorbed and scattered within the water column. We explicitly include the effect of several optically important water constituents (different phytoplankton functional types; detrital particles; and coloured dissolved organic matter, CDOM). The model is evaluated against in situ-observed and satellite-derived products. In particular we compare to concurrently measured biogeochemical, ecosystem, and optical data along a meridional transect of the Atlantic Ocean. The simulation captures the patterns and magnitudes of these data, and estimates surface upwelling irradiance analogous to that observed by ocean colour satellite instruments. We find that incorporating the different optically important constituents explicitly and including spectral irradiance was crucial to capture the variability in the depth of the subsurface chlorophyll a (Chl a) maximum. We conduct a series of sensitivity experiments to demonstrate, globally, the relative importance of each of the water constituents, as well as the crucial feedbacks between the light field, the relative fitness of phytoplankton types, and the biogeochemistry of the ocean. CDOM has proportionally more importance at attenuating light at short wavelengths and in more productive waters, phytoplankton absorption is relatively more important at the subsurface Chl a maximum, and water molecules have the greatest contribution when concentrations of other constituents are low, such as in the oligotrophic gyres. Scattering had less effect on attenuation, but since it is important for the amount and type of upwelling irradiance, it is crucial for setting sea surface reflectance. Strikingly, sensitivity experiments in which absorption by any of the optical constituents was increased led to a decrease in the size of the oligotrophic regions of the subtropical gyres: lateral nutrient supplies were enhanced as a result of decreasing high-latitude productivity. This new model that captures bio-optical feedbacks will be important for improving our understanding of the role of light and optical constituents on ocean biogeochemistry, especially in a changing environment. Further, resolving surface upwelling irradiance will make it easier to connect to satellite-derived products in the future.}, + language = {en}, + number = {14}, + urldate = {2022-08-15}, + journal = {Biogeosciences}, + author = {Dutkiewicz, S. and Hickman, A. E. and Jahn, O. and Gregg, W. W. and Mouw, C. B. and Follows, M. J.}, + month = jul, + year = {2015}, + pages = {4447--4481}, + file = {Dutkiewicz et al. - 2015 - Capturing optically important constituents and pro.pdf:/Users/birdy/Zotero/storage/DEFKPKCD/Dutkiewicz et al. - 2015 - Capturing optically important constituents and pro.pdf:application/pdf}, +} + +@article{han_solving_2018, + title = {Solving high-dimensional partial differential equations using deep learning}, + volume = {115}, + url = {https://www.pnas.org/doi/abs/10.1073/pnas.1718942115}, + doi = {10.1073/pnas.1718942115}, + abstract = {Developing algorithms for solving high-dimensional partial differential equations +(PDEs) has been an exceedingly difficult task for a long time, du...}, + language = {EN}, + number = {34}, + urldate = {2022-08-31}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Han, Jiequn and Jentzen, Arnulf and E, Weinan}, + month = aug, + year = {2018}, + note = {Company: National Academy of Sciences +Distributor: National Academy of Sciences +Institution: National Academy of Sciences +Label: National Academy of Sciences +Publisher: Proceedings of the National Academy of Sciences}, + keywords = {ML}, + pages = {8505--8510}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/PCZ84KRN/Han et al. - 2018 - Solving high-dimensional partial differential equa.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/KSCJDJ7T/pnas.html:text/html}, +} + +@article{follows_emergent_2007, + title = {Emergent {Biogeography} of {Microbial} {Communities} in a {Model} {Ocean}}, + volume = {315}, + issn = {0036-8075, 1095-9203}, + url = {https://www.science.org/doi/10.1126/science.1138544}, + doi = {10.1126/science.1138544}, + abstract = {A marine ecosystem model seeded with many phytoplankton types, whose physiological traits were randomly assigned from ranges defined by field and laboratory data, generated an emergent community structure and biogeography consistent with observed global phytoplankton distributions. The modeled organisms included types analogous to the marine cyanobacterium + Prochlorococcus + . Their emergent global distributions and physiological properties simultaneously correspond to observations. This flexible representation of community structure can be used to explore relations between ecosystems, biogeochemical cycles, and climate change.}, + language = {en}, + number = {5820}, + urldate = {2022-08-31}, + journal = {Science}, + author = {Follows, Michael J. and Dutkiewicz, Stephanie and Grant, Scott and Chisholm, Sallie W.}, + month = mar, + year = {2007}, + pages = {1843--1846}, + file = {Follows et al. - 2007 - Emergent Biogeography of Microbial Communities in .pdf:/Users/birdy/Zotero/storage/5N8359IB/Follows et al. - 2007 - Emergent Biogeography of Microbial Communities in .pdf:application/pdf}, +} + +@article{duyck_sloop_2015, + title = {Sloop: {A} pattern retrieval engine for individual animal identification}, + volume = {48}, + issn = {0031-3203}, + shorttitle = {Sloop}, + url = {https://www.sciencedirect.com/science/article/pii/S0031320314002763}, + doi = {10.1016/j.patcog.2014.07.017}, + abstract = {Identifying individuals in photographs of animals collected over time is a non-invasive approach for ecological monitoring and conservation. This paper describes the design and use of Sloop, the first image retrieval system for individual animal identification incorporating crowd-sourced relevance feedback. Sloop׳s iterative retrieval strategy using hierarchical and aggregated matching and relevance feedback consistently improves deformation and correspondence-based approaches for individual identification across several species. Its crowdsourcing strategy is successful in utilizing relevance feedback on a large scale. Sloop is in operational use. The user experience and results are presented here to facilitate the creation of a community-based individual identification system for conservation planning.}, + language = {en}, + number = {4}, + urldate = {2022-10-01}, + journal = {Pattern Recognition}, + author = {Duyck, James and Finn, Chelsea and Hutcheon, Andy and Vera, Pablo and Salas, Joaquin and Ravela, Sai}, + month = apr, + year = {2015}, + keywords = {Animal biometrics, Conservation, Crowdsourcing, Gecko, Hybrid shape contexts, Individual identification, Local features, Photo-identification, Relevance feedback, Salamander, Scale-cascaded alignment, Skink, Whale shark}, + pages = {1059--1073}, + file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/EVXJTLMW/S0031320314002763.html:text/html}, +} + +@article{taylor_shutdown_2011, + title = {Shutdown of turbulent convection as a new criterion for the onset of spring phytoplankton blooms}, + volume = {56}, + issn = {1939-5590}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lo.2011.56.6.2293}, + doi = {10.4319/lo.2011.56.6.2293}, + abstract = {The onset of phytoplankton blooms in late winter, early spring has been traditionally associated with the shoaling of the mixed layer above a critical depth. Here we show that the onset of a bloom can also be triggered by a reduction in air–sea fluxes at the end of winter. When net cooling subsides at the end of winter, turbulent mixing becomes weak, thereby increasing the residence time of phytoplankton cells in the euphotic layer and allowing a bloom to develop. The necessary change in the air–sea flux generally precedes mixed-layer shoaling, and may provide a better indicator for the onset of the spring bloom than the mixed-layer depth alone. Our hypothesis is supported by numerical simulations and remote sensing data.}, + language = {en}, + number = {6}, + urldate = {2022-10-05}, + journal = {Limnology and Oceanography}, + author = {Taylor, John R. and Ferrari, Raffaele}, + year = {2011}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lo.2011.56.6.2293}, + pages = {2293--2307}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/X7EVVVGL/Taylor and Ferrari - 2011 - Shutdown of turbulent convection as a new criterio.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/SJS6MACH/lo.2011.56.6.html:text/html}, +} + +@article{regos_effects_2019, + title = {Effects of species traits and environmental predictors on performance and transferability of ecological niche models}, + volume = {9}, + issn = {20452322}, + url = {https://www.mendeley.com/catalogue/30ca2198-448a-30e7-bb65-8f00f0bc94ed/?ref=raven&dgcid=raven_md_suggest_email&dgcid=raven_md_suggest_mie_email}, + doi = {10.1038/s41598-019-40766-5}, + abstract = {(2019) Regos et al. Scientific Reports. The ability of ecological niche models (ENMs) to produce robust predictions for different time frames (i.e. temporal transferability) may be hindered by a la...}, + language = {en-GB}, + number = {1}, + urldate = {2022-10-11}, + journal = {Scientific Reports}, + author = {Regos, Adrián and Gagne, Laura and Alcaraz-Segura, Domingo and Honrado, João P. and Domínguez, Jesús}, + year = {2019}, + note = {Number: 1}, + pages = {undefined--undefined}, + file = {Full Text:/Users/birdy/Zotero/storage/83W8A9G9/Regos et al. - 2019 - Effects of species traits and environmental predic.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/CSIQ6CV6/30ca2198-448a-30e7-bb65-8f00f0bc94ed.html:text/html}, +} + +@misc{beery_species_2021, + title = {Species {Distribution} {Modeling} for {Machine} {Learning} {Practitioners}: {A} {Review}}, + shorttitle = {Species {Distribution} {Modeling} for {Machine} {Learning} {Practitioners}}, + url = {http://arxiv.org/abs/2107.10400}, + abstract = {Conservation science depends on an accurate understanding of what's happening in a given ecosystem. How many species live there? What is the makeup of the population? How is that changing over time? Species Distribution Modeling (SDM) seeks to predict the spatial (and sometimes temporal) patterns of species occurrence, i.e. where a species is likely to be found. The last few years have seen a surge of interest in applying powerful machine learning tools to challenging problems in ecology. Despite its considerable importance, SDM has received relatively little attention from the computer science community. Our goal in this work is to provide computer scientists with the necessary background to read the SDM literature and develop ecologically useful ML-based SDM algorithms. In particular, we introduce key SDM concepts and terminology, review standard models, discuss data availability, and highlight technical challenges and pitfalls.}, + urldate = {2022-10-11}, + publisher = {arXiv}, + author = {Beery, Sara and Cole, Elijah and Parker, Joseph and Perona, Pietro and Winner, Kevin}, + month = jul, + year = {2021}, + note = {arXiv:2107.10400 [cs, stat]}, + keywords = {Computer Science - Machine Learning, Statistics - Applications}, + file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/5D6ZSJQL/Beery et al. - 2021 - Species Distribution Modeling for Machine Learning.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/UNSJQTFB/2107.html:text/html}, +} + +@article{beery_scaling_2021, + title = {Scaling biodiversity monitoring for the data age}, + volume = {27}, + issn = {1528-4972, 1528-4980}, + url = {https://dl.acm.org/doi/10.1145/3466857}, + doi = {10.1145/3466857}, + abstract = {Technological advances have made it possible to collect massive amounts of biodiversity data. How can analysis efforts keep up?}, + language = {en}, + number = {4}, + urldate = {2022-10-11}, + journal = {XRDS: Crossroads, The ACM Magazine for Students}, + author = {Beery, Sara}, + month = jun, + year = {2021}, + pages = {14--18}, + file = {Beery - 2021 - Scaling biodiversity monitoring for the data age.pdf:/Users/birdy/Zotero/storage/N366LVCR/Beery - 2021 - Scaling biodiversity monitoring for the data age.pdf:application/pdf}, +} + +@article{olson_submersible_2007, + title = {A submersible imaging-in-flow instrument to analyze nano-and microplankton: {Imaging} {FlowCytobot}}, + volume = {5}, + issn = {1541-5856}, + shorttitle = {A submersible imaging-in-flow instrument to analyze nano-and microplankton}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lom.2007.5.195}, + doi = {10.4319/lom.2007.5.195}, + abstract = {A fundamental understanding of the interaction between physical and biological factors that regulate plankton species composition requires, first of all, detailed and sustained observations. Only now is it becoming possible to acquire these types of observations, as we develop and deploy instruments that can continuously monitor individual organisms in the ocean. Our research group can measure and count the smallest phytoplankton cells using a submersible flow cytometer (FlowCytobot), in which optical properties of individual suspended cells are recorded as they pass through a focused laser beam. However, FlowCytobot cannot efficiently sample or identify the much larger cells (10 to {\textgreater}100 µm) that often dominate the plankton in coastal waters. Because these larger cells often have recognizable morphologies, we have developed a second submersible flow cytometer, with imaging capability and increased water sampling rate (typically, 5 mL seawater analyzed every 20 min), to characterize these nano- and microplankton. Like the original, Imaging FlowCytobot can operate unattended for months at a time; it obtains power from and communicates with a shore laboratory, so we can monitor results and modify sampling procedures when needed. Imaging FlowCytobot was successfully tested for 2 months in Woods Hole Harbor and is presently deployed alongside FlowCytobot at the Martha's Vineyard Coastal Observatory. These combined approaches will allow continuous long-term observations of plankton community structure over a wide range of cell sizes and types, and help to elucidate the processes and interactions that control the life cycles of individual species.}, + language = {en}, + number = {6}, + urldate = {2022-10-20}, + journal = {Limnology and Oceanography: Methods}, + author = {Olson, Robert J. and Sosik, Heidi M.}, + year = {2007}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lom.2007.5.195}, + pages = {195--203}, + file = {Full Text:/Users/birdy/Zotero/storage/JP4F24FI/Olson and Sosik - 2007 - A submersible imaging-in-flow instrument to analyz.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/9E2FX4K7/lom.2007.5.html:text/html}, +} + +@article{sosik_automated_2007, + title = {Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry}, + volume = {5}, + issn = {1541-5856}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lom.2007.5.204}, + doi = {10.4319/lom.2007.5.204}, + abstract = {High-resolution photomicrographs of phytoplankton cells and chains can now be acquired with imaging-in-flow systems at rates that make manual identification impractical for many applications. To address the challenge for automated taxonomic identification of images generated by our custom-built submersible Imaging FlowCytobot, we developed an approach that relies on extraction of image features, which are then presented to a machine learning algorithm for classification. Our approach uses a combination of image feature types including size, shape, symmetry, and texture characteristics, plus orientation invariant moments, diffraction pattern sampling, and co-occurrence matrix statistics. Some of these features required preprocessing with image analysis techniques including edge detection after phase congruency calculations, morphological operations, boundary representation and simplification, and rotation. For the machine learning strategy, we developed an approach that combines a feature selection algorithm and use of a support vector machine specified with a rigorous parameter selection and training approach. After training, a 22-category classifier provides 88\% overall accuracy for an independent test set, with individual category accuracies ranging from 68\% to 99\%. We demonstrate application of this classifier to a nearly uninterrupted 2-month time series of images acquired in Woods Hole Harbor, including use of statistical error correction to derive quantitative concentration estimates, which are shown to be unbiased with respect to manual estimates for random subsamples. Our approach, which provides taxonomically resolved estimates of phytoplankton abundance with fine temporal resolution (hours for many species), permits access to scales of variability from tidal to seasonal and longer.}, + language = {en}, + number = {6}, + urldate = {2022-10-20}, + journal = {Limnology and Oceanography: Methods}, + author = {Sosik, Heidi M. and Olson, Robert J.}, + year = {2007}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lom.2007.5.204}, + pages = {204--216}, + file = {Full Text:/Users/birdy/Zotero/storage/6E2WBCLM/Sosik and Olson - 2007 - Automated taxonomic classification of phytoplankto.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/UIZ3PRI8/lom.2007.5.html:text/html}, +} + +@article{besson_towards_nodate, + title = {Towards the fully automated monitoring of ecological communities}, + volume = {n/a}, + issn = {1461-0248}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ele.14123}, + doi = {10.1111/ele.14123}, + abstract = {High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components—for example, individual behaviours and traits, and species abundance and distribution—is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.}, + language = {en}, + number = {n/a}, + urldate = {2022-10-24}, + journal = {Ecology Letters}, + author = {Besson, Marc and Alison, Jamie and Bjerge, Kim and Gorochowski, Thomas E. and Høye, Toke T. and Jucker, Tommaso and Mann, Hjalte M. R. and Clements, Christopher F.}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ele.14123}, + keywords = {community ecology, computer vision, deep learning, high-resolution monitoring, remote sensing}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/BBZAT6LU/Besson et al. - Towards the fully automated monitoring of ecologic.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/GEGIFAZV/ele.html:text/html}, +} + +@article{pierella_karlusich_coupling_2022, + title = {Coupling {Imaging} and {Omics} in {Plankton} {Surveys}: {State}-of-the-{Art}, {Challenges}, and {Future} {Directions}}, + volume = {9}, + issn = {2296-7745}, + shorttitle = {Coupling {Imaging} and {Omics} in {Plankton} {Surveys}}, + url = {https://www.frontiersin.org/articles/10.3389/fmars.2022.878803}, + abstract = {A major challenge in characterizing plankton communities is the collection, identification and quantification of samples in a time-efficient way. The classical manual microscopy counts are gradually being replaced by high throughput imaging and nucleic acid sequencing. DNA sequencing allows deep taxonomic resolution (including cryptic species) as well as high detection power (detecting rare species), while RNA provides insights on function and potential activity. However, these methods are affected by database limitations, PCR bias, and copy number variability across taxa. Recent developments in high-throughput imaging applied in situ or on collected samples (high-throughput microscopy, Underwater Vision Profiler, FlowCam, ZooScan, etc) has enabled a rapid enumeration of morphologically-distinguished plankton populations, estimates of biovolume/biomass, and provides additional valuable phenotypic information. Although machine learning classifiers generate encouraging results to classify marine plankton images in a time efficient way, there is still a need for large training datasets of manually annotated images. Here we provide workflow examples that couple nucleic acid sequencing with high-throughput imaging for a more complete and robust analysis of microbial communities. We also describe the publicly available and collaborative web application EcoTaxa, which offers tools for the rapid validation of plankton by specialists with the help of automatic recognition algorithms. Finally, we describe how the field is moving with citizen science programs, unmanned autonomous platforms with in situ sensors, and sequencing and digitalization of historical plankton samples.}, + urldate = {2022-10-27}, + journal = {Frontiers in Marine Science}, + author = {Pierella Karlusich, Juan José and Lombard, Fabien and Irisson, Jean-Olivier and Bowler, Chris and Foster, Rachel A.}, + year = {2022}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/7HJUM6PQ/Pierella Karlusich et al. - 2022 - Coupling Imaging and Omics in Plankton Surveys St.pdf:application/pdf}, +} + +@article{pierella_karlusich_global_2021, + title = {Global distribution patterns of marine nitrogen-fixers by imaging and molecular methods}, + volume = {12}, + copyright = {2021 The Author(s)}, + issn = {2041-1723}, + url = {https://www.nature.com/articles/s41467-021-24299-y}, + doi = {10.1038/s41467-021-24299-y}, + abstract = {Nitrogen fixation has a critical role in marine primary production, yet our understanding of marine nitrogen-fixers (diazotrophs) is hindered by limited observations. Here, we report a quantitative image analysis pipeline combined with mapping of molecular markers for mining {\textgreater}2,000,000 images and {\textgreater}1300 metagenomes from surface, deep chlorophyll maximum and mesopelagic seawater samples across 6 size fractions ({\textless}0.2–2000 μm). We use this approach to characterise the diversity, abundance, biovolume and distribution of symbiotic, colony-forming and particle-associated diazotrophs at a global scale. We show that imaging and PCR-free molecular data are congruent. Sequence reads indicate diazotrophs are detected from the ultrasmall bacterioplankton ({\textless}0.2 μm) to mesoplankton (180–2000 μm) communities, while images predict numerous symbiotic and colony-forming diazotrophs ({\textgreater}20 µm). Using imaging and molecular data, we estimate that polyploidy can substantially affect gene abundances of symbiotic versus colony-forming diazotrophs. Our results support the canonical view that larger diazotrophs ({\textgreater}10 μm) dominate the tropical belts, while unicellular cyanobacterial and non-cyanobacterial diazotrophs are globally distributed in surface and mesopelagic layers. We describe co-occurring diazotrophic lineages of different lifestyles and identify high-density regions of diazotrophs in the global ocean. Overall, we provide an update of marine diazotroph biogeographical diversity and present a new bioimaging-bioinformatic workflow.}, + language = {en}, + number = {1}, + urldate = {2022-10-28}, + journal = {Nature Communications}, + author = {Pierella Karlusich, Juan José and Pelletier, Eric and Lombard, Fabien and Carsique, Madeline and Dvorak, Etienne and Colin, Sébastien and Picheral, Marc and Cornejo-Castillo, Francisco M. and Acinas, Silvia G. and Pepperkok, Rainer and Karsenti, Eric and de Vargas, Colomban and Wincker, Patrick and Bowler, Chris and Foster, Rachel A.}, + month = jul, + year = {2021}, + note = {Number: 1 +Publisher: Nature Publishing Group}, + keywords = {Microbial biooceanography, Element cycles, Environmental microbiology, Marine biology}, + pages = {4160}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/6QTE42Z7/Pierella Karlusich et al. - 2021 - Global distribution patterns of marine nitrogen-fi.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/FZ492JKD/s41467-021-24299-y.html:text/html}, +} + +@misc{feichtenhofer_large-scale_2021, + title = {A {Large}-{Scale} {Study} on {Unsupervised} {Spatiotemporal} {Representation} {Learning}}, + url = {http://arxiv.org/abs/2104.14558}, + abstract = {We present a large-scale study on unsupervised spatiotemporal representation learning from videos. With a unified perspective on four recent image-based frameworks, we study a simple objective that can easily generalize all these methods to space-time. Our objective encourages temporally-persistent features in the same video, and in spite of its simplicity, it works surprisingly well across: (i) different unsupervised frameworks, (ii) pre-training datasets, (iii) downstream datasets, and (iv) backbone architectures. We draw a series of intriguing observations from this study, e.g., we discover that encouraging long-spanned persistency can be effective even if the timespan is 60 seconds. In addition to state-of-the-art results in multiple benchmarks, we report a few promising cases in which unsupervised pre-training can outperform its supervised counterpart. Code is made available at https://github.com/facebookresearch/SlowFast}, + urldate = {2022-10-29}, + publisher = {arXiv}, + author = {Feichtenhofer, Christoph and Fan, Haoqi and Xiong, Bo and Girshick, Ross and He, Kaiming}, + month = apr, + year = {2021}, + note = {arXiv:2104.14558 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition}, + file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/P4P752JB/Feichtenhofer et al. - 2021 - A Large-Scale Study on Unsupervised Spatiotemporal.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/LV4YRCJQ/2104.html:text/html}, +} + +@article{lambert_dynamic_2022, + title = {The dynamic trophic architecture of open-ocean protist communities revealed through machine-guided metatranscriptomics}, + volume = {119}, + copyright = {Copyright © 2022 the Author(s). Published by PNAS.}, + url = {https://www.pnas.org/doi/abs/10.1073/pnas.2100916119}, + doi = {10.1073/pnas.2100916119}, + abstract = {Intricate networks of single-celled eukaryotes (protists) dominate carbon flow in +the ocean. Their growth, demise, and interactions with other micr...}, + language = {EN}, + number = {7}, + urldate = {2022-11-01}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Lambert, Bennett S. and Groussman, Ryan D. and Schatz, Megan J. and Coesel, Sacha N. and Durham, Bryndan P. and Alverson, Andrew J. and White, Angelicque E. and Armbrust, E. Virginia}, + month = feb, + year = {2022}, + note = {Company: National Academy of Sciences +Distributor: National Academy of Sciences +Institution: National Academy of Sciences +Label: National Academy of Sciences +Publisher: Proceedings of the National Academy of Sciences}, + pages = {e2100916119}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/U3RTSWWR/Lambert et al. - 2022 - The dynamic trophic architecture of open-ocean pro.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/W4FGUHV8/pnas.html:text/html}, +} + +@article{bourne_insights_2016, + title = {Insights into the {Coral} {Microbiome}: {Underpinning} the {Health} and {Resilience} of {Reef} {Ecosystems}}, + volume = {70}, + issn = {0066-4227, 1545-3251}, + shorttitle = {Insights into the {Coral} {Microbiome}}, + url = {https://www.annualreviews.org/doi/10.1146/annurev-micro-102215-095440}, + doi = {10.1146/annurev-micro-102215-095440}, + abstract = {Corals are fundamental ecosystem engineers, creating large, intricate reefs that support diverse and abundant marine life. At the core of a healthy coral animal is a dynamic relationship with microorganisms, including a mutually beneficial symbiosis with photosynthetic dinoflagellates (Symbiodinium spp.) and enduring partnerships with an array of bacterial, archaeal, fungal, protistan, and viral associates, collectively termed the coral holobiont. The combined genomes of this coral holobiont form a coral hologenome, and genomic interactions within the hologenome ultimately define the coral phenotype. Here we integrate contemporary scientific knowledge regarding the ecological, host-specific, and environmental forces shaping the diversity, specificity, and distribution of microbial symbionts within the coral holobiont, explore physiological pathways that contribute to holobiont fitness, and describe potential mechanisms for holobiont homeostasis. Understanding the role of the microbiome in coral resilience, acclimation, and environmental adaptation is a new frontier in reef science that will require large-scale collaborative research efforts.}, + language = {en}, + number = {1}, + urldate = {2022-11-02}, + journal = {Annual Review of Microbiology}, + author = {Bourne, David G. and Morrow, Kathleen M. and Webster, Nicole S.}, + month = sep, + year = {2016}, + pages = {317--340}, + file = {Bourne et al. - 2016 - Insights into the Coral Microbiome Underpinning t.pdf:/Users/birdy/Zotero/storage/AE37AHKP/Bourne et al. - 2016 - Insights into the Coral Microbiome Underpinning t.pdf:application/pdf}, +} + +@article{zhang_microbially_2019, + title = {Microbially mediated nutrient cycles in marine sponges}, + volume = {95}, + issn = {0168-6496}, + url = {https://doi.org/10.1093/femsec/fiz155}, + doi = {10.1093/femsec/fiz155}, + abstract = {Efficient nutrient cycles mediated by symbiotic microorganisms with their hosts are vital to support the high productivity of coral reef ecosystems. In these ecosystems, marine sponges are important habitat-forming organisms in the benthic community and harbor abundant microbial symbionts. However, few studies have reviewed the critical microbially mediated nutrient cycling processes in marine sponges. To bridge this gap, in this review article, we summarize existing knowledge and recent advances in understanding microbially mediated carbon (C), nitrogen (N), phosphorus (P) and sulfur (S) cycles in sponges, propose a conceptual model that describes potential interactions and constraints in the major nutrient cycles, and suggest that shifting redox state induced by animal behavior like sponge pumping can exert great influence on the activities of symbiotic microbial communities. Constraints include the lack of knowledge on spatial and temporal variations and host behavior; more studies are needed in these areas. Sponge microbiomes may have a significant impact on the nutrient cycles in the world’s coral reef ecosystems.}, + number = {11}, + urldate = {2022-11-02}, + journal = {FEMS Microbiology Ecology}, + author = {Zhang, Fan and Jonas, Lauren and Lin, Hanzhi and Hill, Russell T}, + month = nov, + year = {2019}, + keywords = {EMO}, + pages = {fiz155}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/372EJVV6/Zhang et al. - 2019 - Microbially mediated nutrient cycles in marine spo.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/AN3DK4P7/5582607.html:text/html}, +} + +@article{ceh_nutrient_2013, + title = {Nutrient cycling in early coral life stages: {Pocillopora} damicornis larvae provide their algal symbiont ({Symbiodinium}) with nitrogen acquired from bacterial associates}, + volume = {3}, + issn = {2045-7758}, + shorttitle = {Nutrient cycling in early coral life stages}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.642}, + doi = {10.1002/ece3.642}, + abstract = {The waters surrounding coral reef ecosystems are generally poor in nutrients, yet their levels of primary production are comparable with those reported from tropical rain forests. One explanation of this paradox is the efficient cycling of nutrients between the coral host, its endosymbiotic alga Symbiodinium and a wide array of microorganisms. Despite their importance for the animals' fitness, the cycling of nutrients in early coral life stages and the initial establishment of partnerships with the microbes involved in these processes has received little scrutiny to date. Nitrogen is an essential but limited nutrient in coral reef ecosystems. In order to assess the early nutrient exchange between bacteria and corals, coral larvae of the species Pocillopora damicornis were incubated with two coral-associated bacteria (Alteromonas sp., or Vibrio alginolyticus), prelabeled with the stable nitrogen isotope 15N. The incorporation and translocation of nitrogen from Vibrio- and Alteromonas bacteria into P. damicornis coral larvae and specifically into the coral-symbiotic Symbiodinium were detected by nanoscale secondary ion mass spectrometry (NanoSIMS). A significant increase in the amount of enriched 15N (two to threefold compared to natural abundance) was observed in P. damicornis larvae within 8 h of incubation for both bacterial treatments (one-way ANOVA, F5,53 = 18.03, P = 0.004 for Alteromonas sp. and F5,53 = 18.03, P = 0.0001 for V. alginolyticus). These findings reveal that coral larvae acquire nutrients previously taken up from the environment by bacteria. The additional nitrogen may increase the survival rate and fitness of the developing coral and therefore contribute to the successful maintenance of coral reefs.}, + language = {en}, + number = {8}, + urldate = {2022-11-02}, + journal = {Ecology and Evolution}, + author = {Ceh, Janja and Kilburn, Matt R. and Cliff, John B. and Raina, Jean-Baptiste and van Keulen, Mike and Bourne, David G.}, + year = {2013}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.642}, + keywords = {EMO, Bacteria, coral larvae, nanoSIMS, nitrogen}, + pages = {2393--2400}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/8PI4Q2QV/Ceh et al. - 2013 - Nutrient cycling in early coral life stages Pocil.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/L6GYJYMJ/ece3.html:text/html}, +} + +@article{radecker_heat_2022, + title = {Heat stress reduces the contribution of diazotrophs to coral holobiont nitrogen cycling}, + volume = {16}, + copyright = {2021 The Author(s)}, + issn = {1751-7370}, + url = {https://www.nature.com/articles/s41396-021-01158-8}, + doi = {10.1038/s41396-021-01158-8}, + abstract = {Efficient nutrient cycling in the coral-algal symbiosis requires constant but limited nitrogen availability. Coral-associated diazotrophs, i.e., prokaryotes capable of fixing dinitrogen, may thus support productivity in a stable coral-algal symbiosis but could contribute to its breakdown when overstimulated. However, the effects of environmental conditions on diazotroph communities and their interaction with other members of the coral holobiont remain poorly understood. Here we assessed the effects of heat stress on diazotroph diversity and their contribution to holobiont nutrient cycling in the reef-building coral Stylophora pistillata from the central Red Sea. In a stable symbiotic state, we found that nitrogen fixation by coral-associated diazotrophs constitutes a source of nitrogen to the algal symbionts. Heat stress caused an increase in nitrogen fixation concomitant with a change in diazotroph communities. Yet, this additional fixed nitrogen was not assimilated by the coral tissue or the algal symbionts. We conclude that although diazotrophs may support coral holobiont functioning under low nitrogen availability, altered nutrient cycling during heat stress abates the dependence of the coral host and its algal symbionts on diazotroph-derived nitrogen. Consequently, the role of nitrogen fixation in the coral holobiont is strongly dependent on its nutritional status and varies dynamically with environmental conditions.}, + language = {en}, + number = {4}, + urldate = {2022-11-02}, + journal = {The ISME Journal}, + author = {Rädecker, Nils and Pogoreutz, Claudia and Gegner, Hagen M. and Cárdenas, Anny and Perna, Gabriela and Geißler, Laura and Roth, Florian and Bougoure, Jeremy and Guagliardo, Paul and Struck, Ulrich and Wild, Christian and Pernice, Mathieu and Raina, Jean-Baptiste and Meibom, Anders and Voolstra, Christian R.}, + month = apr, + year = {2022}, + note = {Number: 4 +Publisher: Nature Publishing Group}, + keywords = {Microbial ecology, EMO, Climate-change ecology}, + pages = {1110--1118}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/XMDXIJE7/Rädecker et al. - 2022 - Heat stress reduces the contribution of diazotroph.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/3RSSVZZL/s41396-021-01158-8.html:text/html}, +} + +@article{radecker_nitrogen_2015, + title = {Nitrogen cycling in corals: the key to understanding holobiont functioning?}, + volume = {23}, + issn = {0966-842X}, + shorttitle = {Nitrogen cycling in corals}, + url = {https://www.sciencedirect.com/science/article/pii/S0966842X1500075X}, + doi = {10.1016/j.tim.2015.03.008}, + abstract = {Corals are animals that form close mutualistic associations with endosymbiotic photosynthetic algae of the genus Symbiodinium. Together they provide the calcium carbonate framework of coral reef ecosystems. The importance of the microbiome (i.e., bacteria, archaea, fungi, and viruses) to holobiont functioning has only recently been recognized. Given that growth and density of Symbiodinium within the coral host is highly dependent on nitrogen availability, nitrogen-cycling microbes may be of fundamental importance to the stability of the coral–algae symbiosis and holobiont functioning, in particular under nutrient-enriched and -depleted scenarios. We summarize what is known about nitrogen cycling in corals and conclude that disturbance of microbial nitrogen cycling may be tightly linked to coral bleaching and disease.}, + language = {en}, + number = {8}, + urldate = {2022-11-02}, + journal = {Trends in Microbiology}, + author = {Rädecker, Nils and Pogoreutz, Claudia and Voolstra, Christian R. and Wiedenmann, Jörg and Wild, Christian}, + month = aug, + year = {2015}, + keywords = {EMO, coral bleaching, coral disease, coral holobiont, nutrient limitation, symbiosis}, + pages = {490--497}, + file = {ScienceDirect Full Text PDF:/Users/birdy/Zotero/storage/QY245ZPK/Rädecker et al. - 2015 - Nitrogen cycling in corals the key to understandi.pdf:application/pdf;ScienceDirect Snapshot:/Users/birdy/Zotero/storage/I62WKSM8/S0966842X1500075X.html:text/html}, +} + +@article{zhang_multi-task_nodate, + title = {Multi-task few-shot learning with composed data augmentation for image classification}, + volume = {n/a}, + issn = {1751-9640}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1049/cvi2.12150}, + doi = {10.1049/cvi2.12150}, + abstract = {Few-shot learning (FSL) attempts to learn and optimise the model from a few examples on image classification, which is still threatened by data scarcity. To generate more data as supplements, data augmentation is considered as a powerful and popular technique to enhance the robustness of few-shot models. However, there are still some weaknesses in applying augmentation methods. For example, all augmented samples have similar semantic information with respect to different augmented transformations, which makes these traditional augmentation methods incapable of learning the property being varied. To address this challenge, we introduce multi-task learning to learn a primary few-shot classification task and an auxiliary self-supervised task, simultaneously. The self-supervised task can learn transformation property as auxiliary self-supervision signals to improve the performance of the primary few-shot classification task. Additionally, we propose a simple, flexible, and effective mechanism for decision fusion to further improve the reliability of the classifier, named model-agnostic ensemble inference (MAEI). Specifically, the MAEI mechanism can eliminate the influence of outliers for FSL using non-maximum suppression. Extensive experiment results demonstrate that our method can outperform other state-of-the-art methods by large margins.}, + language = {en}, + number = {n/a}, + urldate = {2022-11-03}, + journal = {IET Computer Vision}, + author = {Zhang, Rui and Yang, Yixin and Li, Yang and Wang, Jiabao and Li, Hang and Miao, Zhuang}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1049/cvi2.12150}, + keywords = {data augmentation, few-shot learning, multi-task learning, non-maximum suppression, self-supervised learning}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/QWAG8GIJ/Zhang et al. - Multi-task few-shot learning with composed data au.pdf:application/pdf}, +} + +@article{mngadi_estimating_2022, + title = {Estimating aboveground net primary productivity of reforested trees in an urban landscape using biophysical variables and remotely sensed data}, + volume = {802}, + issn = {00489697}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0048969721050336}, + doi = {10.1016/j.scitotenv.2021.149958}, + language = {en}, + urldate = {2022-11-21}, + journal = {Science of The Total Environment}, + author = {Mngadi, Mthembeni and Odindi, John and Mutanga, Onisimo and Sibanda, Mbulisi}, + month = jan, + year = {2022}, + pages = {149958}, +} + +@article{henderikx-freitas_diel_2022, + title = {Diel variations in the estimated refractive index of bulk oceanic particles}, + volume = {30}, + copyright = {\&\#169; 2022 Optica Publishing Group}, + issn = {1094-4087}, + url = {https://opg.optica.org/oe/abstract.cfm?uri=oe-30-24-44141}, + doi = {10.1364/OE.469565}, + abstract = {The index of refraction (n) of particles is an important parameter in optical models that aims to extract particle size and carbon concentrations from light scattering measurements. An inadequate choice of n can critically affect the characterization and interpretation of optically-derived parameters, including those from satellite-based models which provide the current view of how biogeochemical processes vary over the global ocean. Yet, little is known about how n varies over time and space to inform such models. Particularly, in situ estimates of n for bulk water samples and at diel-resolving time scales are rare. Here, we demonstrate a method to estimate n using simultaneously and independently collected particulate beam attenuation coefficients, particle size distribution data, and a Mie theory model. We apply this method to surface waters of the North Pacific Subtropical Gyre (NPSG) at hourly resolution. Clear diel cycles in n were observed, marked by minima around local sunrise and maxima around sunset, qualitatively consistent with several laboratory-based estimates of n for specific phytoplankton species. A sensitivity analysis showed that the daily oscillation in n amplitude was somewhat insensitive to broad variations in method assumptions, ranging from 11.3\&\#x2009;\&\#x00B1;\&\#x2009;4.3\&\#x0025; to 16.9\&\#x2009;\&\#x00B1;\&\#x2009;2.9\&\#x0025;. Such estimates are crucial for improvement of algorithms that extract the particle size and production from bulk optical measurements, and could potentially help establish a link between n variations and changes in cellular composition of in situ particles.}, + language = {EN}, + number = {24}, + urldate = {2022-11-22}, + journal = {Optics Express}, + author = {Henderikx-Freitas, Fernanda and Allen, James G. and Lansdorp, Bob M. and White, Angelicque E.}, + month = nov, + year = {2022}, + note = {Publisher: Optica Publishing Group}, + pages = {44141--44159}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/HPPVHGUH/Henderikx-Freitas et al. - 2022 - Diel variations in the estimated refractive index .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/YVISH8DF/fulltext.html:text/html}, +} + +@misc{noauthor_accelerated_nodate, + title = {Accelerated modern human–induced species losses: {Entering} the sixth mass extinction}, + shorttitle = {Accelerated modern human–induced species losses}, + url = {https://www.science.org/doi/10.1126/sciadv.1400253}, + language = {en}, + urldate = {2022-12-01}, + doi = {10.1126/sciadv.1400253}, + file = {Full Text:/Users/birdy/Zotero/storage/Z7BVSY72/Accelerated modern human–induced species losses E.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/AC954VKH/sciadv.html:text/html}, +} + +@article{koning_network_2020, + title = {A network of grassroots reserves protects tropical river fish diversity}, + volume = {588}, + copyright = {2020 The Author(s), under exclusive licence to Springer Nature Limited}, + issn = {1476-4687}, + url = {https://www.nature.com/articles/s41586-020-2944-y}, + doi = {10.1038/s41586-020-2944-y}, + abstract = {Intensive fisheries have reduced fish biodiversity and abundance in aquatic ecosystems worldwide1–3. ‘No-take’ marine reserves have become a cornerstone of marine ecosystem-based fisheries management4–6, and their benefits for adjacent fisheries are maximized when reserve design fosters synergies among nearby reserves7,8. The applicability of this marine reserve network paradigm to riverine biodiversity and inland fisheries remains largely untested. Here we show that reserves created by 23 separate communities in Thailand’s Salween basin have markedly increased fish richness, density, and biomass relative to adjacent areas. Moreover, key correlates of the success of protected areas in marine ecosystems—particularly reserve size and enforcement—predict differences in ecological benefits among riverine reserves. Occupying a central position in the network confers additional gains, underscoring the importance of connectivity within dendritic river systems. The emergence of network-based benefits is remarkable given that these reserves are young (less than 25 years old) and arose without formal coordination. Freshwater ecosystems are under-represented among the world’s protected areas9, and our findings suggest that networks of small, community-based reserves offer a generalizable model for protecting biodiversity and augmenting fisheries as the world’s rivers face unprecedented pressures10,11.}, + language = {en}, + number = {7839}, + urldate = {2022-12-01}, + journal = {Nature}, + author = {Koning, Aaron A. and Perales, K. Martin and Fluet-Chouinard, Etienne and McIntyre, Peter B.}, + month = dec, + year = {2020}, + note = {Number: 7839 +Publisher: Nature Publishing Group}, + keywords = {Conservation biology, Freshwater ecology}, + pages = {631--635}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/U38SBSD4/Koning et al. - 2020 - A network of grassroots reserves protects tropical.pdf:application/pdf}, +} + +@article{robbins_lawns_2001, + title = {Lawns and {Toxins}: {An} {Ecology} of the {City}}, + volume = {18}, + issn = {0264-2751}, + shorttitle = {Lawns and {Toxins}}, + url = {https://www.sciencedirect.com/science/article/pii/S0264275101000294}, + doi = {10.1016/S0264-2751(01)00029-4}, + abstract = {This paper surveys the problems of contemporary urban ecology through the lens of lawn chemical usage, exploring the difficulty of explaining and managing urban ecological dilemmas that, though built from the disaggregated choices of individuals, aggregate into large and serious issues. Introductory discussion surveys the seriousness of lawn chemicals as urban non-point pollution sources and suggests why the issue, and problems like it, it understudied. Analysis proceeds with a case study from the United States city of Columbus, Ohio, utilizing formal survey techniques and aanlysis of county assessor's data. The results suggest lawns and lawn care chemicals are expanding with urban sprewl and that users of high-input lawn chemical systems are more likely to be wealthy, well-educated, and knowledgeable about the negative environmental impacts of the actions than non-users. Further investigation demonstrates the instrumental logics of homeowners in pursuit of property values but also points to the moral and community-oriented institutions that enforce and propel high chemical use. The conclusions point to policy options for dealing with the lawn chemical dilemma but suggest the difficulties of circumventing the deeply structured roots of the problem.}, + language = {en}, + number = {6}, + urldate = {2022-12-01}, + journal = {Cities}, + author = {Robbins, Paul and Polderman, Annemarie and Birkenholtz, Trevor}, + month = dec, + year = {2001}, + keywords = {consumption, political ecology, urban ecology}, + pages = {369--380}, + file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/PAWPGVRF/S0264275101000294.html:text/html}, +} + +@misc{orenstein_whoi-plankton-_2015, + title = {{WHOI}-{Plankton}- {A} {Large} {Scale} {Fine} {Grained} {Visual} {Recognition} {Benchmark} {Dataset} for {Plankton} {Classification}}, + url = {http://arxiv.org/abs/1510.00745}, + abstract = {Planktonic organisms are of fundamental importance to marine ecosystems: they form the basis of the food web, provide the link between the atmosphere and the deep ocean, and influence global-scale biogeochemical cycles. Scientists are increasingly using imaging-based technologies to study these creatures in their natural habit. Images from such systems provide an unique opportunity to model and understand plankton ecosystems, but the collected datasets can be enormous. The Imaging FlowCytobot (IFCB) at Woods Hole Oceanographic Institution, for example, is an {\textbackslash}emph\{in situ\} system that has been continuously imaging plankton since 2006. To date, it has generated more than 700 million samples. Manual classification of such a vast image collection is impractical due to the size of the data set. In addition, the annotation task is challenging due to the large space of relevant classes, intra-class variability, and inter-class similarity. Methods for automated classification exist, but the accuracy is often below that of human experts. Here we introduce WHOI-Plankton: a large scale, fine-grained visual recognition dataset for plankton classification, which comprises over 3.4 million expert-labeled images across 70 classes. The labeled image set is complied from over 8 years of near continuous data collection with the IFCB at the Martha's Vineyard Coastal Observatory (MVCO). We discuss relevant metrics for evaluation of classification performance and provide results for a traditional method based on hand-engineered features and two methods based on convolutional neural networks.}, + urldate = {2023-01-10}, + publisher = {arXiv}, + author = {Orenstein, Eric C. and Beijbom, Oscar and Peacock, Emily E. and Sosik, Heidi M.}, + month = oct, + year = {2015}, + note = {arXiv:1510.00745 [cs]}, + keywords = {Computer Science - Computer Vision and Pattern Recognition}, + file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/V4XB9956/Orenstein et al. - 2015 - WHOI-Plankton- A Large Scale Fine Grained Visual R.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/UKCDAVKQ/1510.html:text/html}, +} + +@article{jones_matthew_b_gries_corinna_proceedings_2011, + title = {Proceedings of the {Environmental} {Information} {Management} {Conference} 2011}, + url = {https://eim.ecoinformatics.org/eim2011/eim-proceedings-2011/at_download/file}, + doi = {10.5060/D2NC5Z4X}, + abstract = {Lack of observations that span the wide range of critical space and time scales continues to limit many aspects of oceanography. As ocean observatories and observing networks mature, the role for optical technologies and approaches in helping to overcome this limitation continues to grow. As a result the quantity and complexity of data produced is increasing at a pace that threatens to overwhelm the capacity of individual researchers who must cope with large high-resolution datasets, complex, multistage analyses, and the challenges of preserving sufficient metadata and provenance information to ensure reproducibility and avoid costly reprocessing or data loss. We have developed approaches to address these new challenges in the context of a case study involving very large numbers ({\textasciitilde}1 billion) of images collected at coastal observatories by Imaging FlowCytobot, an automated submersible flow cytometer that produces high resolution images of plankton and other microscopic particles at rates up to 10 Hz for months to years. By developing partnerships amongst oceanographers generating and using such data and computer scientists focused on improving science outcomes, we have prototyped a replicable system. It provides simple and ubiquitous access to observational data and products via web services in standard formats; accelerates image processing by enabling algorithms developed with desktop applications to be rapidly deployed and evaluated on shared, high-performance servers; and improves data integrity by replacing error-prone manual data management processes with generalized, automated services. The informatics system is currently in operation for multiple Imaging FlowCytobot datasets and being tested with other types of ocean imagery.}, + language = {en}, + urldate = {2023-01-10}, + author = {Jones, Matthew B.; Gries, Corinna}, + year = {2011}, + note = {Publisher: University of California}, + file = {Jones, Matthew B.\; Gries, Corinna - 2011 - Proceedings of the Environmental Information Manag.pdf:/Users/birdy/Zotero/storage/8LGGH6BH/Jones, Matthew B.\; Gries, Corinna - 2011 - Proceedings of the Environmental Information Manag.pdf:application/pdf}, +} + +@misc{noauthor_creative_nodate, + title = {Creative {Team} {\textbar} {Pulse} at {Omega} {Mart}}, + url = {https://credits.meowwolf.com/omega-mart/factory/pulse}, + abstract = {Discover the artists and creative teams behind Meow Wolf's exhibitions. Works include sculpture, painting, fabrication, digital art, writing, film, and more.}, + urldate = {2023-01-13}, + journal = {Credits {\textbar} Meow Wolf}, + file = {Snapshot:/Users/birdy/Zotero/storage/8C2WZPAI/pulse.html:text/html}, +} + +@article{mahaffey_phytoplankton_2012, + title = {Phytoplankton response to deep seawater nutrient addition in the {North} {Pacific} {Subtropical} {Gyre}}, + volume = {460}, + issn = {0171-8630, 1616-1599}, + url = {http://www.int-res.com/abstracts/meps/v460/p13-34/}, + doi = {10.3354/meps09699}, + abstract = {We examined the phytoplankton response to the addition of nutrient-enriched deep seawater (DSW) and of nitrate only during shipboard experiments performed between July 2004 and May 2007 in the North Pacific Subtropical Gyre (NPSG). Chlorophyll a (chl a), community size structure and composition, carbon fixation (CF) rates, and nutrient concentrations were measured daily for 5 to 7 d under simulated in situ conditions. Despite the fact that the NPSG is a permanently stratified, oligotrophic biome, there was a seasonal response to the addition of DSW and nitrate. In summertime experiments, chl a and CF rates increased up to 18- and 22-fold, respectively, relative to unamended controls after an incubation period of 5 to 6 d. Nutrients were assimilated to below control concentrations. A shift from a Prochlorococcus-dominated to a Synechococcus- and diatom-dominated community, and an 8- to 12-fold increase in the chl a content of the {\textgreater} 2 µm size fraction were observed. Addition of nitrate only increased chl a and CF rates 10- and 7-fold, respectively. In wintertime experiments, chl a and CF rates increased up to 4- and 9-fold, respectively, after an incubation period of up to 7 d. Only 50\% of nutrients were assimilated and nitrate alone stimulated a 2-fold increase in chl a and CF rates. We explore the role of nutrient limitation, community composition, grazing, and light in explaining our observations. Findings from this seasonal and multi-year field experiment demonstrate how little we know about bloom development in the NPSG and highlight the potential risk in extrapolating the response of phytoplankton to natural or artificial fertilization from short-term studies.}, + language = {en}, + urldate = {2023-02-22}, + journal = {Marine Ecology Progress Series}, + author = {Mahaffey, C and Björkman, Km and Karl, Dm}, + month = jul, + year = {2012}, + pages = {13--34}, + file = {Mahaffey et al. - 2012 - Phytoplankton response to deep seawater nutrient a.pdf:/Users/birdy/Zotero/storage/BZYUHQ9I/Mahaffey et al. - 2012 - Phytoplankton response to deep seawater nutrient a.pdf:application/pdf}, +} + +@article{tilman_invasions_1984, + title = {Invasions of equilibria: tests of resource competition using two species of algae}, + volume = {61}, + issn = {1432-1939}, + shorttitle = {Invasions of equilibria}, + url = {https://doi.org/10.1007/BF00396760}, + doi = {10.1007/BF00396760}, + abstract = {Single-species, steady-state chemostat cultures of two freshwater diatoms showed that Fragilaria crotonensis had a lower equilibrial requirement for silicate than did Tabellaria fenestratra. Using this information, resource competition theory predicts that Fragilaria should be able to invade silicate-limited equilibrium populations of Tabellaria, but Tabellaria should not be able to invade silicatelimited Fragilaria. This prediction was supported by a series of invasion experiments. The two species did not have detectable differences in their phosphate requirements. Invasion experiments showed that Fragilaria could invade Tabellaria cultures, but that Tabellaria could not invade Fragilaria cultures under phosphate-limited conditions. These results are consistent with predictions based on previous studies of the phosphate physiology of these genera.}, + language = {en}, + number = {2}, + urldate = {2023-02-22}, + journal = {Oecologia}, + author = {Tilman, David and Sterner, Robert W.}, + month = feb, + year = {1984}, + pages = {197--200}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/7S64RFJG/Tilman and Sterner - 1984 - Invasions of equilibria tests of resource competi.pdf:application/pdf}, +} + +@article{tilman_resource_1977, + title = {Resource {Competition} between {Plankton} {Algae}: {An} {Experimental} and {Theoretical} {Approach}}, + volume = {58}, + issn = {00129658}, + shorttitle = {Resource {Competition} between {Plankton} {Algae}}, + url = {http://doi.wiley.com/10.2307/1935608}, + doi = {10.2307/1935608}, + language = {en}, + number = {2}, + urldate = {2023-02-22}, + journal = {Ecology}, + author = {Tilman, David}, + month = mar, + year = {1977}, + pages = {338--348}, + file = {Tilman - 1977 - Resource Competition between Plankton Algae An Ex.pdf:/Users/birdy/Zotero/storage/E6YRP87L/Tilman - 1977 - Resource Competition between Plankton Algae An Ex.pdf:application/pdf}, +} + +@article{burghardt_impact_2009, + title = {Impact of {Native} {Plants} on {Bird} and {Butterfly} {Biodiversity} in {Suburban} {Landscapes}}, + volume = {23}, + issn = {1523-1739}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1523-1739.2008.01076.x}, + doi = {10.1111/j.1523-1739.2008.01076.x}, + abstract = {Abstract: Managed landscapes in which non-native ornamental plants are favored over native vegetation now dominate the United States, particularly east of the Mississippi River. We measured how landscaping with native plants affects the avian and lepidopteran communities on 6 pairs of suburban properties in southeastern Pennsylvania. One property in each pair was landscaped entirely with native plants and the other exhibited a more conventional suburban mixture of plants—a native canopy with non-native groundcover and shrubs. Vegetation sampling confirmed that total plant cover and plant diversity did not differ between treatments, but non-native plant cover was greater on the conventional sites and native plant cover was greater on the native sites. Several avian (abundance, species richness, biomass, and breeding-bird abundance) and larval lepidopteran (abundance and species richness) community parameters were measured from June 2006 to August 2006. Native properties supported significantly more caterpillars and caterpillar species and significantly greater bird abundance, diversity, species richness, biomass, and breeding pairs of native species. Of particular importance is that bird species of regional conservation concern were 8 times more abundant and significantly more diverse on native properties. In our study area, native landscaping positively influenced the avian and lepidopteran carrying capacity of suburbia and provided a mechanism for reducing biodiversity losses in human-dominated landscapes.}, + language = {en}, + number = {1}, + urldate = {2023-02-23}, + journal = {Conservation Biology}, + author = {Burghardt, Karin T. and Tallamy, Douglas W. and Gregory Shriver, W.}, + year = {2009}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1523-1739.2008.01076.x}, + keywords = {biodiversity, aves, biodiversidad, birds, ecosistemas bajo manejo, Lepidoptera, Lepidóptera, managed ecosystems, native plants, non-native plants, paisaje suburbano, plantas nativas, plantas no nativas, suburban landscape}, + pages = {219--224}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/9IP46YJ7/Burghardt et al. - 2009 - Impact of Native Plants on Bird and Butterfly Biod.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/2M776SVN/j.1523-1739.2008.01076.html:text/html}, +} + +@misc{noauthor_pnas2211758120pdf_nodate, + title = {pnas.2211758120.pdf: 1.873 {Systems} {Ecology} in {Theory}}, + url = {https://canvas.mit.edu/courses/18350/files/3301971?module_item_id=856541}, + urldate = {2023-05-02}, + file = {pnas.2211758120.pdf\: 1.873 Systems Ecology in Theory:/Users/birdy/Zotero/storage/FR2ZNW6N/3301971.html:text/html}, +} + +@article{munch_constraining_2023, + title = {Constraining nonlinear time series modeling with the metabolic theory of ecology}, + volume = {120}, + issn = {0027-8424, 1091-6490}, + url = {https://pnas.org/doi/10.1073/pnas.2211758120}, + doi = {10.1073/pnas.2211758120}, + abstract = {Forecasting the response of ecological systems to environmental change is a critical challenge for sustainable management. The metabolic theory of ecology (MTE) posits scaling of biological rates with temperature, but it has had limited application to population dynamic forecasting. Here we use the temperature dependence of the MTE to constrain empirical dynamic modeling (EDM), an equation-free nonlinear machine learning approach for forecasting. By rescaling time with temperature and modeling dynamics on a “metabolic time step,” our method (MTE-EDM) improved forecast accuracy in 18 of 19 empirical ectotherm time series (by 19\% on average), with the largest gains in more seasonal environments. MTE-EDM assumes that temperature affects only the rate, rather than the form, of population dynamics, and that interacting species have approximately similar temperature dependence. A review of laboratory studies suggests these assumptions are reasonable, at least approximately, though not for all ecological systems. Our approach highlights how to combine modern data-driven forecasting techniques with ecological theory and mechanistic understanding to predict the response of complex ecosystems to temperature variability and trends.}, + language = {en}, + number = {12}, + urldate = {2023-05-02}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Munch, Stephan B. and Rogers, Tanya L. and Symons, Celia C. and Anderson, David and Pennekamp, Frank}, + month = mar, + year = {2023}, + pages = {e2211758120}, + file = {pnas.2211758120.pdf:/Users/birdy/Downloads/pnas.2211758120.pdf:application/pdf}, +} + +@article{sugihara_nonlinear_1990, + title = {Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series}, + volume = {344}, + copyright = {1990 Nature Publishing Group}, + issn = {1476-4687}, + url = {https://www.nature.com/articles/344734a0}, + doi = {10.1038/344734a0}, + abstract = {An approach is presented for making short-term predictions about the trajectories of chaotic dynamical systems. The method is applied to data on measles, chickenpox, and marine phytoplankton populations, to show how apparent noise associated with deterministic chaos can be distinguished from sampling error and other sources of externally induced environmental noise.}, + language = {en}, + number = {6268}, + urldate = {2023-05-02}, + journal = {Nature}, + author = {Sugihara, George and May, Robert M.}, + month = apr, + year = {1990}, + note = {Number: 6268 +Publisher: Nature Publishing Group}, + keywords = {Humanities and Social Sciences, multidisciplinary, Science}, + pages = {734--741}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/LDYABKT4/Sugihara and May - 1990 - Nonlinear forecasting as a way of distinguishing c.pdf:application/pdf}, +} + +@article{fujita_alternative_2023, + title = {Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics}, + volume = {11}, + issn = {2049-2618}, + url = {https://doi.org/10.1186/s40168-023-01474-5}, + doi = {10.1186/s40168-023-01474-5}, + abstract = {Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in human microbiomes.}, + language = {en}, + number = {1}, + urldate = {2023-05-02}, + journal = {Microbiome}, + author = {Fujita, Hiroaki and Ushio, Masayuki and Suzuki, Kenta and Abe, Masato S. and Yamamichi, Masato and Iwayama, Koji and Canarini, Alberto and Hayashi, Ibuki and Fukushima, Keitaro and Fukuda, Shinji and Kiers, E. Toby and Toju, Hirokazu}, + month = mar, + year = {2023}, + keywords = {Alternative stable states, Biodiversity, Biological communities, Chaos, Community collapse, Community stability, Dysbiosis, Empirical dynamic modeling, Microbiome dynamics, Non-linear dynamics}, + pages = {63}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/LJZAZT65/Fujita et al. - 2023 - Alternative stable states, nonlinear behavior, and.pdf:application/pdf}, +} + +@inproceedings{kay_caltech_2022, + address = {Cham}, + series = {Lecture {Notes} in {Computer} {Science}}, + title = {The {Caltech} {Fish} {Counting} {Dataset}: {A} {Benchmark} for {Multiple}-{Object} {Tracking} and {Counting}}, + isbn = {978-3-031-20074-8}, + shorttitle = {The {Caltech} {Fish} {Counting} {Dataset}}, + doi = {10.1007/978-3-031-20074-8_17}, + abstract = {We present the Caltech Fish Counting Dataset (CFC ), a large-scale dataset for detecting, tracking, and counting fish in sonar videos. We identify sonar videos as a rich source of data for advancing low signal-to-noise computer vision applications and tackling domain generalization in multiple-object tracking (MOT) and counting. In comparison to existing MOT and counting datasets, which are largely restricted to videos of people and vehicles in cities, CFC is sourced from a natural-world domain where targets are not easily resolvable and appearance features cannot be easily leveraged for target re-identification. With over half a million annotations in over 1,500 videos sourced from seven different sonar cameras, CFC allows researchers to train MOT and counting algorithms and evaluate generalization performance at unseen test locations. We perform extensive baseline experiments and identify key challenges and opportunities for advancing the state of the art in generalization in MOT and counting.}, + language = {en}, + booktitle = {Computer {Vision} – {ECCV} 2022}, + publisher = {Springer Nature Switzerland}, + author = {Kay, Justin and Kulits, Peter and Stathatos, Suzanne and Deng, Siqi and Young, Erik and Beery, Sara and Van Horn, Grant and Perona, Pietro}, + editor = {Avidan, Shai and Brostow, Gabriel and Cissé, Moustapha and Farinella, Giovanni Maria and Hassner, Tal}, + year = {2022}, + keywords = {Counting, Detection, Tracking, Video dataset}, + pages = {290--311}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/M2APBWFQ/Kay et al. - 2022 - The Caltech Fish Counting Dataset A Benchmark for.pdf:application/pdf}, +} + +@article{chang_engineering_2021, + title = {Engineering complex communities by directed evolution}, + volume = {5}, + copyright = {2021 The Author(s), under exclusive licence to Springer Nature Limited}, + issn = {2397-334X}, + url = {https://www.nature.com/articles/s41559-021-01457-5}, + doi = {10.1038/s41559-021-01457-5}, + abstract = {Directed evolution has been used for decades to engineer biological systems at or below the organismal level. Above the organismal level, a small number of studies have attempted to artificially select microbial ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. Here, we have developed a flexible modelling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions. By artificially selecting hundreds of in silico microbial metacommunities under identical conditions, we first show that the main breeding methods used to date, which do not necessarily let communities reach their ecological equilibrium, are outperformed by a simple screen of sufficiently mature communities. We then identify a range of alternative directed evolution strategies that, particularly when applied in combination, are well suited for the top-down engineering of large, diverse and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure–function landscape to be navigated in search of dynamically stable and ecologically resilient communities with desired quantitative attributes.}, + language = {en}, + number = {7}, + urldate = {2023-05-11}, + journal = {Nature Ecology \& Evolution}, + author = {Chang, Chang-Yu and Vila, Jean C. C. and Bender, Madeline and Li, Richard and Mankowski, Madeleine C. and Bassette, Molly and Borden, Julia and Golfier, Stefan and Sanchez, Paul Gerald L. and Waymack, Rachel and Zhu, Xinwen and Diaz-Colunga, Juan and Estrela, Sylvie and Rebolleda-Gomez, Maria and Sanchez, Alvaro}, + month = jul, + year = {2021}, + note = {Number: 7 +Publisher: Nature Publishing Group}, + keywords = {Community ecology, Evolutionary theory}, + pages = {1011--1023}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/28DAPUW2/Chang et al. - 2021 - Engineering complex communities by directed evolut.pdf:application/pdf}, +} + +@article{lazebnik_can_2002, + title = {Can a biologist fix a radio?—{Or}, what {I} learned while studying apoptosis}, + volume = {2}, + issn = {1535-6108, 1878-3686}, + shorttitle = {Can a biologist fix a radio?}, + url = {https://www.cell.com/cancer-cell/abstract/S1535-6108(02)00133-2}, + doi = {10.1016/S1535-6108(02)00133-2}, + language = {English}, + number = {3}, + urldate = {2023-05-11}, + journal = {Cancer Cell}, + author = {Lazebnik, Yuri}, + month = sep, + year = {2002}, + pmid = {12242150}, + note = {Publisher: Elsevier}, + pages = {179--182}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/MM347IX4/Lazebnik - 2002 - Can a biologist fix a radio—Or, what I learned wh.pdf:application/pdf}, +} + +@article{deyle_tracking_2016, + title = {Tracking and forecasting ecosystem interactions in real time}, + volume = {283}, + url = {https://royalsocietypublishing.org/doi/full/10.1098/rspb.2015.2258}, + doi = {10.1098/rspb.2015.2258}, + abstract = {Evidence shows that species interactions are not constant but change as the ecosystem shifts to new states. Although controlled experiments and model investigations demonstrate how nonlinear interactions can arise in principle, empirical tools to track and predict them in nature are lacking. Here we present a practical method, using available time-series data, to measure and forecast changing interactions in real systems, and identify the underlying mechanisms. The method is illustrated with model data from a marine mesocosm experiment and limnologic field data from Sparkling Lake, WI, USA. From simple to complex, these examples demonstrate the feasibility of quantifying, predicting and understanding state-dependent, nonlinear interactions as they occur in situ and in real time—a requirement for managing resources in a nonlinear, non-equilibrium world.}, + number = {1822}, + urldate = {2023-05-16}, + journal = {Proceedings of the Royal Society B: Biological Sciences}, + author = {Deyle, Ethan R. and May, Robert M. and Munch, Stephan B. and Sugihara, George}, + month = jan, + year = {2016}, + note = {Publisher: Royal Society}, + keywords = {changing interaction strength, community matrix, empirical dynamics, nonlinear, S-map, state space reconstruction}, + pages = {20152258}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/GAGG2JHP/Deyle et al. - 2016 - Tracking and forecasting ecosystem interactions in.pdf:application/pdf}, +} + +@article{gonzalez_automatic_2019, + title = {Automatic plankton quantification using deep features}, + volume = {41}, + issn = {0142-7873}, + url = {https://doi.org/10.1093/plankt/fbz023}, + doi = {10.1093/plankt/fbz023}, + abstract = {The study of marine plankton data is vital to monitor the health of the world’s oceans. In recent decades, automatic plankton recognition systems have proved useful to address the vast amount of data collected by specially engineered in situ digital imaging systems. At the beginning, these systems were developed and put into operation using traditional automatic classification techniques, which were fed with hand-designed local image descriptors (such as Fourier features), obtaining quite successful results. In the past few years, there have been many advances in the computer vision community with the rebirth of neural networks. In this paper, we leverage how descriptors computed using convolutional neural networks trained with out-of-domain data are useful to replace hand-designed descriptors in the task of estimating the prevalence of each plankton class in a water sample. To achieve this goal, we have designed a broad set of experiments that show how effective these deep features are when working in combination with state-of-the-art quantification algorithms.}, + number = {4}, + urldate = {2023-05-18}, + journal = {Journal of Plankton Research}, + author = {González, Pablo and Castaño, Alberto and Peacock, Emily E and Díez, Jorge and Del Coz, Juan José and Sosik, Heidi M}, + month = jul, + year = {2019}, + pages = {449--463}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/YS4AGBKW/González et al. - 2019 - Automatic plankton quantification using deep featu.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/M6LWQPI2/5537667.html:text/html}, +} + +@inproceedings{walker_improving_2021, + address = {Montreal, BC, Canada}, + title = {Improving {Rare}-{Class} {Recognition} of {Marine} {Plankton} with {Hard} {Negative} {Mining}}, + isbn = {978-1-66540-191-3}, + url = {https://ieeexplore.ieee.org/document/9607849/}, + doi = {10.1109/ICCVW54120.2021.00410}, + abstract = {Biological oceanographers are increasingly adopting machine learning techniques to conduct quantitative assessments of marine plankton. Most supervised plankton classifiers are trained on labeled image datasets annotated by domain experts under the closed world assumption: all object classes and their priors are the same during both training and deployment. This assumption, however, is hard to satisfy in the actual ocean where data is subject to dataset shift due to shifting populations and from the introduction of object categories not seen during training. Here we present an alternative approach for training and evaluating plankton classifiers under the more realistic open world scenario. We specifically address the problems of out-of-distribution detection and dataset shift under the class imbalance setting where downsampling is needed to reliably detect and classify relatively rare target classes. We apply a hard negative mining approach called Background Resampling to perform downsampling and compare it to other strategies. We show that Background Resampling improves detection of novel particle classes while simultaneously providing competitive classification performance under dataset shift.}, + language = {en}, + urldate = {2023-05-18}, + booktitle = {2021 {IEEE}/{CVF} {International} {Conference} on {Computer} {Vision} {Workshops} ({ICCVW})}, + publisher = {IEEE}, + author = {Walker, Joseph L. and Orenstein, Eric C.}, + month = oct, + year = {2021}, + pages = {3665--3675}, + file = {Walker and Orenstein - 2021 - Improving Rare-Class Recognition of Marine Plankto.pdf:/Users/birdy/Zotero/storage/9IBBTBGB/Walker and Orenstein - 2021 - Improving Rare-Class Recognition of Marine Plankto.pdf:application/pdf}, +} + +@article{kerr_collaborative_2020, + title = {Collaborative {Deep} {Learning} {Models} to {Handle} {Class} {Imbalance} in {FlowCam} {Plankton} {Imagery}}, + volume = {8}, + issn = {2169-3536}, + doi = {10.1109/ACCESS.2020.3022242}, + abstract = {Using automated imaging technologies, it is now possible to generate previously unprecedented volumes of plankton image data which can be used to study the composition of plankton assemblages. However, the current need to manually classify individual images introduces a bottleneck into processing chains. Although Machine Learning techniques have been used to try and address this issue, past efforts have suffered from accuracy limitations, especially in minority classes. Here we use state-of-the-art methods in Deep Learning to investigate suitable architectures for training an automated plankton classification system which achieves high efficacy for both abundant and rare taxa. We collected live plankton from Station L4 in the Western English Channel and imaged 11,371 particles covering 104 taxonomic groups using the automated plankton imaging system FlowCam. The image set contained a severe class imbalance, with some taxa represented by {\textgreater} 600 images while other, rarer taxa were represented by just 14. We demonstrate that by allowing multiple Deep Learning models to collaborate in a single classification system, classification accuracy improves for minority classes when compared with the best individual model. The top collaborative model achieved a 6 \% improvement in F1 accuracy over the best individual model, while overall accuracy improved by 3.2 \%. This resulted in a 97.4 \% overall accuracy score and a 96.2 \% F1 macro score on a separate holdout test set containing 104 taxonomic groups. Based on a survey of similar studies in the literature, we believe collaborative deep learning models can significantly improve the accuracy of existing automated plankton classification systems.}, + journal = {IEEE Access}, + author = {Kerr, Thomas and Clark, James R. and Fileman, Elaine S. and Widdicombe, Claire E. and Pugeault, Nicolas}, + year = {2020}, + note = {Conference Name: IEEE Access}, + keywords = {Machine learning, deep learning, Automated plankton identification, Biological neural networks, Collaboration, Computational modeling, Convolution, convolutional neural networks, Feature extraction, FlowCam, multi-layer perceptron model, Station L4, Training}, + pages = {170013--170032}, + file = {IEEE Xplore Abstract Record:/Users/birdy/Zotero/storage/E9MSTETK/stamp.html:text/html;IEEE Xplore Full Text PDF:/Users/birdy/Zotero/storage/P2XN56XI/Kerr et al. - 2020 - Collaborative Deep Learning Models to Handle Class.pdf:application/pdf}, +} + +@article{ellen_improving_2019, + title = {Improving plankton image classification using context metadata}, + volume = {17}, + issn = {1541-5856}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lom3.10324}, + doi = {10.1002/lom3.10324}, + abstract = {Advances in both hardware and software are enabling rapid proliferation of in situ plankton imaging methods, requiring more effective machine learning approaches to image classification. Deep Learning methods, such as convolutional neural networks (CNNs), show marked improvement over traditional feature-based supervised machine learning algorithms, but require careful optimization of hyperparameters and adequate training sets. Here, we document some best practices in applying CNNs to zooplankton and marine snow images and note where our results differ from contemporary Deep Learning findings in other domains. We boost the performance of CNN classifiers by incorporating metadata of different types and illustrate how to assimilate metadata beyond simple concatenation. We utilize both geotemporal (e.g., sample depth, location, time of day) and hydrographic (e.g., temperature, salinity, chlorophyll a) metadata and show that either type by itself, or both combined, can substantially reduce error rates. Incorporation of context metadata also boosts performance of the feature-based classifiers we evaluated: Random Forest, Extremely Randomized Trees, Gradient Boosted Classifier, Support Vector Machines, and Multilayer Perceptron. For our assessments, we use an original data set of 350,000 in situ images (roughly 50\% marine snow and 50\% non-snow sorted into 26 categories) from a novel in situ Zooglider. We document asymptotically increasing performance with more computationally intensive techniques, such as substantially deeper networks and artificially augmented data sets. Our best model achieves 92.3\% accuracy with our 27-class data set. We provide guidance for further refinements that are likely to provide additional gains in classifier accuracy.}, + language = {en}, + number = {8}, + urldate = {2023-05-18}, + journal = {Limnology and Oceanography: Methods}, + author = {Ellen, Jeffrey S. and Graff, Casey A. and Ohman, Mark D.}, + year = {2019}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10324}, + pages = {439--461}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/3BP4JKLG/Ellen et al. - 2019 - Improving plankton image classification using cont.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/H2X6CN9U/lom3.html:text/html}, +} + +@article{wilchek_human---loop_2023, + title = {Human-in-the-loop for computer vision assurance: {A} survey}, + volume = {123}, + issn = {0952-1976}, + shorttitle = {Human-in-the-loop for computer vision assurance}, + url = {https://www.sciencedirect.com/science/article/pii/S0952197623005602}, + doi = {10.1016/j.engappai.2023.106376}, + abstract = {Human-in-the-loop (HITL), a key branch of Human–Computer Interaction (HCI), is increasingly proposed in the research literature as a key assurance method for automated analyses and predictive application designs. As the need increases to improve methods in Artificial Intelligence (AI) model training, optimize systems performance, provide AI explainability, and monitor AI system operations, the concept of HITL is gaining traction due to its value in solving these challenges. This survey of existing works on HITL from a computer vision system design perspective focuses on the following AI assurance principles: (1) improved data assurance, such as data preparation or automated data labeling; (2) algorithmic assurance, such as managing uncertainty and AI trustworthiness; and (3) critical limitations and capabilities introduced by HITL into a system’s operational efficiency. We survey prior work within these foci, including technical strengths and weaknesses of novel approaches and ongoing research. This review of the state of the art in HITL computer vision research supports an informed discussion of considerations and future opportunities in this critical space.}, + language = {en}, + urldate = {2023-05-19}, + journal = {Engineering Applications of Artificial Intelligence}, + author = {Wilchek, Matthew and Hanley, Will and Lim, Jude and Luther, Kurt and Batarseh, Feras A.}, + month = aug, + year = {2023}, + keywords = {AI assurance, Computer vision, Human-in-the-loop, Human–computer interaction, Learning systems, Object detection}, + pages = {106376}, + file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/XUK4ZLLN/S0952197623005602.html:text/html}, +} + +@article{bian_understanding_2023, + title = {Understanding opposing predictions of {Prochlorococcus} in a changing climate}, + volume = {14}, + copyright = {2023 The Author(s)}, + issn = {2041-1723}, + url = {https://www.nature.com/articles/s41467-023-36928-9}, + doi = {10.1038/s41467-023-36928-9}, + abstract = {Statistically derived species distribution models (SDMs) are increasingly used to predict ecological changes on a warming planet. For Prochlorococcus, the most abundant phytoplankton, an established statistical prediction conflicts with dynamical models as they predict large, opposite, changes in abundance. We probe the SDM at various spatial-temporal scales, showing that light and temperature fail to explain both temporal fluctuations and sharp spatial transitions. Strong correlations between changes in temperature and population emerge only at very large spatial scales, as transects pass through transitions between regions of high and low abundance. Furthermore, a two-state model based on a temperature threshold matches the original SDM in the surface ocean. We conclude that the original SDM has little power to predict changes when Prochlorococcus is already abundant, which resolves the conflict with dynamical models. Our conclusion suggests that SDMs should prove efficacy across multiple spatial-temporal scales before being trusted in a changing ocean.}, + language = {en}, + number = {1}, + urldate = {2023-06-05}, + journal = {Nature Communications}, + author = {Bian, Vincent and Cai, Merrick and Follett, Christopher L.}, + month = mar, + year = {2023}, + note = {Number: 1 +Publisher: Nature Publishing Group}, + keywords = {Biogeography, Projection and prediction, DAR1}, + pages = {1445}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/PQNIBYHE/Bian et al. - 2023 - Understanding opposing predictions of Prochlorococ.pdf:application/pdf}, +} + +@misc{noauthor_ocean_nodate, + title = {Ocean mover’s distance: using optimal transport for analysing oceanographic data}, + shorttitle = {Ocean mover’s distance}, + url = {https://royalsocietypublishing.org/doi/epdf/10.1098/rspa.2021.0875}, + language = {en}, + urldate = {2023-06-05}, + doi = {10.1098/rspa.2021.0875}, + file = {Full Text:/Users/birdy/Zotero/storage/YPAJVFL3/Ocean mover’s distance using optimal transport fo.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/MVL45IZE/rspa.2021.html:text/html}, +} + +@article{irisson_machine_2022, + title = {Machine {Learning} for the {Study} of {Plankton} and {Marine} {Snow} from {Images}}, + volume = {14}, + url = {https://doi.org/10.1146/annurev-marine-041921-013023}, + doi = {10.1146/annurev-marine-041921-013023}, + abstract = {Quantitative imaging instruments produce a large number of images of plankton and marine snow, acquired in a controlled manner, from which the visual characteristics of individual objects and their in situ concentrations can be computed. To exploit this wealth of information, machine learning is necessary to automate tasks such as taxonomic classification. Through a review of the literature, we highlight the progress of those machine classifiers and what they can and still cannot be trusted for. Several examples showcase how the combination of quantitative imaging with machine learning has brought insights on pelagic ecology. They also highlight what is still missing and how images could be exploited further through trait-based approaches. In the future, we suggest deeper interactions with the computer sciences community, the adoption of data standards, and the more systematic sharing of databases to build a global community of pelagic image providers and users.}, + number = {1}, + urldate = {2023-07-14}, + journal = {Annual Review of Marine Science}, + author = {Irisson, Jean-Olivier and Ayata, Sakina-Dorothée and Lindsay, Dhugal J. and Karp-Boss, Lee and Stemmann, Lars}, + year = {2022}, + pmid = {34460314}, + note = {\_eprint: https://doi.org/10.1146/annurev-marine-041921-013023}, + pages = {277--301}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/IWC2B69M/Irisson et al. - 2022 - Machine Learning for the Study of Plankton and Mar.pdf:application/pdf}, +} + +@article{macneil_plankton_2021, + title = {Plankton classification with high-throughput submersible holographic microscopy and transfer learning}, + volume = {21}, + issn = {2730-7182}, + url = {https://doi.org/10.1186/s12862-021-01839-0}, + doi = {10.1186/s12862-021-01839-0}, + abstract = {Plankton are foundational to marine food webs and an important feature for characterizing ocean health. Recent developments in quantitative imaging devices provide in-flow high-throughput sampling from bulk volumes—opening new ecological challenges exploring microbial eukaryotic variation and diversity, alongside technical hurdles to automate classification from large datasets. However, a limited number of deployable imaging instruments have been coupled with the most prominent classification algorithms—effectively limiting the extraction of curated observations from field deployments. Holography offers relatively simple coherent microscopy designs with non-intrusive 3-D image information, and rapid frame rates that support data-driven plankton imaging tasks. Classification benchmarks across different domains have been set with transfer learning approaches, focused on repurposing pre-trained, state-of-the-art deep learning models as classifiers to learn new image features without protracted model training times. Combining the data production of holography, digital image processing, and computer vision could improve in-situ monitoring of plankton communities and contribute to sampling the diversity of microbial eukaryotes.}, + number = {1}, + urldate = {2023-07-14}, + journal = {BMC Ecology and Evolution}, + author = {MacNeil, Liam and Missan, Sergey and Luo, Junliang and Trappenberg, Thomas and LaRoche, Julie}, + month = jun, + year = {2021}, + keywords = {Classification workflow, Convolutional neural networks, Deep learning, Deployable microscope, High-throughput imaging, Holographic microscopy, Plankton}, + pages = {123}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/983GMGI8/MacNeil et al. - 2021 - Plankton classification with high-throughput subme.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/SAXVJK45/s12862-021-01839-0.html:text/html}, +} + +@article{scotti_ecosystem-based_2022, + title = {Ecosystem-based fisheries management increases catch and carbon sequestration through recovery of exploited stocks: {The} western {Baltic} {Sea} case study}, + volume = {9}, + issn = {2296-7745}, + shorttitle = {Ecosystem-based fisheries management increases catch and carbon sequestration through recovery of exploited stocks}, + url = {https://www.frontiersin.org/articles/10.3389/fmars.2022.879998}, + abstract = {Legal requirement in Europe asks for Ecosystem-Based Fisheries Management (EBFM) in European seas, including consideration of trophic interactions and minimization of negative impacts of fishing on food webs and ecosystem functioning. This study presents the first mass-balanced ecosystem model focused on the western Baltic Sea (WBS). Results show that heavy fishing pressure exerted on the WBS has forced top predators such as harbour porpoise and cod to cover their dietary needs by shifting from forage fish to other prey or find food outside of the model area. The model was then developed to explore the dynamics of four future fishery scenarios: (1) business as usual (BAU), (2) maximum sustainable fishing (F = FMSY), (3) half of FMSY, and (4) EBFM with F = 0.5 FMSY for forage fish and F = 0.8 FMSY for other fish. Simulations show that BAU would perpetuate low catches from depleted stocks with a high risk of extinction for harbour porpoise. In contrast, the EBFM scenario would allow the recovery of harbour porpoise, forage fish and cod with increases in catch of herring and cod. EBFM promotes ecosystem resilience to eutrophication and ocean warming, and through the rebuilding of commercial stocks increases by more than three times carbon sequestration compared to BAU. The model provides an interrelated assessment of trophic guilds in the WBS, as required by European law to assess whether European seas are in good environmental status.}, + urldate = {2023-07-14}, + journal = {Frontiers in Marine Science}, + author = {Scotti, Marco and Opitz, Silvia and MacNeil, Liam and Kreutle, Axel and Pusch, Christian and Froese, Rainer}, + year = {2022}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/DFDLU7N4/Scotti et al. - 2022 - Ecosystem-based fisheries management increases cat.pdf:application/pdf}, +} + +@article{tsakalakis_diel_2022, + title = {Diel light cycles affect phytoplankton competition in the global ocean}, + volume = {31}, + copyright = {© 2022 The Authors. Global Ecology and Biogeography published by John Wiley \& Sons Ltd.}, + issn = {1466-8238}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/geb.13562}, + doi = {10.1111/geb.13562}, + abstract = {Aim Light, essential for photosynthesis, is present in two periodic cycles in nature: seasonal and diel. Although seasonality of light is typically resolved in ocean biogeochemical–ecosystem models because of its significance for seasonal succession and biogeography of phytoplankton, the diel light cycle is generally not resolved. The goal of this study is to demonstrate the impact of diel light cycles on phytoplankton competition and biogeography in the global ocean. Location Global ocean. Major taxa studied Phytoplankton. Methods We use a three-dimensional global ocean model and compare simulations of high temporal resolution with and without diel light cycles. The model simulates 15 phytoplankton types with different cell sizes, encompassing two broad ecological strategies: small cells with high nutrient affinity (gleaners) and larger cells with high maximal growth rate (opportunists). Both are grazed by zooplankton and limited by nitrogen, phosphorus and iron. Results Simulations show that diel cycles of light induce diel cycles in limiting nutrients in the global ocean. Diel nutrient cycles are associated with higher concentrations of limiting nutrients, by 100\% at low latitudes (−40° to 40°), a process that increases the relative abundance of opportunists over gleaners. Size classes with the highest maximal growth rates from both gleaner and opportunist groups are favoured by diel light cycles. This mechanism weakens as latitude increases, because the effects of the seasonal cycle dominate over those of the diel cycle. Main conclusions Understanding the mechanisms that govern phytoplankton biogeography is crucial for predicting ocean ecosystem functioning and biogeochemical cycles. We show that the diel light cycle has a significant impact on phytoplankton competition and biogeography, indicating the need for understanding the role of diel processes in shaping macroecological patterns in the global ocean.}, + language = {en}, + number = {9}, + urldate = {2023-08-04}, + journal = {Global Ecology and Biogeography}, + author = {Tsakalakis, Ioannis and Follows, Michael J. and Dutkiewicz, Stephanie and Follett, Christopher L. and Vallino, Joseph J.}, + year = {2022}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/geb.13562}, + keywords = {phytoplankton, resource competition, modelling, biogeography, diel light cycle, global ocean, nutrient cycles, diel}, + pages = {1838--1849}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/4IRAUGAH/Tsakalakis et al. - 2022 - Diel light cycles affect phytoplankton competition.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/QKKTIJ3P/geb.html:text/html}, +} + +@article{tsakalakis_diel_2018, + title = {Diel light cycle as a key factor for modelling phytoplankton biogeography and diversity}, + volume = {384}, + issn = {0304-3800}, + url = {https://www.sciencedirect.com/science/article/pii/S0304380018302278}, + doi = {10.1016/j.ecolmodel.2018.06.022}, + abstract = {Understanding the mechanisms driving species biogeography and biodiversity remains a major challenge in phytoplankton ecology. Using a model of two phytoplankton species with a gleaner-opportunist trade-off and competing for light and a limiting nutrient, we show that the diel light cycle may be an essential factor to explain large-scale ecological patterns. When only the seasonal light cycle is considered (control scenario) the model predicts that, independently of the nutrient supply, gleaners should dominate across all latitudes and opportunists can obtain a temporal niche only at high latitudes. However, the diel light cycle makes the competition outcome also a function of nutrient supply by affecting the amplitude of diel nutrient oscillations, with gleaners dominating when nutrient supply is low, opportunists when nutrient supply is high, and both species coexisting at intermediate levels of nutrient supply. The combined effects of seasonal and diel light cycles (diel scenario) shape a latitudinal diversity gradient with decreasing diversity towards higher latitudes and a unimodal dependence of diversity on nutrient supply and, therefore, on ecosystem productivity. The proposed mechanism can help interpret the biogeography of major phytoplankton functional groups in the global ocean and link them with large-scale biodiversity patterns.}, + language = {en}, + urldate = {2023-08-04}, + journal = {Ecological Modelling}, + author = {Tsakalakis, Ioannis and Pahlow, Markus and Oschlies, Andreas and Blasius, Bernd and Ryabov, Alexey B.}, + month = sep, + year = {2018}, + keywords = {Biodiversity, Biogeography, diel, Diel light cycle, Nutrient oscillations, Phytoplankton, Resource competition}, + pages = {241--248}, + file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/IRUYQ5WD/S0304380018302278.html:text/html;Tsakalakis et al. - 2018 - Diel light cycle as a key factor for modelling phy.pdf:/Users/birdy/Zotero/storage/EW5BGQ4D/Tsakalakis et al. - 2018 - Diel light cycle as a key factor for modelling phy.pdf:application/pdf}, +} + +@article{grilli_macroecological_2020, + title = {Macroecological laws describe variation and diversity in microbial communities}, + volume = {11}, + copyright = {2020 The Author(s)}, + issn = {2041-1723}, + url = {https://www.nature.com/articles/s41467-020-18529-y}, + doi = {10.1038/s41467-020-18529-y}, + abstract = {How the coexistence of many species is maintained is a fundamental and unresolved question in ecology. Coexistence is a puzzle because we lack a mechanistic understanding of the variation in species presence and abundance. Whether variation in ecological communities is driven by deterministic or random processes is one of the most controversial issues in ecology. Here, I study the variation of species presence and abundance in microbial communities from a macroecological standpoint. I identify three macroecological laws that quantitatively characterize the fluctuation of species abundance across communities and over time. Using these three laws, one can predict species’ presence and absence, diversity, and commonly studied macroecological patterns. I show that a mathematical model based on environmental stochasticity, the stochastic logistic model, quantitatively predicts the three macroecological laws, as well as non-stationary properties of community dynamics.}, + language = {en}, + number = {1}, + urldate = {2023-08-04}, + journal = {Nature Communications}, + author = {Grilli, Jacopo}, + month = sep, + year = {2020}, + note = {Number: 1 +Publisher: Nature Publishing Group}, + keywords = {Microbial ecology, Biodiversity, Community ecology, Ecological modelling, Macroecology, microbes}, + pages = {4743}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/XTDE7UWR/Grilli - 2020 - Macroecological laws describe variation and divers.pdf:application/pdf}, +} + +@article{williamson_beneficial_2001, + title = {Beneficial and {Detrimental} {Effects} of {Uv} on {Aquatic} {Organisms}: {Implications} of {Spectral} {Variation}}, + volume = {11}, + copyright = {© 2001 by the Ecological Society of America}, + issn = {1939-5582}, + shorttitle = {Beneficial and {Detrimental} {Effects} of {Uv} on {Aquatic} {Organisms}}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1890/1051-0761%282001%29011%5B1843%3ABADEOU%5D2.0.CO%3B2}, + doi = {10.1890/1051-0761(2001)011[1843:BADEOU]2.0.CO;2}, + abstract = {Solar ultraviolet radiation (UVR) may have beneficial as well as detrimental effects on living systems. For example, UV-B radiation (280–320 nm) is generally damaging, while UV-A radiation (320–400 nm) may cause damage or stimulate beneficial photorepair of UV-B damage. The nature of both direct and indirect effects of UVR in nature depends on both the photon flux density and the spectral composition of the radiation incident on aquatic organisms across environmental UVR gradients in space (depth, transparency, elevation) and time (diel, seasonal, interannual). Here we use the common and widespread freshwater cladoceran Daphnia pulicaria as a model organism to demonstrate the potential importance of these wavelength-specific effects of UVR to the ecology of aquatic organisms. UVR-exposure experiments are used to manipulate both natural solar and artificial UVR sources to examine the beneficial as well as detrimental effects of different wavelengths of UVR. Changes in the spectral composition of solar radiation are also examined along several natural environmental gradients including diel gradients, depth gradients, and dissolved organic carbon (DOC) gradients. The implications of variation in the spectral composition of UVR for aquatic organisms are discussed. The first biological weighting function (BWF) for a freshwater cladoceran is presented here. It demonstrates that the shortest UV-B wavelengths in sunlight are potentially the most damaging per photon. However, due to the greater photon flux density of longer wavelength UVR in sunlight, the net potential damage to Daphnia in nature is greatest for the longer wavelength UV-B and shorter wavelength UV-A radiation in the 305–322 nm range. Overall the contribution of UV-B to the total mortality response of Daphnia exposed to full-spectrum solar radiation for 7 h on a sunny summer day is 64\% while UV-A contributes 36\%. The BWF for Daphnia is used with the transmission spectrum for Mylar D to demonstrate that Mylar D cuts out only about half of the damaging UVR in sunlight. Following exposure to damaging UV-B, Daphnia exhibits a dramatic increase in survival in the presence of longer wavelength UV-A and visible radiation due to the stimulation of photoenzymatic repair. We present data that demonstrate the importance of both atmospheric ozone and DOC in creating strong environmental gradients in the intensity (irradiance) and spectral composition of solar UVR in nature. The light-absorbing component of DOC, chromophoric dissolved organic matter (CDOM), is particularly important in creating depth refugia from damaging UV-B in freshwater ecosystems. CDOM may also cause intense variations in the ratio of potentially beneficial UV-A to detrimental UV-B radiation to which aquatic organisms are exposed. In addition to changes in atmospheric ozone, future changes in CDOM related to climate change or other environmental disturbances may substantially alter the underwater exposure of a variety of aquatic organisms to different wavelengths of solar UVR.}, + language = {en}, + number = {6}, + urldate = {2023-08-04}, + journal = {Ecological Applications}, + author = {Williamson, Craig E. and Neale, Patrick J. and Grad, Gabriella and De Lange, Hendrika J. and Hargreaves, Bruce R.}, + year = {2001}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1890/1051-0761\%282001\%29011\%5B1843\%3ABADEOU\%5D2.0.CO\%3B2}, + keywords = {diel, biological weighting function, Daphnia pulicaria, dissolved organic carbon, environmental gradients, lake optics, ozone depletion, relative damage to aquatic organisms, spectral composition of solar radiation, tolerance of UV radiation, ultraviolet radiation, UV-A and UV-B, zooplankton}, + pages = {1843--1857}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/C65AT69Z/Williamson et al. - 2001 - Beneficial and Detrimental Effects of Uv on Aquati.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/AQL7Y8NU/1051-0761(2001)011[1843BADEOU]2.0.html:text/html}, +} + +@incollection{fasham_phytoplankton_2003, + address = {Berlin, Heidelberg}, + title = {Phytoplankton and {Their} {Role} in {Primary}, {New}, and {Export} {Production}}, + isbn = {978-3-642-62691-3 978-3-642-55844-3}, + url = {http://link.springer.com/10.1007/978-3-642-55844-3_5}, + language = {en}, + urldate = {2023-08-15}, + booktitle = {Ocean {Biogeochemistry}}, + publisher = {Springer Berlin Heidelberg}, + author = {Falkowski, Paul G. and Laws, Edward A. and Barber, Richard T. and Murray, James W.}, + editor = {Fasham, Michael J. R.}, + year = {2003}, + doi = {10.1007/978-3-642-55844-3_5}, + keywords = {DAR1}, + pages = {99--121}, + file = {Falkowski et al. - 2003 - Phytoplankton and Their Role in Primary, New, and .pdf:/Users/birdy/Zotero/storage/W6TWZWCA/Falkowski et al. - 2003 - Phytoplankton and Their Role in Primary, New, and .pdf:application/pdf}, +} + +@article{tilman_resource_1977-1, + title = {Resource {Competition} between {Plankton} {Algae}: {An} {Experimental} and {Theoretical} {Approach}}, + volume = {58}, + issn = {0012-9658}, + shorttitle = {Resource {Competition} between {Plankton} {Algae}}, + url = {https://www.jstor.org/stable/1935608}, + doi = {10.2307/1935608}, + abstract = {The results of 76 long-term competition experiments between two species of freshwater algae (Asterionella formosa and Cyclotella meneghiniana) grown along a resource gradient agree with the predictions of two different models of resource competition. Both models are based on the functional resource-utilization response of each species to limiting resources. The Monod and the Variable Internal Stores model of competition made similar predictions. Asterionella was observed to be competitively dominant when both species were phosphate limited; Cyclotella was dominant when both species were silicate limited; and both species stably coexisted when each species was growth-rate limited by a different resource. Almost 75\% of the variance in the relative abundances of these two species along a natural silicate-phosphate gradient in Lake Michigan is explained by the Monod model.}, + number = {2}, + urldate = {2023-08-24}, + journal = {Ecology}, + author = {Tilman, David}, + year = {1977}, + note = {Publisher: Ecological Society of America}, + pages = {338--348}, +} + +@article{dallolmo_inferring_2011, + title = {Inferring phytoplankton carbon and eco-physiological rates from diel cycles of spectral particulate beam-attenuation coefficient}, + volume = {8}, + issn = {1726-4170}, + url = {https://bg.copernicus.org/articles/8/3423/2011/bg-8-3423-2011.html}, + doi = {10.5194/bg-8-3423-2011}, + abstract = {The diurnal fluctuations in solar irradiance impose a fundamental frequency on ocean biogeochemistry. Observations of the ocean carbon cycle at these frequencies are rare, but could be considerably expanded by measuring and interpreting the inherent optical properties. A method is presented to analyze diel cycles in particulate beam-attenuation coefficient (cp) measured at multiple wavelengths. The method is based on fitting observations with a size-structured population model coupled to an optical model to infer the particle size distribution and physiologically relevant parameters of the cells responsible for the measured diel cycle in cp. Results show that the information related to size and contained in the spectral data can be exploited to independently estimate growth and loss rates during the day and night. In addition, the model can characterize the population of particles affecting the diel variability in cp. Application of this method to spectral cp measured at a station in the oligotrophic Mediterranean Sea suggests that most of the observed variations in cp can be ascribed to a synchronized population of cells with an equivalent spherical diameter around 4.6±1.5 μm. The inferred carbon biomass of these cells was about 5.2–6.0 mg m−3 and accounted for approximately 10\% of the total particulate organic carbon. If successfully validated, this method may improve our in situ estimates of primary productivity.}, + language = {English}, + number = {11}, + urldate = {2023-08-25}, + journal = {Biogeosciences}, + author = {Dall'Olmo, G. and Boss, E. and Behrenfeld, M. J. and Westberry, T. K. and Courties, C. and Prieur, L. and Pujo-Pay, M. and Hardman-Mountford, N. and Moutin, T.}, + month = nov, + year = {2011}, + note = {Publisher: Copernicus GmbH}, + pages = {3423--3439}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/C6C247B3/Dall'Olmo et al. - 2011 - Inferring phytoplankton carbon and eco-physiologic.pdf:application/pdf}, +} + +@misc{noauthor_ai_nodate, + title = {The {AI} gig economy is coming for you}, + file = {[Hao] The AI gig economy is coming for you _ MIT Technology Review.pdf:/Users/birdy/Downloads/[Hao] The AI gig economy is coming for you _ MIT Technology Review.pdf:application/pdf}, +} + +@article{noauthor_architecture_nodate, + title = {Architecture of a {Database} {System}}, + file = {Architecture of a Database System.pdf:/Users/birdy/Zotero/storage/2DKEVTBM/Architecture of a Database System.pdf:application/pdf}, +} + +@article{isola_lecture_nodate, + title = {Lecture 4: {Architectures} for {Grids}}, + language = {en}, + author = {Isola, Phillip}, + file = {4_grid_archs.pdf:/Users/birdy/Documents/Meng Classes/Deep Learning/4_grid_archs.pdf:application/pdf}, +} + +@article{isola_lecture_nodate-1, + title = {Lecture 5: {Graph} {Neural} {Networks}}, + language = {en}, + author = {Isola, Phillip}, + file = {5_graph_nn.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/5_graph_nn.pdf:application/pdf}, +} + +@misc{noauthor_fast_nodate, + title = {Fast {Forward}: {MIT}'s {Climate} {Action} {Plan} for the {Decade}}, + shorttitle = {Fast {Forward}}, + url = {https://climate.mit.edu/climateaction/fastforward}, + language = {en}, + urldate = {2023-09-22}, + journal = {MIT Climate Portal}, + file = {Snapshot:/Users/birdy/Zotero/storage/E37ZPUKM/fastforward.html:text/html}, +} + +@article{bernstein_scaling_nodate, + title = {Scaling rules for optimisation}, + language = {en}, + author = {Bernstein, Jeremy}, + file = {6_scaling_JB.pdf:/Users/birdy/Documents/Meng Classes/Deep Learning/6_scaling_JB.pdf:application/pdf}, +} + +@article{isola_lecture_nodate-2, + title = {Lecture 3: {Approximation} {Theory}}, + language = {en}, + author = {Isola, Phillip}, + file = {3_approximation_PI.pdf:/Users/birdy/Documents/Meng Classes/Deep Learning/3_approximation_PI.pdf:application/pdf}, +} + +@misc{noauthor_6s898_nodate, + title = {6.{S898} {Lecture} on 9/12/2023 ({Tue})}, + url = {https://mit.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=885feb13-1128-46a6-94fa-b077010e681c}, + abstract = {Special subject in Computer Science}, + language = {en}, + urldate = {2023-09-30}, + file = {Snapshot:/Users/birdy/Zotero/storage/8KZHLHN6/Viewer.html:text/html}, +} + +@inproceedings{andreas_neural_2016, + address = {Las Vegas, NV, USA}, + title = {Neural {Module} {Networks}}, + isbn = {978-1-4673-8851-1}, + url = {http://ieeexplore.ieee.org/document/7780381/}, + doi = {10.1109/CVPR.2016.12}, + abstract = {Visual question answering is fundamentally compositional in nature—a question like where is the dog? shares substructure with questions like what color is the dog? and where is the cat? This paper seeks to simultaneously exploit the representational capacity of deep networks and the compositional linguistic structure of questions. We describe a procedure for constructing and learning neural module networks, which compose collections of jointly-trained neural “modules” into deep networks for question answering. Our approach decomposes questions into their linguistic substructures, and uses these structures to dynamically instantiate modular networks (with reusable components for recognizing dogs, classifying colors, etc.). The resulting compound networks are jointly trained. We evaluate our approach on two challenging datasets for visual question answering, achieving state-of-the-art results on both the VQA natural image dataset and a new dataset of complex questions about abstract shapes.}, + language = {en}, + urldate = {2023-09-30}, + booktitle = {2016 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})}, + publisher = {IEEE}, + author = {Andreas, Jacob and Rohrbach, Marcus and Darrell, Trevor and Klein, Dan}, + month = jun, + year = {2016}, + pages = {39--48}, + file = {Andreas et al. - 2016 - Neural Module Networks.pdf:/Users/birdy/Zotero/storage/YK4HIHHM/Andreas et al. - 2016 - Neural Module Networks.pdf:application/pdf}, +} + +@article{beery_lecture_nodate, + title = {Lecture 2: {How} to train a neural net}, + language = {en}, + author = {Beery, Sara}, + file = {2_backprop_SB.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/2_backprop_SB.pdf:application/pdf}, +} + +@misc{ruswurm_geographic_2023, + title = {Geographic {Location} {Encoding} with {Spherical} {Harmonics} and {Sinusoidal} {Representation} {Networks}}, + url = {http://arxiv.org/abs/2310.06743}, + abstract = {Learning feature representations of geographical space is vital for any machine learning model that integrates geolocated data, spanning application domains such as remote sensing, ecology, or epidemiology. Recent work mostly embeds coordinates using sine and cosine projections based on Double Fourier Sphere (DFS) features -- these embeddings assume a rectangular data domain even on global data, which can lead to artifacts, especially at the poles. At the same time, relatively little attention has been paid to the exact design of the neural network architectures these functional embeddings are combined with. This work proposes a novel location encoder for globally distributed geographic data that combines spherical harmonic basis functions, natively defined on spherical surfaces, with sinusoidal representation networks (SirenNets) that can be interpreted as learned Double Fourier Sphere embedding. We systematically evaluate the cross-product of positional embeddings and neural network architectures across various classification and regression benchmarks and synthetic evaluation datasets. In contrast to previous approaches that require the combination of both positional encoding and neural networks to learn meaningful representations, we show that both spherical harmonics and sinusoidal representation networks are competitive on their own but set state-of-the-art performances across tasks when combined. We provide source code at www.github.com/marccoru/locationencoder}, + urldate = {2023-10-12}, + publisher = {arXiv}, + author = {Rußwurm, Marc and Klemmer, Konstantin and Rolf, Esther and Zbinden, Robin and Tuia, Devis}, + month = oct, + year = {2023}, + note = {arXiv:2310.06743 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/N6J75A2A/2310.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/XSF9TGN7/Rußwurm et al. - 2023 - Geographic Location Encoding with Spherical Harmon.pdf:application/pdf}, +} + +@misc{eerola_survey_2023, + title = {Survey of {Automatic} {Plankton} {Image} {Recognition}: {Challenges}, {Existing} {Solutions} and {Future} {Perspectives}}, + shorttitle = {Survey of {Automatic} {Plankton} {Image} {Recognition}}, + url = {http://arxiv.org/abs/2305.11739}, + abstract = {Planktonic organisms are key components of aquatic ecosystems and respond quickly to changes in the environment, therefore their monitoring is vital to understand the changes in the environment. Yet, monitoring plankton at appropriate scales still remains a challenge, limiting our understanding of functioning of aquatic systems and their response to changes. Modern plankton imaging instruments can be utilized to sample at high frequencies, enabling novel possibilities to study plankton populations. However, manual analysis of the data is costly, time consuming and expert based, making such approach unsuitable for large-scale application and urging for automatic solutions. The key problem related to the utilization of plankton datasets through image analysis is plankton recognition. Despite the large amount of research done, automatic methods have not been widely adopted for operational use. In this paper, a comprehensive survey on existing solutions for automatic plankton recognition is presented. First, we identify the most notable challenges that that make the development of plankton recognition systems difficult. Then, we provide a detailed description of solutions for these challenges proposed in plankton recognition literature. Finally, we propose a workflow to identify the specific challenges in new datasets and the recommended approaches to address them. For many of the challenges, applicable solutions exist. However, important challenges remain unsolved: 1) the domain shift between the datasets hindering the development of a general plankton recognition system that would work across different imaging instruments, 2) the difficulty to identify and process the images of previously unseen classes, and 3) the uncertainty in expert annotations that affects the training of the machine learning models for recognition. These challenges should be addressed in the future research.}, + urldate = {2023-10-16}, + publisher = {arXiv}, + author = {Eerola, Tuomas and Batrakhanov, Daniel and Barazandeh, Nastaran Vatankhah and Kraft, Kaisa and Haraguchi, Lumi and Lensu, Lasse and Suikkanen, Sanna and Seppälä, Jukka and Tamminen, Timo and Kälviäinen, Heikki}, + month = may, + year = {2023}, + note = {arXiv:2305.11739 [cs]}, + keywords = {Computer Science - Computer Vision and Pattern Recognition}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/FG4ZBSVN/2305.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/NZX46TSK/Eerola et al. - 2023 - Survey of Automatic Plankton Image Recognition Ch.pdf:application/pdf}, +} + +@article{gonzalez_review_2018, + title = {A {Review} on {Quantification} {Learning}}, + volume = {50}, + issn = {0360-0300, 1557-7341}, + url = {https://dl.acm.org/doi/10.1145/3117807}, + doi = {10.1145/3117807}, + abstract = {The task of quantification consists in providing an aggregate estimation (e.g. the class distribution in a classification problem) for unseen test sets, applying a model that is trained using a training set with a different data distribution. Several real-world applications demand this kind of methods that do not require predictions for individual examples and just focus on obtaining accurate estimates at an aggregate level. During the past few years, several quantification methods have been proposed from different perspectives and with different goals. This paper presents a unified review of the main approaches with the aim of serving as an introductory tutorial for newcomers in the field.}, + language = {en}, + number = {5}, + urldate = {2023-10-16}, + journal = {ACM Computing Surveys}, + author = {González, Pablo and Castaño, Alberto and Chawla, Nitesh V. and Coz, Juan José Del}, + month = sep, + year = {2018}, + pages = {1--40}, + file = {González et al. - 2018 - A Review on Quantification Learning.pdf:/Users/birdy/Zotero/storage/76A4J8ML/quant-review-grAcc.pdf:application/pdf}, +} + +@article{sosa-trejo_vision-based_2023, + title = {Vision-based techniques for automatic marine plankton classification}, + volume = {56}, + issn = {1573-7462}, + url = {https://doi.org/10.1007/s10462-023-10456-w}, + doi = {10.1007/s10462-023-10456-w}, + abstract = {Plankton are an important component of life on Earth. Since the 19th century, scientists have attempted to quantify species distributions using many techniques, such as direct counting, sizing, and classification with microscopes. Since then, extraordinary work has been performed regarding the development of plankton imaging systems, producing a massive backlog of images that await classification. Automatic image processing and classification approaches are opening new avenues for avoiding time-consuming manual procedures. While some algorithms have been adapted from many other applications for use with plankton, other exciting techniques have been developed exclusively for this issue. Achieving higher accuracy than that of human taxonomists is not yet possible, but an expeditious analysis is essential for discovering the world beyond plankton. Recent studies have shown the imminent development of real-time, in situ plankton image classification systems, which have only been slowed down by the complex implementations of algorithms on low-power processing hardware. This article compiles the techniques that have been proposed for classifying marine plankton, focusing on automatic methods that utilize image processing, from the beginnings of this field to the present day.}, + language = {en}, + number = {11}, + urldate = {2023-10-16}, + journal = {Artificial Intelligence Review}, + author = {Sosa-Trejo, David and Bandera, Antonio and González, Martín and Hernández-León, Santiago}, + month = nov, + year = {2023}, + keywords = {Image processing, Marine plankton, Pattern recognition, Plankton classification}, + pages = {12853--12884}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/U7RIA6IX/Sosa-Trejo et al. - 2023 - Vision-based techniques for automatic marine plank.pdf:application/pdf}, +} + +@article{kerr_collaborative_2020-1, + title = {Collaborative {Deep} {Learning} {Models} to {Handle} {Class} {Imbalance} in {FlowCam} {Plankton} {Imagery}}, + volume = {8}, + issn = {2169-3536}, + url = {https://ieeexplore.ieee.org/abstract/document/9187202/authors#authors}, + doi = {10.1109/ACCESS.2020.3022242}, + abstract = {Using automated imaging technologies, it is now possible to generate previously unprecedented volumes of plankton image data which can be used to study the composition of plankton assemblages. However, the current need to manually classify individual images introduces a bottleneck into processing chains. Although Machine Learning techniques have been used to try and address this issue, past efforts have suffered from accuracy limitations, especially in minority classes. Here we use state-of-the-art methods in Deep Learning to investigate suitable architectures for training an automated plankton classification system which achieves high efficacy for both abundant and rare taxa. We collected live plankton from Station L4 in the Western English Channel and imaged 11,371 particles covering 104 taxonomic groups using the automated plankton imaging system FlowCam. The image set contained a severe class imbalance, with some taxa represented by {\textgreater} 600 images while other, rarer taxa were represented by just 14. We demonstrate that by allowing multiple Deep Learning models to collaborate in a single classification system, classification accuracy improves for minority classes when compared with the best individual model. The top collaborative model achieved a 6 \% improvement in F1 accuracy over the best individual model, while overall accuracy improved by 3.2 \%. This resulted in a 97.4 \% overall accuracy score and a 96.2 \% F1 macro score on a separate holdout test set containing 104 taxonomic groups. Based on a survey of similar studies in the literature, we believe collaborative deep learning models can significantly improve the accuracy of existing automated plankton classification systems.}, + urldate = {2023-10-16}, + journal = {IEEE Access}, + author = {Kerr, Thomas and Clark, James R. and Fileman, Elaine S. and Widdicombe, Claire E. and Pugeault, Nicolas}, + year = {2020}, + note = {Conference Name: IEEE Access}, + pages = {170013--170032}, + file = {IEEE Xplore Abstract Record:/Users/birdy/Zotero/storage/WPTTWRTB/authors.html:text/html;IEEE Xplore Full Text PDF:/Users/birdy/Zotero/storage/VLJWVUVD/Kerr et al. - 2020 - Collaborative Deep Learning Models to Handle Class.pdf:application/pdf}, +} + +@article{conradt_automated_2022, + title = {Automated {Plankton} {Classification} {With} a {Dynamic} {Optimization} and {Adaptation} {Cycle}}, + volume = {9}, + issn = {2296-7745}, + url = {https://www.frontiersin.org/articles/10.3389/fmars.2022.868420}, + abstract = {With recent advances in Machine Learning techniques based on Deep Neural Networks (DNNs), automated plankton image classification is becoming increasingly popular within the marine ecological sciences. Yet, while the most advanced methods can achieve human-level performance on the classification of everyday images, plankton image data possess properties that frequently require a final manual validation step. On the one hand, this is due to morphological properties manifesting in high intra-class and low inter-class variability, and, on the other hand is due to spatial-temporal changes in the composition and structure of the plankton community. Composition changes enforce a frequent updating of the classifier model via training with new user-generated training datasets. Here, we present a Dynamic Optimization Cycle (DOC), a processing pipeline that systematizes and streamlines the model adaptation process via an automatic updating of the training dataset based on manual-validation results. We find that frequent adaptation using the DOC pipeline yields strong maintenance of performance with respect to precision, recall and prediction of community composition, compared to more limited adaptation schemes. The DOC is therefore particularly useful when analyzing plankton at novel locations or time periods, where community differences are likely to occur. In order to enable an easy implementation of the DOC pipeline, we provide an end-to-end application with graphical user interface, as well as an initial dataset of training images. The DOC pipeline thus allows for high-throughput plankton classification and quick and systematized model adaptation, thus providing the means for highly-accelerated plankton analysis.}, + urldate = {2023-10-16}, + journal = {Frontiers in Marine Science}, + author = {Conradt, Jan and Börner, Gregor and López-Urrutia, Ángel and Möllmann, Christian and Moyano, Marta}, + year = {2022}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/YBEYVF4R/Conradt et al. - 2022 - Automated Plankton Classification With a Dynamic O.pdf:application/pdf}, +} + +@misc{ic_du_toit_automated_nodate, + title = {Automated machine learning plankton taxonomy pipeline}, + url = {http://vital.seals.ac.za:8080/vital/access/manager/PdfViewer/vital:59003/SOURCE1?viewPdfInternal=1}, + urldate = {2023-10-16}, + author = {I.C. du Toit}, + file = {document.pdf:/Users/birdy/Downloads/document.pdf:application/pdf;SOURCE1:/Users/birdy/Zotero/storage/8EZC4XHT/SOURCE1.html:text/html}, +} + +@article{gonzalez_validation_2017, + title = {Validation methods for plankton image classification systems}, + volume = {15}, + copyright = {© 2016 Association for the Sciences of Limnology and Oceanography}, + issn = {1541-5856}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lom3.10151}, + doi = {10.1002/lom3.10151}, + abstract = {In recent decades, the automatic study and analysis of plankton communities using imaging techniques has advanced significantly. The effectiveness of these automated systems appears to have improved, reaching acceptable levels of accuracy. However, plankton ecologists often find that classification systems do not work as well as expected when applied to new samples. This paper proposes a methodology to assess the efficacy of learned models which takes into account the fact that the data distribution (the plankton composition of the sample) can vary between the model building phase and the production phase. As opposed to most validation methods that consider the individual organism as the unit of validation, our approach uses a validation-by-sample, which is more appropriate when the objective is to estimate the abundance of different morphological groups. We argue that, in these cases, the base unit to correctly estimate the error is the sample, not the individual. Thus, model assessment processes require groups of samples with sufficient variability in order to provide precise error estimates.}, + language = {en}, + number = {3}, + urldate = {2023-10-16}, + journal = {Limnology and Oceanography: Methods}, + author = {González, Pablo and Álvarez, Eva and Díez, Jorge and López-Urrutia, Ángel and del Coz, Juan José}, + year = {2017}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10151}, + pages = {221--237}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/SW5FNZDQ/González et al. - 2017 - Validation methods for plankton image classificati.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/UB2XMFYB/lom3.html:text/html}, +} + +@article{isola_lecture_nodate-3, + title = {Lecture 11: {Representation} {Learning} {I}}, + language = {en}, + author = {Isola, Phillip}, + file = {11-rep_learning_1.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/11-rep_learning_1.pdf:application/pdf}, +} + +@article{beery_lecture_nodate-1, + title = {Lecture 12: {Similarity}-based {Representation} {Learning}}, + language = {en}, + author = {Beery, Sara}, + file = {12_similarity_based_rep_learning-SB.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/12_similarity_based_rep_learning-SB.pdf:application/pdf}, +} + +@inproceedings{van_horn_benchmarking_2021, + address = {Nashville, TN, USA}, + title = {Benchmarking {Representation} {Learning} for {Natural} {World} {Image} {Collections}}, + isbn = {978-1-66544-509-2}, + url = {https://ieeexplore.ieee.org/document/9577285/}, + doi = {10.1109/CVPR46437.2021.01269}, + abstract = {Recent progress in self-supervised learning has resulted in models that are capable of extracting rich representations from image collections without requiring any explicit label supervision. However, to date the vast majority of these approaches have restricted themselves to training on standard benchmark datasets such as ImageNet. We argue that fine-grained visual categorization problems, such as plant and animal species classification, provide an informative testbed for self-supervised learning. In order to facilitate progress in this area we present two new natural world visual classification datasets, iNat2021 and NeWT. The former consists of 2.7M images from 10k different species uploaded by users of the citizen science application iNaturalist. We designed the latter, NeWT, in collaboration with domain experts with the aim of benchmarking the performance of representation learning algorithms on a suite of challenging natural world binary classification tasks that go beyond standard species classification. These two new datasets allow us to explore questions related to large-scale representation and transfer learning in the context of finegrained categories. We provide a comprehensive analysis of feature extractors trained with and without supervision on ImageNet and iNat2021, shedding light on the strengths and weaknesses of different learned features across a diverse set of tasks. We find that features produced by standard supervised methods still outperform those produced by self-supervised approaches such as SimCLR. However, improved self-supervised learning methods are constantly being released and the iNat2021 and NeWT datasets are a valuable resource for tracking their progress.}, + language = {en}, + urldate = {2023-10-24}, + booktitle = {2021 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})}, + publisher = {IEEE}, + author = {Van Horn, Grant and Cole, Elijah and Beery, Sara and Wilber, Kimberly and Belongie, Serge and MacAodha, Oisin}, + month = jun, + year = {2021}, + pages = {12879--12888}, + file = {Van Horn et al. - 2021 - Benchmarking Representation Learning for Natural W.pdf:/Users/birdy/Zotero/storage/PYLIPA9T/Van Horn et al. - 2021 - Benchmarking Representation Learning for Natural W.pdf:application/pdf}, +} + +@article{orenstein_machine_2022, + title = {Machine learning techniques to characterize functional traits of plankton from image data}, + volume = {67}, + issn = {0024-3590, 1939-5590}, + url = {https://aslopubs.onlinelibrary.wiley.com/doi/10.1002/lno.12101}, + doi = {10.1002/lno.12101}, + abstract = {Plankton imaging systems supported by automated classification and analysis have improved ecologists’ ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.}, + language = {en}, + number = {8}, + urldate = {2023-10-25}, + journal = {Limnology and Oceanography}, + author = {Orenstein, Eric C. and Ayata, Sakina‐Dorothée and Maps, Frédéric and Becker, Érica C. and Benedetti, Fabio and Biard, Tristan and De Garidel‐Thoron, Thibault and Ellen, Jeffrey S. and Ferrario, Filippo and Giering, Sarah L. C. and Guy‐Haim, Tamar and Hoebeke, Laura and Iversen, Morten Hvitfeldt and Kiørboe, Thomas and Lalonde, Jean‐François and Lana, Arancha and Laviale, Martin and Lombard, Fabien and Lorimer, Tom and Martini, Séverine and Meyer, Albin and Möller, Klas Ove and Niehoff, Barbara and Ohman, Mark D. and Pradalier, Cédric and Romagnan, Jean‐Baptiste and Schröder, Simon‐Martin and Sonnet, Virginie and Sosik, Heidi M. and Stemmann, Lars S. and Stock, Michiel and Terbiyik‐Kurt, Tuba and Valcárcel‐Pérez, Nerea and Vilgrain, Laure and Wacquet, Guillaume and Waite, Anya M. and Irisson, Jean‐Olivier}, + month = aug, + year = {2022}, + pages = {1647--1669}, + file = {Orensteinal._2022.pdf:/Users/birdy/Documents/mit-meng-thesis/papers/Orensteinal._2022.pdf:application/pdf}, +} + +@article{schanz_robust_2023, + title = {Robust detection of marine life with label-free image feature learning and probability calibration}, + volume = {4}, + issn = {2632-2153}, + url = {https://iopscience.iop.org/article/10.1088/2632-2153/ace417}, + doi = {10.1088/2632-2153/ace417}, + abstract = {Abstract + + Advances in + in situ + marine life imaging have significantly increased the size and quality of available datasets, but automatic image analysis has not kept pace. Machine learning has shown promise for image processing, but its effectiveness is limited by several open challenges: the requirement for large expert-labeled training datasets, disagreement among experts, under-representation of various species and unreliable or overconfident predictions. To overcome these obstacles for automated underwater imaging, we combine and test recent developments in deep classifier networks and self-supervised feature learning. We use unlabeled images for pretraining deep neural networks to extract task-relevant image features, allowing learning algorithms to cope with scarcity in expert labels, and carefully evaluate performance in subsequent label-based tasks. Performance on rare classes is improved by applying data rebalancing together with a Bayesian correction to avoid biasing inferred + in situ + class frequencies. A divergence-based loss allows training on multiple, conflicting labels for the same image, leading to better estimates of uncertainty which we quantify with a novel accuracy measure. Together, these techniques can reduce the required label counts ∼100-fold while maintaining the accuracy of standard supervised training, shorten training time, cope with expert disagreement and reduce overconfidence.}, + language = {en}, + number = {3}, + urldate = {2023-10-25}, + journal = {Machine Learning: Science and Technology}, + author = {Schanz, Tobias and Ove Möller, Klas and Rühl, Saskia and Greenberg, David S}, + month = sep, + year = {2023}, + pages = {035007}, + file = {Schanz et al. - 2023 - Robust detection of marine life with label-free im.pdf:/Users/birdy/Zotero/storage/KBQL5Q2G/Schanz et al. - 2023 - Robust detection of marine life with label-free im.pdf:application/pdf}, +} + +@article{catlett_temperature_2023, + title = {Temperature dependence of parasitoid infection and abundance of a diatom revealed by automated imaging and classification}, + volume = {120}, + issn = {0027-8424, 1091-6490}, + url = {https://pnas.org/doi/10.1073/pnas.2303356120}, + doi = {10.1073/pnas.2303356120}, + abstract = {Diatoms are a group of phytoplankton that contribute disproportionately to global primary production. Traditional paradigms that suggest diatoms are consumed primarily by larger zooplankton are challenged by sporadic parasitic “epidemics” within diatom populations. However, our understanding of diatom parasitism is limited by difficulties in quantifying these interactions. Here, we observe the dynamics of + Cryothecomonas aestivalis + (a protist) infection of an important diatom on the Northeast U.S. Shelf (NES), + Guinardia delicatula + , with a combination of automated imaging-in-flow cytometry and a convolutional neural network image classifier. Application of the classifier to {\textgreater}1 billion images from a nearshore time series and {\textgreater}20 survey cruises across the broader NES reveals the spatiotemporal gradients and temperature dependence of + G. delicatula + abundance and infection dynamics. Suppression of parasitoid infection at temperatures {\textless}4 °C drives annual cycles in both + G. delicatula + infection and abundance, with an annual maximum in infection observed in the fall-winter preceding an annual maximum in host abundance in the winter-spring. This annual cycle likely varies spatially across the NES in response to variable annual cycles in water temperature. We show that infection remains suppressed for {\textasciitilde}2 mo following cold periods, possibly due to temperature-induced local extinctions of the + C. aestivalis + strain(s) that infect + G. delicatula + . These findings have implications for predicting impacts of a warming NES surface ocean on + G. delicatula + abundance and infection dynamics and demonstrate the potential of automated plankton imaging and classification to quantify phytoplankton parasitism in nature across unprecedented spatiotemporal scales.}, + language = {en}, + number = {28}, + urldate = {2023-10-25}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Catlett, Dylan and Peacock, Emily E. and Crockford, E. Taylor and Futrelle, Joe and Batchelder, Sidney and Stevens, Bethany L. F. and Gast, Rebecca J. and Zhang, Weifeng G. and Sosik, Heidi M.}, + month = jul, + year = {2023}, + pages = {e2303356120}, + file = {catlett-et-al-2023-temperature-dependence-of-parasitoid-infection-and-abundance-of-a-diatom-revealed-by-automated.pdf:/Users/birdy/Documents/mit-meng-thesis/papers/catlett-et-al-2023-temperature-dependence-of-parasitoid-infection-and-abundance-of-a-diatom-revealed-by-automated.pdf:application/pdf}, +} + +@article{culverhouse_empirical_2014, + title = {An empirical assessment of the consistency of taxonomic identifications}, + volume = {10}, + issn = {1745-1000}, + url = {https://doi.org/10.1080/17451000.2013.810762}, + doi = {10.1080/17451000.2013.810762}, + abstract = {Plankton counting and analysis is essential in ecological study, yet scant literature exists as to the reliability of those counts and the consistency of the experts who make the counts. To assess how variable expert taxonomic identifications are, a set of six archived mesozooplankton samples from a series of Longhurst Hardy Plankton Recorder net hauls were counted by expert zooplankton analysts located at six marine laboratories. Sample identifications were repeated on two separate days with over 700 target specimens counted and identified on each day across the samples. Twenty percent of the analysts returned counts that varied by more than 10\%. Thirty-three percent of analysts exhibited low identification consistencies, returning Intraclass Correlation Coefficient scores of less than 0.80. Statistical analyses of these data suggest that over 83\% of the observed categorical count variance can be attributed to inconsistencies within analysts. We suggest this is the root cause of variation in expert specimen labelling consistency.}, + number = {1}, + urldate = {2023-10-25}, + journal = {Marine Biology Research}, + author = {Culverhouse, Philip F. and Macleod, Norman and Williams, Robert and Benfield, Mark C. and Lopes, Rubens M. and Picheral, Marc}, + month = jan, + year = {2014}, + note = {Publisher: Taylor \& Francis +\_eprint: https://doi.org/10.1080/17451000.2013.810762}, + keywords = {expert performance, Human factors, mesozooplankton identification, root cause of variation}, + pages = {73--84}, +} + +@article{schroder_morphocluster_2020, + title = {{MorphoCluster}: {Efficient} {Annotation} of {Plankton} {Images} by {Clustering}}, + volume = {20}, + copyright = {http://creativecommons.org/licenses/by/3.0/}, + issn = {1424-8220}, + shorttitle = {{MorphoCluster}}, + url = {https://www.mdpi.com/1424-8220/20/11/3060}, + doi = {10.3390/s20113060}, + abstract = {In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue to increase in the coming years. Still, this data requires interpretation. MorphoCluster augments the human ability to discover patterns and perform object classification in large amounts of data by embedding unsupervised clustering in an interactive process. By aggregating similar images into clusters, our novel approach to image annotation increases consistency, multiplies the throughput of an annotator, and allows experts to adapt the granularity of their sorting scheme to the structure in the data. By sorting a set of 1.2 M objects into 280 data-driven classes in 71 h (16 k objects per hour), with 90\% of these classes having a precision of 0.889 or higher. This shows that MorphoCluster is at the same time fast, accurate, and consistent; provides a fine-grained and data-driven classification; and enables novelty detection.}, + language = {en}, + number = {11}, + urldate = {2023-10-26}, + journal = {Sensors}, + author = {Schröder, Simon-Martin and Kiko, Rainer and Koch, Reinhard}, + month = jan, + year = {2020}, + note = {Number: 11 +Publisher: Multidisciplinary Digital Publishing Institute}, + keywords = {deep learning, clustering, machine learning, marine image annotation, marine image recognition, plankton image classification}, + pages = {3060}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/ZHNATHDJ/Schröder et al. - 2020 - MorphoCluster Efficient Annotation of Plankton Im.pdf:application/pdf}, +} + +@article{henrichs_application_2021, + title = {Application of a convolutional neural network to improve automated early warning of harmful algal blooms}, + volume = {28}, + issn = {1614-7499}, + url = {https://doi.org/10.1007/s11356-021-12471-2}, + doi = {10.1007/s11356-021-12471-2}, + abstract = {Continuous monitoring and early warning together represent an important mitigation strategy for harmful algal blooms (HAB). The coast of Texas experiences periodic blooms of three HAB dinoflagellates: Karenia brevis, Dinophysis ovum, and Prorocentrum texanum. A plankton image data set acquired by an Imaging FlowCytobot over a decade of operation was used to train and evaluate two new automated image classifiers. A 112 class, random forest classifier (RF\_112) and a 112 class, convolutional neural network classifier (CNN\_112) were developed and compared with an existing, 54 class, random forest classifier (RF\_54) already in use as an early warning notification system. Both 112 class classifiers exhibited improved performance over the RF\_54 classifier when tested on three different HAB species with the CNN\_112 classifier producing fewer false positives and false negatives in most of the cases tested. For K. brevis and P. texanum, the current threshold of 2 cells.mL−1 was identified as the best threshold to minimize the number of false positives and false negatives. For D. ovum, a threshold of 1 cell.mL−1 was found to produce the best results with regard to the number of false positives/negatives. A lower threshold will result in earlier notification of an increase in cell concentration and will provide state health managers with increased lead time to prepare for an impending HAB.}, + language = {en}, + number = {22}, + urldate = {2023-10-26}, + journal = {Environmental Science and Pollution Research}, + author = {Henrichs, Darren W. and Anglès, Sílvia and Gaonkar, Chetan C. and Campbell, Lisa}, + month = jun, + year = {2021}, + keywords = {CNN, Dinophysis ovum, Gulf of Mexico, HAB, Imaging FlowCytobot, Karenia brevis, Prorocentrum texanum}, + pages = {28544--28555}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/HPYZHY65/Henrichs et al. - 2021 - Application of a convolutional neural network to i.pdf:application/pdf}, +} + +@inproceedings{vaswani_attention_2017, + title = {Attention is {All} you {Need}}, + volume = {30}, + url = {https://proceedings.neurips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html}, + abstract = {The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration. The best performing such models also connect the encoder and decoder through an attentionm echanisms. We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely.Experiments on two machine translation tasks show these models to be superiorin quality while being more parallelizable and requiring significantly less timeto train. Our single model with 165 million parameters, achieves 27.5 BLEU onEnglish-to-German translation, improving over the existing best ensemble result by over 1 BLEU. On English-to-French translation, we outperform the previoussingle state-of-the-art with model by 0.7 BLEU, achieving a BLEU score of 41.1.}, + urldate = {2023-10-28}, + booktitle = {Advances in {Neural} {Information} {Processing} {Systems}}, + publisher = {Curran Associates, Inc.}, + author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, Łukasz and Polosukhin, Illia}, + year = {2017}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/G3A56AFM/Vaswani et al. - 2017 - Attention is All you Need.pdf:application/pdf}, +} + +@article{isola_lecture_nodate-4, + title = {Lecture 15: {Deep} {Generative} {Models} {I}}, + language = {en}, + author = {Isola, Phillip}, + file = {15_gen_models_1.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/15_gen_models_1.pdf:application/pdf}, +} + +@article{isola_lecture_nodate-5, + title = {Lecture 16: {Deep} {Generative} {Models} {II}}, + language = {en}, + author = {Isola, Phillip}, + file = {16_gen_models_2.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/16_gen_models_2.pdf:application/pdf}, +} + +@article{isola_lecture_nodate-6, + title = {Lecture 17: {Deep} {Generative} {Models} {III}}, + language = {en}, + author = {Isola, Phillip}, + file = {17_gen_models_3.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/17_gen_models_3.pdf:application/pdf}, +} + +@misc{1, + title = {How {Neural} {Networks} {Extrapolate}: {From} {Feedforward} to {Graph} {Neural} {Networks}}, + shorttitle = {How {Neural} {Networks} {Extrapolate}}, + url = {http://arxiv.org/abs/2009.11848}, + doi = {10.48550/arXiv.2009.11848}, + abstract = {We study how neural networks trained by gradient descent extrapolate, i.e., what they learn outside the support of the training distribution. Previous works report mixed empirical results when extrapolating with neural networks: while feedforward neural networks, a.k.a. multilayer perceptrons (MLPs), do not extrapolate well in certain simple tasks, Graph Neural Networks (GNNs) -- structured networks with MLP modules -- have shown some success in more complex tasks. Working towards a theoretical explanation, we identify conditions under which MLPs and GNNs extrapolate well. First, we quantify the observation that ReLU MLPs quickly converge to linear functions along any direction from the origin, which implies that ReLU MLPs do not extrapolate most nonlinear functions. But, they can provably learn a linear target function when the training distribution is sufficiently "diverse". Second, in connection to analyzing the successes and limitations of GNNs, these results suggest a hypothesis for which we provide theoretical and empirical evidence: the success of GNNs in extrapolating algorithmic tasks to new data (e.g., larger graphs or edge weights) relies on encoding task-specific non-linearities in the architecture or features. Our theoretical analysis builds on a connection of over-parameterized networks to the neural tangent kernel. Empirically, our theory holds across different training settings.}, + urldate = {2023-11-12}, + publisher = {arXiv}, + author = {Xu, Keyulu and Zhang, Mozhi and Li, Jingling and Du, Simon S. and Kawarabayashi, Ken-ichi and Jegelka, Stefanie}, + month = mar, + year = {2021}, + note = {arXiv:2009.11848 [cs, stat]}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/MAMEYIFL/2009.html:text/html;Xu et al. - 2021 - How Neural Networks Extrapolate From Feedforward .pdf:/Users/birdy/Zotero/storage/XUU8Z7E2/Xu et al. - 2021 - How Neural Networks Extrapolate From Feedforward .pdf:application/pdf}, +} + +@misc{xiao_noise_2020, + title = {Noise or {Signal}: {The} {Role} of {Image} {Backgrounds} in {Object} {Recognition}}, + shorttitle = {Noise or {Signal}}, + url = {http://arxiv.org/abs/2006.09994}, + abstract = {We assess the tendency of state-of-the-art object recognition models to depend on signals from image backgrounds. We create a toolkit for disentangling foreground and background signal on ImageNet images, and find that (a) models can achieve non-trivial accuracy by relying on the background alone, (b) models often misclassify images even in the presence of correctly classified foregrounds--up to 87.5\% of the time with adversarially chosen backgrounds, and (c) more accurate models tend to depend on backgrounds less. Our analysis of backgrounds brings us closer to understanding which correlations machine learning models use, and how they determine models' out of distribution performance.}, + urldate = {2023-11-12}, + publisher = {arXiv}, + author = {Xiao, Kai and Engstrom, Logan and Ilyas, Andrew and Madry, Aleksander}, + month = jun, + year = {2020}, + note = {arXiv:2006.09994 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/W2NV28Q2/2006.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/MZIZ22HW/Xiao et al. - 2020 - Noise or Signal The Role of Image Backgrounds in .pdf:application/pdf}, +} + +@misc{yang_generalized_2022, + title = {Generalized {Out}-of-{Distribution} {Detection}: {A} {Survey}}, + shorttitle = {Generalized {Out}-of-{Distribution} {Detection}}, + url = {http://arxiv.org/abs/2110.11334}, + doi = {10.48550/arXiv.2110.11334}, + abstract = {Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of machine learning systems. For instance, in autonomous driving, we would like the driving system to issue an alert and hand over the control to humans when it detects unusual scenes or objects that it has never seen during training time and cannot make a safe decision. The term, OOD detection, first emerged in 2017 and since then has received increasing attention from the research community, leading to a plethora of methods developed, ranging from classification-based to density-based to distance-based ones. Meanwhile, several other problems, including anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD), are closely related to OOD detection in terms of motivation and methodology. Despite common goals, these topics develop in isolation, and their subtle differences in definition and problem setting often confuse readers and practitioners. In this survey, we first present a unified framework called generalized OOD detection, which encompasses the five aforementioned problems, i.e., AD, ND, OSR, OOD detection, and OD. Under our framework, these five problems can be seen as special cases or sub-tasks, and are easier to distinguish. We then review each of these five areas by summarizing their recent technical developments, with a special focus on OOD detection methodologies. We conclude this survey with open challenges and potential research directions.}, + urldate = {2023-11-12}, + publisher = {arXiv}, + author = {Yang, Jingkang and Zhou, Kaiyang and Li, Yixuan and Liu, Ziwei}, + month = aug, + year = {2022}, + note = {arXiv:2110.11334 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition}, + file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/MA6JB7TD/Yang et al. - 2022 - Generalized Out-of-Distribution Detection A Surve.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/BH3RP2BX/2110.html:text/html}, +} + +@article{cui_out--distribution_2022, + title = {Out-of-{Distribution} ({OOD}) {Detection} {Based} on {Deep} {Learning}: {A} {Review}}, + volume = {11}, + copyright = {http://creativecommons.org/licenses/by/3.0/}, + issn = {2079-9292}, + shorttitle = {Out-of-{Distribution} ({OOD}) {Detection} {Based} on {Deep} {Learning}}, + url = {https://www.mdpi.com/2079-9292/11/21/3500}, + doi = {10.3390/electronics11213500}, + abstract = {Out-of-Distribution (OOD) detection separates ID (In-Distribution) data and OOD data from input data through a model. This problem has attracted increasing attention in the area of machine learning. OOD detection has achieved good intrusion detection, fraud detection, system health monitoring, sensor network event detection, and ecosystem interference detection. The method based on deep learning is the most studied in OOD detection. In this paper , related basic information on OOD detection based on deep learning is described, and we categorize methods according to the training data. OOD detection is divided into supervised, semisupervised, and unsupervised. Where supervised data are used, the methods are categorized according to technical means: model-based, distance-based, and density-based. Each classification is introduced with background, examples, and applications. In addition, we present the latest applications of OOD detection based on deep learning and the problems and expectations in this field.}, + language = {en}, + number = {21}, + urldate = {2023-11-12}, + journal = {Electronics}, + author = {Cui, Peng and Wang, Jinjia}, + month = jan, + year = {2022}, + note = {Number: 21 +Publisher: Multidisciplinary Digital Publishing Institute}, + keywords = {deep learning, anomaly detection, neural networks, novelty detection, Out-of-Distribution detection, outlier detection}, + pages = {3500}, + file = {Cui and Wang - 2022 - Out-of-Distribution (OOD) Detection Based on Deep .pdf:/Users/birdy/Zotero/storage/MK3M7WBW/Cui and Wang - 2022 - Out-of-Distribution (OOD) Detection Based on Deep .pdf:application/pdf}, +} + +@misc{ghassemi_comprehensive_2022, + title = {A {Comprehensive} {Review} of {Trends}, {Applications} and {Challenges} {In} {Out}-of-{Distribution} {Detection}}, + url = {http://arxiv.org/abs/2209.12935}, + abstract = {With recent advancements in artificial intelligence, its applications can be seen in every aspect of humans' daily life. From voice assistants to mobile healthcare and autonomous driving, we rely on the performance of AI methods for many critical tasks; therefore, it is essential to assert the performance of models in proper means to prevent damage. One of the shortfalls of AI models in general, and deep machine learning in particular, is a drop in performance when faced with shifts in the distribution of data. Nonetheless, these shifts are always expected in real-world applications; thus, a field of study has emerged, focusing on detecting out-of-distribution data subsets and enabling a more comprehensive generalization. Furthermore, as many deep learning based models have achieved near-perfect results on benchmark datasets, the need to evaluate these models' reliability and trustworthiness for pushing towards real-world applications is felt more strongly than ever. This has given rise to a growing number of studies in the field of out-of-distribution detection and domain generalization, which begs the need for surveys that compare these studies from various perspectives and highlight their straightens and weaknesses. This paper presents a survey that, in addition to reviewing more than 70 papers in this field, presents challenges and directions for future works and offers a unifying look into various types of data shifts and solutions for better generalization.}, + urldate = {2023-11-12}, + publisher = {arXiv}, + author = {Ghassemi, Navid and Fazl-Ersi, Ehsan}, + month = sep, + year = {2022}, + note = {arXiv:2209.12935 [cs]}, + keywords = {Computer Science - Machine Learning}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/GJU4ZCYR/2209.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/TWZAVEUQ/Ghassemi and Fazl-Ersi - 2022 - A Comprehensive Review of Trends, Applications and.pdf:application/pdf}, +} + +@book{quinonero-candela_dataset_2009, + address = {Cambridge, Mass}, + series = {Neural information processing series}, + title = {Dataset shift in machine learning}, + isbn = {978-0-262-17005-5 978-0-262-54587-7}, + language = {en}, + publisher = {MIT Press}, + editor = {Quiñonero-Candela, Joaquin}, + year = {2009}, + note = {OCLC: ocn227205909}, + keywords = {Machine learning}, + file = {Quiñonero-Candela - 2009 - Dataset shift in machine learning.pdf:/Users/birdy/Zotero/storage/JJT973SR/Quiñonero-Candela - 2009 - Dataset shift in machine learning.pdf:application/pdf}, +} + +@article{may_will_1972, + title = {Will a {Large} {Complex} {System} be {Stable}?}, + volume = {238}, + copyright = {1972 Springer Nature Limited}, + issn = {1476-4687}, + url = {https://www.nature.com/articles/238413a0}, + doi = {10.1038/238413a0}, + abstract = {Gardner and Ashby1 have suggested that large complex systems which are assembled (connected) at random may be expected to be stable up to a certain critical level of connectance, and then, as this increases, to suddenly become unstable. Their conclusions were based on the trend of computer studies of systems with 4, 7 and 10 variables.}, + language = {en}, + number = {5364}, + urldate = {2023-11-13}, + journal = {Nature}, + author = {May, Robert M.}, + month = aug, + year = {1972}, + note = {Number: 5364 +Publisher: Nature Publishing Group}, + keywords = {Humanities and Social Sciences, multidisciplinary, Science}, + pages = {413--414}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/K3ZKQCSD/May - 1972 - Will a Large Complex System be Stable.pdf:application/pdf}, +} + +@article{armstrong_stable_1999, + title = {Stable model structures for representing biogeochemical diversity and size spectra in plankton communities}, + volume = {21}, + issn = {0142-7873}, + url = {https://doi.org/10.1093/plankt/21.3.445}, + doi = {10.1093/plankt/21.3.445}, + abstract = {There is a fundamental duality in the way we view planktonic communities. On one hand, we see these communities as composed of a diverse mixture of taxonomically and biogeochemically distinct species; pelagic ecosystem models capable of predicting the ocean's role in the global carbon cycle will need to resolve these multiple plankton taxa. However, we have also come to appreciate that superimposed on this diversity are regular patterns of size structure, where plots of abundance within size classes typically show a power-law dependence on size. A truly complete model description of planktonic community structure must reflect both aspects of this duality. Here I examine two classes of ecosystem models that are capable of representing this duality. The first structure is based on multiple (\<it\>n\</it\>) couplets of phytoplankton +(\<it\>P\</it\>) and zooplankton (\<it\>Z\</it\>) [an '\<it\>n\</it\>(\<it\>PZ\</it\>)' model]. Within certain regions of parameter space, this model structure can produce anomalous oscillatory behaviors, the mathematical origin of which is explored in detail. I then examine an alternative food web structure in which total +herbivore abundance is represented by a single state variable,\<it\>G\</it\>, and where grazing pressure is distributed among +taxa in proportion to their abundances. This '(\<it\>nP)G\</it\>' +system is stabilized by the redistribution of grazing intensity among taxa +in response to changes in their densities. This 'distributed grazing' model +also naturally produces size spectra of plankton abundance; this last +observation argues that the model with distributed grazing can help in +uncovering and representing the mechanistic basis for the genesis and +maintenance of planktonic size spectra of phytoplankton and bacteria.}, + number = {3}, + urldate = {2023-11-13}, + journal = {Journal of Plankton Research}, + author = {Armstrong, Robert A.}, + month = mar, + year = {1999}, + pages = {445--464}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/LP2EYBJD/1999 - Stable model structures for representing biogeoche.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/MN45F6HS/1395820.html:text/html}, +} + +@article{ashkezari_simons_2021, + title = {Simons {Collaborative} {Marine} {Atlas} {Project} ({Simons} {CMAP}): {An} open-source portal to share, visualize, and analyze ocean data}, + volume = {19}, + copyright = {© 2021 The Authors. Limnology and Oceanography: Methods published by Wiley Periodicals LLC on behalf of Association for the Sciences of Limnology and Oceanography.}, + issn = {1541-5856}, + shorttitle = {Simons {Collaborative} {Marine} {Atlas} {Project} ({Simons} {CMAP})}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lom3.10439}, + doi = {10.1002/lom3.10439}, + abstract = {Simons Collaborative Marine Atlas Project (Simons CMAP) is an open-source data portal that interconnects large, complex, and diverse public data sets currently dispersed in different formats across different Oceanography discipline-specific databases. Simons CMAP is designed to streamline the retrieval of custom subsets of data, the generation of data visualizations, and the analyses of diverse data, thus expanding the power of these potentially underutilized data sets for cross-disciplinary studies of ocean processes. We describe a unified architecture that allows numerical model outputs, satellite products, and field observations to be readily shared, mined, and integrated regardless of data set size or resolution. A current focus of Simons CMAP is integration of physical, chemical, and biological data sets essential for characterizing the biogeography of key marine microbes across ocean basins and seasonal cycles. Using a practical example, we demonstrate how our unifying data harmonization plans significantly simplifies and allows for systematic data integration across all Simons CMAP data sets.}, + language = {en}, + number = {7}, + urldate = {2023-11-13}, + journal = {Limnology and Oceanography: Methods}, + author = {Ashkezari, Mohammad D. and Hagen, Norland R. and Denholtz, Michael and Neang, Andrew and Burns, Tansy C. and Morales, Rhonda L. and Lee, Charlotte P. and Hill, Christopher N. and Armbrust, E. Virginia}, + year = {2021}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10439}, + pages = {488--496}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/DUNQVLE5/Ashkezari et al. - 2021 - Simons Collaborative Marine Atlas Project (Simons .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/CPQTRWWV/lom3.html:text/html}, +} + +@article{aumont_pisces-v2_2015, + title = {{PISCES}-v2: an ocean biogeochemical model for carbon and ecosystem studies}, + volume = {8}, + url = {https://gmd.copernicus.org/articles/8/2465/2015/}, + doi = {10.5194/gmd-8-2465-2015}, + number = {8}, + journal = {Geoscientific Model Development}, + author = {Aumont, O. and Ethé, C. and Tagliabue, A. and Bopp, L. and Gehlen, M.}, + year = {2015}, + pages = {2465--2513}, +} + +@article{bardon_testing_2021-1, + title = {Testing the {Skill} of a {Species} {Distribution} {Model} {Using} a 21st {Century} {Virtual} {Ecosystem}}, + volume = {48}, + copyright = {© 2021. The Authors.}, + issn = {1944-8007}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2021GL093455}, + doi = {10.1029/2021GL093455}, + abstract = {Plankton communities play an important role in marine food webs, in biogeochemical cycling, and in Earth's climate; yet observations are sparse, and predictions of how they might respond to climate change vary. Correlative species distribution models (SDM's) have been applied to predicting biogeography based on relationships to observed environmental variables. To investigate sources of uncertainty, we use a correlative SDM to predict the plankton biogeography of a 21st century marine ecosystem model (Darwin). Darwin output is sampled to mimic historical ocean observations, and the SDM is trained using generalized additive models. We find that predictive skill varies across test cases, and between functional groups, with errors that are more attributable to spatiotemporal sampling bias than sample size. End-of-century predictions are poor, limited by changes in target-predictor relationships over time. Our findings illustrate the fundamental challenges faced by empirical models in using limited observational data to predict complex, dynamic systems.}, + language = {en}, + number = {22}, + urldate = {2023-11-13}, + journal = {Geophysical Research Letters}, + author = {Bardon, L. R. and Ward, B. A. and Dutkiewicz, S. and Cael, B. B.}, + year = {2021}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021GL093455}, + keywords = {climate change, complex systems, numerical models, oceanography, plankton ecology, statistical learning}, + pages = {e2021GL093455}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/CD5IBV65/Bardon et al. - 2021 - Testing the Skill of a Species Distribution Model .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/DK86CFJT/2021GL093455.html:text/html}, +} + +@article{bian_understanding_2023-1, + title = {Understanding opposing predictions of {Prochlorococcus} in a changing climate}, + volume = {14}, + copyright = {2023 The Author(s)}, + issn = {2041-1723}, + url = {https://www.nature.com/articles/s41467-023-36928-9}, + doi = {10.1038/s41467-023-36928-9}, + abstract = {Statistically derived species distribution models (SDMs) are increasingly used to predict ecological changes on a warming planet. For Prochlorococcus, the most abundant phytoplankton, an established statistical prediction conflicts with dynamical models as they predict large, opposite, changes in abundance. We probe the SDM at various spatial-temporal scales, showing that light and temperature fail to explain both temporal fluctuations and sharp spatial transitions. Strong correlations between changes in temperature and population emerge only at very large spatial scales, as transects pass through transitions between regions of high and low abundance. Furthermore, a two-state model based on a temperature threshold matches the original SDM in the surface ocean. We conclude that the original SDM has little power to predict changes when Prochlorococcus is already abundant, which resolves the conflict with dynamical models. Our conclusion suggests that SDMs should prove efficacy across multiple spatial-temporal scales before being trusted in a changing ocean.}, + language = {en}, + number = {1}, + urldate = {2023-11-13}, + journal = {Nature Communications}, + author = {Bian, Vincent and Cai, Merrick and Follett, Christopher L.}, + month = mar, + year = {2023}, + note = {Number: 1 +Publisher: Nature Publishing Group}, + keywords = {Biogeography, Projection and prediction}, + pages = {1445}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/257L3PIB/Bian et al. - 2023 - Understanding opposing predictions of Prochlorococ.pdf:application/pdf}, +} + +@misc{winkens_contrastive_2020, + title = {Contrastive {Training} for {Improved} {Out}-of-{Distribution} {Detection}}, + url = {http://arxiv.org/abs/2007.05566}, + abstract = {Reliable detection of out-of-distribution (OOD) inputs is increasingly understood to be a precondition for deployment of machine learning systems. This paper proposes and investigates the use of contrastive training to boost OOD detection performance. Unlike leading methods for OOD detection, our approach does not require access to examples labeled explicitly as OOD, which can be difficult to collect in practice. We show in extensive experiments that contrastive training significantly helps OOD detection performance on a number of common benchmarks. By introducing and employing the Confusion Log Probability (CLP) score, which quantifies the difficulty of the OOD detection task by capturing the similarity of inlier and outlier datasets, we show that our method especially improves performance in the `near OOD' classes -- a particularly challenging setting for previous methods.}, + urldate = {2023-11-13}, + publisher = {arXiv}, + author = {Winkens, Jim and Bunel, Rudy and Roy, Abhijit Guha and Stanforth, Robert and Natarajan, Vivek and Ledsam, Joseph R. and MacWilliams, Patricia and Kohli, Pushmeet and Karthikesalingam, Alan and Kohl, Simon and Cemgil, Taylan and Eslami, S. M. Ali and Ronneberger, Olaf}, + month = jul, + year = {2020}, + note = {arXiv:2007.05566 [cs, stat]}, + keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/ZW3DG7B6/2007.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/5M6W3QNV/Winkens et al. - 2020 - Contrastive Training for Improved Out-of-Distribut.pdf:application/pdf}, +} + +@inproceedings{zhou_contrastive_2021, + title = {Contrastive {Out}-of-{Distribution} {Detection} for {Pretrained} {Transformers}}, + url = {http://arxiv.org/abs/2104.08812}, + doi = {10.18653/v1/2021.emnlp-main.84}, + abstract = {Pretrained Transformers achieve remarkable performance when training and test data are from the same distribution. However, in real-world scenarios, the model often faces out-of-distribution (OOD) instances that can cause severe semantic shift problems at inference time. Therefore, in practice, a reliable model should identify such instances, and then either reject them during inference or pass them over to models that handle another distribution. In this paper, we develop an unsupervised OOD detection method, in which only the in-distribution (ID) data are used in training. We propose to fine-tune the Transformers with a contrastive loss, which improves the compactness of representations, such that OOD instances can be better differentiated from ID ones. These OOD instances can then be accurately detected using the Mahalanobis distance in the model's penultimate layer. We experiment with comprehensive settings and achieve near-perfect OOD detection performance, outperforming baselines drastically. We further investigate the rationales behind the improvement, finding that more compact representations through margin-based contrastive learning bring the improvement. We release our code to the community for future research.}, + urldate = {2023-11-13}, + booktitle = {Proceedings of the 2021 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}}, + author = {Zhou, Wenxuan and Liu, Fangyu and Chen, Muhao}, + year = {2021}, + note = {arXiv:2104.08812 [cs]}, + keywords = {Computer Science - Computation and Language}, + pages = {1100--1111}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/IB8D8VD8/2104.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/XFIETIDR/Zhou et al. - 2021 - Contrastive Out-of-Distribution Detection for Pret.pdf:application/pdf}, +} + +@article{beery_lecture_nodate-2, + title = {Lecture 20: {Transfer} {Learning} {II}}, + language = {en}, + author = {Beery, Sara}, + file = {20_transfer_learning_2_SB.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/20_transfer_learning_2_SB.pdf:application/pdf}, +} + +@misc{kingma_auto-encoding_2022, + title = {Auto-{Encoding} {Variational} {Bayes}}, + url = {http://arxiv.org/abs/1312.6114}, + abstract = {How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions are two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.}, + urldate = {2023-11-27}, + publisher = {arXiv}, + author = {Kingma, Diederik P. and Welling, Max}, + month = dec, + year = {2022}, + note = {arXiv:1312.6114 [cs, stat]}, + keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/H7BQXHMR/1312.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/QR4A6NNA/Kingma and Welling - 2022 - Auto-Encoding Variational Bayes.pdf:application/pdf}, +} + +@article{sonnewald_elucidating_2020, + title = {Elucidating ecological complexity: {Unsupervised} learning determines global marine eco-provinces}, + volume = {6}, + shorttitle = {Elucidating ecological complexity}, + url = {https://www.science.org/doi/full/10.1126/sciadv.aay4740}, + doi = {10.1126/sciadv.aay4740}, + abstract = {An unsupervised learning method is presented for determining global marine ecological provinces (eco-provinces) from plankton community structure and nutrient flux data. The systematic aggregated eco-province (SAGE) method identifies eco-provinces within a highly nonlinear ecosystem model. To accommodate the non-Gaussian covariance of the data, SAGE uses t-stochastic neighbor embedding (t-SNE) to reduce dimensionality. Over a hundred eco-provinces are identified with the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Using a connectivity graph with ecological dissimilarity as the distance metric, robust aggregated eco-provinces (AEPs) are objectively defined by nesting the eco-provinces. Using the AEPs, the control of nutrient supply rates on community structure is explored. Eco-provinces and AEPs are unique and aid model interpretation. They could facilitate model intercomparison and potentially improve understanding and monitoring of marine ecosystems.}, + number = {22}, + urldate = {2023-11-28}, + journal = {Science Advances}, + author = {Sonnewald, Maike and Dutkiewicz, Stephanie and Hill, Christopher and Forget, Gael}, + month = may, + year = {2020}, + note = {Publisher: American Association for the Advancement of Science}, + pages = {eaay4740}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/FSUVU9LS/Sonnewald et al. - 2020 - Elucidating ecological complexity Unsupervised le.pdf:application/pdf}, +} + +@misc{ho_denoising_2020, + title = {Denoising {Diffusion} {Probabilistic} {Models}}, + url = {http://arxiv.org/abs/2006.11239}, + doi = {10.48550/arXiv.2006.11239}, + abstract = {We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at https://github.com/hojonathanho/diffusion}, + urldate = {2023-11-30}, + publisher = {arXiv}, + author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter}, + month = dec, + year = {2020}, + note = {arXiv:2006.11239 [cs, stat]}, + keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, + file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/A854EPRR/Ho et al. - 2020 - Denoising Diffusion Probabilistic Models.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/R4HY8MGJ/2006.html:text/html}, +} + +@inproceedings{wang_partial_2022, + title = {Partial and {Asymmetric} {Contrastive} {Learning} for {Out}-of-{Distribution} {Detection} in {Long}-{Tailed} {Recognition}}, + url = {https://proceedings.mlr.press/v162/wang22aq.html}, + abstract = {Existing out-of-distribution (OOD) detection methods are typically benchmarked on training sets with balanced class distributions. However, in real-world applications, it is common for the training sets to have long-tailed distributions. In this work, we first demonstrate that existing OOD detection methods commonly suffer from significant performance degradation when the training set is long-tail distributed. Through analysis, we posit that this is because the models struggle to distinguish the minority tail-class in-distribution samples, from the true OOD samples, making the tail classes more prone to be falsely detected as OOD. To solve this problem, we propose Partial and Asymmetric Supervised Contrastive Learning (PASCL), which explicitly encourages the model to distinguish between tail-class in-distribution samples and OOD samples. To further boost in-distribution classification accuracy, we propose Auxiliary Branch Finetuning, which uses two separate branches of BN and classification layers for anomaly detection and in-distribution classification, respectively. The intuition is that in-distribution and OOD anomaly data have different underlying distributions. Our method outperforms previous state-of-the-art method by \$1.29\%\$, \$1.45\%\$, \$0.69\%\$ anomaly detection false positive rate (FPR) and \$3.24\%\$, \$4.06\%\$, \$7.89\%\$ in-distribution classification accuracy on CIFAR10-LT, CIFAR100-LT, and ImageNet-LT, respectively. Code and pre-trained models are available at https://github.com/amazon-research/long-tailed-ood-detection.}, + language = {en}, + urldate = {2023-12-04}, + booktitle = {Proceedings of the 39th {International} {Conference} on {Machine} {Learning}}, + publisher = {PMLR}, + author = {Wang, Haotao and Zhang, Aston and Zhu, Yi and Zheng, Shuai and Li, Mu and Smola, Alex J. and Wang, Zhangyang}, + month = jun, + year = {2022}, + note = {ISSN: 2640-3498}, + pages = {23446--23458}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/Y2THTBUA/Wang et al. - 2022 - Partial and Asymmetric Contrastive Learning for Ou.pdf:application/pdf}, +} + +@article{higgins_-vae_2017, + title = {β-{VAE}: {LEARNING} {BASIC} {VISUAL} {CONCEPTS} {WITH} {A} {CONSTRAINED} {VARIATIONAL} {FRAMEWORK}}, + abstract = {Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to learn and reason in the same way that humans do. We introduce β-VAE, a new state-of-the-art framework for automated discovery of interpretable factorised latent representations from raw image data in a completely unsupervised manner. Our approach is a modification of the variational autoencoder (VAE) framework. We introduce an adjustable hyperparameter β that balances latent channel capacity and independence constraints with reconstruction accuracy. We demonstrate that β-VAE with appropriately tuned β {\textgreater} 1 qualitatively outperforms VAE (β = 1), as well as state of the art unsupervised (InfoGAN) and semi-supervised (DC-IGN) approaches to disentangled factor learning on a variety of datasets (celebA, faces and chairs). Furthermore, we devise a protocol to quantitatively compare the degree of disentanglement learnt by different models, and show that our approach also significantly outperforms all baselines quantitatively. Unlike InfoGAN, β-VAE is stable to train, makes few assumptions about the data and relies on tuning a single hyperparameter β, which can be directly optimised through a hyperparameter search using weakly labelled data or through heuristic visual inspection for purely unsupervised data.}, + language = {en}, + author = {Higgins, Irina and Matthey, Loic and Pal, Arka and Burgess, Christopher and Glorot, Xavier and Botvinick, Matthew and Mohamed, Shakir and Lerchner, Alexander}, + year = {2017}, + file = {Higgins et al. - 2017 - β-VAE LEARNING BASIC VISUAL CONCEPTS WITH A CONST.pdf:/Users/birdy/Zotero/storage/9VGRX2XM/Higgins et al. - 2017 - β-VAE LEARNING BASIC VISUAL CONCEPTS WITH A CONST.pdf:application/pdf}, +} + +@article{schmarje_fuzzy_2021, + title = {Fuzzy {Overclustering}: {Semi}-{Supervised} {Classification} of {Fuzzy} {Labels} with {Overclustering} and {Inverse} {Cross}-{Entropy}}, + volume = {21}, + copyright = {http://creativecommons.org/licenses/by/3.0/}, + issn = {1424-8220}, + shorttitle = {Fuzzy {Overclustering}}, + url = {https://www.mdpi.com/1424-8220/21/19/6661}, + doi = {10.3390/s21196661}, + abstract = {Deep learning has been successfully applied to many classification problems including underwater challenges. However, a long-standing issue with deep learning is the need for large and consistently labeled datasets. Although current approaches in semi-supervised learning can decrease the required amount of annotated data by a factor of 10 or even more, this line of research still uses distinct classes. For underwater classification, and uncurated real-world datasets in general, clean class boundaries can often not be given due to a limited information content in the images and transitional stages of the depicted objects. This leads to different experts having different opinions and thus producing fuzzy labels which could also be considered ambiguous or divergent. We propose a novel framework for handling semi-supervised classifications of such fuzzy labels. It is based on the idea of overclustering to detect substructures in these fuzzy labels. We propose a novel loss to improve the overclustering capability of our framework and show the benefit of overclustering for fuzzy labels. We show that our framework is superior to previous state-of-the-art semi-supervised methods when applied to real-world plankton data with fuzzy labels. Moreover, we acquire 5 to 10\% more consistent predictions of substructures.}, + language = {en}, + number = {19}, + urldate = {2023-12-04}, + journal = {Sensors}, + author = {Schmarje, Lars and Brünger, Johannes and Santarossa, Monty and Schröder, Simon-Martin and Kiko, Rainer and Koch, Reinhard}, + month = jan, + year = {2021}, + note = {Number: 19 +Publisher: Multidisciplinary Digital Publishing Institute}, + keywords = {deep learning, fuzzy, marine, noisy, plankton, real-world, semi-supervised}, + pages = {6661}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/WILN3PSB/Schmarje et al. - 2021 - Fuzzy Overclustering Semi-Supervised Classificati.pdf:application/pdf}, +} + +@inproceedings{liu_good-d_2023, + address = {New York, NY, USA}, + series = {{WSDM} '23}, + title = {{GOOD}-{D}: {On} {Unsupervised} {Graph} {Out}-{Of}-{Distribution} {Detection}}, + isbn = {978-1-4503-9407-9}, + shorttitle = {{GOOD}-{D}}, + url = {https://dl.acm.org/doi/10.1145/3539597.3570446}, + doi = {10.1145/3539597.3570446}, + abstract = {Most existing deep learning models are trained based on the closed-world assumption, where the test data is assumed to be drawn i.i.d. from the same distribution as the training data, known as in-distribution (ID). However, when models are deployed in an open-world scenario, test samples can be out-of-distribution (OOD) and therefore should be handled with caution. To detect such OOD samples drawn from unknown distribution, OOD detection has received increasing attention lately. However, current endeavors mostly focus on grid-structured data and its application for graph-structured data remains under-explored. Considering the fact that data labeling on graphs is commonly time-expensive and labor-intensive, in this work we study the problem of unsupervised graph OOD detection, aiming at detecting OOD graphs solely based on unlabeled ID data. To achieve this goal, we develop a new graph contrastive learning framework GOOD-D for detecting OOD graphs without using any ground-truth labels. By performing hierarchical contrastive learning on the augmented graphs generated by our perturbation-free graph data augmentation method, GOOD-D is able to capture the latent ID patterns and accurately detect OOD graphs based on the semantic inconsistency in different granularities (i.e., node-level, graph-level, and group-level). As a pioneering work in unsupervised graph-level OOD detection, we build a comprehensive benchmark to compare our proposed approach with different state-of-the-art methods. The experiment results demonstrate the superiority of our approach over different methods on various datasets.}, + urldate = {2023-12-04}, + booktitle = {Proceedings of the {Sixteenth} {ACM} {International} {Conference} on {Web} {Search} and {Data} {Mining}}, + publisher = {Association for Computing Machinery}, + author = {Liu, Yixin and Ding, Kaize and Liu, Huan and Pan, Shirui}, + month = feb, + year = {2023}, + keywords = {contrastive learning, graph neural networks, out-of-distribution detection}, + pages = {339--347}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/I8T5QYSC/Liu et al. - 2023 - GOOD-D On Unsupervised Graph Out-Of-Distribution .pdf:application/pdf}, +} + +@misc{wang_understanding_2022, + title = {Understanding {Contrastive} {Representation} {Learning} through {Alignment} and {Uniformity} on the {Hypersphere}}, + url = {http://arxiv.org/abs/2005.10242}, + abstract = {Contrastive representation learning has been outstandingly successful in practice. In this work, we identify two key properties related to the contrastive loss: (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere. We prove that, asymptotically, the contrastive loss optimizes these properties, and analyze their positive effects on downstream tasks. Empirically, we introduce an optimizable metric to quantify each property. Extensive experiments on standard vision and language datasets confirm the strong agreement between both metrics and downstream task performance. Remarkably, directly optimizing for these two metrics leads to representations with comparable or better performance at downstream tasks than contrastive learning. Project Page: https://tongzhouwang.info/hypersphere Code: https://github.com/SsnL/align\_uniform , https://github.com/SsnL/moco\_align\_uniform}, + urldate = {2023-12-04}, + publisher = {arXiv}, + author = {Wang, Tongzhou and Isola, Phillip}, + month = aug, + year = {2022}, + note = {arXiv:2005.10242 [cs, stat]}, + keywords = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/WFW3P94A/2005.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/KJZGVSLC/Wang and Isola - 2022 - Understanding Contrastive Representation Learning .pdf:application/pdf}, +} + +@inproceedings{chen_atom_2021, + address = {Cham}, + series = {Lecture {Notes} in {Computer} {Science}}, + title = {{ATOM}: {Robustifying} {Out}-of-{Distribution} {Detection} {Using} {Outlier} {Mining}}, + isbn = {978-3-030-86523-8}, + shorttitle = {{ATOM}}, + doi = {10.1007/978-3-030-86523-8_26}, + abstract = {Detecting out-of-distribution (OOD) inputs is critical for safely deploying deep learning models in an open-world setting. However, existing OOD detection solutions can be brittle in the open world, facing various types of adversarial OOD inputs. While methods leveraging auxiliary OOD data have emerged, our analysis on illuminative examples reveals a key insight that the majority of auxiliary OOD examples may not meaningfully improve or even hurt the decision boundary of the OOD detector, which is also observed in empirical results on real data. In this paper, we provide a theoretically motivated method, Adversarial Training with informative Outlier Mining (ATOM), which improves the robustness of OOD detection. We show that, by mining informative auxiliary OOD data, one can significantly improve OOD detection performance, and somewhat surprisingly, generalize to unseen adversarial attacks. ATOM achieves state-of-the-art performance under a broad family of classic and adversarial OOD evaluation tasks. For example, on the CIFAR-10 in-distribution dataset, ATOM reduces the FPR (at TPR 95\%) by up to 57.99\% under adversarial OOD inputs, surpassing the previous best baseline by a large margin.}, + language = {en}, + booktitle = {Machine {Learning} and {Knowledge} {Discovery} in {Databases}. {Research} {Track}}, + publisher = {Springer International Publishing}, + author = {Chen, Jiefeng and Li, Yixuan and Wu, Xi and Liang, Yingyu and Jha, Somesh}, + editor = {Oliver, Nuria and Pérez-Cruz, Fernando and Kramer, Stefan and Read, Jesse and Lozano, Jose A.}, + year = {2021}, + keywords = {Out-of-distribution detection, Outlier mining, Robustness}, + pages = {430--445}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/5PNM3HAW/Chen et al. - 2021 - ATOM Robustifying Out-of-Distribution Detection U.pdf:application/pdf}, +} + +@misc{chen_simple_2020, + title = {A {Simple} {Framework} for {Contrastive} {Learning} of {Visual} {Representations}}, + url = {http://arxiv.org/abs/2002.05709}, + abstract = {This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5\% top-1 accuracy, which is a 7\% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1\% of the labels, we achieve 85.8\% top-5 accuracy, outperforming AlexNet with 100X fewer labels.}, + urldate = {2024-01-04}, + publisher = {arXiv}, + author = {Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey}, + month = jun, + year = {2020}, + note = {arXiv:2002.05709 [cs, stat]}, + keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/QSFZN5RI/2002.html:text/html;Chen et al. - 2020 - A Simple Framework for Contrastive Learning of Vis.pdf:/Users/birdy/Zotero/storage/UA8HJXR5/Chen et al. - 2020 - A Simple Framework for Contrastive Learning of Vis.pdf:application/pdf}, +} + +@misc{pang_rethinking_2020, + title = {Rethinking {Softmax} {Cross}-{Entropy} {Loss} for {Adversarial} {Robustness}}, + url = {http://arxiv.org/abs/1905.10626}, + abstract = {Previous work shows that adversarially robust generalization requires larger sample complexity, and the same dataset, e.g., CIFAR-10, which enables good standard accuracy may not suffice to train robust models. Since collecting new training data could be costly, we focus on better utilizing the given data by inducing the regions with high sample density in the feature space, which could lead to locally sufficient samples for robust learning. We first formally show that the softmax cross-entropy (SCE) loss and its variants convey inappropriate supervisory signals, which encourage the learned feature points to spread over the space sparsely in training. This inspires us to propose the Max-Mahalanobis center (MMC) loss to explicitly induce dense feature regions in order to benefit robustness. Namely, the MMC loss encourages the model to concentrate on learning ordered and compact representations, which gather around the preset optimal centers for different classes. We empirically demonstrate that applying the MMC loss can significantly improve robustness even under strong adaptive attacks, while keeping state-of-the-art accuracy on clean inputs with little extra computation compared to the SCE loss.}, + urldate = {2024-01-06}, + publisher = {arXiv}, + author = {Pang, Tianyu and Xu, Kun and Dong, Yinpeng and Du, Chao and Chen, Ning and Zhu, Jun}, + month = feb, + year = {2020}, + note = {arXiv:1905.10626 [cs, stat]}, + keywords = {Computer Science - Cryptography and Security, Computer Science - Machine Learning, Statistics - Machine Learning}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/ZQWMNTQ4/1905.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/L6RK8EVE/Pang et al. - 2020 - Rethinking Softmax Cross-Entropy Loss for Adversar.pdf:application/pdf}, +} + +@inproceedings{zhai_s4l_2019, + address = {Seoul, Korea (South)}, + title = {{S4L}: {Self}-{Supervised} {Semi}-{Supervised} {Learning}}, + isbn = {978-1-72814-803-8}, + shorttitle = {{S4L}}, + url = {https://ieeexplore.ieee.org/document/9010283/}, + doi = {10.1109/ICCV.2019.00156}, + abstract = {This work tackles the problem of semi-supervised learning of image classifiers. Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual representation learning. Unifying these two approaches, we propose the framework of self-supervised semi-supervised learning (S4L) and use it to derive two novel semi-supervised image classification methods. We demonstrate the effectiveness of these methods in comparison to both carefully tuned baselines, and existing semi-supervised learning methods. We then show that S4L and existing semi-supervised methods can be jointly trained, yielding a new state-of-the-art result on semi-supervised ILSVRC-2012 with 10\% of labels.}, + language = {en}, + urldate = {2024-01-06}, + booktitle = {2019 {IEEE}/{CVF} {International} {Conference} on {Computer} {Vision} ({ICCV})}, + publisher = {IEEE}, + author = {Zhai, Xiaohua and Oliver, Avital and Kolesnikov, Alexander and Beyer, Lucas}, + month = oct, + year = {2019}, + pages = {1476--1485}, + file = {Zhai et al. - 2019 - S4L Self-Supervised Semi-Supervised Learning.pdf:/Users/birdy/Zotero/storage/8NNHKTJM/Zhai et al. - 2019 - S4L Self-Supervised Semi-Supervised Learning.pdf:application/pdf}, +} + +@misc{duckworth_dar1_2023, + title = {{DAR1} - {An} {Easy} to {Use}, {Single}-{Box}, {Configuration} of a {Global} {Scale} {Biogeochemical} {Ecosystem} {Model}}, + copyright = {© 2023, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/}, + url = {https://www.biorxiv.org/content/10.1101/2023.11.23.568480v1}, + doi = {10.1101/2023.11.23.568480}, + abstract = {Global scale biogeochemical ocean models are key to understanding the mechanisms which drive elemental cycles and their links to marine ecosystems. However, these models are rarely straightforward to use and their complexity can make them difficult to interpret. Here, we aim to address this problem by developing DAR1 – a single box configuration of the MIT Darwin model, which is itself a global scale biogeochemical ecosystem model designed for understanding the emergent patterns of global plankton distributions. Our configuration is packaged inside Docker and implemented in the Julia programming language. This helps make it both computationally efficient and straightforward to implement on any local machine, independent of hardware and operating system. It allows for the replication of field and laboratory experiments in a model ocean and the rapid comparison of global runs with local predictions. We provide three case studies: a framework for nutrient amendment bottle experiments; a test of steady state assumptions on plankton community structure at the global scale; and an exploration of the niche space of nitrogen-fixing plankton. Our method has the potential to facilitate broader direct testing of global model assumptions in the context of small-scale measurements. DAR1 will also allow observational and laboratory oceanographers to directly test their results against model predictions in a straightforward, easy to use, manner.}, + language = {en}, + urldate = {2024-01-06}, + publisher = {bioRxiv}, + author = {Duckworth, Barbara and Dutkiewicz, Stephanie and Jahn, Oliver and Hill, Christopher N. and Follett, Christopher L.}, + month = nov, + year = {2023}, + note = {Pages: 2023.11.23.568480 +Section: New Results}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/3376RTZG/Duckworth et al. - 2023 - DAR1 - An Easy to Use, Single-Box, Configuration o.pdf:application/pdf}, +} + +@article{falkowski_role_1994, + title = {The role of phytoplankton photosynthesis in global biogeochemical cycles}, + volume = {39}, + issn = {1573-5079}, + url = {https://doi.org/10.1007/BF00014586}, + doi = {10.1007/BF00014586}, + abstract = {Phytoplankton biomass in the world's oceans amounts to only ∽1–2\% of the total global plant carbon, yet these organisms fix between 30 and 50 billion metric tons of carbon annually, which is about 40\% of the total. On geological time scales there is profound evidence of the importance of phytoplankton photosynthesis in biogeochemical cycles. It is generally assumed that present phytoplankton productivity is in a quasi steady-state (on the time scale of decades). However, in a global context, the stability of oceanic photosynthetic processes is dependent on the physical circulation of the upper ocean and is therefore strongly influenced by the atmosphere. The net flux of atmospheric radiation is critical to determining the depth of the upper mixed layer and the vertical fluxes of nutrients. These latter two parameters are keys to determining the intensity, and spatial and temporal distributions of phytoplankton blooms. Atmospheric radiation budgets are not in steady-state. Driven largely by anthropogenic activities in the 20th century, increased levels of IR- absorbing gases such as CO2, CH4 and CFC's and NOx will potentially increase atmospheric temperatures on a global scale. The atmospheric radiation budget can affect phytoplankton photosynthesis directly and indirectly. Increased temperature differences between the continents and oceans have been implicated in higher wind stresses at the ocean margins. Increased wind speeds can lead to higher nutrient fluxes. Throughout most of the central oceans, nitrate concentrations are sub-micromolar and there is strong evidence that the quantum efficiency of Photosystem II is impaired by nutrient stress. Higher nutrient fluxes would lead to both an increase in phytoplankton biomass and higher biomass-specific rates of carbon fixation. However, in the center of the ocean gyres, increased radiative heating could reduce the vertical flux of nutrients to the euphotic zone, and hence lead to a reduction in phytoplankton carbon fixation. Increased desertification in terrestrial ecosystems can lead to increased aeolean loadings of essential micronutrients, such as iron. An increased flux of aeolean micronutrients could fertilize nutrient-replete areas of the open ocean with limiting trace elements, thereby stimulating photosynthetic rates. The factors which limit phytoplankton biomass and photosynthesis are discussed and examined with regard to potential changes in the Earth climate system which can lead the oceans away from steady-state. While it is difficult to confidently deduce changes in either phytoplankton biomass or photosynthetic rates on decadal time scales, time-series analysis of ocean transparency data suggest long-term trends have occurred in the North Pacific Ocean in the 20th century. However, calculations of net carbon uptake by the oceans resulting from phytoplankton photosynthesis suggest that without a supply of nutrients external to the ocean, carbon fixation in the open ocean is not presently a significant sink for excess atmospheric CO2.}, + language = {en}, + number = {3}, + urldate = {2024-01-06}, + journal = {Photosynthesis Research}, + author = {Falkowski, Paul G.}, + month = mar, + year = {1994}, + keywords = {biogeochemical cycles, oceans, photoacclimation, Photosystem II nutrient limitation, phytoplankton quantum efficiency of photosynthesis}, + pages = {235--258}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/6ECKTMBR/Falkowski - 1994 - The role of phytoplankton photosynthesis in global.pdf:application/pdf}, +} + +@article{treguer_influence_2018, + title = {Influence of diatom diversity on the ocean biological carbon pump}, + volume = {11}, + copyright = {2017 Springer Nature Limited}, + issn = {1752-0908}, + url = {https://www.nature.com/articles/s41561-017-0028-x}, + doi = {10.1038/s41561-017-0028-x}, + abstract = {Diatoms sustain the marine food web and contribute to the export of carbon from the surface ocean to depth. They account for about 40\% of marine primary productivity and particulate carbon exported to depth as part of the biological pump. Diatoms have long been known to be abundant in turbulent, nutrient-rich waters, but observations and simulations indicate that they are dominant also in meso- and submesoscale structures such as fronts and filaments, and in the deep chlorophyll maximum. Diatoms vary widely in size, morphology and elemental composition, all of which control the quality, quantity and sinking speed of biogenic matter to depth. In particular, their silica shells provide ballast to marine snow and faecal pellets, and can help transport carbon to both the mesopelagic layer and deep ocean. Herein we show that the extent to which diatoms contribute to the export of carbon varies by diatom type, with carbon transfer modulated by the Si/C ratio of diatom cells, the thickness of the shells and their life strategies; for instance, the tendency to form aggregates or resting spores. Model simulations project a decline in the contribution of diatoms to primary production everywhere outside of the Southern Ocean. We argue that we need to understand changes in diatom diversity, life cycle and plankton interactions in a warmer and more acidic ocean in much more detail to fully assess any changes in their contribution to the biological pump.}, + language = {en}, + number = {1}, + urldate = {2024-01-06}, + journal = {Nature Geoscience}, + author = {Tréguer, Paul and Bowler, Chris and Moriceau, Brivaela and Dutkiewicz, Stephanie and Gehlen, Marion and Aumont, Olivier and Bittner, Lucie and Dugdale, Richard and Finkel, Zoe and Iudicone, Daniele and Jahn, Oliver and Guidi, Lionel and Lasbleiz, Marine and Leblanc, Karine and Levy, Marina and Pondaven, Philippe}, + month = jan, + year = {2018}, + note = {Number: 1 +Publisher: Nature Publishing Group}, + keywords = {Biogeochemistry, Carbon cycle, Marine biology, Ocean sciences}, + pages = {27--37}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/VLLSAC7H/Tréguer et al. - 2018 - Influence of diatom diversity on the ocean biologi.pdf:application/pdf}, +} + +@misc{bengio_representation_2014, + title = {Representation {Learning}: {A} {Review} and {New} {Perspectives}}, + shorttitle = {Representation {Learning}}, + url = {http://arxiv.org/abs/1206.5538}, + abstract = {The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning.}, + urldate = {2024-01-06}, + publisher = {arXiv}, + author = {Bengio, Yoshua and Courville, Aaron and Vincent, Pascal}, + month = apr, + year = {2014}, + note = {arXiv:1206.5538 [cs]}, + keywords = {Computer Science - Machine Learning}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/EYA2B52H/1206.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/2CFSH6MT/Bengio et al. - 2014 - Representation Learning A Review and New Perspect.pdf:application/pdf}, +} + +@misc{white_gradients2-mgl1704-ifcb-abundance_2020-04-01_v10_2020, + title = {Gradients2-{MGL1704}-{IFCB}-{Abundance}\_2020-04-01\_v1.0}, + url = {https://zenodo.org/records/4267140}, + doi = {10.5281/zenodo.4267140}, + abstract = {Cruise: Gradients 2, MGL1704 Project Name: Simons Foundation, Gradient NPSG Dataset Description: The Imaging FlowCytoBot (IFCB) is an in situ automated imaging flow cytometer that generates images of particles suspended in seawater, in this case from the underway uncontaminated seawater system aboard the R/V Langseth (intake 5m).   The IFCB uses a recycled sheath fluid (0.2 µm filtered seawater) to align and drive particles individually towards a light source (red laser, 4.5 mW) in order to detect and identify single or colonial cells using a combination of optical properties (red fluorescence and light scattering intensities) and high resolution images (3.2 pixels per micron) by a mounted camera. Both optical properties are used to trigger targeted image acquisition of suspended particles in the size range {\textless}4 to 100 μm. The instrument continuously samples (few seconds) from  {\textasciitilde}5 ml aliquots from the intake, and processes all particles contained in that volume for the next 20 mins.  Images corresponding to the "Sample" variable are available on the cruise's dashboard (http://ifcb-data.soest.hawaii.edu/IFCB\_NPTZ).  This dataset is for the abundance of imaged cells by genus.  For each sample, the total number of cells classified to the genus-level by a random forest algorithm (Sosik and Olson, 2007 doi:10.4319/lom.2007.5.204) is counted and divided by the corresponding volume analyzed ({\textasciitilde}5 mL). Note that we used all the images collected during Gradients 2.0 to train the random forest algorithm and that classification is therefore highly accurate for this dataset. Using 7 µm calibration beads, we estimated that the error of cell concentration due to cell detection during sample acquisition averages 11 ± 10 \%, independently of concentrations in the range 1-10000 cell/mL.}, + urldate = {2024-01-06}, + publisher = {Zenodo}, + author = {White, Angelicque}, + month = apr, + year = {2020}, + file = {Zenodo Snapshot:/Users/birdy/Zotero/storage/456EVCSQ/4267140.html:text/html}, +} + +@article{kanungo_efficient_2002, + title = {An efficient k-means clustering algorithm: analysis and implementation}, + volume = {24}, + issn = {1939-3539}, + shorttitle = {An efficient k-means clustering algorithm}, + url = {https://ieeexplore.ieee.org/document/1017616}, + doi = {10.1109/TPAMI.2002.1017616}, + abstract = {In k-means clustering, we are given a set of n data points in d-dimensional space R/sup d/ and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd's (1982) algorithm. We present a simple and efficient implementation of Lloyd's k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm's running time, which shows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in color quantization, data compression, and image segmentation.}, + number = {7}, + urldate = {2024-01-06}, + journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, + author = {Kanungo, T. and Mount, D.M. and Netanyahu, N.S. and Piatko, C.D. and Silverman, R. and Wu, A.Y.}, + month = jul, + year = {2002}, + note = {Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence}, + pages = {881--892}, + file = {IEEE Xplore Abstract Record:/Users/birdy/Zotero/storage/RC5LGQ6V/1017616.html:text/html;IEEE Xplore Full Text PDF:/Users/birdy/Zotero/storage/NUTPDLG4/Kanungo et al. - 2002 - An efficient k-means clustering algorithm analysi.pdf:application/pdf}, +} + +@inproceedings{he_deep_2016, + title = {Deep {Residual} {Learning} for {Image} {Recognition}}, + url = {https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html}, + urldate = {2024-01-06}, + author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, + year = {2016}, + pages = {770--778}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/RYXX2LD8/He et al. - 2016 - Deep Residual Learning for Image Recognition.pdf:application/pdf}, +} diff --git a/assets/bibliography/PlanktonRepresentation.bib b/assets/bibliography/PlanktonRepresentation.bib new file mode 100644 index 00000000..b201fa10 --- /dev/null +++ b/assets/bibliography/PlanktonRepresentation.bib @@ -0,0 +1,2462 @@ + +@article{araujo_validation_2005, + title = {Validation of species–climate impact models under climate change}, + volume = {11}, + issn = {1365-2486}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-2486.2005.01000.x}, + doi = {10.1111/j.1365-2486.2005.01000.x}, + abstract = {Increasing concern over the implications of climate change for biodiversity has led to the use of species–climate envelope models to project species extinction risk under climate-change scenarios. However, recent studies have demonstrated significant variability in model predictions and there remains a pressing need to validate models and to reduce uncertainties. Model validation is problematic as predictions are made for events that have not yet occurred. Resubstituition and data partitioning of present-day data sets are, therefore, commonly used to test the predictive performance of models. However, these approaches suffer from the problems of spatial and temporal autocorrelation in the calibration and validation sets. Using observed distribution shifts among 116 British breeding-bird species over the past ∼20 years, we are able to provide a first independent validation of four envelope modelling techniques under climate change. Results showed good to fair predictive performance on independent validation, although rules used to assess model performance are difficult to interpret in a decision-planning context. We also showed that measures of performance on nonindependent data provided optimistic estimates of models' predictive ability on independent data. Artificial neural networks and generalized additive models provided generally more accurate predictions of species range shifts than generalized linear models or classification tree analysis. Data for independent model validation and replication of this study are rare and we argue that perfect validation may not in fact be conceptually possible. We also note that usefulness of models is contingent on both the questions being asked and the techniques used. Implementations of species–climate envelope models for testing hypotheses and predicting future events may prove wrong, while being potentially useful if put into appropriate context.}, + language = {en}, + number = {9}, + urldate = {2021-12-15}, + journal = {Global Change Biology}, + author = {Araújo, Miguel B. and Pearson, Richard G. and Thuiller, Wilfried and Erhard, Markus}, + year = {2005}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2486.2005.01000.x}, + keywords = {bioclimatic-envelope models, breeding birds, Britain, climate change, model accuracy, uncertainty, validation, unread, not printed}, + pages = {1504--1513}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/6EVK36UH/Araújo et al. - 2005 - Validation of species–climate impact models under .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/C68Y6MBK/j.1365-2486.2005.01000.html:text/html}, +} + +@incollection{kolter_unveiling_2012, + title = {Unveiling {Prochlorococcus}}, + isbn = {978-1-55581-540-0 978-1-55581-847-0}, + url = {http://www.asmscience.org/content/book/10.1128/9781555818470.chap23}, + language = {en}, + urldate = {2021-12-15}, + booktitle = {Microbes and {Evolution}}, + publisher = {American Society of Microbiology}, + author = {Chisholm, Sallie W.}, + editor = {{Kolter} and {Maloy}}, + month = jan, + year = {2012}, + doi = {10.1128/9781555818470.ch23}, + keywords = {read, printed}, + pages = {165--171}, + file = {Chisholm - 2012 - Unveiling Prochlorococcus.pdf:/Users/birdy/Zotero/storage/D2MZS4I5/Chisholm - 2012 - Unveiling Prochlorococcus.pdf:application/pdf}, +} + +@article{follett_moving_2021, + title = {Moving ecological and biogeochemical transitions across the {North} {Pacific}}, + volume = {66}, + issn = {1939-5590}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lno.11763}, + doi = {10.1002/lno.11763}, + abstract = {In the North Pacific Ocean, nutrient rich surface waters flow south from the subpolar gyre through a transitional region and into the subtropics. Along the way, nutrients are used, recycled, and exported, leading to lower biomass and a commensurate change in ecosystem structure moving southward. We focus on the region between the two gyres (the Transition Zone) using a coupled biophysical ocean model, remote sensing, floats, and cruise data to explore the nature of the physical, biogeochemical, and ecological fields in this region. Nonlinear interactions between biological processes and the meridional gradient in nutrient supply lead to sharp shifts across this zone. These transitions between a southern region with more uniform biological and biogeochemical properties and steep meridional gradients to the north are diagnosed from extrema in the first derivative of the properties with latitude. Some transitions like that for chlorophyll a (the transition zone chlorophyll front [TZCF]) experience large seasonal excursions while the location of the transitions in other properties moves very little. The seasonal shifts are not caused by changes in the horizontal flow field, but rather by the interaction of seasonal, depth related, forcing with the mean latitudinal gradients. Focusing on the TZCF as a case study, we express its phase velocity in terms of vertical nutrient flux and internal ecosystem processes, demonstrating their nearly equal influence on its motion. This framework of propagating biogeochemical transitions can be systematically expanded to better understand the processes that structure ecosystems and biogeochemistry in the North Pacific and beyond.}, + language = {en}, + number = {6}, + urldate = {2021-12-15}, + journal = {Limnology and Oceanography}, + author = {Follett, Christopher L. and Dutkiewicz, Stephanie and Forget, Gael and Cael, B. B. and Follows, Michael J.}, + year = {2021}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lno.11763}, + keywords = {read, darwin, printed}, + pages = {2442--2454}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/XUKLUSS4/Follett et al. - 2021 - Moving ecological and biogeochemical transitions a.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/MASWCC2S/lno.html:text/html}, +} + +@article{guisan_predictive_2000, + title = {Predictive habitat distribution models in ecology}, + volume = {135}, + issn = {03043800}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0304380000003549}, + doi = {10.1016/S0304-3800(00)00354-9}, + abstract = {With the rise of new powerful statistical techniques and GIS tools, the development of predictive habitat distribution models has rapidly increased in ecology. Such models are static and probabilistic in nature, since they statistically relate the geographical distribution of species or communities to their present environment. A wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate change research, and habitat or species management. In this paper, we present a review of predictive habitat distribution modeling. The variety of statistical techniques used is growing. Ordinary multiple regression and its generalized form (GLM) are very popular and are often used for modeling species distributions. Other methods include neural networks, ordination and classification methods, Bayesian models, locally weighted approaches (e.g. GAM), environmental envelopes or even combinations of these models. The selection of an appropriate method should not depend solely on statistical considerations. Some models are better suited to reflect theoretical findings on the shape and nature of the species’ response (or realized niche). Conceptual considerations include e.g. the trade-off between optimizing accuracy versus optimizing generality. In the field of static distribution modeling, the latter is mostly related to selecting appropriate predictor variables and to designing an appropriate procedure for model selection. New methods, including threshold-independent measures (e.g. receiver operating characteristic (ROC)-plots) and resampling techniques (e.g. bootstrap, cross-validation) have been introduced in ecology for testing the accuracy of predictive models. The choice of an evaluation measure should be driven primarily by the goals of the study. This may possibly lead to the attribution of different weights to the various types of prediction errors (e.g. omission, commission or confusion). Testing the model in a wider range of situations (in space and time) will permit one to define the range of applications for which the model predictions are suitable. In turn, the qualification of the model depends primarily on the goals of the study that define the qualification criteria and on the usability of the model, rather than on statistics alone. © 2000 Elsevier Science B.V. All rights reserved.}, + language = {en}, + number = {2-3}, + urldate = {2021-12-15}, + journal = {Ecological Modelling}, + author = {Guisan, Antoine and Zimmermann, Niklaus E.}, + month = dec, + year = {2000}, + keywords = {unread, sdm, printed}, + pages = {147--186}, + file = {Guisan and Zimmermann - 2000 - Predictive habitat distribution models in ecology.pdf:/Users/birdy/Zotero/storage/SGKKM6TL/Guisan and Zimmermann - 2000 - Predictive habitat distribution models in ecology.pdf:application/pdf}, +} + +@article{bardon_testing_2021, + title = {Testing the {Skill} of a {Species} {Distribution} {Model} {Using} a 21st {Century} {Virtual} {Ecosystem}}, + volume = {48}, + issn = {1944-8007}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2021GL093455}, + doi = {10.1029/2021GL093455}, + abstract = {Plankton communities play an important role in marine food webs, in biogeochemical cycling, and in Earth's climate; yet observations are sparse, and predictions of how they might respond to climate change vary. Correlative species distribution models (SDM's) have been applied to predicting biogeography based on relationships to observed environmental variables. To investigate sources of uncertainty, we use a correlative SDM to predict the plankton biogeography of a 21st century marine ecosystem model (Darwin). Darwin output is sampled to mimic historical ocean observations, and the SDM is trained using generalized additive models. We find that predictive skill varies across test cases, and between functional groups, with errors that are more attributable to spatiotemporal sampling bias than sample size. End-of-century predictions are poor, limited by changes in target-predictor relationships over time. Our findings illustrate the fundamental challenges faced by empirical models in using limited observational data to predict complex, dynamic systems.}, + language = {en}, + number = {22}, + urldate = {2021-12-15}, + journal = {Geophysical Research Letters}, + author = {Bardon, L. R. and Ward, B. A. and Dutkiewicz, S. and Cael, B. B.}, + year = {2021}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021GL093455}, + keywords = {climate change, unread, darwin, sdm, complex systems, numerical models, oceanography, plankton ecology, statistical learning, printed}, + pages = {e2021GL093455}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/RKQ6X8MD/Bardon et al. - 2021 - Testing the Skill of a Species Distribution Model .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/53EFUGZ7/2021GL093455.html:text/html}, +} + +@article{elith_species_2009, + title = {Species {Distribution} {Models}: {Ecological} {Explanation} and {Prediction} {Across} {Space} and {Time}}, + volume = {40}, + shorttitle = {Species {Distribution} {Models}}, + url = {https://doi.org/10.1146/annurev.ecolsys.110308.120159}, + doi = {10.1146/annurev.ecolsys.110308.120159}, + abstract = {Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in “established use.” Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.}, + number = {1}, + urldate = {2021-12-15}, + journal = {Annual Review of Ecology, Evolution, and Systematics}, + author = {Elith, Jane and Leathwick, John R.}, + year = {2009}, + note = {\_eprint: https://doi.org/10.1146/annurev.ecolsys.110308.120159}, + keywords = {read, sdm, printed}, + pages = {677--697}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/94HVWZRY/Elith and Leathwick - 2009 - Species Distribution Models Ecological Explanatio.pdf:application/pdf}, +} + +@article{henson_future_2021, + title = {Future phytoplankton diversity in a changing climate}, + volume = {12}, + issn = {2041-1723}, + url = {https://www.nature.com/articles/s41467-021-25699-w}, + doi = {10.1038/s41467-021-25699-w}, + abstract = {Abstract + The future response of marine ecosystem diversity to continued anthropogenic forcing is poorly constrained. Phytoplankton are a diverse set of organisms that form the base of the marine ecosystem. Currently, ocean biogeochemistry and ecosystem models used for climate change projections typically include only 2−3 phytoplankton types and are, therefore, too simple to adequately assess the potential for changes in plankton community structure. Here, we analyse a complex ecosystem model with 35 phytoplankton types to evaluate the changes in phytoplankton community composition, turnover and size structure over the 21st century. We find that the rate of turnover in the phytoplankton community becomes faster during this century, that is, the community structure becomes increasingly unstable in response to climate change. Combined with alterations to phytoplankton diversity, our results imply a loss of ecological resilience with likely knock-on effects on the productivity and functioning of the marine environment.}, + language = {en}, + number = {1}, + urldate = {2021-12-15}, + journal = {Nature Communications}, + author = {Henson, Stephanie A. and Cael, B. B. and Allen, Stephanie R. and Dutkiewicz, Stephanie}, + month = dec, + year = {2021}, + keywords = {unread, not printed}, + pages = {5372}, + file = {Henson et al. - 2021 - Future phytoplankton diversity in a changing clima.pdf:/Users/birdy/Zotero/storage/GLZDZK9S/Henson et al. - 2021 - Future phytoplankton diversity in a changing clima.pdf:application/pdf}, +} + +@article{wu_modeling_2021, + title = {Modeling {Photosynthesis} and {Exudation} in {Subtropical} {Oceans}}, + volume = {35}, + issn = {1944-9224}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2021GB006941}, + doi = {10.1029/2021GB006941}, + abstract = {Marine phytoplankton contributes nearly half of the total primary production on Earth through photosynthesis. Parameterizations of algal photosynthesis commonly employed in global biogeochemical simulations generally fail to capture the observed vertical structure of primary production. Here we examined the consequences of decoupling photosynthesis (carbon fixation) and biosynthesis (biomass building) with accumulation or exudation of excess photosynthate under energy rich conditions in both regional and global models. The results show that the decoupling of these two processes improved the simulated vertical profile of primary production, increased modeled primary production over 30\% globally and over 40\% in subtropical oceans, improved simulated meridional patterns of particulate C:N:P and increased modeled surface pool of labile/semi-labile dissolved organic carbon. More generally, these results highlight the importance of exudation, which results from the decoupling of photosynthesis and biosynthesis, as a major physiological process affecting ocean biogeochemistry.}, + language = {en}, + number = {9}, + urldate = {2021-12-15}, + journal = {Global Biogeochemical Cycles}, + author = {Wu, Zhen and Dutkiewicz, Stephanie and Jahn, Oliver and Sher, Daniel and White, Angelicque and Follows, Michael J.}, + year = {2021}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021GB006941}, + keywords = {unread, not printed, biogeochemical model, exudation, photosynthesis, phytoplankton, primary production, subtropical gyres, EAPS}, + pages = {e2021GB006941}, +} + +@article{ser-giacomi_lagrangian_2021, + title = {Lagrangian betweenness as a measure of bottlenecks in dynamical systems with oceanographic examples}, + volume = {12}, + issn = {2041-1723}, + url = {https://www.nature.com/articles/s41467-021-25155-9}, + doi = {10.1038/s41467-021-25155-9}, + abstract = {Abstract + The study of connectivity patterns in networks has brought novel insights across diverse fields ranging from neurosciences to epidemic spreading or climate. In this context, betweenness centrality has demonstrated to be a very effective measure to identify nodes that act as focus of congestion, or bottlenecks, in the network. However, there is not a way to define betweenness outside the network framework. By analytically linking dynamical systems and network theory, we provide a trajectory-based formulation of betweenness, called Lagrangian betweenness, as a function of Lyapunov exponents. This extends the concept of betweenness beyond the context of network theory relating hyperbolic points and heteroclinic connections in any dynamical system to the structural bottlenecks of the network associated with it. Using modeled and observational velocity fields, we show that such bottlenecks are present and surprisingly persistent in the oceanic circulation across different spatio-temporal scales and we illustrate the role of these areas in driving fluid transport over vast oceanic regions. Analyzing plankton abundance data from the Kuroshio region of the Pacific Ocean, we find significant spatial correlations between measures of diversity and betweenness, suggesting promise for ecological applications.}, + language = {en}, + number = {1}, + urldate = {2021-12-15}, + journal = {Nature Communications}, + author = {Ser-Giacomi, Enrico and Baudena, Alberto and Rossi, Vincent and Follows, Mick and Clayton, Sophie and Vasile, Ruggero and López, Cristóbal and Hernández-García, Emilio}, + month = dec, + year = {2021}, + keywords = {unread, not printed, EAPS}, + pages = {4935}, + file = {Ser-Giacomi et al. - 2021 - Lagrangian betweenness as a measure of bottlenecks.pdf:/Users/birdy/Zotero/storage/ENVD6A4Z/Ser-Giacomi et al. - 2021 - Lagrangian betweenness as a measure of bottlenecks.pdf:application/pdf}, +} + +@article{juranek_importance_2020, + title = {The {Importance} of the {Phytoplankton} “{Middle} {Class}” to {Ocean} {Net} {Community} {Production}}, + volume = {34}, + issn = {1944-9224}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2020GB006702}, + doi = {10.1029/2020GB006702}, + abstract = {The net balance between photosynthesis and respiration in the surface ocean is a key regulator of ocean-atmosphere carbon dioxide (CO2) partitioning, and by extension, Earth's climate. The slight excess of photosynthesis over community respiration in sunlit waters, known as net community production (NCP), sets the upper bound on the sequestration of carbon via biologically mediated export. Prevailing paradigms suggest a high/low binary where net primary production (NPP), NCP, and export are highest in ecosystems characterized by microplankton ({\textgreater}20 μm) and lowest in ecosystems dominated by picoplankton ({\textless}2 μm). This bifurcation model neglects the potential importance of nanoplankton (2–20 μm)—i.e., the “middle” size class—toward global biological pump functioning. Here, we show a relationship between the biomass of nanoplankton and oxygen-based estimates of NCP across natural ecological gradients in the North Pacific Ocean. Using a suite of high-resolution optical imaging approaches including SeaFlow, Imaging FlowCytobot, and laser-based scattering, nanoplankton dynamics are observed to dominate the particle size distribution throughout an 1,000 km transition between the subtropical and subpolar North Pacific, where NCP rates are threefold to fivefold higher than subtropical values. Based on ecological theory applied to the Darwin size-based ecosystem model, we hypothesize that intermediate size class organisms are capable of high rates of production via an optimization of bottom-up and top-down control inherent to the “middle class.” More broadly, the model indicates the global importance of nanoplankton for ocean biological production.}, + language = {en}, + number = {12}, + urldate = {2021-12-15}, + journal = {Global Biogeochemical Cycles}, + author = {Juranek, Lauren W. and White, Angelicque E. and Dugenne, Mathilde and Henderikx Freitas, Fernanda and Dutkiewicz, Stephanie and Ribalet, Francois and Ferrón, Sara and Armbrust, E. Virginia and Karl, David M.}, + year = {2020}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020GB006702}, + keywords = {read, not printed, EAPS}, + pages = {e2020GB006702}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/FI7EY7HA/Juranek et al. - 2020 - The Importance of the Phytoplankton “Middle Class”.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/48CTG827/2020GB006702.html:text/html}, +} + +@article{kempes_growth_2012, + title = {Growth, metabolic partitioning, and the size of microorganisms}, + volume = {109}, + copyright = {© . Freely available online through the PNAS open access option.}, + issn = {0027-8424, 1091-6490}, + url = {https://www.pnas.org/content/109/2/495}, + doi = {10.1073/pnas.1115585109}, + abstract = {Population growth rate is a fundamental ecological and evolutionary characteristic of living organisms, but individuals must balance the metabolism devoted to biosynthesis and reproduction against the maintenance of existing structure and other functionality. Here we present a mathematical model that relates metabolic partitioning to the form of growth. The model captures the observed growth trajectory of single cells and individuals for a variety of species and taxa spanning prokaryotes, unicellular eukaryotes, and small multicellular eukaryotes. Our analysis suggests that the per-unit costs of biosynthesis and maintenance are conserved across prokaryotes and eukaryotes. However, the relative metabolic expenditure on growth and maintenance of whole organisms clearly differentiates taxa: prokaryotes spend an increasing fraction of their entire metabolism on growth with increasing cell size, whereas eukaryotes devote a diminishing fraction. These differences allow us to predict the minimum and maximum size for each taxonomic group, anticipating observed evolutionary life-history transitions. The framework provides energetic insights into taxonomic tradeoffs related to growth and metabolism and constrains traits that are important for size-structured modeling of microbial communities and their ecological and biogeochemical effects.}, + language = {en}, + number = {2}, + urldate = {2022-01-28}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Kempes, Christopher P. and Dutkiewicz, Stephanie and Follows, Michael J.}, + month = jan, + year = {2012}, + pmid = {22203990}, + note = {Publisher: National Academy of Sciences +Section: Biological Sciences}, + keywords = {unread, evolutionary transitions, maintenance metabolism, metabolic ecology, ontogenetic growth, single cell growth}, + pages = {495--500}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/72H8W27A/Kempes et al. - 2012 - Growth, metabolic partitioning, and the size of mi.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/4LRVBPN7/495.html:text/html}, +} + +@article{dutkiewicz_dimensions_2020, + title = {Dimensions of marine phytoplankton diversity}, + volume = {17}, + issn = {1726-4170}, + url = {https://bg.copernicus.org/articles/17/609/2020/}, + doi = {10.5194/bg-17-609-2020}, + abstract = {{\textless}p{\textgreater}{\textless}strong class="journal-contentHeaderColor"{\textgreater}Abstract.{\textless}/strong{\textgreater} Biodiversity of phytoplankton is important for ecosystem stability and marine biogeochemistry. However, the large-scale patterns of diversity are not well understood and are often poorly characterized in terms of statistical relationships with factors such as latitude, temperature and productivity. Here we use ecological theory and a global trait-based ecosystem model to provide mechanistic understanding of patterns of phytoplankton diversity. Our study suggests that phytoplankton diversity across three dimensions of trait space (size, biogeochemical function and thermal tolerance) is controlled by disparate combinations of drivers: the supply rate of the limiting resource, the imbalance in different resource supplies relative to competing phytoplankton demands, size-selective grazing and transport by the moving ocean. Using sensitivity studies we show that each dimension of diversity is controlled by different drivers. Models including only one (or two) of the trait dimensions will have different patterns of diversity than one which incorporates another trait dimension. We use the results of our model exploration to infer the controls on the diversity patterns derived from field observations along meridional transects in the Atlantic and to explain why different taxa and size classes have differing patterns.{\textless}/p{\textgreater}}, + language = {English}, + number = {3}, + urldate = {2022-02-02}, + journal = {Biogeosciences}, + author = {Dutkiewicz, Stephanie and Cermeno, Pedro and Jahn, Oliver and Follows, Michael J. and Hickman, Anna E. and Taniguchi, Darcy A. A. and Ward, Ben A.}, + month = feb, + year = {2020}, + note = {Publisher: Copernicus GmbH}, + keywords = {read}, + pages = {609--634}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/GDPB2N66/Dutkiewicz et al. - 2020 - Dimensions of marine phytoplankton diversity.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/6J7BBRZ4/2020.html:text/html}, +} + +@article{armstrong_grazing_1994, + title = {Grazing limitation and nutrient limitation in marine ecosystems: {Steady} state solutions of an ecosystem model with multiple food chains}, + volume = {39}, + issn = {1939-5590}, + shorttitle = {Grazing limitation and nutrient limitation in marine ecosystems}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lo.1994.39.3.0597}, + doi = {10.4319/lo.1994.39.3.0597}, + abstract = {Here I develop a model with multiple phytoplankton-zooplankton food chains where each food chain is based on a different size class of algae. Model parameters were chosen to allow food chains based on smaller algal size classes to dominate under oligotrophic conditions, with larger size classes being added sequentially with increased nutrient loading. The model makes use of allometric relations among size classes to minimize the number of free parameters, facilitating numerical exploration of its steady state behavior. The model is simple, yet it is complex enough to allow simultaneous predator limitation of algal size classes (or species) and nutrient limitation of total phytoplankton biomass. This perspective, not obtainable from models with single phytoplankton and zooplankton size classes, is applied to the particular case of elevated nitrate levels in the equatorial Pacific. The model shows clearly that it is possible for each phytoplankton size class to be limited by its herbivores, while at the same time micronutrient (notably iron) deficiency may limit the number of size classes that can exist in the community and hence the total phytoplankton biomass that can be supported.}, + language = {en}, + number = {3}, + urldate = {2022-02-07}, + journal = {Limnology and Oceanography}, + author = {Armstrong, Robert A.}, + year = {1994}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lo.1994.39.3.0597}, + keywords = {unread}, + pages = {597--608}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/62GDW2AR/Armstrong - 1994 - Grazing limitation and nutrient limitation in mari.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/FJKQ3LLF/lo.1994.39.3.html:text/html}, +} + +@article{dutkiewicz_modeling_2009, + title = {Modeling the coupling of ocean ecology and biogeochemistry}, + volume = {23}, + issn = {1944-9224}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2008GB003405}, + doi = {10.1029/2008GB003405}, + abstract = {We examine the interplay between ecology and biogeochemical cycles in the context of a global three-dimensional ocean model where self-assembling phytoplankton communities emerge from a wide set of potentially viable cell types. We consider the complex model solutions in the light of resource competition theory. The emergent community structures and ecological regimes vary across different physical environments in the model ocean: Strongly seasonal, high-nutrient regions are dominated by fast growing bloom specialists, while stable, low-seasonality regions are dominated by organisms that can grow at low nutrient concentrations and are suited to oligotrophic conditions. In the latter regions, the framework of resource competition theory provides a useful qualitative and quantitative diagnostic tool with which to interpret the outcome of competition between model organisms, their regulation of the resource environment, and the sensitivity of the system to changes in key physiological characteristics of the cells.}, + language = {en}, + number = {4}, + urldate = {2022-02-14}, + journal = {Global Biogeochemical Cycles}, + author = {Dutkiewicz, S. and Follows, M. J. and Bragg, J. G.}, + year = {2009}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2008GB003405}, + keywords = {unread, ecology, ocean biogeochemistry, resource competition}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/ME3K4FVT/Dutkiewicz et al. - 2009 - Modeling the coupling of ocean ecology and biogeoc.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/JWID8TMI/2008GB003405.html:text/html}, +} + +@article{maranon_resource_2015, + title = {Resource supply alone explains the variability of marine phytoplankton size structure}, + volume = {60}, + issn = {1939-5590}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lno.10138}, + doi = {10.1002/lno.10138}, + abstract = {Due to the covariation between temperature and resource availability in the surface ocean, a correct assessment of resource supply is crucial to determine if temperature has a direct effect on phytoplankton size structure. To remove the effect of resources, López-Urrutia and Morán analyzed data subsets with narrow ranges of variation in Chlorophyll a (Chl a) concentration and found that temperature is correlated with Chl a partitioning among size classes, from which they concluded that temperature is an important variable to explain the variability of phytoplankton size structure. Our analysis, however, shows that resource supply varies widely also within these subsets and, importantly, that it is inversely correlated with temperature. Therefore, the relationship between temperature and size structure reflects instead the effect of resources. When groups of samples with similar resource supply conditions are considered, no correlation between temperature and phytoplankton size structure is observed, which invalidates the conclusion of López-Urrutia and Morán. Even within restricted ranges of variation for phytoplankton biomass and production, changes in resource supply alone are sufficient to explain the variability of phytoplankton size structure in the sea.}, + language = {en}, + number = {5}, + urldate = {2022-02-16}, + journal = {Limnology and Oceanography}, + author = {Marañón, Emilio and Cermeño, Pedro and Latasa, Mikel and Tadonléké, Rémy D.}, + year = {2015}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lno.10138}, + keywords = {unread, prosynref}, + pages = {1848--1854}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/PFJ4GLDA/Marañón et al. - 2015 - Resource supply alone explains the variability of .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/HSLF3VCJ/lno.html:text/html}, +} + +@article{follett_trophic_2022, + title = {Trophic interactions with heterotrophic bacteria limit the range of \textit{{Prochlorococcus}}}, + volume = {119}, + issn = {0027-8424, 1091-6490}, + url = {http://www.pnas.org/lookup/doi/10.1073/pnas.2110993118}, + doi = {10.1073/pnas.2110993118}, + abstract = {Prochlorococcus + is both the smallest and numerically most abundant photosynthesizing organism on the planet. While thriving in the warm oligotrophic gyres, + Prochlorococcus + concentrations drop rapidly in higher-latitude regions. Transect data from the North Pacific show the collapse occurring at a wide range of temperatures and latitudes (temperature is often hypothesized to cause this shift), suggesting an ecological mechanism may be at play. An often used size-based theory of phytoplankton community structure that has been incorporated into computational models correctly predicts the dominance of + Prochlorococcus + in the gyres, and the relative dominance of larger cells at high latitudes. However, both theory and computational models fail to explain the poleward collapse. When heterotrophic bacteria and predators that prey nonspecifically on both + Prochlorococcus + and bacteria are included in the theoretical framework, the collapse of + Prochlorococcus + occurs with increasing nutrient supplies. The poleward collapse of + Prochlorococcus + populations then naturally emerges when this mechanism of “shared predation” is implemented in a complex global ecosystem model. Additionally, the theory correctly predicts trends in both the abundance and mean size of the heterotrophic bacteria. These results suggest that ecological controls need to be considered to understand the biogeography of + Prochlorococcus + and predict its changes under future ocean conditions. Indirect interactions within a microbial network can be essential in setting community structure.}, + language = {en}, + number = {2}, + urldate = {2022-02-23}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Follett, Christopher L. and Dutkiewicz, Stephanie and Ribalet, François and Zakem, Emily and Caron, David and Armbrust, E. Virginia and Follows, Michael J.}, + month = jan, + year = {2022}, + pages = {e2110993118}, + file = {Follett et al. - 2022 - Trophic interactions with heterotrophic bacteria l.pdf:/Users/birdy/Zotero/storage/PJMSAXIG/Follett et al. - 2022 - Trophic interactions with heterotrophic bacteria l.pdf:application/pdf}, +} + +@article{muratore_complex_2022, + title = {Complex marine microbial communities partition metabolism of scarce resources over the diel cycle}, + volume = {6}, + copyright = {2022 The Author(s), under exclusive licence to Springer Nature Limited}, + issn = {2397-334X}, + url = {https://www.nature.com/articles/s41559-021-01606-w}, + doi = {10.1038/s41559-021-01606-w}, + abstract = {Complex assemblages of microbes in the surface ocean are responsible for approximately half of global carbon fixation. The persistence of high taxonomic diversity despite competition for a small suite of relatively homogeneously distributed nutrients, that is, ‘the paradox of the plankton’, represents a long-standing challenge for ecological theory. Here we find evidence consistent with temporal niche partitioning of nitrogen assimilation processes over a diel cycle in the North Pacific Subtropical Gyre. We jointly analysed transcript abundances, lipids and metabolites and discovered that a small number of diel archetypes can explain pervasive periodic dynamics. Metabolic pathway analysis of identified diel signals revealed asynchronous timing in the transcription of nitrogen uptake and assimilation genes among different microbial groups—cyanobacteria, heterotrophic bacteria and eukaryotes. This temporal niche partitioning of nitrogen uptake emerged despite synchronous transcription of photosynthesis and central carbon metabolism genes and associated macromolecular abundances. Temporal niche partitioning may be a mechanism by which microorganisms in the open ocean mitigate competition for scarce resources, supporting community coexistence.}, + language = {en}, + number = {2}, + urldate = {2022-02-25}, + journal = {Nature Ecology \& Evolution}, + author = {Muratore, Daniel and Boysen, Angela K. and Harke, Matthew J. and Becker, Kevin W. and Casey, John R. and Coesel, Sacha N. and Mende, Daniel R. and Wilson, Samuel T. and Aylward, Frank O. and Eppley, John M. and Vislova, Alice and Peng, Shengyun and Rodriguez-Gonzalez, Rogelio A. and Beckett, Stephen J. and Virginia Armbrust, E. and DeLong, Edward F. and Karl, David M. and White, Angelicque E. and Zehr, Jonathan P. and Van Mooy, Benjamin A. S. and Dyhrman, Sonya T. and Ingalls, Anitra E. and Weitz, Joshua S.}, + month = feb, + year = {2022}, + note = {Number: 2 +Publisher: Nature Publishing Group}, + keywords = {unread, Microbial biooceanography, Microbial ecology}, + pages = {218--229}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/T58JCF6Q/Muratore et al. - 2022 - Complex marine microbial communities partition met.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/HC4GW85C/s41559-021-01606-w.html:text/html}, +} + +@article{mangolte_plankton_2022, + title = {Plankton community response to fronts : winners and losers}, + shorttitle = {Plankton community response to fronts}, + url = {https://hal.archives-ouvertes.fr/hal-03607937}, + doi = {10.1093/plankt/fbac010}, + abstract = {Fronts are particularly productive regions of the ocean, and biodiversity hotspots for many marine species. Here we use an ocean-ecosystem model to investigate the effect of fronts on plankton ecology. We focus on energetic fronts in Western Boundary Current systems that efficiently inject nutrients into the euphotic layer and which are physical boundaries between productive and oligotrophic provinces. We found that the fronts form an environment distinct from both provinces, favorable to some plankton groups (diatoms, dinoflagellates and large carnivorous zooplankton) and less favorable to others (pico-phytoplankton, coccolithophores, small grazers), and with an overall larger diversity. In agreement with previous understanding, we find that bottom-up abiotic processes (nutrient enrichment) explain the prevalence of groups with fast growing rates (the "winners"). Importantly, our results also show that biotic interactions within the ecosystem may play a larger role than previously thought. We show that the winners can have a negative impact on other plankton species (the "losers") through two indirect competitive processes: community shading (modification of the light environment by the plankton community leading to light-limitation of some plankton groups) and shared predation (where an increase in one functional group leads to increased grazing by a shared predator on another functional group).}, + urldate = {2022-03-23}, + journal = {Journal of Plankton Research}, + author = {Mangolte, Inès and Lévy, Marina and Dutkiewicz, Stephanie and Clayton, Sophie and Jahn, Oliver}, + month = mar, + year = {2022}, + note = {Publisher: Oxford University Press (OUP)}, + keywords = {unread, phytoplankton, biodiversity, fronts, marine ecology, modelling}, + file = {HAL PDF Full Text:/Users/birdy/Zotero/storage/65HVTJEW/Mangolte et al. - 2022 - Plankton community response to fronts winners an.pdf:application/pdf}, +} + +@article{shang_oxidative_2022, + title = {Oxidative metabolisms catalyzed {Earth}’s oxygenation}, + volume = {13}, + copyright = {2022 The Author(s)}, + issn = {2041-1723}, + url = {https://www.nature.com/articles/s41467-022-28996-0}, + doi = {10.1038/s41467-022-28996-0}, + abstract = {The burial of organic carbon, which prevents its remineralization via oxygen-consuming processes, is considered one of the causes of Earth’s oxygenation. Yet, higher levels of oxygen are thought to inhibit burial. Here we propose a resolution of this conundrum, wherein Earth’s initial oxygenation is favored by oxidative metabolisms generating partially oxidized organic matter (POOM), increasing burial via interaction with minerals in sediments. First, we introduce the POOM hypothesis via a mathematical argument. Second, we reconstruct the evolutionary history of one key enzyme family, flavin-dependent Baeyer–Villiger monooxygenases, that generates POOM, and show the temporal consistency of its diversification with the Proterozoic and Phanerozoic atmospheric oxygenation. Finally, we propose that the expansion of oxidative metabolisms instigated a positive feedback, which was amplified by the chemical changes to minerals on Earth’s surface. Collectively, these results suggest that Earth’s oxygenation is an autocatalytic transition induced by a combination of biological innovations and geological changes.}, + language = {en}, + number = {1}, + urldate = {2022-04-29}, + journal = {Nature Communications}, + author = {Shang, Haitao and Rothman, Daniel H. and Fournier, Gregory P.}, + month = mar, + year = {2022}, + note = {Number: 1 +Publisher: Nature Publishing Group}, + keywords = {unread, Coevolution, Element cycles}, + pages = {1328}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/JAG35KDC/Shang et al. - 2022 - Oxidative metabolisms catalyzed Earth’s oxygenatio.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/28GUMIIB/s41467-022-28996-0.html:text/html}, +} + +@article{ganopolski_climber-2_2001, + title = {{CLIMBER}-2: a climate system model of intermediate complexity. {Part} {II}: model sensitivity}, + volume = {17}, + issn = {0930-7575, 1432-0894}, + shorttitle = {{CLIMBER}-2}, + url = {http://link.springer.com/10.1007/s003820000144}, + doi = {10.1007/s003820000144}, + language = {en}, + number = {10}, + urldate = {2022-05-02}, + journal = {Climate Dynamics}, + author = {Ganopolski, A. and Petoukhov, V. and Rahmstorf, S. and Brovkin, V. and Claussen, M. and Eliseev, A. and Kubatzki, C.}, + month = jul, + year = {2001}, + keywords = {read}, + pages = {735--751}, + file = {Ganopolski et al. - 2001 - CLIMBER-2 a climate system model of intermediate .pdf:/Users/birdy/Zotero/storage/X4G6F5HW/Ganopolski et al. - 2001 - CLIMBER-2 a climate system model of intermediate .pdf:application/pdf}, +} + +@article{bhatia_its_2022, + chapter = {The Upshot}, + title = {‘{It}’s {Super} {Spectacular}.’ {See} {How} the {Tonga} {Volcano} {Unleashed} a {Once}-in-a-{Century} {Shockwave}.}, + issn = {0362-4331}, + url = {https://www.nytimes.com/interactive/2022/04/14/upshot/tonga-pressure-wave.html}, + abstract = {A new simulation gives a detailed look at a shockwave that circled the planet for days.}, + language = {en-US}, + urldate = {2022-05-06}, + journal = {The New York Times}, + author = {Bhatia, Aatish and Catangui, Sean}, + month = apr, + year = {2022}, + keywords = {Hunga Tonga-Hunga Ha'apai (Tonga), Tidal Waves and Tsunamis, Tonga, Volcanoes}, + file = {Snapshot:/Users/birdy/Zotero/storage/K9I8BQD2/tonga-pressure-wave.html:text/html}, +} + +@article{lee_improved_2018, + title = {Improved {Prediction} of {Harmful} {Algal} {Blooms} in {Four} {Major} {South} {Korea}’s {Rivers} {Using} {Deep} {Learning} {Models}}, + volume = {15}, + issn = {1661-7827}, + url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6069434/}, + doi = {10.3390/ijerph15071322}, + abstract = {Harmful algal blooms are an annual phenomenon that cause environmental damage, economic losses, and disease outbreaks. A fundamental solution to this problem is still lacking, thus, the best option for counteracting the effects of algal blooms is to improve advance warnings (predictions). However, existing physical prediction models have difficulties setting a clear coefficient indicating the relationship between each factor when predicting algal blooms, and many variable data sources are required for the analysis. These limitations are accompanied by high time and economic costs. Meanwhile, artificial intelligence and deep learning methods have become increasingly common in scientific research; attempts to apply the long short-term memory (LSTM) model to environmental research problems are increasing because the LSTM model exhibits good performance for time-series data prediction. However, few studies have applied deep learning models or LSTM to algal bloom prediction, especially in South Korea, where algal blooms occur annually. Therefore, we employed the LSTM model for algal bloom prediction in four major rivers of South Korea. We conducted short-term (one week) predictions by employing regression analysis and deep learning techniques on a newly constructed water quality and quantity dataset drawn from 16 dammed pools on the rivers. Three deep learning models (multilayer perceptron, MLP; recurrent neural network, RNN; and long short-term memory, LSTM) were used to predict chlorophyll-a, a recognized proxy for algal activity. The results were compared to those from OLS (ordinary least square) regression analysis and actual data based on the root mean square error (RSME). The LSTM model showed the highest prediction rate for harmful algal blooms and all deep learning models out-performed the OLS regression analysis. Our results reveal the potential for predicting algal blooms using LSTM and deep learning.}, + number = {7}, + urldate = {2022-05-19}, + journal = {International Journal of Environmental Research and Public Health}, + author = {Lee, Sangmok and Lee, Donghyun}, + month = jul, + year = {2018}, + pmid = {29937531}, + pmcid = {PMC6069434}, + pages = {1322}, + file = {Full Text:/Users/birdy/Zotero/storage/AA2K29UM/Lee and Lee - 2018 - Improved Prediction of Harmful Algal Blooms in Fou.pdf:application/pdf}, +} + +@article{yu_predicting_2021, + title = {Predicting coastal algal blooms with environmental factors by machine learning methods}, + volume = {123}, + issn = {1470-160X}, + url = {https://www.sciencedirect.com/science/article/pii/S1470160X20312760}, + doi = {10.1016/j.ecolind.2020.107334}, + abstract = {Harmful algal blooms are a major type of marine disaster that endangers the marine ecological environment and human health. Since the algal bloom is a complex nonlinear process with time characteristics, traditional statistical methods often cannot provide good predictions. In this paper, we propose a method based on machine learning with the aim to predict the occurrence of algal blooms by environmental parameters. The features related to algal bloom growth have been experimented for achieving a good prediction of algal concentrations by a combination strategy. We validate the prediction performance on two real datasets from two locations in US and China, i.e., Scripps Pier, California and Sishili Bay, Shandong, respectively. The models and feature subsets have been selected to complete the missing data and predict the phytoplankton concentration. The results demonstrate the efficiency of our method in the short-term prediction of concentrations by selecting appropriate features. The comparison studies prove the advantage of our developed machine learning method. The importance of every features for the prediction performance reveals crucial factors for the outbreak of harmful algal blooms.}, + language = {en}, + urldate = {2022-05-24}, + journal = {Ecological Indicators}, + author = {Yu, Peixuan and Gao, Rui and Zhang, Dezhen and Liu, Zhi-Ping}, + month = apr, + year = {2021}, + keywords = {unread, Feature importance, Feature selection, GBDT, Harmful algal bloom, Machine learning}, + pages = {107334}, + file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/XICJL7KH/S1470160X20312760.html:text/html}, +} + +@article{dutkiewicz_capturing_2015, + title = {Capturing optically important constituents and properties in a marine biogeochemical and ecosystem model}, + volume = {12}, + issn = {1726-4189}, + url = {https://bg.copernicus.org/articles/12/4447/2015/}, + doi = {10.5194/bg-12-4447-2015}, + abstract = {Abstract. We present a numerical model of the ocean that couples a three-stream radiative transfer component with a marine biogeochemical–ecosystem component in a dynamic three-dimensional physical framework. The radiative transfer component resolves the penetration of spectral irradiance as it is absorbed and scattered within the water column. We explicitly include the effect of several optically important water constituents (different phytoplankton functional types; detrital particles; and coloured dissolved organic matter, CDOM). The model is evaluated against in situ-observed and satellite-derived products. In particular we compare to concurrently measured biogeochemical, ecosystem, and optical data along a meridional transect of the Atlantic Ocean. The simulation captures the patterns and magnitudes of these data, and estimates surface upwelling irradiance analogous to that observed by ocean colour satellite instruments. We find that incorporating the different optically important constituents explicitly and including spectral irradiance was crucial to capture the variability in the depth of the subsurface chlorophyll a (Chl a) maximum. We conduct a series of sensitivity experiments to demonstrate, globally, the relative importance of each of the water constituents, as well as the crucial feedbacks between the light field, the relative fitness of phytoplankton types, and the biogeochemistry of the ocean. CDOM has proportionally more importance at attenuating light at short wavelengths and in more productive waters, phytoplankton absorption is relatively more important at the subsurface Chl a maximum, and water molecules have the greatest contribution when concentrations of other constituents are low, such as in the oligotrophic gyres. Scattering had less effect on attenuation, but since it is important for the amount and type of upwelling irradiance, it is crucial for setting sea surface reflectance. Strikingly, sensitivity experiments in which absorption by any of the optical constituents was increased led to a decrease in the size of the oligotrophic regions of the subtropical gyres: lateral nutrient supplies were enhanced as a result of decreasing high-latitude productivity. This new model that captures bio-optical feedbacks will be important for improving our understanding of the role of light and optical constituents on ocean biogeochemistry, especially in a changing environment. Further, resolving surface upwelling irradiance will make it easier to connect to satellite-derived products in the future.}, + language = {en}, + number = {14}, + urldate = {2022-08-15}, + journal = {Biogeosciences}, + author = {Dutkiewicz, S. and Hickman, A. E. and Jahn, O. and Gregg, W. W. and Mouw, C. B. and Follows, M. J.}, + month = jul, + year = {2015}, + pages = {4447--4481}, + file = {Dutkiewicz et al. - 2015 - Capturing optically important constituents and pro.pdf:/Users/birdy/Zotero/storage/DEFKPKCD/Dutkiewicz et al. - 2015 - Capturing optically important constituents and pro.pdf:application/pdf}, +} + +@article{han_solving_2018, + title = {Solving high-dimensional partial differential equations using deep learning}, + volume = {115}, + url = {https://www.pnas.org/doi/abs/10.1073/pnas.1718942115}, + doi = {10.1073/pnas.1718942115}, + abstract = {Developing algorithms for solving high-dimensional partial differential equations +(PDEs) has been an exceedingly difficult task for a long time, du...}, + language = {EN}, + number = {34}, + urldate = {2022-08-31}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Han, Jiequn and Jentzen, Arnulf and E, Weinan}, + month = aug, + year = {2018}, + note = {Company: National Academy of Sciences +Distributor: National Academy of Sciences +Institution: National Academy of Sciences +Label: National Academy of Sciences +Publisher: Proceedings of the National Academy of Sciences}, + keywords = {ML}, + pages = {8505--8510}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/PCZ84KRN/Han et al. - 2018 - Solving high-dimensional partial differential equa.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/KSCJDJ7T/pnas.html:text/html}, +} + +@article{follows_emergent_2007, + title = {Emergent {Biogeography} of {Microbial} {Communities} in a {Model} {Ocean}}, + volume = {315}, + issn = {0036-8075, 1095-9203}, + url = {https://www.science.org/doi/10.1126/science.1138544}, + doi = {10.1126/science.1138544}, + abstract = {A marine ecosystem model seeded with many phytoplankton types, whose physiological traits were randomly assigned from ranges defined by field and laboratory data, generated an emergent community structure and biogeography consistent with observed global phytoplankton distributions. The modeled organisms included types analogous to the marine cyanobacterium + Prochlorococcus + . Their emergent global distributions and physiological properties simultaneously correspond to observations. This flexible representation of community structure can be used to explore relations between ecosystems, biogeochemical cycles, and climate change.}, + language = {en}, + number = {5820}, + urldate = {2022-08-31}, + journal = {Science}, + author = {Follows, Michael J. and Dutkiewicz, Stephanie and Grant, Scott and Chisholm, Sallie W.}, + month = mar, + year = {2007}, + pages = {1843--1846}, + file = {Follows et al. - 2007 - Emergent Biogeography of Microbial Communities in .pdf:/Users/birdy/Zotero/storage/5N8359IB/Follows et al. - 2007 - Emergent Biogeography of Microbial Communities in .pdf:application/pdf}, +} + +@article{duyck_sloop_2015, + title = {Sloop: {A} pattern retrieval engine for individual animal identification}, + volume = {48}, + issn = {0031-3203}, + shorttitle = {Sloop}, + url = {https://www.sciencedirect.com/science/article/pii/S0031320314002763}, + doi = {10.1016/j.patcog.2014.07.017}, + abstract = {Identifying individuals in photographs of animals collected over time is a non-invasive approach for ecological monitoring and conservation. This paper describes the design and use of Sloop, the first image retrieval system for individual animal identification incorporating crowd-sourced relevance feedback. Sloop׳s iterative retrieval strategy using hierarchical and aggregated matching and relevance feedback consistently improves deformation and correspondence-based approaches for individual identification across several species. Its crowdsourcing strategy is successful in utilizing relevance feedback on a large scale. Sloop is in operational use. The user experience and results are presented here to facilitate the creation of a community-based individual identification system for conservation planning.}, + language = {en}, + number = {4}, + urldate = {2022-10-01}, + journal = {Pattern Recognition}, + author = {Duyck, James and Finn, Chelsea and Hutcheon, Andy and Vera, Pablo and Salas, Joaquin and Ravela, Sai}, + month = apr, + year = {2015}, + keywords = {Animal biometrics, Conservation, Crowdsourcing, Gecko, Hybrid shape contexts, Individual identification, Local features, Photo-identification, Relevance feedback, Salamander, Scale-cascaded alignment, Skink, Whale shark}, + pages = {1059--1073}, + file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/EVXJTLMW/S0031320314002763.html:text/html}, +} + +@article{taylor_shutdown_2011, + title = {Shutdown of turbulent convection as a new criterion for the onset of spring phytoplankton blooms}, + volume = {56}, + issn = {1939-5590}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lo.2011.56.6.2293}, + doi = {10.4319/lo.2011.56.6.2293}, + abstract = {The onset of phytoplankton blooms in late winter, early spring has been traditionally associated with the shoaling of the mixed layer above a critical depth. Here we show that the onset of a bloom can also be triggered by a reduction in air–sea fluxes at the end of winter. When net cooling subsides at the end of winter, turbulent mixing becomes weak, thereby increasing the residence time of phytoplankton cells in the euphotic layer and allowing a bloom to develop. The necessary change in the air–sea flux generally precedes mixed-layer shoaling, and may provide a better indicator for the onset of the spring bloom than the mixed-layer depth alone. Our hypothesis is supported by numerical simulations and remote sensing data.}, + language = {en}, + number = {6}, + urldate = {2022-10-05}, + journal = {Limnology and Oceanography}, + author = {Taylor, John R. and Ferrari, Raffaele}, + year = {2011}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lo.2011.56.6.2293}, + pages = {2293--2307}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/X7EVVVGL/Taylor and Ferrari - 2011 - Shutdown of turbulent convection as a new criterio.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/SJS6MACH/lo.2011.56.6.html:text/html}, +} + +@article{regos_effects_2019, + title = {Effects of species traits and environmental predictors on performance and transferability of ecological niche models}, + volume = {9}, + issn = {20452322}, + url = {https://www.mendeley.com/catalogue/30ca2198-448a-30e7-bb65-8f00f0bc94ed/?ref=raven&dgcid=raven_md_suggest_email&dgcid=raven_md_suggest_mie_email}, + doi = {10.1038/s41598-019-40766-5}, + abstract = {(2019) Regos et al. Scientific Reports. The ability of ecological niche models (ENMs) to produce robust predictions for different time frames (i.e. temporal transferability) may be hindered by a la...}, + language = {en-GB}, + number = {1}, + urldate = {2022-10-11}, + journal = {Scientific Reports}, + author = {Regos, Adrián and Gagne, Laura and Alcaraz-Segura, Domingo and Honrado, João P. and Domínguez, Jesús}, + year = {2019}, + note = {Number: 1}, + pages = {undefined--undefined}, + file = {Full Text:/Users/birdy/Zotero/storage/83W8A9G9/Regos et al. - 2019 - Effects of species traits and environmental predic.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/CSIQ6CV6/30ca2198-448a-30e7-bb65-8f00f0bc94ed.html:text/html}, +} + +@misc{beery_species_2021, + title = {Species {Distribution} {Modeling} for {Machine} {Learning} {Practitioners}: {A} {Review}}, + shorttitle = {Species {Distribution} {Modeling} for {Machine} {Learning} {Practitioners}}, + url = {http://arxiv.org/abs/2107.10400}, + abstract = {Conservation science depends on an accurate understanding of what's happening in a given ecosystem. How many species live there? What is the makeup of the population? How is that changing over time? Species Distribution Modeling (SDM) seeks to predict the spatial (and sometimes temporal) patterns of species occurrence, i.e. where a species is likely to be found. The last few years have seen a surge of interest in applying powerful machine learning tools to challenging problems in ecology. Despite its considerable importance, SDM has received relatively little attention from the computer science community. Our goal in this work is to provide computer scientists with the necessary background to read the SDM literature and develop ecologically useful ML-based SDM algorithms. In particular, we introduce key SDM concepts and terminology, review standard models, discuss data availability, and highlight technical challenges and pitfalls.}, + urldate = {2022-10-11}, + publisher = {arXiv}, + author = {Beery, Sara and Cole, Elijah and Parker, Joseph and Perona, Pietro and Winner, Kevin}, + month = jul, + year = {2021}, + note = {arXiv:2107.10400 [cs, stat]}, + keywords = {Computer Science - Machine Learning, Statistics - Applications}, + file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/5D6ZSJQL/Beery et al. - 2021 - Species Distribution Modeling for Machine Learning.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/UNSJQTFB/2107.html:text/html}, +} + +@article{beery_scaling_2021, + title = {Scaling biodiversity monitoring for the data age}, + volume = {27}, + issn = {1528-4972, 1528-4980}, + url = {https://dl.acm.org/doi/10.1145/3466857}, + doi = {10.1145/3466857}, + abstract = {Technological advances have made it possible to collect massive amounts of biodiversity data. How can analysis efforts keep up?}, + language = {en}, + number = {4}, + urldate = {2022-10-11}, + journal = {XRDS: Crossroads, The ACM Magazine for Students}, + author = {Beery, Sara}, + month = jun, + year = {2021}, + pages = {14--18}, + file = {Beery - 2021 - Scaling biodiversity monitoring for the data age.pdf:/Users/birdy/Zotero/storage/N366LVCR/Beery - 2021 - Scaling biodiversity monitoring for the data age.pdf:application/pdf}, +} + +@article{olson_submersible_2007, + title = {A submersible imaging-in-flow instrument to analyze nano-and microplankton: {Imaging} {FlowCytobot}}, + volume = {5}, + issn = {1541-5856}, + shorttitle = {A submersible imaging-in-flow instrument to analyze nano-and microplankton}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lom.2007.5.195}, + doi = {10.4319/lom.2007.5.195}, + abstract = {A fundamental understanding of the interaction between physical and biological factors that regulate plankton species composition requires, first of all, detailed and sustained observations. Only now is it becoming possible to acquire these types of observations, as we develop and deploy instruments that can continuously monitor individual organisms in the ocean. Our research group can measure and count the smallest phytoplankton cells using a submersible flow cytometer (FlowCytobot), in which optical properties of individual suspended cells are recorded as they pass through a focused laser beam. However, FlowCytobot cannot efficiently sample or identify the much larger cells (10 to {\textgreater}100 µm) that often dominate the plankton in coastal waters. Because these larger cells often have recognizable morphologies, we have developed a second submersible flow cytometer, with imaging capability and increased water sampling rate (typically, 5 mL seawater analyzed every 20 min), to characterize these nano- and microplankton. Like the original, Imaging FlowCytobot can operate unattended for months at a time; it obtains power from and communicates with a shore laboratory, so we can monitor results and modify sampling procedures when needed. Imaging FlowCytobot was successfully tested for 2 months in Woods Hole Harbor and is presently deployed alongside FlowCytobot at the Martha's Vineyard Coastal Observatory. These combined approaches will allow continuous long-term observations of plankton community structure over a wide range of cell sizes and types, and help to elucidate the processes and interactions that control the life cycles of individual species.}, + language = {en}, + number = {6}, + urldate = {2022-10-20}, + journal = {Limnology and Oceanography: Methods}, + author = {Olson, Robert J. and Sosik, Heidi M.}, + year = {2007}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lom.2007.5.195}, + pages = {195--203}, + file = {Full Text:/Users/birdy/Zotero/storage/JP4F24FI/Olson and Sosik - 2007 - A submersible imaging-in-flow instrument to analyz.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/9E2FX4K7/lom.2007.5.html:text/html}, +} + +@article{sosik_automated_2007, + title = {Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry}, + volume = {5}, + issn = {1541-5856}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lom.2007.5.204}, + doi = {10.4319/lom.2007.5.204}, + abstract = {High-resolution photomicrographs of phytoplankton cells and chains can now be acquired with imaging-in-flow systems at rates that make manual identification impractical for many applications. To address the challenge for automated taxonomic identification of images generated by our custom-built submersible Imaging FlowCytobot, we developed an approach that relies on extraction of image features, which are then presented to a machine learning algorithm for classification. Our approach uses a combination of image feature types including size, shape, symmetry, and texture characteristics, plus orientation invariant moments, diffraction pattern sampling, and co-occurrence matrix statistics. Some of these features required preprocessing with image analysis techniques including edge detection after phase congruency calculations, morphological operations, boundary representation and simplification, and rotation. For the machine learning strategy, we developed an approach that combines a feature selection algorithm and use of a support vector machine specified with a rigorous parameter selection and training approach. After training, a 22-category classifier provides 88\% overall accuracy for an independent test set, with individual category accuracies ranging from 68\% to 99\%. We demonstrate application of this classifier to a nearly uninterrupted 2-month time series of images acquired in Woods Hole Harbor, including use of statistical error correction to derive quantitative concentration estimates, which are shown to be unbiased with respect to manual estimates for random subsamples. Our approach, which provides taxonomically resolved estimates of phytoplankton abundance with fine temporal resolution (hours for many species), permits access to scales of variability from tidal to seasonal and longer.}, + language = {en}, + number = {6}, + urldate = {2022-10-20}, + journal = {Limnology and Oceanography: Methods}, + author = {Sosik, Heidi M. and Olson, Robert J.}, + year = {2007}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lom.2007.5.204}, + pages = {204--216}, + file = {Full Text:/Users/birdy/Zotero/storage/6E2WBCLM/Sosik and Olson - 2007 - Automated taxonomic classification of phytoplankto.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/UIZ3PRI8/lom.2007.5.html:text/html}, +} + +@article{besson_towards_nodate, + title = {Towards the fully automated monitoring of ecological communities}, + volume = {n/a}, + issn = {1461-0248}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ele.14123}, + doi = {10.1111/ele.14123}, + abstract = {High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components—for example, individual behaviours and traits, and species abundance and distribution—is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.}, + language = {en}, + number = {n/a}, + urldate = {2022-10-24}, + journal = {Ecology Letters}, + author = {Besson, Marc and Alison, Jamie and Bjerge, Kim and Gorochowski, Thomas E. and Høye, Toke T. and Jucker, Tommaso and Mann, Hjalte M. R. and Clements, Christopher F.}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ele.14123}, + keywords = {community ecology, computer vision, deep learning, high-resolution monitoring, remote sensing}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/BBZAT6LU/Besson et al. - Towards the fully automated monitoring of ecologic.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/GEGIFAZV/ele.html:text/html}, +} + +@article{pierella_karlusich_coupling_2022, + title = {Coupling {Imaging} and {Omics} in {Plankton} {Surveys}: {State}-of-the-{Art}, {Challenges}, and {Future} {Directions}}, + volume = {9}, + issn = {2296-7745}, + shorttitle = {Coupling {Imaging} and {Omics} in {Plankton} {Surveys}}, + url = {https://www.frontiersin.org/articles/10.3389/fmars.2022.878803}, + abstract = {A major challenge in characterizing plankton communities is the collection, identification and quantification of samples in a time-efficient way. The classical manual microscopy counts are gradually being replaced by high throughput imaging and nucleic acid sequencing. DNA sequencing allows deep taxonomic resolution (including cryptic species) as well as high detection power (detecting rare species), while RNA provides insights on function and potential activity. However, these methods are affected by database limitations, PCR bias, and copy number variability across taxa. Recent developments in high-throughput imaging applied in situ or on collected samples (high-throughput microscopy, Underwater Vision Profiler, FlowCam, ZooScan, etc) has enabled a rapid enumeration of morphologically-distinguished plankton populations, estimates of biovolume/biomass, and provides additional valuable phenotypic information. Although machine learning classifiers generate encouraging results to classify marine plankton images in a time efficient way, there is still a need for large training datasets of manually annotated images. Here we provide workflow examples that couple nucleic acid sequencing with high-throughput imaging for a more complete and robust analysis of microbial communities. We also describe the publicly available and collaborative web application EcoTaxa, which offers tools for the rapid validation of plankton by specialists with the help of automatic recognition algorithms. Finally, we describe how the field is moving with citizen science programs, unmanned autonomous platforms with in situ sensors, and sequencing and digitalization of historical plankton samples.}, + urldate = {2022-10-27}, + journal = {Frontiers in Marine Science}, + author = {Pierella Karlusich, Juan José and Lombard, Fabien and Irisson, Jean-Olivier and Bowler, Chris and Foster, Rachel A.}, + year = {2022}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/7HJUM6PQ/Pierella Karlusich et al. - 2022 - Coupling Imaging and Omics in Plankton Surveys St.pdf:application/pdf}, +} + +@article{pierella_karlusich_global_2021, + title = {Global distribution patterns of marine nitrogen-fixers by imaging and molecular methods}, + volume = {12}, + copyright = {2021 The Author(s)}, + issn = {2041-1723}, + url = {https://www.nature.com/articles/s41467-021-24299-y}, + doi = {10.1038/s41467-021-24299-y}, + abstract = {Nitrogen fixation has a critical role in marine primary production, yet our understanding of marine nitrogen-fixers (diazotrophs) is hindered by limited observations. Here, we report a quantitative image analysis pipeline combined with mapping of molecular markers for mining {\textgreater}2,000,000 images and {\textgreater}1300 metagenomes from surface, deep chlorophyll maximum and mesopelagic seawater samples across 6 size fractions ({\textless}0.2–2000 μm). We use this approach to characterise the diversity, abundance, biovolume and distribution of symbiotic, colony-forming and particle-associated diazotrophs at a global scale. We show that imaging and PCR-free molecular data are congruent. Sequence reads indicate diazotrophs are detected from the ultrasmall bacterioplankton ({\textless}0.2 μm) to mesoplankton (180–2000 μm) communities, while images predict numerous symbiotic and colony-forming diazotrophs ({\textgreater}20 µm). Using imaging and molecular data, we estimate that polyploidy can substantially affect gene abundances of symbiotic versus colony-forming diazotrophs. Our results support the canonical view that larger diazotrophs ({\textgreater}10 μm) dominate the tropical belts, while unicellular cyanobacterial and non-cyanobacterial diazotrophs are globally distributed in surface and mesopelagic layers. We describe co-occurring diazotrophic lineages of different lifestyles and identify high-density regions of diazotrophs in the global ocean. Overall, we provide an update of marine diazotroph biogeographical diversity and present a new bioimaging-bioinformatic workflow.}, + language = {en}, + number = {1}, + urldate = {2022-10-28}, + journal = {Nature Communications}, + author = {Pierella Karlusich, Juan José and Pelletier, Eric and Lombard, Fabien and Carsique, Madeline and Dvorak, Etienne and Colin, Sébastien and Picheral, Marc and Cornejo-Castillo, Francisco M. and Acinas, Silvia G. and Pepperkok, Rainer and Karsenti, Eric and de Vargas, Colomban and Wincker, Patrick and Bowler, Chris and Foster, Rachel A.}, + month = jul, + year = {2021}, + note = {Number: 1 +Publisher: Nature Publishing Group}, + keywords = {Microbial biooceanography, Element cycles, Environmental microbiology, Marine biology}, + pages = {4160}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/6QTE42Z7/Pierella Karlusich et al. - 2021 - Global distribution patterns of marine nitrogen-fi.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/FZ492JKD/s41467-021-24299-y.html:text/html}, +} + +@misc{feichtenhofer_large-scale_2021, + title = {A {Large}-{Scale} {Study} on {Unsupervised} {Spatiotemporal} {Representation} {Learning}}, + url = {http://arxiv.org/abs/2104.14558}, + abstract = {We present a large-scale study on unsupervised spatiotemporal representation learning from videos. With a unified perspective on four recent image-based frameworks, we study a simple objective that can easily generalize all these methods to space-time. Our objective encourages temporally-persistent features in the same video, and in spite of its simplicity, it works surprisingly well across: (i) different unsupervised frameworks, (ii) pre-training datasets, (iii) downstream datasets, and (iv) backbone architectures. We draw a series of intriguing observations from this study, e.g., we discover that encouraging long-spanned persistency can be effective even if the timespan is 60 seconds. In addition to state-of-the-art results in multiple benchmarks, we report a few promising cases in which unsupervised pre-training can outperform its supervised counterpart. Code is made available at https://github.com/facebookresearch/SlowFast}, + urldate = {2022-10-29}, + publisher = {arXiv}, + author = {Feichtenhofer, Christoph and Fan, Haoqi and Xiong, Bo and Girshick, Ross and He, Kaiming}, + month = apr, + year = {2021}, + note = {arXiv:2104.14558 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition}, + file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/P4P752JB/Feichtenhofer et al. - 2021 - A Large-Scale Study on Unsupervised Spatiotemporal.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/LV4YRCJQ/2104.html:text/html}, +} + +@article{lambert_dynamic_2022, + title = {The dynamic trophic architecture of open-ocean protist communities revealed through machine-guided metatranscriptomics}, + volume = {119}, + copyright = {Copyright © 2022 the Author(s). Published by PNAS.}, + url = {https://www.pnas.org/doi/abs/10.1073/pnas.2100916119}, + doi = {10.1073/pnas.2100916119}, + abstract = {Intricate networks of single-celled eukaryotes (protists) dominate carbon flow in +the ocean. Their growth, demise, and interactions with other micr...}, + language = {EN}, + number = {7}, + urldate = {2022-11-01}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Lambert, Bennett S. and Groussman, Ryan D. and Schatz, Megan J. and Coesel, Sacha N. and Durham, Bryndan P. and Alverson, Andrew J. and White, Angelicque E. and Armbrust, E. Virginia}, + month = feb, + year = {2022}, + note = {Company: National Academy of Sciences +Distributor: National Academy of Sciences +Institution: National Academy of Sciences +Label: National Academy of Sciences +Publisher: Proceedings of the National Academy of Sciences}, + pages = {e2100916119}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/U3RTSWWR/Lambert et al. - 2022 - The dynamic trophic architecture of open-ocean pro.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/W4FGUHV8/pnas.html:text/html}, +} + +@article{bourne_insights_2016, + title = {Insights into the {Coral} {Microbiome}: {Underpinning} the {Health} and {Resilience} of {Reef} {Ecosystems}}, + volume = {70}, + issn = {0066-4227, 1545-3251}, + shorttitle = {Insights into the {Coral} {Microbiome}}, + url = {https://www.annualreviews.org/doi/10.1146/annurev-micro-102215-095440}, + doi = {10.1146/annurev-micro-102215-095440}, + abstract = {Corals are fundamental ecosystem engineers, creating large, intricate reefs that support diverse and abundant marine life. At the core of a healthy coral animal is a dynamic relationship with microorganisms, including a mutually beneficial symbiosis with photosynthetic dinoflagellates (Symbiodinium spp.) and enduring partnerships with an array of bacterial, archaeal, fungal, protistan, and viral associates, collectively termed the coral holobiont. The combined genomes of this coral holobiont form a coral hologenome, and genomic interactions within the hologenome ultimately define the coral phenotype. Here we integrate contemporary scientific knowledge regarding the ecological, host-specific, and environmental forces shaping the diversity, specificity, and distribution of microbial symbionts within the coral holobiont, explore physiological pathways that contribute to holobiont fitness, and describe potential mechanisms for holobiont homeostasis. Understanding the role of the microbiome in coral resilience, acclimation, and environmental adaptation is a new frontier in reef science that will require large-scale collaborative research efforts.}, + language = {en}, + number = {1}, + urldate = {2022-11-02}, + journal = {Annual Review of Microbiology}, + author = {Bourne, David G. and Morrow, Kathleen M. and Webster, Nicole S.}, + month = sep, + year = {2016}, + pages = {317--340}, + file = {Bourne et al. - 2016 - Insights into the Coral Microbiome Underpinning t.pdf:/Users/birdy/Zotero/storage/AE37AHKP/Bourne et al. - 2016 - Insights into the Coral Microbiome Underpinning t.pdf:application/pdf}, +} + +@article{zhang_microbially_2019, + title = {Microbially mediated nutrient cycles in marine sponges}, + volume = {95}, + issn = {0168-6496}, + url = {https://doi.org/10.1093/femsec/fiz155}, + doi = {10.1093/femsec/fiz155}, + abstract = {Efficient nutrient cycles mediated by symbiotic microorganisms with their hosts are vital to support the high productivity of coral reef ecosystems. In these ecosystems, marine sponges are important habitat-forming organisms in the benthic community and harbor abundant microbial symbionts. However, few studies have reviewed the critical microbially mediated nutrient cycling processes in marine sponges. To bridge this gap, in this review article, we summarize existing knowledge and recent advances in understanding microbially mediated carbon (C), nitrogen (N), phosphorus (P) and sulfur (S) cycles in sponges, propose a conceptual model that describes potential interactions and constraints in the major nutrient cycles, and suggest that shifting redox state induced by animal behavior like sponge pumping can exert great influence on the activities of symbiotic microbial communities. Constraints include the lack of knowledge on spatial and temporal variations and host behavior; more studies are needed in these areas. Sponge microbiomes may have a significant impact on the nutrient cycles in the world’s coral reef ecosystems.}, + number = {11}, + urldate = {2022-11-02}, + journal = {FEMS Microbiology Ecology}, + author = {Zhang, Fan and Jonas, Lauren and Lin, Hanzhi and Hill, Russell T}, + month = nov, + year = {2019}, + keywords = {EMO}, + pages = {fiz155}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/372EJVV6/Zhang et al. - 2019 - Microbially mediated nutrient cycles in marine spo.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/AN3DK4P7/5582607.html:text/html}, +} + +@article{ceh_nutrient_2013, + title = {Nutrient cycling in early coral life stages: {Pocillopora} damicornis larvae provide their algal symbiont ({Symbiodinium}) with nitrogen acquired from bacterial associates}, + volume = {3}, + issn = {2045-7758}, + shorttitle = {Nutrient cycling in early coral life stages}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.642}, + doi = {10.1002/ece3.642}, + abstract = {The waters surrounding coral reef ecosystems are generally poor in nutrients, yet their levels of primary production are comparable with those reported from tropical rain forests. One explanation of this paradox is the efficient cycling of nutrients between the coral host, its endosymbiotic alga Symbiodinium and a wide array of microorganisms. Despite their importance for the animals' fitness, the cycling of nutrients in early coral life stages and the initial establishment of partnerships with the microbes involved in these processes has received little scrutiny to date. Nitrogen is an essential but limited nutrient in coral reef ecosystems. In order to assess the early nutrient exchange between bacteria and corals, coral larvae of the species Pocillopora damicornis were incubated with two coral-associated bacteria (Alteromonas sp., or Vibrio alginolyticus), prelabeled with the stable nitrogen isotope 15N. The incorporation and translocation of nitrogen from Vibrio- and Alteromonas bacteria into P. damicornis coral larvae and specifically into the coral-symbiotic Symbiodinium were detected by nanoscale secondary ion mass spectrometry (NanoSIMS). A significant increase in the amount of enriched 15N (two to threefold compared to natural abundance) was observed in P. damicornis larvae within 8 h of incubation for both bacterial treatments (one-way ANOVA, F5,53 = 18.03, P = 0.004 for Alteromonas sp. and F5,53 = 18.03, P = 0.0001 for V. alginolyticus). These findings reveal that coral larvae acquire nutrients previously taken up from the environment by bacteria. The additional nitrogen may increase the survival rate and fitness of the developing coral and therefore contribute to the successful maintenance of coral reefs.}, + language = {en}, + number = {8}, + urldate = {2022-11-02}, + journal = {Ecology and Evolution}, + author = {Ceh, Janja and Kilburn, Matt R. and Cliff, John B. and Raina, Jean-Baptiste and van Keulen, Mike and Bourne, David G.}, + year = {2013}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.642}, + keywords = {EMO, Bacteria, coral larvae, nanoSIMS, nitrogen}, + pages = {2393--2400}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/8PI4Q2QV/Ceh et al. - 2013 - Nutrient cycling in early coral life stages Pocil.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/L6GYJYMJ/ece3.html:text/html}, +} + +@article{radecker_heat_2022, + title = {Heat stress reduces the contribution of diazotrophs to coral holobiont nitrogen cycling}, + volume = {16}, + copyright = {2021 The Author(s)}, + issn = {1751-7370}, + url = {https://www.nature.com/articles/s41396-021-01158-8}, + doi = {10.1038/s41396-021-01158-8}, + abstract = {Efficient nutrient cycling in the coral-algal symbiosis requires constant but limited nitrogen availability. Coral-associated diazotrophs, i.e., prokaryotes capable of fixing dinitrogen, may thus support productivity in a stable coral-algal symbiosis but could contribute to its breakdown when overstimulated. However, the effects of environmental conditions on diazotroph communities and their interaction with other members of the coral holobiont remain poorly understood. Here we assessed the effects of heat stress on diazotroph diversity and their contribution to holobiont nutrient cycling in the reef-building coral Stylophora pistillata from the central Red Sea. In a stable symbiotic state, we found that nitrogen fixation by coral-associated diazotrophs constitutes a source of nitrogen to the algal symbionts. Heat stress caused an increase in nitrogen fixation concomitant with a change in diazotroph communities. Yet, this additional fixed nitrogen was not assimilated by the coral tissue or the algal symbionts. We conclude that although diazotrophs may support coral holobiont functioning under low nitrogen availability, altered nutrient cycling during heat stress abates the dependence of the coral host and its algal symbionts on diazotroph-derived nitrogen. Consequently, the role of nitrogen fixation in the coral holobiont is strongly dependent on its nutritional status and varies dynamically with environmental conditions.}, + language = {en}, + number = {4}, + urldate = {2022-11-02}, + journal = {The ISME Journal}, + author = {Rädecker, Nils and Pogoreutz, Claudia and Gegner, Hagen M. and Cárdenas, Anny and Perna, Gabriela and Geißler, Laura and Roth, Florian and Bougoure, Jeremy and Guagliardo, Paul and Struck, Ulrich and Wild, Christian and Pernice, Mathieu and Raina, Jean-Baptiste and Meibom, Anders and Voolstra, Christian R.}, + month = apr, + year = {2022}, + note = {Number: 4 +Publisher: Nature Publishing Group}, + keywords = {Microbial ecology, EMO, Climate-change ecology}, + pages = {1110--1118}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/XMDXIJE7/Rädecker et al. - 2022 - Heat stress reduces the contribution of diazotroph.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/3RSSVZZL/s41396-021-01158-8.html:text/html}, +} + +@article{radecker_nitrogen_2015, + title = {Nitrogen cycling in corals: the key to understanding holobiont functioning?}, + volume = {23}, + issn = {0966-842X}, + shorttitle = {Nitrogen cycling in corals}, + url = {https://www.sciencedirect.com/science/article/pii/S0966842X1500075X}, + doi = {10.1016/j.tim.2015.03.008}, + abstract = {Corals are animals that form close mutualistic associations with endosymbiotic photosynthetic algae of the genus Symbiodinium. Together they provide the calcium carbonate framework of coral reef ecosystems. The importance of the microbiome (i.e., bacteria, archaea, fungi, and viruses) to holobiont functioning has only recently been recognized. Given that growth and density of Symbiodinium within the coral host is highly dependent on nitrogen availability, nitrogen-cycling microbes may be of fundamental importance to the stability of the coral–algae symbiosis and holobiont functioning, in particular under nutrient-enriched and -depleted scenarios. We summarize what is known about nitrogen cycling in corals and conclude that disturbance of microbial nitrogen cycling may be tightly linked to coral bleaching and disease.}, + language = {en}, + number = {8}, + urldate = {2022-11-02}, + journal = {Trends in Microbiology}, + author = {Rädecker, Nils and Pogoreutz, Claudia and Voolstra, Christian R. and Wiedenmann, Jörg and Wild, Christian}, + month = aug, + year = {2015}, + keywords = {EMO, coral bleaching, coral disease, coral holobiont, nutrient limitation, symbiosis}, + pages = {490--497}, + file = {ScienceDirect Full Text PDF:/Users/birdy/Zotero/storage/QY245ZPK/Rädecker et al. - 2015 - Nitrogen cycling in corals the key to understandi.pdf:application/pdf;ScienceDirect Snapshot:/Users/birdy/Zotero/storage/I62WKSM8/S0966842X1500075X.html:text/html}, +} + +@article{zhang_multi-task_nodate, + title = {Multi-task few-shot learning with composed data augmentation for image classification}, + volume = {n/a}, + issn = {1751-9640}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1049/cvi2.12150}, + doi = {10.1049/cvi2.12150}, + abstract = {Few-shot learning (FSL) attempts to learn and optimise the model from a few examples on image classification, which is still threatened by data scarcity. To generate more data as supplements, data augmentation is considered as a powerful and popular technique to enhance the robustness of few-shot models. However, there are still some weaknesses in applying augmentation methods. For example, all augmented samples have similar semantic information with respect to different augmented transformations, which makes these traditional augmentation methods incapable of learning the property being varied. To address this challenge, we introduce multi-task learning to learn a primary few-shot classification task and an auxiliary self-supervised task, simultaneously. The self-supervised task can learn transformation property as auxiliary self-supervision signals to improve the performance of the primary few-shot classification task. Additionally, we propose a simple, flexible, and effective mechanism for decision fusion to further improve the reliability of the classifier, named model-agnostic ensemble inference (MAEI). Specifically, the MAEI mechanism can eliminate the influence of outliers for FSL using non-maximum suppression. Extensive experiment results demonstrate that our method can outperform other state-of-the-art methods by large margins.}, + language = {en}, + number = {n/a}, + urldate = {2022-11-03}, + journal = {IET Computer Vision}, + author = {Zhang, Rui and Yang, Yixin and Li, Yang and Wang, Jiabao and Li, Hang and Miao, Zhuang}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1049/cvi2.12150}, + keywords = {data augmentation, few-shot learning, multi-task learning, non-maximum suppression, self-supervised learning}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/QWAG8GIJ/Zhang et al. - Multi-task few-shot learning with composed data au.pdf:application/pdf}, +} + +@article{mngadi_estimating_2022, + title = {Estimating aboveground net primary productivity of reforested trees in an urban landscape using biophysical variables and remotely sensed data}, + volume = {802}, + issn = {00489697}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0048969721050336}, + doi = {10.1016/j.scitotenv.2021.149958}, + language = {en}, + urldate = {2022-11-21}, + journal = {Science of The Total Environment}, + author = {Mngadi, Mthembeni and Odindi, John and Mutanga, Onisimo and Sibanda, Mbulisi}, + month = jan, + year = {2022}, + pages = {149958}, +} + +@article{henderikx-freitas_diel_2022, + title = {Diel variations in the estimated refractive index of bulk oceanic particles}, + volume = {30}, + copyright = {\&\#169; 2022 Optica Publishing Group}, + issn = {1094-4087}, + url = {https://opg.optica.org/oe/abstract.cfm?uri=oe-30-24-44141}, + doi = {10.1364/OE.469565}, + abstract = {The index of refraction (n) of particles is an important parameter in optical models that aims to extract particle size and carbon concentrations from light scattering measurements. An inadequate choice of n can critically affect the characterization and interpretation of optically-derived parameters, including those from satellite-based models which provide the current view of how biogeochemical processes vary over the global ocean. Yet, little is known about how n varies over time and space to inform such models. Particularly, in situ estimates of n for bulk water samples and at diel-resolving time scales are rare. Here, we demonstrate a method to estimate n using simultaneously and independently collected particulate beam attenuation coefficients, particle size distribution data, and a Mie theory model. We apply this method to surface waters of the North Pacific Subtropical Gyre (NPSG) at hourly resolution. Clear diel cycles in n were observed, marked by minima around local sunrise and maxima around sunset, qualitatively consistent with several laboratory-based estimates of n for specific phytoplankton species. A sensitivity analysis showed that the daily oscillation in n amplitude was somewhat insensitive to broad variations in method assumptions, ranging from 11.3\&\#x2009;\&\#x00B1;\&\#x2009;4.3\&\#x0025; to 16.9\&\#x2009;\&\#x00B1;\&\#x2009;2.9\&\#x0025;. Such estimates are crucial for improvement of algorithms that extract the particle size and production from bulk optical measurements, and could potentially help establish a link between n variations and changes in cellular composition of in situ particles.}, + language = {EN}, + number = {24}, + urldate = {2022-11-22}, + journal = {Optics Express}, + author = {Henderikx-Freitas, Fernanda and Allen, James G. and Lansdorp, Bob M. and White, Angelicque E.}, + month = nov, + year = {2022}, + note = {Publisher: Optica Publishing Group}, + pages = {44141--44159}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/HPPVHGUH/Henderikx-Freitas et al. - 2022 - Diel variations in the estimated refractive index .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/YVISH8DF/fulltext.html:text/html}, +} + +@misc{noauthor_accelerated_nodate, + title = {Accelerated modern human–induced species losses: {Entering} the sixth mass extinction}, + shorttitle = {Accelerated modern human–induced species losses}, + url = {https://www.science.org/doi/10.1126/sciadv.1400253}, + language = {en}, + urldate = {2022-12-01}, + doi = {10.1126/sciadv.1400253}, + file = {Full Text:/Users/birdy/Zotero/storage/Z7BVSY72/Accelerated modern human–induced species losses E.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/AC954VKH/sciadv.html:text/html}, +} + +@article{koning_network_2020, + title = {A network of grassroots reserves protects tropical river fish diversity}, + volume = {588}, + copyright = {2020 The Author(s), under exclusive licence to Springer Nature Limited}, + issn = {1476-4687}, + url = {https://www.nature.com/articles/s41586-020-2944-y}, + doi = {10.1038/s41586-020-2944-y}, + abstract = {Intensive fisheries have reduced fish biodiversity and abundance in aquatic ecosystems worldwide1–3. ‘No-take’ marine reserves have become a cornerstone of marine ecosystem-based fisheries management4–6, and their benefits for adjacent fisheries are maximized when reserve design fosters synergies among nearby reserves7,8. The applicability of this marine reserve network paradigm to riverine biodiversity and inland fisheries remains largely untested. Here we show that reserves created by 23 separate communities in Thailand’s Salween basin have markedly increased fish richness, density, and biomass relative to adjacent areas. Moreover, key correlates of the success of protected areas in marine ecosystems—particularly reserve size and enforcement—predict differences in ecological benefits among riverine reserves. Occupying a central position in the network confers additional gains, underscoring the importance of connectivity within dendritic river systems. The emergence of network-based benefits is remarkable given that these reserves are young (less than 25 years old) and arose without formal coordination. Freshwater ecosystems are under-represented among the world’s protected areas9, and our findings suggest that networks of small, community-based reserves offer a generalizable model for protecting biodiversity and augmenting fisheries as the world’s rivers face unprecedented pressures10,11.}, + language = {en}, + number = {7839}, + urldate = {2022-12-01}, + journal = {Nature}, + author = {Koning, Aaron A. and Perales, K. Martin and Fluet-Chouinard, Etienne and McIntyre, Peter B.}, + month = dec, + year = {2020}, + note = {Number: 7839 +Publisher: Nature Publishing Group}, + keywords = {Conservation biology, Freshwater ecology}, + pages = {631--635}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/U38SBSD4/Koning et al. - 2020 - A network of grassroots reserves protects tropical.pdf:application/pdf}, +} + +@article{robbins_lawns_2001, + title = {Lawns and {Toxins}: {An} {Ecology} of the {City}}, + volume = {18}, + issn = {0264-2751}, + shorttitle = {Lawns and {Toxins}}, + url = {https://www.sciencedirect.com/science/article/pii/S0264275101000294}, + doi = {10.1016/S0264-2751(01)00029-4}, + abstract = {This paper surveys the problems of contemporary urban ecology through the lens of lawn chemical usage, exploring the difficulty of explaining and managing urban ecological dilemmas that, though built from the disaggregated choices of individuals, aggregate into large and serious issues. Introductory discussion surveys the seriousness of lawn chemicals as urban non-point pollution sources and suggests why the issue, and problems like it, it understudied. Analysis proceeds with a case study from the United States city of Columbus, Ohio, utilizing formal survey techniques and aanlysis of county assessor's data. The results suggest lawns and lawn care chemicals are expanding with urban sprewl and that users of high-input lawn chemical systems are more likely to be wealthy, well-educated, and knowledgeable about the negative environmental impacts of the actions than non-users. Further investigation demonstrates the instrumental logics of homeowners in pursuit of property values but also points to the moral and community-oriented institutions that enforce and propel high chemical use. The conclusions point to policy options for dealing with the lawn chemical dilemma but suggest the difficulties of circumventing the deeply structured roots of the problem.}, + language = {en}, + number = {6}, + urldate = {2022-12-01}, + journal = {Cities}, + author = {Robbins, Paul and Polderman, Annemarie and Birkenholtz, Trevor}, + month = dec, + year = {2001}, + keywords = {consumption, political ecology, urban ecology}, + pages = {369--380}, + file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/PAWPGVRF/S0264275101000294.html:text/html}, +} + +@misc{orenstein_whoi-plankton-_2015, + title = {{WHOI}-{Plankton}- {A} {Large} {Scale} {Fine} {Grained} {Visual} {Recognition} {Benchmark} {Dataset} for {Plankton} {Classification}}, + url = {http://arxiv.org/abs/1510.00745}, + abstract = {Planktonic organisms are of fundamental importance to marine ecosystems: they form the basis of the food web, provide the link between the atmosphere and the deep ocean, and influence global-scale biogeochemical cycles. Scientists are increasingly using imaging-based technologies to study these creatures in their natural habit. Images from such systems provide an unique opportunity to model and understand plankton ecosystems, but the collected datasets can be enormous. The Imaging FlowCytobot (IFCB) at Woods Hole Oceanographic Institution, for example, is an {\textbackslash}emph\{in situ\} system that has been continuously imaging plankton since 2006. To date, it has generated more than 700 million samples. Manual classification of such a vast image collection is impractical due to the size of the data set. In addition, the annotation task is challenging due to the large space of relevant classes, intra-class variability, and inter-class similarity. Methods for automated classification exist, but the accuracy is often below that of human experts. Here we introduce WHOI-Plankton: a large scale, fine-grained visual recognition dataset for plankton classification, which comprises over 3.4 million expert-labeled images across 70 classes. The labeled image set is complied from over 8 years of near continuous data collection with the IFCB at the Martha's Vineyard Coastal Observatory (MVCO). We discuss relevant metrics for evaluation of classification performance and provide results for a traditional method based on hand-engineered features and two methods based on convolutional neural networks.}, + urldate = {2023-01-10}, + publisher = {arXiv}, + author = {Orenstein, Eric C. and Beijbom, Oscar and Peacock, Emily E. and Sosik, Heidi M.}, + month = oct, + year = {2015}, + note = {arXiv:1510.00745 [cs]}, + keywords = {Computer Science - Computer Vision and Pattern Recognition}, + file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/V4XB9956/Orenstein et al. - 2015 - WHOI-Plankton- A Large Scale Fine Grained Visual R.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/UKCDAVKQ/1510.html:text/html}, +} + +@article{jones_matthew_b_gries_corinna_proceedings_2011, + title = {Proceedings of the {Environmental} {Information} {Management} {Conference} 2011}, + url = {https://eim.ecoinformatics.org/eim2011/eim-proceedings-2011/at_download/file}, + doi = {10.5060/D2NC5Z4X}, + abstract = {Lack of observations that span the wide range of critical space and time scales continues to limit many aspects of oceanography. As ocean observatories and observing networks mature, the role for optical technologies and approaches in helping to overcome this limitation continues to grow. As a result the quantity and complexity of data produced is increasing at a pace that threatens to overwhelm the capacity of individual researchers who must cope with large high-resolution datasets, complex, multistage analyses, and the challenges of preserving sufficient metadata and provenance information to ensure reproducibility and avoid costly reprocessing or data loss. We have developed approaches to address these new challenges in the context of a case study involving very large numbers ({\textasciitilde}1 billion) of images collected at coastal observatories by Imaging FlowCytobot, an automated submersible flow cytometer that produces high resolution images of plankton and other microscopic particles at rates up to 10 Hz for months to years. By developing partnerships amongst oceanographers generating and using such data and computer scientists focused on improving science outcomes, we have prototyped a replicable system. It provides simple and ubiquitous access to observational data and products via web services in standard formats; accelerates image processing by enabling algorithms developed with desktop applications to be rapidly deployed and evaluated on shared, high-performance servers; and improves data integrity by replacing error-prone manual data management processes with generalized, automated services. The informatics system is currently in operation for multiple Imaging FlowCytobot datasets and being tested with other types of ocean imagery.}, + language = {en}, + urldate = {2023-01-10}, + author = {Jones, Matthew B.; Gries, Corinna}, + year = {2011}, + note = {Publisher: University of California}, + file = {Jones, Matthew B.\; Gries, Corinna - 2011 - Proceedings of the Environmental Information Manag.pdf:/Users/birdy/Zotero/storage/8LGGH6BH/Jones, Matthew B.\; Gries, Corinna - 2011 - Proceedings of the Environmental Information Manag.pdf:application/pdf}, +} + +@misc{noauthor_creative_nodate, + title = {Creative {Team} {\textbar} {Pulse} at {Omega} {Mart}}, + url = {https://credits.meowwolf.com/omega-mart/factory/pulse}, + abstract = {Discover the artists and creative teams behind Meow Wolf's exhibitions. Works include sculpture, painting, fabrication, digital art, writing, film, and more.}, + urldate = {2023-01-13}, + journal = {Credits {\textbar} Meow Wolf}, + file = {Snapshot:/Users/birdy/Zotero/storage/8C2WZPAI/pulse.html:text/html}, +} + +@article{mahaffey_phytoplankton_2012, + title = {Phytoplankton response to deep seawater nutrient addition in the {North} {Pacific} {Subtropical} {Gyre}}, + volume = {460}, + issn = {0171-8630, 1616-1599}, + url = {http://www.int-res.com/abstracts/meps/v460/p13-34/}, + doi = {10.3354/meps09699}, + abstract = {We examined the phytoplankton response to the addition of nutrient-enriched deep seawater (DSW) and of nitrate only during shipboard experiments performed between July 2004 and May 2007 in the North Pacific Subtropical Gyre (NPSG). Chlorophyll a (chl a), community size structure and composition, carbon fixation (CF) rates, and nutrient concentrations were measured daily for 5 to 7 d under simulated in situ conditions. Despite the fact that the NPSG is a permanently stratified, oligotrophic biome, there was a seasonal response to the addition of DSW and nitrate. In summertime experiments, chl a and CF rates increased up to 18- and 22-fold, respectively, relative to unamended controls after an incubation period of 5 to 6 d. Nutrients were assimilated to below control concentrations. A shift from a Prochlorococcus-dominated to a Synechococcus- and diatom-dominated community, and an 8- to 12-fold increase in the chl a content of the {\textgreater} 2 µm size fraction were observed. Addition of nitrate only increased chl a and CF rates 10- and 7-fold, respectively. In wintertime experiments, chl a and CF rates increased up to 4- and 9-fold, respectively, after an incubation period of up to 7 d. Only 50\% of nutrients were assimilated and nitrate alone stimulated a 2-fold increase in chl a and CF rates. We explore the role of nutrient limitation, community composition, grazing, and light in explaining our observations. Findings from this seasonal and multi-year field experiment demonstrate how little we know about bloom development in the NPSG and highlight the potential risk in extrapolating the response of phytoplankton to natural or artificial fertilization from short-term studies.}, + language = {en}, + urldate = {2023-02-22}, + journal = {Marine Ecology Progress Series}, + author = {Mahaffey, C and Björkman, Km and Karl, Dm}, + month = jul, + year = {2012}, + pages = {13--34}, + file = {Mahaffey et al. - 2012 - Phytoplankton response to deep seawater nutrient a.pdf:/Users/birdy/Zotero/storage/BZYUHQ9I/Mahaffey et al. - 2012 - Phytoplankton response to deep seawater nutrient a.pdf:application/pdf}, +} + +@article{tilman_invasions_1984, + title = {Invasions of equilibria: tests of resource competition using two species of algae}, + volume = {61}, + issn = {1432-1939}, + shorttitle = {Invasions of equilibria}, + url = {https://doi.org/10.1007/BF00396760}, + doi = {10.1007/BF00396760}, + abstract = {Single-species, steady-state chemostat cultures of two freshwater diatoms showed that Fragilaria crotonensis had a lower equilibrial requirement for silicate than did Tabellaria fenestratra. Using this information, resource competition theory predicts that Fragilaria should be able to invade silicate-limited equilibrium populations of Tabellaria, but Tabellaria should not be able to invade silicatelimited Fragilaria. This prediction was supported by a series of invasion experiments. The two species did not have detectable differences in their phosphate requirements. Invasion experiments showed that Fragilaria could invade Tabellaria cultures, but that Tabellaria could not invade Fragilaria cultures under phosphate-limited conditions. These results are consistent with predictions based on previous studies of the phosphate physiology of these genera.}, + language = {en}, + number = {2}, + urldate = {2023-02-22}, + journal = {Oecologia}, + author = {Tilman, David and Sterner, Robert W.}, + month = feb, + year = {1984}, + pages = {197--200}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/7S64RFJG/Tilman and Sterner - 1984 - Invasions of equilibria tests of resource competi.pdf:application/pdf}, +} + +@article{tilman_resource_1977, + title = {Resource {Competition} between {Plankton} {Algae}: {An} {Experimental} and {Theoretical} {Approach}}, + volume = {58}, + issn = {00129658}, + shorttitle = {Resource {Competition} between {Plankton} {Algae}}, + url = {http://doi.wiley.com/10.2307/1935608}, + doi = {10.2307/1935608}, + language = {en}, + number = {2}, + urldate = {2023-02-22}, + journal = {Ecology}, + author = {Tilman, David}, + month = mar, + year = {1977}, + pages = {338--348}, + file = {Tilman - 1977 - Resource Competition between Plankton Algae An Ex.pdf:/Users/birdy/Zotero/storage/E6YRP87L/Tilman - 1977 - Resource Competition between Plankton Algae An Ex.pdf:application/pdf}, +} + +@article{burghardt_impact_2009, + title = {Impact of {Native} {Plants} on {Bird} and {Butterfly} {Biodiversity} in {Suburban} {Landscapes}}, + volume = {23}, + issn = {1523-1739}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1523-1739.2008.01076.x}, + doi = {10.1111/j.1523-1739.2008.01076.x}, + abstract = {Abstract: Managed landscapes in which non-native ornamental plants are favored over native vegetation now dominate the United States, particularly east of the Mississippi River. We measured how landscaping with native plants affects the avian and lepidopteran communities on 6 pairs of suburban properties in southeastern Pennsylvania. One property in each pair was landscaped entirely with native plants and the other exhibited a more conventional suburban mixture of plants—a native canopy with non-native groundcover and shrubs. Vegetation sampling confirmed that total plant cover and plant diversity did not differ between treatments, but non-native plant cover was greater on the conventional sites and native plant cover was greater on the native sites. Several avian (abundance, species richness, biomass, and breeding-bird abundance) and larval lepidopteran (abundance and species richness) community parameters were measured from June 2006 to August 2006. Native properties supported significantly more caterpillars and caterpillar species and significantly greater bird abundance, diversity, species richness, biomass, and breeding pairs of native species. Of particular importance is that bird species of regional conservation concern were 8 times more abundant and significantly more diverse on native properties. In our study area, native landscaping positively influenced the avian and lepidopteran carrying capacity of suburbia and provided a mechanism for reducing biodiversity losses in human-dominated landscapes.}, + language = {en}, + number = {1}, + urldate = {2023-02-23}, + journal = {Conservation Biology}, + author = {Burghardt, Karin T. and Tallamy, Douglas W. and Gregory Shriver, W.}, + year = {2009}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1523-1739.2008.01076.x}, + keywords = {biodiversity, aves, biodiversidad, birds, ecosistemas bajo manejo, Lepidoptera, Lepidóptera, managed ecosystems, native plants, non-native plants, paisaje suburbano, plantas nativas, plantas no nativas, suburban landscape}, + pages = {219--224}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/9IP46YJ7/Burghardt et al. - 2009 - Impact of Native Plants on Bird and Butterfly Biod.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/2M776SVN/j.1523-1739.2008.01076.html:text/html}, +} + +@misc{noauthor_pnas2211758120pdf_nodate, + title = {pnas.2211758120.pdf: 1.873 {Systems} {Ecology} in {Theory}}, + url = {https://canvas.mit.edu/courses/18350/files/3301971?module_item_id=856541}, + urldate = {2023-05-02}, + file = {pnas.2211758120.pdf\: 1.873 Systems Ecology in Theory:/Users/birdy/Zotero/storage/FR2ZNW6N/3301971.html:text/html}, +} + +@article{munch_constraining_2023, + title = {Constraining nonlinear time series modeling with the metabolic theory of ecology}, + volume = {120}, + issn = {0027-8424, 1091-6490}, + url = {https://pnas.org/doi/10.1073/pnas.2211758120}, + doi = {10.1073/pnas.2211758120}, + abstract = {Forecasting the response of ecological systems to environmental change is a critical challenge for sustainable management. The metabolic theory of ecology (MTE) posits scaling of biological rates with temperature, but it has had limited application to population dynamic forecasting. Here we use the temperature dependence of the MTE to constrain empirical dynamic modeling (EDM), an equation-free nonlinear machine learning approach for forecasting. By rescaling time with temperature and modeling dynamics on a “metabolic time step,” our method (MTE-EDM) improved forecast accuracy in 18 of 19 empirical ectotherm time series (by 19\% on average), with the largest gains in more seasonal environments. MTE-EDM assumes that temperature affects only the rate, rather than the form, of population dynamics, and that interacting species have approximately similar temperature dependence. A review of laboratory studies suggests these assumptions are reasonable, at least approximately, though not for all ecological systems. Our approach highlights how to combine modern data-driven forecasting techniques with ecological theory and mechanistic understanding to predict the response of complex ecosystems to temperature variability and trends.}, + language = {en}, + number = {12}, + urldate = {2023-05-02}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Munch, Stephan B. and Rogers, Tanya L. and Symons, Celia C. and Anderson, David and Pennekamp, Frank}, + month = mar, + year = {2023}, + pages = {e2211758120}, + file = {pnas.2211758120.pdf:/Users/birdy/Downloads/pnas.2211758120.pdf:application/pdf}, +} + +@article{sugihara_nonlinear_1990, + title = {Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series}, + volume = {344}, + copyright = {1990 Nature Publishing Group}, + issn = {1476-4687}, + url = {https://www.nature.com/articles/344734a0}, + doi = {10.1038/344734a0}, + abstract = {An approach is presented for making short-term predictions about the trajectories of chaotic dynamical systems. The method is applied to data on measles, chickenpox, and marine phytoplankton populations, to show how apparent noise associated with deterministic chaos can be distinguished from sampling error and other sources of externally induced environmental noise.}, + language = {en}, + number = {6268}, + urldate = {2023-05-02}, + journal = {Nature}, + author = {Sugihara, George and May, Robert M.}, + month = apr, + year = {1990}, + note = {Number: 6268 +Publisher: Nature Publishing Group}, + keywords = {Humanities and Social Sciences, multidisciplinary, Science}, + pages = {734--741}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/LDYABKT4/Sugihara and May - 1990 - Nonlinear forecasting as a way of distinguishing c.pdf:application/pdf}, +} + +@article{fujita_alternative_2023, + title = {Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics}, + volume = {11}, + issn = {2049-2618}, + url = {https://doi.org/10.1186/s40168-023-01474-5}, + doi = {10.1186/s40168-023-01474-5}, + abstract = {Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in human microbiomes.}, + language = {en}, + number = {1}, + urldate = {2023-05-02}, + journal = {Microbiome}, + author = {Fujita, Hiroaki and Ushio, Masayuki and Suzuki, Kenta and Abe, Masato S. and Yamamichi, Masato and Iwayama, Koji and Canarini, Alberto and Hayashi, Ibuki and Fukushima, Keitaro and Fukuda, Shinji and Kiers, E. Toby and Toju, Hirokazu}, + month = mar, + year = {2023}, + keywords = {Alternative stable states, Biodiversity, Biological communities, Chaos, Community collapse, Community stability, Dysbiosis, Empirical dynamic modeling, Microbiome dynamics, Non-linear dynamics}, + pages = {63}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/LJZAZT65/Fujita et al. - 2023 - Alternative stable states, nonlinear behavior, and.pdf:application/pdf}, +} + +@inproceedings{kay_caltech_2022, + address = {Cham}, + series = {Lecture {Notes} in {Computer} {Science}}, + title = {The {Caltech} {Fish} {Counting} {Dataset}: {A} {Benchmark} for {Multiple}-{Object} {Tracking} and {Counting}}, + isbn = {978-3-031-20074-8}, + shorttitle = {The {Caltech} {Fish} {Counting} {Dataset}}, + doi = {10.1007/978-3-031-20074-8_17}, + abstract = {We present the Caltech Fish Counting Dataset (CFC ), a large-scale dataset for detecting, tracking, and counting fish in sonar videos. We identify sonar videos as a rich source of data for advancing low signal-to-noise computer vision applications and tackling domain generalization in multiple-object tracking (MOT) and counting. In comparison to existing MOT and counting datasets, which are largely restricted to videos of people and vehicles in cities, CFC is sourced from a natural-world domain where targets are not easily resolvable and appearance features cannot be easily leveraged for target re-identification. With over half a million annotations in over 1,500 videos sourced from seven different sonar cameras, CFC allows researchers to train MOT and counting algorithms and evaluate generalization performance at unseen test locations. We perform extensive baseline experiments and identify key challenges and opportunities for advancing the state of the art in generalization in MOT and counting.}, + language = {en}, + booktitle = {Computer {Vision} – {ECCV} 2022}, + publisher = {Springer Nature Switzerland}, + author = {Kay, Justin and Kulits, Peter and Stathatos, Suzanne and Deng, Siqi and Young, Erik and Beery, Sara and Van Horn, Grant and Perona, Pietro}, + editor = {Avidan, Shai and Brostow, Gabriel and Cissé, Moustapha and Farinella, Giovanni Maria and Hassner, Tal}, + year = {2022}, + keywords = {Counting, Detection, Tracking, Video dataset}, + pages = {290--311}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/M2APBWFQ/Kay et al. - 2022 - The Caltech Fish Counting Dataset A Benchmark for.pdf:application/pdf}, +} + +@article{chang_engineering_2021, + title = {Engineering complex communities by directed evolution}, + volume = {5}, + copyright = {2021 The Author(s), under exclusive licence to Springer Nature Limited}, + issn = {2397-334X}, + url = {https://www.nature.com/articles/s41559-021-01457-5}, + doi = {10.1038/s41559-021-01457-5}, + abstract = {Directed evolution has been used for decades to engineer biological systems at or below the organismal level. Above the organismal level, a small number of studies have attempted to artificially select microbial ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. Here, we have developed a flexible modelling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions. By artificially selecting hundreds of in silico microbial metacommunities under identical conditions, we first show that the main breeding methods used to date, which do not necessarily let communities reach their ecological equilibrium, are outperformed by a simple screen of sufficiently mature communities. We then identify a range of alternative directed evolution strategies that, particularly when applied in combination, are well suited for the top-down engineering of large, diverse and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure–function landscape to be navigated in search of dynamically stable and ecologically resilient communities with desired quantitative attributes.}, + language = {en}, + number = {7}, + urldate = {2023-05-11}, + journal = {Nature Ecology \& Evolution}, + author = {Chang, Chang-Yu and Vila, Jean C. C. and Bender, Madeline and Li, Richard and Mankowski, Madeleine C. and Bassette, Molly and Borden, Julia and Golfier, Stefan and Sanchez, Paul Gerald L. and Waymack, Rachel and Zhu, Xinwen and Diaz-Colunga, Juan and Estrela, Sylvie and Rebolleda-Gomez, Maria and Sanchez, Alvaro}, + month = jul, + year = {2021}, + note = {Number: 7 +Publisher: Nature Publishing Group}, + keywords = {Community ecology, Evolutionary theory}, + pages = {1011--1023}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/28DAPUW2/Chang et al. - 2021 - Engineering complex communities by directed evolut.pdf:application/pdf}, +} + +@article{lazebnik_can_2002, + title = {Can a biologist fix a radio?—{Or}, what {I} learned while studying apoptosis}, + volume = {2}, + issn = {1535-6108, 1878-3686}, + shorttitle = {Can a biologist fix a radio?}, + url = {https://www.cell.com/cancer-cell/abstract/S1535-6108(02)00133-2}, + doi = {10.1016/S1535-6108(02)00133-2}, + language = {English}, + number = {3}, + urldate = {2023-05-11}, + journal = {Cancer Cell}, + author = {Lazebnik, Yuri}, + month = sep, + year = {2002}, + pmid = {12242150}, + note = {Publisher: Elsevier}, + pages = {179--182}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/MM347IX4/Lazebnik - 2002 - Can a biologist fix a radio—Or, what I learned wh.pdf:application/pdf}, +} + +@article{deyle_tracking_2016, + title = {Tracking and forecasting ecosystem interactions in real time}, + volume = {283}, + url = {https://royalsocietypublishing.org/doi/full/10.1098/rspb.2015.2258}, + doi = {10.1098/rspb.2015.2258}, + abstract = {Evidence shows that species interactions are not constant but change as the ecosystem shifts to new states. Although controlled experiments and model investigations demonstrate how nonlinear interactions can arise in principle, empirical tools to track and predict them in nature are lacking. Here we present a practical method, using available time-series data, to measure and forecast changing interactions in real systems, and identify the underlying mechanisms. The method is illustrated with model data from a marine mesocosm experiment and limnologic field data from Sparkling Lake, WI, USA. From simple to complex, these examples demonstrate the feasibility of quantifying, predicting and understanding state-dependent, nonlinear interactions as they occur in situ and in real time—a requirement for managing resources in a nonlinear, non-equilibrium world.}, + number = {1822}, + urldate = {2023-05-16}, + journal = {Proceedings of the Royal Society B: Biological Sciences}, + author = {Deyle, Ethan R. and May, Robert M. and Munch, Stephan B. and Sugihara, George}, + month = jan, + year = {2016}, + note = {Publisher: Royal Society}, + keywords = {changing interaction strength, community matrix, empirical dynamics, nonlinear, S-map, state space reconstruction}, + pages = {20152258}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/GAGG2JHP/Deyle et al. - 2016 - Tracking and forecasting ecosystem interactions in.pdf:application/pdf}, +} + +@article{gonzalez_automatic_2019, + title = {Automatic plankton quantification using deep features}, + volume = {41}, + issn = {0142-7873}, + url = {https://doi.org/10.1093/plankt/fbz023}, + doi = {10.1093/plankt/fbz023}, + abstract = {The study of marine plankton data is vital to monitor the health of the world’s oceans. In recent decades, automatic plankton recognition systems have proved useful to address the vast amount of data collected by specially engineered in situ digital imaging systems. At the beginning, these systems were developed and put into operation using traditional automatic classification techniques, which were fed with hand-designed local image descriptors (such as Fourier features), obtaining quite successful results. In the past few years, there have been many advances in the computer vision community with the rebirth of neural networks. In this paper, we leverage how descriptors computed using convolutional neural networks trained with out-of-domain data are useful to replace hand-designed descriptors in the task of estimating the prevalence of each plankton class in a water sample. To achieve this goal, we have designed a broad set of experiments that show how effective these deep features are when working in combination with state-of-the-art quantification algorithms.}, + number = {4}, + urldate = {2023-05-18}, + journal = {Journal of Plankton Research}, + author = {González, Pablo and Castaño, Alberto and Peacock, Emily E and Díez, Jorge and Del Coz, Juan José and Sosik, Heidi M}, + month = jul, + year = {2019}, + pages = {449--463}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/YS4AGBKW/González et al. - 2019 - Automatic plankton quantification using deep featu.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/M6LWQPI2/5537667.html:text/html}, +} + +@inproceedings{walker_improving_2021, + address = {Montreal, BC, Canada}, + title = {Improving {Rare}-{Class} {Recognition} of {Marine} {Plankton} with {Hard} {Negative} {Mining}}, + isbn = {978-1-66540-191-3}, + url = {https://ieeexplore.ieee.org/document/9607849/}, + doi = {10.1109/ICCVW54120.2021.00410}, + abstract = {Biological oceanographers are increasingly adopting machine learning techniques to conduct quantitative assessments of marine plankton. Most supervised plankton classifiers are trained on labeled image datasets annotated by domain experts under the closed world assumption: all object classes and their priors are the same during both training and deployment. This assumption, however, is hard to satisfy in the actual ocean where data is subject to dataset shift due to shifting populations and from the introduction of object categories not seen during training. Here we present an alternative approach for training and evaluating plankton classifiers under the more realistic open world scenario. We specifically address the problems of out-of-distribution detection and dataset shift under the class imbalance setting where downsampling is needed to reliably detect and classify relatively rare target classes. We apply a hard negative mining approach called Background Resampling to perform downsampling and compare it to other strategies. We show that Background Resampling improves detection of novel particle classes while simultaneously providing competitive classification performance under dataset shift.}, + language = {en}, + urldate = {2023-05-18}, + booktitle = {2021 {IEEE}/{CVF} {International} {Conference} on {Computer} {Vision} {Workshops} ({ICCVW})}, + publisher = {IEEE}, + author = {Walker, Joseph L. and Orenstein, Eric C.}, + month = oct, + year = {2021}, + pages = {3665--3675}, + file = {Walker and Orenstein - 2021 - Improving Rare-Class Recognition of Marine Plankto.pdf:/Users/birdy/Zotero/storage/9IBBTBGB/Walker and Orenstein - 2021 - Improving Rare-Class Recognition of Marine Plankto.pdf:application/pdf}, +} + +@article{kerr_collaborative_2020, + title = {Collaborative {Deep} {Learning} {Models} to {Handle} {Class} {Imbalance} in {FlowCam} {Plankton} {Imagery}}, + volume = {8}, + issn = {2169-3536}, + doi = {10.1109/ACCESS.2020.3022242}, + abstract = {Using automated imaging technologies, it is now possible to generate previously unprecedented volumes of plankton image data which can be used to study the composition of plankton assemblages. However, the current need to manually classify individual images introduces a bottleneck into processing chains. Although Machine Learning techniques have been used to try and address this issue, past efforts have suffered from accuracy limitations, especially in minority classes. Here we use state-of-the-art methods in Deep Learning to investigate suitable architectures for training an automated plankton classification system which achieves high efficacy for both abundant and rare taxa. We collected live plankton from Station L4 in the Western English Channel and imaged 11,371 particles covering 104 taxonomic groups using the automated plankton imaging system FlowCam. The image set contained a severe class imbalance, with some taxa represented by {\textgreater} 600 images while other, rarer taxa were represented by just 14. We demonstrate that by allowing multiple Deep Learning models to collaborate in a single classification system, classification accuracy improves for minority classes when compared with the best individual model. The top collaborative model achieved a 6 \% improvement in F1 accuracy over the best individual model, while overall accuracy improved by 3.2 \%. This resulted in a 97.4 \% overall accuracy score and a 96.2 \% F1 macro score on a separate holdout test set containing 104 taxonomic groups. Based on a survey of similar studies in the literature, we believe collaborative deep learning models can significantly improve the accuracy of existing automated plankton classification systems.}, + journal = {IEEE Access}, + author = {Kerr, Thomas and Clark, James R. and Fileman, Elaine S. and Widdicombe, Claire E. and Pugeault, Nicolas}, + year = {2020}, + note = {Conference Name: IEEE Access}, + keywords = {Machine learning, deep learning, Automated plankton identification, Biological neural networks, Collaboration, Computational modeling, Convolution, convolutional neural networks, Feature extraction, FlowCam, multi-layer perceptron model, Station L4, Training}, + pages = {170013--170032}, + file = {IEEE Xplore Abstract Record:/Users/birdy/Zotero/storage/E9MSTETK/stamp.html:text/html;IEEE Xplore Full Text PDF:/Users/birdy/Zotero/storage/P2XN56XI/Kerr et al. - 2020 - Collaborative Deep Learning Models to Handle Class.pdf:application/pdf}, +} + +@article{ellen_improving_2019, + title = {Improving plankton image classification using context metadata}, + volume = {17}, + issn = {1541-5856}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lom3.10324}, + doi = {10.1002/lom3.10324}, + abstract = {Advances in both hardware and software are enabling rapid proliferation of in situ plankton imaging methods, requiring more effective machine learning approaches to image classification. Deep Learning methods, such as convolutional neural networks (CNNs), show marked improvement over traditional feature-based supervised machine learning algorithms, but require careful optimization of hyperparameters and adequate training sets. Here, we document some best practices in applying CNNs to zooplankton and marine snow images and note where our results differ from contemporary Deep Learning findings in other domains. We boost the performance of CNN classifiers by incorporating metadata of different types and illustrate how to assimilate metadata beyond simple concatenation. We utilize both geotemporal (e.g., sample depth, location, time of day) and hydrographic (e.g., temperature, salinity, chlorophyll a) metadata and show that either type by itself, or both combined, can substantially reduce error rates. Incorporation of context metadata also boosts performance of the feature-based classifiers we evaluated: Random Forest, Extremely Randomized Trees, Gradient Boosted Classifier, Support Vector Machines, and Multilayer Perceptron. For our assessments, we use an original data set of 350,000 in situ images (roughly 50\% marine snow and 50\% non-snow sorted into 26 categories) from a novel in situ Zooglider. We document asymptotically increasing performance with more computationally intensive techniques, such as substantially deeper networks and artificially augmented data sets. Our best model achieves 92.3\% accuracy with our 27-class data set. We provide guidance for further refinements that are likely to provide additional gains in classifier accuracy.}, + language = {en}, + number = {8}, + urldate = {2023-05-18}, + journal = {Limnology and Oceanography: Methods}, + author = {Ellen, Jeffrey S. and Graff, Casey A. and Ohman, Mark D.}, + year = {2019}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10324}, + pages = {439--461}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/3BP4JKLG/Ellen et al. - 2019 - Improving plankton image classification using cont.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/H2X6CN9U/lom3.html:text/html}, +} + +@article{wilchek_human---loop_2023, + title = {Human-in-the-loop for computer vision assurance: {A} survey}, + volume = {123}, + issn = {0952-1976}, + shorttitle = {Human-in-the-loop for computer vision assurance}, + url = {https://www.sciencedirect.com/science/article/pii/S0952197623005602}, + doi = {10.1016/j.engappai.2023.106376}, + abstract = {Human-in-the-loop (HITL), a key branch of Human–Computer Interaction (HCI), is increasingly proposed in the research literature as a key assurance method for automated analyses and predictive application designs. As the need increases to improve methods in Artificial Intelligence (AI) model training, optimize systems performance, provide AI explainability, and monitor AI system operations, the concept of HITL is gaining traction due to its value in solving these challenges. This survey of existing works on HITL from a computer vision system design perspective focuses on the following AI assurance principles: (1) improved data assurance, such as data preparation or automated data labeling; (2) algorithmic assurance, such as managing uncertainty and AI trustworthiness; and (3) critical limitations and capabilities introduced by HITL into a system’s operational efficiency. We survey prior work within these foci, including technical strengths and weaknesses of novel approaches and ongoing research. This review of the state of the art in HITL computer vision research supports an informed discussion of considerations and future opportunities in this critical space.}, + language = {en}, + urldate = {2023-05-19}, + journal = {Engineering Applications of Artificial Intelligence}, + author = {Wilchek, Matthew and Hanley, Will and Lim, Jude and Luther, Kurt and Batarseh, Feras A.}, + month = aug, + year = {2023}, + keywords = {AI assurance, Computer vision, Human-in-the-loop, Human–computer interaction, Learning systems, Object detection}, + pages = {106376}, + file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/XUK4ZLLN/S0952197623005602.html:text/html}, +} + +@article{bian_understanding_2023, + title = {Understanding opposing predictions of {Prochlorococcus} in a changing climate}, + volume = {14}, + copyright = {2023 The Author(s)}, + issn = {2041-1723}, + url = {https://www.nature.com/articles/s41467-023-36928-9}, + doi = {10.1038/s41467-023-36928-9}, + abstract = {Statistically derived species distribution models (SDMs) are increasingly used to predict ecological changes on a warming planet. For Prochlorococcus, the most abundant phytoplankton, an established statistical prediction conflicts with dynamical models as they predict large, opposite, changes in abundance. We probe the SDM at various spatial-temporal scales, showing that light and temperature fail to explain both temporal fluctuations and sharp spatial transitions. Strong correlations between changes in temperature and population emerge only at very large spatial scales, as transects pass through transitions between regions of high and low abundance. Furthermore, a two-state model based on a temperature threshold matches the original SDM in the surface ocean. We conclude that the original SDM has little power to predict changes when Prochlorococcus is already abundant, which resolves the conflict with dynamical models. Our conclusion suggests that SDMs should prove efficacy across multiple spatial-temporal scales before being trusted in a changing ocean.}, + language = {en}, + number = {1}, + urldate = {2023-06-05}, + journal = {Nature Communications}, + author = {Bian, Vincent and Cai, Merrick and Follett, Christopher L.}, + month = mar, + year = {2023}, + note = {Number: 1 +Publisher: Nature Publishing Group}, + keywords = {Biogeography, Projection and prediction, DAR1}, + pages = {1445}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/PQNIBYHE/Bian et al. - 2023 - Understanding opposing predictions of Prochlorococ.pdf:application/pdf}, +} + +@misc{noauthor_ocean_nodate, + title = {Ocean mover’s distance: using optimal transport for analysing oceanographic data}, + shorttitle = {Ocean mover’s distance}, + url = {https://royalsocietypublishing.org/doi/epdf/10.1098/rspa.2021.0875}, + language = {en}, + urldate = {2023-06-05}, + doi = {10.1098/rspa.2021.0875}, + file = {Full Text:/Users/birdy/Zotero/storage/YPAJVFL3/Ocean mover’s distance using optimal transport fo.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/MVL45IZE/rspa.2021.html:text/html}, +} + +@article{irisson_machine_2022, + title = {Machine {Learning} for the {Study} of {Plankton} and {Marine} {Snow} from {Images}}, + volume = {14}, + url = {https://doi.org/10.1146/annurev-marine-041921-013023}, + doi = {10.1146/annurev-marine-041921-013023}, + abstract = {Quantitative imaging instruments produce a large number of images of plankton and marine snow, acquired in a controlled manner, from which the visual characteristics of individual objects and their in situ concentrations can be computed. To exploit this wealth of information, machine learning is necessary to automate tasks such as taxonomic classification. Through a review of the literature, we highlight the progress of those machine classifiers and what they can and still cannot be trusted for. Several examples showcase how the combination of quantitative imaging with machine learning has brought insights on pelagic ecology. They also highlight what is still missing and how images could be exploited further through trait-based approaches. In the future, we suggest deeper interactions with the computer sciences community, the adoption of data standards, and the more systematic sharing of databases to build a global community of pelagic image providers and users.}, + number = {1}, + urldate = {2023-07-14}, + journal = {Annual Review of Marine Science}, + author = {Irisson, Jean-Olivier and Ayata, Sakina-Dorothée and Lindsay, Dhugal J. and Karp-Boss, Lee and Stemmann, Lars}, + year = {2022}, + pmid = {34460314}, + note = {\_eprint: https://doi.org/10.1146/annurev-marine-041921-013023}, + pages = {277--301}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/IWC2B69M/Irisson et al. - 2022 - Machine Learning for the Study of Plankton and Mar.pdf:application/pdf}, +} + +@article{macneil_plankton_2021, + title = {Plankton classification with high-throughput submersible holographic microscopy and transfer learning}, + volume = {21}, + issn = {2730-7182}, + url = {https://doi.org/10.1186/s12862-021-01839-0}, + doi = {10.1186/s12862-021-01839-0}, + abstract = {Plankton are foundational to marine food webs and an important feature for characterizing ocean health. Recent developments in quantitative imaging devices provide in-flow high-throughput sampling from bulk volumes—opening new ecological challenges exploring microbial eukaryotic variation and diversity, alongside technical hurdles to automate classification from large datasets. However, a limited number of deployable imaging instruments have been coupled with the most prominent classification algorithms—effectively limiting the extraction of curated observations from field deployments. Holography offers relatively simple coherent microscopy designs with non-intrusive 3-D image information, and rapid frame rates that support data-driven plankton imaging tasks. Classification benchmarks across different domains have been set with transfer learning approaches, focused on repurposing pre-trained, state-of-the-art deep learning models as classifiers to learn new image features without protracted model training times. Combining the data production of holography, digital image processing, and computer vision could improve in-situ monitoring of plankton communities and contribute to sampling the diversity of microbial eukaryotes.}, + number = {1}, + urldate = {2023-07-14}, + journal = {BMC Ecology and Evolution}, + author = {MacNeil, Liam and Missan, Sergey and Luo, Junliang and Trappenberg, Thomas and LaRoche, Julie}, + month = jun, + year = {2021}, + keywords = {Classification workflow, Convolutional neural networks, Deep learning, Deployable microscope, High-throughput imaging, Holographic microscopy, Plankton}, + pages = {123}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/983GMGI8/MacNeil et al. - 2021 - Plankton classification with high-throughput subme.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/SAXVJK45/s12862-021-01839-0.html:text/html}, +} + +@article{scotti_ecosystem-based_2022, + title = {Ecosystem-based fisheries management increases catch and carbon sequestration through recovery of exploited stocks: {The} western {Baltic} {Sea} case study}, + volume = {9}, + issn = {2296-7745}, + shorttitle = {Ecosystem-based fisheries management increases catch and carbon sequestration through recovery of exploited stocks}, + url = {https://www.frontiersin.org/articles/10.3389/fmars.2022.879998}, + abstract = {Legal requirement in Europe asks for Ecosystem-Based Fisheries Management (EBFM) in European seas, including consideration of trophic interactions and minimization of negative impacts of fishing on food webs and ecosystem functioning. This study presents the first mass-balanced ecosystem model focused on the western Baltic Sea (WBS). Results show that heavy fishing pressure exerted on the WBS has forced top predators such as harbour porpoise and cod to cover their dietary needs by shifting from forage fish to other prey or find food outside of the model area. The model was then developed to explore the dynamics of four future fishery scenarios: (1) business as usual (BAU), (2) maximum sustainable fishing (F = FMSY), (3) half of FMSY, and (4) EBFM with F = 0.5 FMSY for forage fish and F = 0.8 FMSY for other fish. Simulations show that BAU would perpetuate low catches from depleted stocks with a high risk of extinction for harbour porpoise. In contrast, the EBFM scenario would allow the recovery of harbour porpoise, forage fish and cod with increases in catch of herring and cod. EBFM promotes ecosystem resilience to eutrophication and ocean warming, and through the rebuilding of commercial stocks increases by more than three times carbon sequestration compared to BAU. The model provides an interrelated assessment of trophic guilds in the WBS, as required by European law to assess whether European seas are in good environmental status.}, + urldate = {2023-07-14}, + journal = {Frontiers in Marine Science}, + author = {Scotti, Marco and Opitz, Silvia and MacNeil, Liam and Kreutle, Axel and Pusch, Christian and Froese, Rainer}, + year = {2022}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/DFDLU7N4/Scotti et al. - 2022 - Ecosystem-based fisheries management increases cat.pdf:application/pdf}, +} + +@article{tsakalakis_diel_2022, + title = {Diel light cycles affect phytoplankton competition in the global ocean}, + volume = {31}, + copyright = {© 2022 The Authors. Global Ecology and Biogeography published by John Wiley \& Sons Ltd.}, + issn = {1466-8238}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/geb.13562}, + doi = {10.1111/geb.13562}, + abstract = {Aim Light, essential for photosynthesis, is present in two periodic cycles in nature: seasonal and diel. Although seasonality of light is typically resolved in ocean biogeochemical–ecosystem models because of its significance for seasonal succession and biogeography of phytoplankton, the diel light cycle is generally not resolved. The goal of this study is to demonstrate the impact of diel light cycles on phytoplankton competition and biogeography in the global ocean. Location Global ocean. Major taxa studied Phytoplankton. Methods We use a three-dimensional global ocean model and compare simulations of high temporal resolution with and without diel light cycles. The model simulates 15 phytoplankton types with different cell sizes, encompassing two broad ecological strategies: small cells with high nutrient affinity (gleaners) and larger cells with high maximal growth rate (opportunists). Both are grazed by zooplankton and limited by nitrogen, phosphorus and iron. Results Simulations show that diel cycles of light induce diel cycles in limiting nutrients in the global ocean. Diel nutrient cycles are associated with higher concentrations of limiting nutrients, by 100\% at low latitudes (−40° to 40°), a process that increases the relative abundance of opportunists over gleaners. Size classes with the highest maximal growth rates from both gleaner and opportunist groups are favoured by diel light cycles. This mechanism weakens as latitude increases, because the effects of the seasonal cycle dominate over those of the diel cycle. Main conclusions Understanding the mechanisms that govern phytoplankton biogeography is crucial for predicting ocean ecosystem functioning and biogeochemical cycles. We show that the diel light cycle has a significant impact on phytoplankton competition and biogeography, indicating the need for understanding the role of diel processes in shaping macroecological patterns in the global ocean.}, + language = {en}, + number = {9}, + urldate = {2023-08-04}, + journal = {Global Ecology and Biogeography}, + author = {Tsakalakis, Ioannis and Follows, Michael J. and Dutkiewicz, Stephanie and Follett, Christopher L. and Vallino, Joseph J.}, + year = {2022}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/geb.13562}, + keywords = {phytoplankton, resource competition, modelling, biogeography, diel light cycle, global ocean, nutrient cycles, diel}, + pages = {1838--1849}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/4IRAUGAH/Tsakalakis et al. - 2022 - Diel light cycles affect phytoplankton competition.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/QKKTIJ3P/geb.html:text/html}, +} + +@article{tsakalakis_diel_2018, + title = {Diel light cycle as a key factor for modelling phytoplankton biogeography and diversity}, + volume = {384}, + issn = {0304-3800}, + url = {https://www.sciencedirect.com/science/article/pii/S0304380018302278}, + doi = {10.1016/j.ecolmodel.2018.06.022}, + abstract = {Understanding the mechanisms driving species biogeography and biodiversity remains a major challenge in phytoplankton ecology. Using a model of two phytoplankton species with a gleaner-opportunist trade-off and competing for light and a limiting nutrient, we show that the diel light cycle may be an essential factor to explain large-scale ecological patterns. When only the seasonal light cycle is considered (control scenario) the model predicts that, independently of the nutrient supply, gleaners should dominate across all latitudes and opportunists can obtain a temporal niche only at high latitudes. However, the diel light cycle makes the competition outcome also a function of nutrient supply by affecting the amplitude of diel nutrient oscillations, with gleaners dominating when nutrient supply is low, opportunists when nutrient supply is high, and both species coexisting at intermediate levels of nutrient supply. The combined effects of seasonal and diel light cycles (diel scenario) shape a latitudinal diversity gradient with decreasing diversity towards higher latitudes and a unimodal dependence of diversity on nutrient supply and, therefore, on ecosystem productivity. The proposed mechanism can help interpret the biogeography of major phytoplankton functional groups in the global ocean and link them with large-scale biodiversity patterns.}, + language = {en}, + urldate = {2023-08-04}, + journal = {Ecological Modelling}, + author = {Tsakalakis, Ioannis and Pahlow, Markus and Oschlies, Andreas and Blasius, Bernd and Ryabov, Alexey B.}, + month = sep, + year = {2018}, + keywords = {Biodiversity, Biogeography, diel, Diel light cycle, Nutrient oscillations, Phytoplankton, Resource competition}, + pages = {241--248}, + file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/IRUYQ5WD/S0304380018302278.html:text/html;Tsakalakis et al. - 2018 - Diel light cycle as a key factor for modelling phy.pdf:/Users/birdy/Zotero/storage/EW5BGQ4D/Tsakalakis et al. - 2018 - Diel light cycle as a key factor for modelling phy.pdf:application/pdf}, +} + +@article{grilli_macroecological_2020, + title = {Macroecological laws describe variation and diversity in microbial communities}, + volume = {11}, + copyright = {2020 The Author(s)}, + issn = {2041-1723}, + url = {https://www.nature.com/articles/s41467-020-18529-y}, + doi = {10.1038/s41467-020-18529-y}, + abstract = {How the coexistence of many species is maintained is a fundamental and unresolved question in ecology. Coexistence is a puzzle because we lack a mechanistic understanding of the variation in species presence and abundance. Whether variation in ecological communities is driven by deterministic or random processes is one of the most controversial issues in ecology. Here, I study the variation of species presence and abundance in microbial communities from a macroecological standpoint. I identify three macroecological laws that quantitatively characterize the fluctuation of species abundance across communities and over time. Using these three laws, one can predict species’ presence and absence, diversity, and commonly studied macroecological patterns. I show that a mathematical model based on environmental stochasticity, the stochastic logistic model, quantitatively predicts the three macroecological laws, as well as non-stationary properties of community dynamics.}, + language = {en}, + number = {1}, + urldate = {2023-08-04}, + journal = {Nature Communications}, + author = {Grilli, Jacopo}, + month = sep, + year = {2020}, + note = {Number: 1 +Publisher: Nature Publishing Group}, + keywords = {Microbial ecology, Biodiversity, Community ecology, Ecological modelling, Macroecology, microbes}, + pages = {4743}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/XTDE7UWR/Grilli - 2020 - Macroecological laws describe variation and divers.pdf:application/pdf}, +} + +@article{williamson_beneficial_2001, + title = {Beneficial and {Detrimental} {Effects} of {Uv} on {Aquatic} {Organisms}: {Implications} of {Spectral} {Variation}}, + volume = {11}, + copyright = {© 2001 by the Ecological Society of America}, + issn = {1939-5582}, + shorttitle = {Beneficial and {Detrimental} {Effects} of {Uv} on {Aquatic} {Organisms}}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1890/1051-0761%282001%29011%5B1843%3ABADEOU%5D2.0.CO%3B2}, + doi = {10.1890/1051-0761(2001)011[1843:BADEOU]2.0.CO;2}, + abstract = {Solar ultraviolet radiation (UVR) may have beneficial as well as detrimental effects on living systems. For example, UV-B radiation (280–320 nm) is generally damaging, while UV-A radiation (320–400 nm) may cause damage or stimulate beneficial photorepair of UV-B damage. The nature of both direct and indirect effects of UVR in nature depends on both the photon flux density and the spectral composition of the radiation incident on aquatic organisms across environmental UVR gradients in space (depth, transparency, elevation) and time (diel, seasonal, interannual). Here we use the common and widespread freshwater cladoceran Daphnia pulicaria as a model organism to demonstrate the potential importance of these wavelength-specific effects of UVR to the ecology of aquatic organisms. UVR-exposure experiments are used to manipulate both natural solar and artificial UVR sources to examine the beneficial as well as detrimental effects of different wavelengths of UVR. Changes in the spectral composition of solar radiation are also examined along several natural environmental gradients including diel gradients, depth gradients, and dissolved organic carbon (DOC) gradients. The implications of variation in the spectral composition of UVR for aquatic organisms are discussed. The first biological weighting function (BWF) for a freshwater cladoceran is presented here. It demonstrates that the shortest UV-B wavelengths in sunlight are potentially the most damaging per photon. However, due to the greater photon flux density of longer wavelength UVR in sunlight, the net potential damage to Daphnia in nature is greatest for the longer wavelength UV-B and shorter wavelength UV-A radiation in the 305–322 nm range. Overall the contribution of UV-B to the total mortality response of Daphnia exposed to full-spectrum solar radiation for 7 h on a sunny summer day is 64\% while UV-A contributes 36\%. The BWF for Daphnia is used with the transmission spectrum for Mylar D to demonstrate that Mylar D cuts out only about half of the damaging UVR in sunlight. Following exposure to damaging UV-B, Daphnia exhibits a dramatic increase in survival in the presence of longer wavelength UV-A and visible radiation due to the stimulation of photoenzymatic repair. We present data that demonstrate the importance of both atmospheric ozone and DOC in creating strong environmental gradients in the intensity (irradiance) and spectral composition of solar UVR in nature. The light-absorbing component of DOC, chromophoric dissolved organic matter (CDOM), is particularly important in creating depth refugia from damaging UV-B in freshwater ecosystems. CDOM may also cause intense variations in the ratio of potentially beneficial UV-A to detrimental UV-B radiation to which aquatic organisms are exposed. In addition to changes in atmospheric ozone, future changes in CDOM related to climate change or other environmental disturbances may substantially alter the underwater exposure of a variety of aquatic organisms to different wavelengths of solar UVR.}, + language = {en}, + number = {6}, + urldate = {2023-08-04}, + journal = {Ecological Applications}, + author = {Williamson, Craig E. and Neale, Patrick J. and Grad, Gabriella and De Lange, Hendrika J. and Hargreaves, Bruce R.}, + year = {2001}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1890/1051-0761\%282001\%29011\%5B1843\%3ABADEOU\%5D2.0.CO\%3B2}, + keywords = {diel, biological weighting function, Daphnia pulicaria, dissolved organic carbon, environmental gradients, lake optics, ozone depletion, relative damage to aquatic organisms, spectral composition of solar radiation, tolerance of UV radiation, ultraviolet radiation, UV-A and UV-B, zooplankton}, + pages = {1843--1857}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/C65AT69Z/Williamson et al. - 2001 - Beneficial and Detrimental Effects of Uv on Aquati.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/AQL7Y8NU/1051-0761(2001)011[1843BADEOU]2.0.html:text/html}, +} + +@incollection{fasham_phytoplankton_2003, + address = {Berlin, Heidelberg}, + title = {Phytoplankton and {Their} {Role} in {Primary}, {New}, and {Export} {Production}}, + isbn = {978-3-642-62691-3 978-3-642-55844-3}, + url = {http://link.springer.com/10.1007/978-3-642-55844-3_5}, + language = {en}, + urldate = {2023-08-15}, + booktitle = {Ocean {Biogeochemistry}}, + publisher = {Springer Berlin Heidelberg}, + author = {Falkowski, Paul G. and Laws, Edward A. and Barber, Richard T. and Murray, James W.}, + editor = {Fasham, Michael J. R.}, + year = {2003}, + doi = {10.1007/978-3-642-55844-3_5}, + keywords = {DAR1}, + pages = {99--121}, + file = {Falkowski et al. - 2003 - Phytoplankton and Their Role in Primary, New, and .pdf:/Users/birdy/Zotero/storage/W6TWZWCA/Falkowski et al. - 2003 - Phytoplankton and Their Role in Primary, New, and .pdf:application/pdf}, +} + +@article{tilman_resource_1977-1, + title = {Resource {Competition} between {Plankton} {Algae}: {An} {Experimental} and {Theoretical} {Approach}}, + volume = {58}, + issn = {0012-9658}, + shorttitle = {Resource {Competition} between {Plankton} {Algae}}, + url = {https://www.jstor.org/stable/1935608}, + doi = {10.2307/1935608}, + abstract = {The results of 76 long-term competition experiments between two species of freshwater algae (Asterionella formosa and Cyclotella meneghiniana) grown along a resource gradient agree with the predictions of two different models of resource competition. Both models are based on the functional resource-utilization response of each species to limiting resources. The Monod and the Variable Internal Stores model of competition made similar predictions. Asterionella was observed to be competitively dominant when both species were phosphate limited; Cyclotella was dominant when both species were silicate limited; and both species stably coexisted when each species was growth-rate limited by a different resource. Almost 75\% of the variance in the relative abundances of these two species along a natural silicate-phosphate gradient in Lake Michigan is explained by the Monod model.}, + number = {2}, + urldate = {2023-08-24}, + journal = {Ecology}, + author = {Tilman, David}, + year = {1977}, + note = {Publisher: Ecological Society of America}, + pages = {338--348}, +} + +@article{dallolmo_inferring_2011, + title = {Inferring phytoplankton carbon and eco-physiological rates from diel cycles of spectral particulate beam-attenuation coefficient}, + volume = {8}, + issn = {1726-4170}, + url = {https://bg.copernicus.org/articles/8/3423/2011/bg-8-3423-2011.html}, + doi = {10.5194/bg-8-3423-2011}, + abstract = {The diurnal fluctuations in solar irradiance impose a fundamental frequency on ocean biogeochemistry. Observations of the ocean carbon cycle at these frequencies are rare, but could be considerably expanded by measuring and interpreting the inherent optical properties. A method is presented to analyze diel cycles in particulate beam-attenuation coefficient (cp) measured at multiple wavelengths. The method is based on fitting observations with a size-structured population model coupled to an optical model to infer the particle size distribution and physiologically relevant parameters of the cells responsible for the measured diel cycle in cp. Results show that the information related to size and contained in the spectral data can be exploited to independently estimate growth and loss rates during the day and night. In addition, the model can characterize the population of particles affecting the diel variability in cp. Application of this method to spectral cp measured at a station in the oligotrophic Mediterranean Sea suggests that most of the observed variations in cp can be ascribed to a synchronized population of cells with an equivalent spherical diameter around 4.6±1.5 μm. The inferred carbon biomass of these cells was about 5.2–6.0 mg m−3 and accounted for approximately 10\% of the total particulate organic carbon. If successfully validated, this method may improve our in situ estimates of primary productivity.}, + language = {English}, + number = {11}, + urldate = {2023-08-25}, + journal = {Biogeosciences}, + author = {Dall'Olmo, G. and Boss, E. and Behrenfeld, M. J. and Westberry, T. K. and Courties, C. and Prieur, L. and Pujo-Pay, M. and Hardman-Mountford, N. and Moutin, T.}, + month = nov, + year = {2011}, + note = {Publisher: Copernicus GmbH}, + pages = {3423--3439}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/C6C247B3/Dall'Olmo et al. - 2011 - Inferring phytoplankton carbon and eco-physiologic.pdf:application/pdf}, +} + +@misc{noauthor_ai_nodate, + title = {The {AI} gig economy is coming for you}, + file = {[Hao] The AI gig economy is coming for you _ MIT Technology Review.pdf:/Users/birdy/Downloads/[Hao] The AI gig economy is coming for you _ MIT Technology Review.pdf:application/pdf}, +} + +@article{noauthor_architecture_nodate, + title = {Architecture of a {Database} {System}}, + file = {Architecture of a Database System.pdf:/Users/birdy/Zotero/storage/2DKEVTBM/Architecture of a Database System.pdf:application/pdf}, +} + +@article{isola_lecture_nodate, + title = {Lecture 4: {Architectures} for {Grids}}, + language = {en}, + author = {Isola, Phillip}, + file = {4_grid_archs.pdf:/Users/birdy/Documents/Meng Classes/Deep Learning/4_grid_archs.pdf:application/pdf}, +} + +@article{isola_lecture_nodate-1, + title = {Lecture 5: {Graph} {Neural} {Networks}}, + language = {en}, + author = {Isola, Phillip}, + file = {5_graph_nn.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/5_graph_nn.pdf:application/pdf}, +} + +@misc{noauthor_fast_nodate, + title = {Fast {Forward}: {MIT}'s {Climate} {Action} {Plan} for the {Decade}}, + shorttitle = {Fast {Forward}}, + url = {https://climate.mit.edu/climateaction/fastforward}, + language = {en}, + urldate = {2023-09-22}, + journal = {MIT Climate Portal}, + file = {Snapshot:/Users/birdy/Zotero/storage/E37ZPUKM/fastforward.html:text/html}, +} + +@article{bernstein_scaling_nodate, + title = {Scaling rules for optimisation}, + language = {en}, + author = {Bernstein, Jeremy}, + file = {6_scaling_JB.pdf:/Users/birdy/Documents/Meng Classes/Deep Learning/6_scaling_JB.pdf:application/pdf}, +} + +@article{isola_lecture_nodate-2, + title = {Lecture 3: {Approximation} {Theory}}, + language = {en}, + author = {Isola, Phillip}, + file = {3_approximation_PI.pdf:/Users/birdy/Documents/Meng Classes/Deep Learning/3_approximation_PI.pdf:application/pdf}, +} + +@misc{noauthor_6s898_nodate, + title = {6.{S898} {Lecture} on 9/12/2023 ({Tue})}, + url = {https://mit.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=885feb13-1128-46a6-94fa-b077010e681c}, + abstract = {Special subject in Computer Science}, + language = {en}, + urldate = {2023-09-30}, + file = {Snapshot:/Users/birdy/Zotero/storage/8KZHLHN6/Viewer.html:text/html}, +} + +@inproceedings{andreas_neural_2016, + address = {Las Vegas, NV, USA}, + title = {Neural {Module} {Networks}}, + isbn = {978-1-4673-8851-1}, + url = {http://ieeexplore.ieee.org/document/7780381/}, + doi = {10.1109/CVPR.2016.12}, + abstract = {Visual question answering is fundamentally compositional in nature—a question like where is the dog? shares substructure with questions like what color is the dog? and where is the cat? This paper seeks to simultaneously exploit the representational capacity of deep networks and the compositional linguistic structure of questions. We describe a procedure for constructing and learning neural module networks, which compose collections of jointly-trained neural “modules” into deep networks for question answering. Our approach decomposes questions into their linguistic substructures, and uses these structures to dynamically instantiate modular networks (with reusable components for recognizing dogs, classifying colors, etc.). The resulting compound networks are jointly trained. We evaluate our approach on two challenging datasets for visual question answering, achieving state-of-the-art results on both the VQA natural image dataset and a new dataset of complex questions about abstract shapes.}, + language = {en}, + urldate = {2023-09-30}, + booktitle = {2016 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})}, + publisher = {IEEE}, + author = {Andreas, Jacob and Rohrbach, Marcus and Darrell, Trevor and Klein, Dan}, + month = jun, + year = {2016}, + pages = {39--48}, + file = {Andreas et al. - 2016 - Neural Module Networks.pdf:/Users/birdy/Zotero/storage/YK4HIHHM/Andreas et al. - 2016 - Neural Module Networks.pdf:application/pdf}, +} + +@article{beery_lecture_nodate, + title = {Lecture 2: {How} to train a neural net}, + language = {en}, + author = {Beery, Sara}, + file = {2_backprop_SB.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/2_backprop_SB.pdf:application/pdf}, +} + +@misc{ruswurm_geographic_2023, + title = {Geographic {Location} {Encoding} with {Spherical} {Harmonics} and {Sinusoidal} {Representation} {Networks}}, + url = {http://arxiv.org/abs/2310.06743}, + abstract = {Learning feature representations of geographical space is vital for any machine learning model that integrates geolocated data, spanning application domains such as remote sensing, ecology, or epidemiology. Recent work mostly embeds coordinates using sine and cosine projections based on Double Fourier Sphere (DFS) features -- these embeddings assume a rectangular data domain even on global data, which can lead to artifacts, especially at the poles. At the same time, relatively little attention has been paid to the exact design of the neural network architectures these functional embeddings are combined with. This work proposes a novel location encoder for globally distributed geographic data that combines spherical harmonic basis functions, natively defined on spherical surfaces, with sinusoidal representation networks (SirenNets) that can be interpreted as learned Double Fourier Sphere embedding. We systematically evaluate the cross-product of positional embeddings and neural network architectures across various classification and regression benchmarks and synthetic evaluation datasets. In contrast to previous approaches that require the combination of both positional encoding and neural networks to learn meaningful representations, we show that both spherical harmonics and sinusoidal representation networks are competitive on their own but set state-of-the-art performances across tasks when combined. We provide source code at www.github.com/marccoru/locationencoder}, + urldate = {2023-10-12}, + publisher = {arXiv}, + author = {Rußwurm, Marc and Klemmer, Konstantin and Rolf, Esther and Zbinden, Robin and Tuia, Devis}, + month = oct, + year = {2023}, + note = {arXiv:2310.06743 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/N6J75A2A/2310.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/XSF9TGN7/Rußwurm et al. - 2023 - Geographic Location Encoding with Spherical Harmon.pdf:application/pdf}, +} + +@misc{eerola_survey_2023, + title = {Survey of {Automatic} {Plankton} {Image} {Recognition}: {Challenges}, {Existing} {Solutions} and {Future} {Perspectives}}, + shorttitle = {Survey of {Automatic} {Plankton} {Image} {Recognition}}, + url = {http://arxiv.org/abs/2305.11739}, + abstract = {Planktonic organisms are key components of aquatic ecosystems and respond quickly to changes in the environment, therefore their monitoring is vital to understand the changes in the environment. Yet, monitoring plankton at appropriate scales still remains a challenge, limiting our understanding of functioning of aquatic systems and their response to changes. Modern plankton imaging instruments can be utilized to sample at high frequencies, enabling novel possibilities to study plankton populations. However, manual analysis of the data is costly, time consuming and expert based, making such approach unsuitable for large-scale application and urging for automatic solutions. The key problem related to the utilization of plankton datasets through image analysis is plankton recognition. Despite the large amount of research done, automatic methods have not been widely adopted for operational use. In this paper, a comprehensive survey on existing solutions for automatic plankton recognition is presented. First, we identify the most notable challenges that that make the development of plankton recognition systems difficult. Then, we provide a detailed description of solutions for these challenges proposed in plankton recognition literature. Finally, we propose a workflow to identify the specific challenges in new datasets and the recommended approaches to address them. For many of the challenges, applicable solutions exist. However, important challenges remain unsolved: 1) the domain shift between the datasets hindering the development of a general plankton recognition system that would work across different imaging instruments, 2) the difficulty to identify and process the images of previously unseen classes, and 3) the uncertainty in expert annotations that affects the training of the machine learning models for recognition. These challenges should be addressed in the future research.}, + urldate = {2023-10-16}, + publisher = {arXiv}, + author = {Eerola, Tuomas and Batrakhanov, Daniel and Barazandeh, Nastaran Vatankhah and Kraft, Kaisa and Haraguchi, Lumi and Lensu, Lasse and Suikkanen, Sanna and Seppälä, Jukka and Tamminen, Timo and Kälviäinen, Heikki}, + month = may, + year = {2023}, + note = {arXiv:2305.11739 [cs]}, + keywords = {Computer Science - Computer Vision and Pattern Recognition}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/FG4ZBSVN/2305.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/NZX46TSK/Eerola et al. - 2023 - Survey of Automatic Plankton Image Recognition Ch.pdf:application/pdf}, +} + +@article{gonzalez_review_2018, + title = {A {Review} on {Quantification} {Learning}}, + volume = {50}, + issn = {0360-0300, 1557-7341}, + url = {https://dl.acm.org/doi/10.1145/3117807}, + doi = {10.1145/3117807}, + abstract = {The task of quantification consists in providing an aggregate estimation (e.g. the class distribution in a classification problem) for unseen test sets, applying a model that is trained using a training set with a different data distribution. Several real-world applications demand this kind of methods that do not require predictions for individual examples and just focus on obtaining accurate estimates at an aggregate level. During the past few years, several quantification methods have been proposed from different perspectives and with different goals. This paper presents a unified review of the main approaches with the aim of serving as an introductory tutorial for newcomers in the field.}, + language = {en}, + number = {5}, + urldate = {2023-10-16}, + journal = {ACM Computing Surveys}, + author = {González, Pablo and Castaño, Alberto and Chawla, Nitesh V. and Coz, Juan José Del}, + month = sep, + year = {2018}, + pages = {1--40}, + file = {González et al. - 2018 - A Review on Quantification Learning.pdf:/Users/birdy/Zotero/storage/76A4J8ML/quant-review-grAcc.pdf:application/pdf}, +} + +@article{sosa-trejo_vision-based_2023, + title = {Vision-based techniques for automatic marine plankton classification}, + volume = {56}, + issn = {1573-7462}, + url = {https://doi.org/10.1007/s10462-023-10456-w}, + doi = {10.1007/s10462-023-10456-w}, + abstract = {Plankton are an important component of life on Earth. Since the 19th century, scientists have attempted to quantify species distributions using many techniques, such as direct counting, sizing, and classification with microscopes. Since then, extraordinary work has been performed regarding the development of plankton imaging systems, producing a massive backlog of images that await classification. Automatic image processing and classification approaches are opening new avenues for avoiding time-consuming manual procedures. While some algorithms have been adapted from many other applications for use with plankton, other exciting techniques have been developed exclusively for this issue. Achieving higher accuracy than that of human taxonomists is not yet possible, but an expeditious analysis is essential for discovering the world beyond plankton. Recent studies have shown the imminent development of real-time, in situ plankton image classification systems, which have only been slowed down by the complex implementations of algorithms on low-power processing hardware. This article compiles the techniques that have been proposed for classifying marine plankton, focusing on automatic methods that utilize image processing, from the beginnings of this field to the present day.}, + language = {en}, + number = {11}, + urldate = {2023-10-16}, + journal = {Artificial Intelligence Review}, + author = {Sosa-Trejo, David and Bandera, Antonio and González, Martín and Hernández-León, Santiago}, + month = nov, + year = {2023}, + keywords = {Image processing, Marine plankton, Pattern recognition, Plankton classification}, + pages = {12853--12884}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/U7RIA6IX/Sosa-Trejo et al. - 2023 - Vision-based techniques for automatic marine plank.pdf:application/pdf}, +} + +@article{kerr_collaborative_2020-1, + title = {Collaborative {Deep} {Learning} {Models} to {Handle} {Class} {Imbalance} in {FlowCam} {Plankton} {Imagery}}, + volume = {8}, + issn = {2169-3536}, + url = {https://ieeexplore.ieee.org/abstract/document/9187202/authors#authors}, + doi = {10.1109/ACCESS.2020.3022242}, + abstract = {Using automated imaging technologies, it is now possible to generate previously unprecedented volumes of plankton image data which can be used to study the composition of plankton assemblages. However, the current need to manually classify individual images introduces a bottleneck into processing chains. Although Machine Learning techniques have been used to try and address this issue, past efforts have suffered from accuracy limitations, especially in minority classes. Here we use state-of-the-art methods in Deep Learning to investigate suitable architectures for training an automated plankton classification system which achieves high efficacy for both abundant and rare taxa. We collected live plankton from Station L4 in the Western English Channel and imaged 11,371 particles covering 104 taxonomic groups using the automated plankton imaging system FlowCam. The image set contained a severe class imbalance, with some taxa represented by {\textgreater} 600 images while other, rarer taxa were represented by just 14. We demonstrate that by allowing multiple Deep Learning models to collaborate in a single classification system, classification accuracy improves for minority classes when compared with the best individual model. The top collaborative model achieved a 6 \% improvement in F1 accuracy over the best individual model, while overall accuracy improved by 3.2 \%. This resulted in a 97.4 \% overall accuracy score and a 96.2 \% F1 macro score on a separate holdout test set containing 104 taxonomic groups. Based on a survey of similar studies in the literature, we believe collaborative deep learning models can significantly improve the accuracy of existing automated plankton classification systems.}, + urldate = {2023-10-16}, + journal = {IEEE Access}, + author = {Kerr, Thomas and Clark, James R. and Fileman, Elaine S. and Widdicombe, Claire E. and Pugeault, Nicolas}, + year = {2020}, + note = {Conference Name: IEEE Access}, + pages = {170013--170032}, + file = {IEEE Xplore Abstract Record:/Users/birdy/Zotero/storage/WPTTWRTB/authors.html:text/html;IEEE Xplore Full Text PDF:/Users/birdy/Zotero/storage/VLJWVUVD/Kerr et al. - 2020 - Collaborative Deep Learning Models to Handle Class.pdf:application/pdf}, +} + +@article{conradt_automated_2022, + title = {Automated {Plankton} {Classification} {With} a {Dynamic} {Optimization} and {Adaptation} {Cycle}}, + volume = {9}, + issn = {2296-7745}, + url = {https://www.frontiersin.org/articles/10.3389/fmars.2022.868420}, + abstract = {With recent advances in Machine Learning techniques based on Deep Neural Networks (DNNs), automated plankton image classification is becoming increasingly popular within the marine ecological sciences. Yet, while the most advanced methods can achieve human-level performance on the classification of everyday images, plankton image data possess properties that frequently require a final manual validation step. On the one hand, this is due to morphological properties manifesting in high intra-class and low inter-class variability, and, on the other hand is due to spatial-temporal changes in the composition and structure of the plankton community. Composition changes enforce a frequent updating of the classifier model via training with new user-generated training datasets. Here, we present a Dynamic Optimization Cycle (DOC), a processing pipeline that systematizes and streamlines the model adaptation process via an automatic updating of the training dataset based on manual-validation results. We find that frequent adaptation using the DOC pipeline yields strong maintenance of performance with respect to precision, recall and prediction of community composition, compared to more limited adaptation schemes. The DOC is therefore particularly useful when analyzing plankton at novel locations or time periods, where community differences are likely to occur. In order to enable an easy implementation of the DOC pipeline, we provide an end-to-end application with graphical user interface, as well as an initial dataset of training images. The DOC pipeline thus allows for high-throughput plankton classification and quick and systematized model adaptation, thus providing the means for highly-accelerated plankton analysis.}, + urldate = {2023-10-16}, + journal = {Frontiers in Marine Science}, + author = {Conradt, Jan and Börner, Gregor and López-Urrutia, Ángel and Möllmann, Christian and Moyano, Marta}, + year = {2022}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/YBEYVF4R/Conradt et al. - 2022 - Automated Plankton Classification With a Dynamic O.pdf:application/pdf}, +} + +@misc{ic_du_toit_automated_nodate, + title = {Automated machine learning plankton taxonomy pipeline}, + url = {http://vital.seals.ac.za:8080/vital/access/manager/PdfViewer/vital:59003/SOURCE1?viewPdfInternal=1}, + urldate = {2023-10-16}, + author = {I.C. du Toit}, + file = {document.pdf:/Users/birdy/Downloads/document.pdf:application/pdf;SOURCE1:/Users/birdy/Zotero/storage/8EZC4XHT/SOURCE1.html:text/html}, +} + +@article{gonzalez_validation_2017, + title = {Validation methods for plankton image classification systems}, + volume = {15}, + copyright = {© 2016 Association for the Sciences of Limnology and Oceanography}, + issn = {1541-5856}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lom3.10151}, + doi = {10.1002/lom3.10151}, + abstract = {In recent decades, the automatic study and analysis of plankton communities using imaging techniques has advanced significantly. The effectiveness of these automated systems appears to have improved, reaching acceptable levels of accuracy. However, plankton ecologists often find that classification systems do not work as well as expected when applied to new samples. This paper proposes a methodology to assess the efficacy of learned models which takes into account the fact that the data distribution (the plankton composition of the sample) can vary between the model building phase and the production phase. As opposed to most validation methods that consider the individual organism as the unit of validation, our approach uses a validation-by-sample, which is more appropriate when the objective is to estimate the abundance of different morphological groups. We argue that, in these cases, the base unit to correctly estimate the error is the sample, not the individual. Thus, model assessment processes require groups of samples with sufficient variability in order to provide precise error estimates.}, + language = {en}, + number = {3}, + urldate = {2023-10-16}, + journal = {Limnology and Oceanography: Methods}, + author = {González, Pablo and Álvarez, Eva and Díez, Jorge and López-Urrutia, Ángel and del Coz, Juan José}, + year = {2017}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10151}, + pages = {221--237}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/SW5FNZDQ/González et al. - 2017 - Validation methods for plankton image classificati.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/UB2XMFYB/lom3.html:text/html}, +} + +@article{isola_lecture_nodate-3, + title = {Lecture 11: {Representation} {Learning} {I}}, + language = {en}, + author = {Isola, Phillip}, + file = {11-rep_learning_1.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/11-rep_learning_1.pdf:application/pdf}, +} + +@article{beery_lecture_nodate-1, + title = {Lecture 12: {Similarity}-based {Representation} {Learning}}, + language = {en}, + author = {Beery, Sara}, + file = {12_similarity_based_rep_learning-SB.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/12_similarity_based_rep_learning-SB.pdf:application/pdf}, +} + +@inproceedings{van_horn_benchmarking_2021, + address = {Nashville, TN, USA}, + title = {Benchmarking {Representation} {Learning} for {Natural} {World} {Image} {Collections}}, + isbn = {978-1-66544-509-2}, + url = {https://ieeexplore.ieee.org/document/9577285/}, + doi = {10.1109/CVPR46437.2021.01269}, + abstract = {Recent progress in self-supervised learning has resulted in models that are capable of extracting rich representations from image collections without requiring any explicit label supervision. However, to date the vast majority of these approaches have restricted themselves to training on standard benchmark datasets such as ImageNet. We argue that fine-grained visual categorization problems, such as plant and animal species classification, provide an informative testbed for self-supervised learning. In order to facilitate progress in this area we present two new natural world visual classification datasets, iNat2021 and NeWT. The former consists of 2.7M images from 10k different species uploaded by users of the citizen science application iNaturalist. We designed the latter, NeWT, in collaboration with domain experts with the aim of benchmarking the performance of representation learning algorithms on a suite of challenging natural world binary classification tasks that go beyond standard species classification. These two new datasets allow us to explore questions related to large-scale representation and transfer learning in the context of finegrained categories. We provide a comprehensive analysis of feature extractors trained with and without supervision on ImageNet and iNat2021, shedding light on the strengths and weaknesses of different learned features across a diverse set of tasks. We find that features produced by standard supervised methods still outperform those produced by self-supervised approaches such as SimCLR. However, improved self-supervised learning methods are constantly being released and the iNat2021 and NeWT datasets are a valuable resource for tracking their progress.}, + language = {en}, + urldate = {2023-10-24}, + booktitle = {2021 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})}, + publisher = {IEEE}, + author = {Van Horn, Grant and Cole, Elijah and Beery, Sara and Wilber, Kimberly and Belongie, Serge and MacAodha, Oisin}, + month = jun, + year = {2021}, + pages = {12879--12888}, + file = {Van Horn et al. - 2021 - Benchmarking Representation Learning for Natural W.pdf:/Users/birdy/Zotero/storage/PYLIPA9T/Van Horn et al. - 2021 - Benchmarking Representation Learning for Natural W.pdf:application/pdf}, +} + +@article{orenstein_machine_2022, + title = {Machine learning techniques to characterize functional traits of plankton from image data}, + volume = {67}, + issn = {0024-3590, 1939-5590}, + url = {https://aslopubs.onlinelibrary.wiley.com/doi/10.1002/lno.12101}, + doi = {10.1002/lno.12101}, + abstract = {Plankton imaging systems supported by automated classification and analysis have improved ecologists’ ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.}, + language = {en}, + number = {8}, + urldate = {2023-10-25}, + journal = {Limnology and Oceanography}, + author = {Orenstein, Eric C. and Ayata, Sakina‐Dorothée and Maps, Frédéric and Becker, Érica C. and Benedetti, Fabio and Biard, Tristan and De Garidel‐Thoron, Thibault and Ellen, Jeffrey S. and Ferrario, Filippo and Giering, Sarah L. C. and Guy‐Haim, Tamar and Hoebeke, Laura and Iversen, Morten Hvitfeldt and Kiørboe, Thomas and Lalonde, Jean‐François and Lana, Arancha and Laviale, Martin and Lombard, Fabien and Lorimer, Tom and Martini, Séverine and Meyer, Albin and Möller, Klas Ove and Niehoff, Barbara and Ohman, Mark D. and Pradalier, Cédric and Romagnan, Jean‐Baptiste and Schröder, Simon‐Martin and Sonnet, Virginie and Sosik, Heidi M. and Stemmann, Lars S. and Stock, Michiel and Terbiyik‐Kurt, Tuba and Valcárcel‐Pérez, Nerea and Vilgrain, Laure and Wacquet, Guillaume and Waite, Anya M. and Irisson, Jean‐Olivier}, + month = aug, + year = {2022}, + pages = {1647--1669}, + file = {Orensteinal._2022.pdf:/Users/birdy/Documents/mit-meng-thesis/papers/Orensteinal._2022.pdf:application/pdf}, +} + +@article{schanz_robust_2023, + title = {Robust detection of marine life with label-free image feature learning and probability calibration}, + volume = {4}, + issn = {2632-2153}, + url = {https://iopscience.iop.org/article/10.1088/2632-2153/ace417}, + doi = {10.1088/2632-2153/ace417}, + abstract = {Abstract + + Advances in + in situ + marine life imaging have significantly increased the size and quality of available datasets, but automatic image analysis has not kept pace. Machine learning has shown promise for image processing, but its effectiveness is limited by several open challenges: the requirement for large expert-labeled training datasets, disagreement among experts, under-representation of various species and unreliable or overconfident predictions. To overcome these obstacles for automated underwater imaging, we combine and test recent developments in deep classifier networks and self-supervised feature learning. We use unlabeled images for pretraining deep neural networks to extract task-relevant image features, allowing learning algorithms to cope with scarcity in expert labels, and carefully evaluate performance in subsequent label-based tasks. Performance on rare classes is improved by applying data rebalancing together with a Bayesian correction to avoid biasing inferred + in situ + class frequencies. A divergence-based loss allows training on multiple, conflicting labels for the same image, leading to better estimates of uncertainty which we quantify with a novel accuracy measure. Together, these techniques can reduce the required label counts ∼100-fold while maintaining the accuracy of standard supervised training, shorten training time, cope with expert disagreement and reduce overconfidence.}, + language = {en}, + number = {3}, + urldate = {2023-10-25}, + journal = {Machine Learning: Science and Technology}, + author = {Schanz, Tobias and Ove Möller, Klas and Rühl, Saskia and Greenberg, David S}, + month = sep, + year = {2023}, + pages = {035007}, + file = {Schanz et al. - 2023 - Robust detection of marine life with label-free im.pdf:/Users/birdy/Zotero/storage/KBQL5Q2G/Schanz et al. - 2023 - Robust detection of marine life with label-free im.pdf:application/pdf}, +} + +@article{catlett_temperature_2023, + title = {Temperature dependence of parasitoid infection and abundance of a diatom revealed by automated imaging and classification}, + volume = {120}, + issn = {0027-8424, 1091-6490}, + url = {https://pnas.org/doi/10.1073/pnas.2303356120}, + doi = {10.1073/pnas.2303356120}, + abstract = {Diatoms are a group of phytoplankton that contribute disproportionately to global primary production. Traditional paradigms that suggest diatoms are consumed primarily by larger zooplankton are challenged by sporadic parasitic “epidemics” within diatom populations. However, our understanding of diatom parasitism is limited by difficulties in quantifying these interactions. Here, we observe the dynamics of + Cryothecomonas aestivalis + (a protist) infection of an important diatom on the Northeast U.S. Shelf (NES), + Guinardia delicatula + , with a combination of automated imaging-in-flow cytometry and a convolutional neural network image classifier. Application of the classifier to {\textgreater}1 billion images from a nearshore time series and {\textgreater}20 survey cruises across the broader NES reveals the spatiotemporal gradients and temperature dependence of + G. delicatula + abundance and infection dynamics. Suppression of parasitoid infection at temperatures {\textless}4 °C drives annual cycles in both + G. delicatula + infection and abundance, with an annual maximum in infection observed in the fall-winter preceding an annual maximum in host abundance in the winter-spring. This annual cycle likely varies spatially across the NES in response to variable annual cycles in water temperature. We show that infection remains suppressed for {\textasciitilde}2 mo following cold periods, possibly due to temperature-induced local extinctions of the + C. aestivalis + strain(s) that infect + G. delicatula + . These findings have implications for predicting impacts of a warming NES surface ocean on + G. delicatula + abundance and infection dynamics and demonstrate the potential of automated plankton imaging and classification to quantify phytoplankton parasitism in nature across unprecedented spatiotemporal scales.}, + language = {en}, + number = {28}, + urldate = {2023-10-25}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Catlett, Dylan and Peacock, Emily E. and Crockford, E. Taylor and Futrelle, Joe and Batchelder, Sidney and Stevens, Bethany L. F. and Gast, Rebecca J. and Zhang, Weifeng G. and Sosik, Heidi M.}, + month = jul, + year = {2023}, + pages = {e2303356120}, + file = {catlett-et-al-2023-temperature-dependence-of-parasitoid-infection-and-abundance-of-a-diatom-revealed-by-automated.pdf:/Users/birdy/Documents/mit-meng-thesis/papers/catlett-et-al-2023-temperature-dependence-of-parasitoid-infection-and-abundance-of-a-diatom-revealed-by-automated.pdf:application/pdf}, +} + +@article{culverhouse_empirical_2014, + title = {An empirical assessment of the consistency of taxonomic identifications}, + volume = {10}, + issn = {1745-1000}, + url = {https://doi.org/10.1080/17451000.2013.810762}, + doi = {10.1080/17451000.2013.810762}, + abstract = {Plankton counting and analysis is essential in ecological study, yet scant literature exists as to the reliability of those counts and the consistency of the experts who make the counts. To assess how variable expert taxonomic identifications are, a set of six archived mesozooplankton samples from a series of Longhurst Hardy Plankton Recorder net hauls were counted by expert zooplankton analysts located at six marine laboratories. Sample identifications were repeated on two separate days with over 700 target specimens counted and identified on each day across the samples. Twenty percent of the analysts returned counts that varied by more than 10\%. Thirty-three percent of analysts exhibited low identification consistencies, returning Intraclass Correlation Coefficient scores of less than 0.80. Statistical analyses of these data suggest that over 83\% of the observed categorical count variance can be attributed to inconsistencies within analysts. We suggest this is the root cause of variation in expert specimen labelling consistency.}, + number = {1}, + urldate = {2023-10-25}, + journal = {Marine Biology Research}, + author = {Culverhouse, Philip F. and Macleod, Norman and Williams, Robert and Benfield, Mark C. and Lopes, Rubens M. and Picheral, Marc}, + month = jan, + year = {2014}, + note = {Publisher: Taylor \& Francis +\_eprint: https://doi.org/10.1080/17451000.2013.810762}, + keywords = {expert performance, Human factors, mesozooplankton identification, root cause of variation}, + pages = {73--84}, +} + +@article{schroder_morphocluster_2020, + title = {{MorphoCluster}: {Efficient} {Annotation} of {Plankton} {Images} by {Clustering}}, + volume = {20}, + copyright = {http://creativecommons.org/licenses/by/3.0/}, + issn = {1424-8220}, + shorttitle = {{MorphoCluster}}, + url = {https://www.mdpi.com/1424-8220/20/11/3060}, + doi = {10.3390/s20113060}, + abstract = {In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue to increase in the coming years. Still, this data requires interpretation. MorphoCluster augments the human ability to discover patterns and perform object classification in large amounts of data by embedding unsupervised clustering in an interactive process. By aggregating similar images into clusters, our novel approach to image annotation increases consistency, multiplies the throughput of an annotator, and allows experts to adapt the granularity of their sorting scheme to the structure in the data. By sorting a set of 1.2 M objects into 280 data-driven classes in 71 h (16 k objects per hour), with 90\% of these classes having a precision of 0.889 or higher. This shows that MorphoCluster is at the same time fast, accurate, and consistent; provides a fine-grained and data-driven classification; and enables novelty detection.}, + language = {en}, + number = {11}, + urldate = {2023-10-26}, + journal = {Sensors}, + author = {Schröder, Simon-Martin and Kiko, Rainer and Koch, Reinhard}, + month = jan, + year = {2020}, + note = {Number: 11 +Publisher: Multidisciplinary Digital Publishing Institute}, + keywords = {deep learning, clustering, machine learning, marine image annotation, marine image recognition, plankton image classification}, + pages = {3060}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/ZHNATHDJ/Schröder et al. - 2020 - MorphoCluster Efficient Annotation of Plankton Im.pdf:application/pdf}, +} + +@article{henrichs_application_2021, + title = {Application of a convolutional neural network to improve automated early warning of harmful algal blooms}, + volume = {28}, + issn = {1614-7499}, + url = {https://doi.org/10.1007/s11356-021-12471-2}, + doi = {10.1007/s11356-021-12471-2}, + abstract = {Continuous monitoring and early warning together represent an important mitigation strategy for harmful algal blooms (HAB). The coast of Texas experiences periodic blooms of three HAB dinoflagellates: Karenia brevis, Dinophysis ovum, and Prorocentrum texanum. A plankton image data set acquired by an Imaging FlowCytobot over a decade of operation was used to train and evaluate two new automated image classifiers. A 112 class, random forest classifier (RF\_112) and a 112 class, convolutional neural network classifier (CNN\_112) were developed and compared with an existing, 54 class, random forest classifier (RF\_54) already in use as an early warning notification system. Both 112 class classifiers exhibited improved performance over the RF\_54 classifier when tested on three different HAB species with the CNN\_112 classifier producing fewer false positives and false negatives in most of the cases tested. For K. brevis and P. texanum, the current threshold of 2 cells.mL−1 was identified as the best threshold to minimize the number of false positives and false negatives. For D. ovum, a threshold of 1 cell.mL−1 was found to produce the best results with regard to the number of false positives/negatives. A lower threshold will result in earlier notification of an increase in cell concentration and will provide state health managers with increased lead time to prepare for an impending HAB.}, + language = {en}, + number = {22}, + urldate = {2023-10-26}, + journal = {Environmental Science and Pollution Research}, + author = {Henrichs, Darren W. and Anglès, Sílvia and Gaonkar, Chetan C. and Campbell, Lisa}, + month = jun, + year = {2021}, + keywords = {CNN, Dinophysis ovum, Gulf of Mexico, HAB, Imaging FlowCytobot, Karenia brevis, Prorocentrum texanum}, + pages = {28544--28555}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/HPYZHY65/Henrichs et al. - 2021 - Application of a convolutional neural network to i.pdf:application/pdf}, +} + +@inproceedings{vaswani_attention_2017, + title = {Attention is {All} you {Need}}, + volume = {30}, + url = {https://proceedings.neurips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html}, + abstract = {The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration. The best performing such models also connect the encoder and decoder through an attentionm echanisms. We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely.Experiments on two machine translation tasks show these models to be superiorin quality while being more parallelizable and requiring significantly less timeto train. Our single model with 165 million parameters, achieves 27.5 BLEU onEnglish-to-German translation, improving over the existing best ensemble result by over 1 BLEU. On English-to-French translation, we outperform the previoussingle state-of-the-art with model by 0.7 BLEU, achieving a BLEU score of 41.1.}, + urldate = {2023-10-28}, + booktitle = {Advances in {Neural} {Information} {Processing} {Systems}}, + publisher = {Curran Associates, Inc.}, + author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, Łukasz and Polosukhin, Illia}, + year = {2017}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/G3A56AFM/Vaswani et al. - 2017 - Attention is All you Need.pdf:application/pdf}, +} + +@article{isola_lecture_nodate-4, + title = {Lecture 15: {Deep} {Generative} {Models} {I}}, + language = {en}, + author = {Isola, Phillip}, + file = {15_gen_models_1.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/15_gen_models_1.pdf:application/pdf}, +} + +@article{isola_lecture_nodate-5, + title = {Lecture 16: {Deep} {Generative} {Models} {II}}, + language = {en}, + author = {Isola, Phillip}, + file = {16_gen_models_2.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/16_gen_models_2.pdf:application/pdf}, +} + +@article{isola_lecture_nodate-6, + title = {Lecture 17: {Deep} {Generative} {Models} {III}}, + language = {en}, + author = {Isola, Phillip}, + file = {17_gen_models_3.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/17_gen_models_3.pdf:application/pdf}, +} + +@misc{1, + title = {How {Neural} {Networks} {Extrapolate}: {From} {Feedforward} to {Graph} {Neural} {Networks}}, + shorttitle = {How {Neural} {Networks} {Extrapolate}}, + url = {http://arxiv.org/abs/2009.11848}, + doi = {10.48550/arXiv.2009.11848}, + abstract = {We study how neural networks trained by gradient descent extrapolate, i.e., what they learn outside the support of the training distribution. Previous works report mixed empirical results when extrapolating with neural networks: while feedforward neural networks, a.k.a. multilayer perceptrons (MLPs), do not extrapolate well in certain simple tasks, Graph Neural Networks (GNNs) -- structured networks with MLP modules -- have shown some success in more complex tasks. Working towards a theoretical explanation, we identify conditions under which MLPs and GNNs extrapolate well. First, we quantify the observation that ReLU MLPs quickly converge to linear functions along any direction from the origin, which implies that ReLU MLPs do not extrapolate most nonlinear functions. But, they can provably learn a linear target function when the training distribution is sufficiently "diverse". Second, in connection to analyzing the successes and limitations of GNNs, these results suggest a hypothesis for which we provide theoretical and empirical evidence: the success of GNNs in extrapolating algorithmic tasks to new data (e.g., larger graphs or edge weights) relies on encoding task-specific non-linearities in the architecture or features. Our theoretical analysis builds on a connection of over-parameterized networks to the neural tangent kernel. Empirically, our theory holds across different training settings.}, + urldate = {2023-11-12}, + publisher = {arXiv}, + author = {Xu, Keyulu and Zhang, Mozhi and Li, Jingling and Du, Simon S. and Kawarabayashi, Ken-ichi and Jegelka, Stefanie}, + month = mar, + year = {2021}, + note = {arXiv:2009.11848 [cs, stat]}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/MAMEYIFL/2009.html:text/html;Xu et al. - 2021 - How Neural Networks Extrapolate From Feedforward .pdf:/Users/birdy/Zotero/storage/XUU8Z7E2/Xu et al. - 2021 - How Neural Networks Extrapolate From Feedforward .pdf:application/pdf}, +} + +@misc{xiao_noise_2020, + title = {Noise or {Signal}: {The} {Role} of {Image} {Backgrounds} in {Object} {Recognition}}, + shorttitle = {Noise or {Signal}}, + url = {http://arxiv.org/abs/2006.09994}, + abstract = {We assess the tendency of state-of-the-art object recognition models to depend on signals from image backgrounds. We create a toolkit for disentangling foreground and background signal on ImageNet images, and find that (a) models can achieve non-trivial accuracy by relying on the background alone, (b) models often misclassify images even in the presence of correctly classified foregrounds--up to 87.5\% of the time with adversarially chosen backgrounds, and (c) more accurate models tend to depend on backgrounds less. Our analysis of backgrounds brings us closer to understanding which correlations machine learning models use, and how they determine models' out of distribution performance.}, + urldate = {2023-11-12}, + publisher = {arXiv}, + author = {Xiao, Kai and Engstrom, Logan and Ilyas, Andrew and Madry, Aleksander}, + month = jun, + year = {2020}, + note = {arXiv:2006.09994 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/W2NV28Q2/2006.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/MZIZ22HW/Xiao et al. - 2020 - Noise or Signal The Role of Image Backgrounds in .pdf:application/pdf}, +} + +@misc{yang_generalized_2022, + title = {Generalized {Out}-of-{Distribution} {Detection}: {A} {Survey}}, + shorttitle = {Generalized {Out}-of-{Distribution} {Detection}}, + url = {http://arxiv.org/abs/2110.11334}, + doi = {10.48550/arXiv.2110.11334}, + abstract = {Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of machine learning systems. For instance, in autonomous driving, we would like the driving system to issue an alert and hand over the control to humans when it detects unusual scenes or objects that it has never seen during training time and cannot make a safe decision. The term, OOD detection, first emerged in 2017 and since then has received increasing attention from the research community, leading to a plethora of methods developed, ranging from classification-based to density-based to distance-based ones. Meanwhile, several other problems, including anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD), are closely related to OOD detection in terms of motivation and methodology. Despite common goals, these topics develop in isolation, and their subtle differences in definition and problem setting often confuse readers and practitioners. In this survey, we first present a unified framework called generalized OOD detection, which encompasses the five aforementioned problems, i.e., AD, ND, OSR, OOD detection, and OD. Under our framework, these five problems can be seen as special cases or sub-tasks, and are easier to distinguish. We then review each of these five areas by summarizing their recent technical developments, with a special focus on OOD detection methodologies. We conclude this survey with open challenges and potential research directions.}, + urldate = {2023-11-12}, + publisher = {arXiv}, + author = {Yang, Jingkang and Zhou, Kaiyang and Li, Yixuan and Liu, Ziwei}, + month = aug, + year = {2022}, + note = {arXiv:2110.11334 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition}, + file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/MA6JB7TD/Yang et al. - 2022 - Generalized Out-of-Distribution Detection A Surve.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/BH3RP2BX/2110.html:text/html}, +} + +@article{cui_out--distribution_2022, + title = {Out-of-{Distribution} ({OOD}) {Detection} {Based} on {Deep} {Learning}: {A} {Review}}, + volume = {11}, + copyright = {http://creativecommons.org/licenses/by/3.0/}, + issn = {2079-9292}, + shorttitle = {Out-of-{Distribution} ({OOD}) {Detection} {Based} on {Deep} {Learning}}, + url = {https://www.mdpi.com/2079-9292/11/21/3500}, + doi = {10.3390/electronics11213500}, + abstract = {Out-of-Distribution (OOD) detection separates ID (In-Distribution) data and OOD data from input data through a model. This problem has attracted increasing attention in the area of machine learning. OOD detection has achieved good intrusion detection, fraud detection, system health monitoring, sensor network event detection, and ecosystem interference detection. The method based on deep learning is the most studied in OOD detection. In this paper , related basic information on OOD detection based on deep learning is described, and we categorize methods according to the training data. OOD detection is divided into supervised, semisupervised, and unsupervised. Where supervised data are used, the methods are categorized according to technical means: model-based, distance-based, and density-based. Each classification is introduced with background, examples, and applications. In addition, we present the latest applications of OOD detection based on deep learning and the problems and expectations in this field.}, + language = {en}, + number = {21}, + urldate = {2023-11-12}, + journal = {Electronics}, + author = {Cui, Peng and Wang, Jinjia}, + month = jan, + year = {2022}, + note = {Number: 21 +Publisher: Multidisciplinary Digital Publishing Institute}, + keywords = {deep learning, anomaly detection, neural networks, novelty detection, Out-of-Distribution detection, outlier detection}, + pages = {3500}, + file = {Cui and Wang - 2022 - Out-of-Distribution (OOD) Detection Based on Deep .pdf:/Users/birdy/Zotero/storage/MK3M7WBW/Cui and Wang - 2022 - Out-of-Distribution (OOD) Detection Based on Deep .pdf:application/pdf}, +} + +@misc{ghassemi_comprehensive_2022, + title = {A {Comprehensive} {Review} of {Trends}, {Applications} and {Challenges} {In} {Out}-of-{Distribution} {Detection}}, + url = {http://arxiv.org/abs/2209.12935}, + abstract = {With recent advancements in artificial intelligence, its applications can be seen in every aspect of humans' daily life. From voice assistants to mobile healthcare and autonomous driving, we rely on the performance of AI methods for many critical tasks; therefore, it is essential to assert the performance of models in proper means to prevent damage. One of the shortfalls of AI models in general, and deep machine learning in particular, is a drop in performance when faced with shifts in the distribution of data. Nonetheless, these shifts are always expected in real-world applications; thus, a field of study has emerged, focusing on detecting out-of-distribution data subsets and enabling a more comprehensive generalization. Furthermore, as many deep learning based models have achieved near-perfect results on benchmark datasets, the need to evaluate these models' reliability and trustworthiness for pushing towards real-world applications is felt more strongly than ever. This has given rise to a growing number of studies in the field of out-of-distribution detection and domain generalization, which begs the need for surveys that compare these studies from various perspectives and highlight their straightens and weaknesses. This paper presents a survey that, in addition to reviewing more than 70 papers in this field, presents challenges and directions for future works and offers a unifying look into various types of data shifts and solutions for better generalization.}, + urldate = {2023-11-12}, + publisher = {arXiv}, + author = {Ghassemi, Navid and Fazl-Ersi, Ehsan}, + month = sep, + year = {2022}, + note = {arXiv:2209.12935 [cs]}, + keywords = {Computer Science - Machine Learning}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/GJU4ZCYR/2209.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/TWZAVEUQ/Ghassemi and Fazl-Ersi - 2022 - A Comprehensive Review of Trends, Applications and.pdf:application/pdf}, +} + +@book{quinonero-candela_dataset_2009, + address = {Cambridge, Mass}, + series = {Neural information processing series}, + title = {Dataset shift in machine learning}, + isbn = {978-0-262-17005-5 978-0-262-54587-7}, + language = {en}, + publisher = {MIT Press}, + editor = {Quiñonero-Candela, Joaquin}, + year = {2009}, + note = {OCLC: ocn227205909}, + keywords = {Machine learning}, + file = {Quiñonero-Candela - 2009 - Dataset shift in machine learning.pdf:/Users/birdy/Zotero/storage/JJT973SR/Quiñonero-Candela - 2009 - Dataset shift in machine learning.pdf:application/pdf}, +} + +@article{may_will_1972, + title = {Will a {Large} {Complex} {System} be {Stable}?}, + volume = {238}, + copyright = {1972 Springer Nature Limited}, + issn = {1476-4687}, + url = {https://www.nature.com/articles/238413a0}, + doi = {10.1038/238413a0}, + abstract = {Gardner and Ashby1 have suggested that large complex systems which are assembled (connected) at random may be expected to be stable up to a certain critical level of connectance, and then, as this increases, to suddenly become unstable. Their conclusions were based on the trend of computer studies of systems with 4, 7 and 10 variables.}, + language = {en}, + number = {5364}, + urldate = {2023-11-13}, + journal = {Nature}, + author = {May, Robert M.}, + month = aug, + year = {1972}, + note = {Number: 5364 +Publisher: Nature Publishing Group}, + keywords = {Humanities and Social Sciences, multidisciplinary, Science}, + pages = {413--414}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/K3ZKQCSD/May - 1972 - Will a Large Complex System be Stable.pdf:application/pdf}, +} + +@article{armstrong_stable_1999, + title = {Stable model structures for representing biogeochemical diversity and size spectra in plankton communities}, + volume = {21}, + issn = {0142-7873}, + url = {https://doi.org/10.1093/plankt/21.3.445}, + doi = {10.1093/plankt/21.3.445}, + abstract = {There is a fundamental duality in the way we view planktonic communities. On one hand, we see these communities as composed of a diverse mixture of taxonomically and biogeochemically distinct species; pelagic ecosystem models capable of predicting the ocean's role in the global carbon cycle will need to resolve these multiple plankton taxa. However, we have also come to appreciate that superimposed on this diversity are regular patterns of size structure, where plots of abundance within size classes typically show a power-law dependence on size. A truly complete model description of planktonic community structure must reflect both aspects of this duality. Here I examine two classes of ecosystem models that are capable of representing this duality. The first structure is based on multiple (\<it\>n\</it\>) couplets of phytoplankton +(\<it\>P\</it\>) and zooplankton (\<it\>Z\</it\>) [an '\<it\>n\</it\>(\<it\>PZ\</it\>)' model]. Within certain regions of parameter space, this model structure can produce anomalous oscillatory behaviors, the mathematical origin of which is explored in detail. I then examine an alternative food web structure in which total +herbivore abundance is represented by a single state variable,\<it\>G\</it\>, and where grazing pressure is distributed among +taxa in proportion to their abundances. This '(\<it\>nP)G\</it\>' +system is stabilized by the redistribution of grazing intensity among taxa +in response to changes in their densities. This 'distributed grazing' model +also naturally produces size spectra of plankton abundance; this last +observation argues that the model with distributed grazing can help in +uncovering and representing the mechanistic basis for the genesis and +maintenance of planktonic size spectra of phytoplankton and bacteria.}, + number = {3}, + urldate = {2023-11-13}, + journal = {Journal of Plankton Research}, + author = {Armstrong, Robert A.}, + month = mar, + year = {1999}, + pages = {445--464}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/LP2EYBJD/1999 - Stable model structures for representing biogeoche.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/MN45F6HS/1395820.html:text/html}, +} + +@article{ashkezari_simons_2021, + title = {Simons {Collaborative} {Marine} {Atlas} {Project} ({Simons} {CMAP}): {An} open-source portal to share, visualize, and analyze ocean data}, + volume = {19}, + copyright = {© 2021 The Authors. Limnology and Oceanography: Methods published by Wiley Periodicals LLC on behalf of Association for the Sciences of Limnology and Oceanography.}, + issn = {1541-5856}, + shorttitle = {Simons {Collaborative} {Marine} {Atlas} {Project} ({Simons} {CMAP})}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lom3.10439}, + doi = {10.1002/lom3.10439}, + abstract = {Simons Collaborative Marine Atlas Project (Simons CMAP) is an open-source data portal that interconnects large, complex, and diverse public data sets currently dispersed in different formats across different Oceanography discipline-specific databases. Simons CMAP is designed to streamline the retrieval of custom subsets of data, the generation of data visualizations, and the analyses of diverse data, thus expanding the power of these potentially underutilized data sets for cross-disciplinary studies of ocean processes. We describe a unified architecture that allows numerical model outputs, satellite products, and field observations to be readily shared, mined, and integrated regardless of data set size or resolution. A current focus of Simons CMAP is integration of physical, chemical, and biological data sets essential for characterizing the biogeography of key marine microbes across ocean basins and seasonal cycles. Using a practical example, we demonstrate how our unifying data harmonization plans significantly simplifies and allows for systematic data integration across all Simons CMAP data sets.}, + language = {en}, + number = {7}, + urldate = {2023-11-13}, + journal = {Limnology and Oceanography: Methods}, + author = {Ashkezari, Mohammad D. and Hagen, Norland R. and Denholtz, Michael and Neang, Andrew and Burns, Tansy C. and Morales, Rhonda L. and Lee, Charlotte P. and Hill, Christopher N. and Armbrust, E. Virginia}, + year = {2021}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10439}, + pages = {488--496}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/DUNQVLE5/Ashkezari et al. - 2021 - Simons Collaborative Marine Atlas Project (Simons .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/CPQTRWWV/lom3.html:text/html}, +} + +@article{aumont_pisces-v2_2015, + title = {{PISCES}-v2: an ocean biogeochemical model for carbon and ecosystem studies}, + volume = {8}, + url = {https://gmd.copernicus.org/articles/8/2465/2015/}, + doi = {10.5194/gmd-8-2465-2015}, + number = {8}, + journal = {Geoscientific Model Development}, + author = {Aumont, O. and Ethé, C. and Tagliabue, A. and Bopp, L. and Gehlen, M.}, + year = {2015}, + pages = {2465--2513}, +} + +@article{bardon_testing_2021-1, + title = {Testing the {Skill} of a {Species} {Distribution} {Model} {Using} a 21st {Century} {Virtual} {Ecosystem}}, + volume = {48}, + copyright = {© 2021. The Authors.}, + issn = {1944-8007}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2021GL093455}, + doi = {10.1029/2021GL093455}, + abstract = {Plankton communities play an important role in marine food webs, in biogeochemical cycling, and in Earth's climate; yet observations are sparse, and predictions of how they might respond to climate change vary. Correlative species distribution models (SDM's) have been applied to predicting biogeography based on relationships to observed environmental variables. To investigate sources of uncertainty, we use a correlative SDM to predict the plankton biogeography of a 21st century marine ecosystem model (Darwin). Darwin output is sampled to mimic historical ocean observations, and the SDM is trained using generalized additive models. We find that predictive skill varies across test cases, and between functional groups, with errors that are more attributable to spatiotemporal sampling bias than sample size. End-of-century predictions are poor, limited by changes in target-predictor relationships over time. Our findings illustrate the fundamental challenges faced by empirical models in using limited observational data to predict complex, dynamic systems.}, + language = {en}, + number = {22}, + urldate = {2023-11-13}, + journal = {Geophysical Research Letters}, + author = {Bardon, L. R. and Ward, B. A. and Dutkiewicz, S. and Cael, B. B.}, + year = {2021}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021GL093455}, + keywords = {climate change, complex systems, numerical models, oceanography, plankton ecology, statistical learning}, + pages = {e2021GL093455}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/CD5IBV65/Bardon et al. - 2021 - Testing the Skill of a Species Distribution Model .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/DK86CFJT/2021GL093455.html:text/html}, +} + +@article{bian_understanding_2023-1, + title = {Understanding opposing predictions of {Prochlorococcus} in a changing climate}, + volume = {14}, + copyright = {2023 The Author(s)}, + issn = {2041-1723}, + url = {https://www.nature.com/articles/s41467-023-36928-9}, + doi = {10.1038/s41467-023-36928-9}, + abstract = {Statistically derived species distribution models (SDMs) are increasingly used to predict ecological changes on a warming planet. For Prochlorococcus, the most abundant phytoplankton, an established statistical prediction conflicts with dynamical models as they predict large, opposite, changes in abundance. We probe the SDM at various spatial-temporal scales, showing that light and temperature fail to explain both temporal fluctuations and sharp spatial transitions. Strong correlations between changes in temperature and population emerge only at very large spatial scales, as transects pass through transitions between regions of high and low abundance. Furthermore, a two-state model based on a temperature threshold matches the original SDM in the surface ocean. We conclude that the original SDM has little power to predict changes when Prochlorococcus is already abundant, which resolves the conflict with dynamical models. Our conclusion suggests that SDMs should prove efficacy across multiple spatial-temporal scales before being trusted in a changing ocean.}, + language = {en}, + number = {1}, + urldate = {2023-11-13}, + journal = {Nature Communications}, + author = {Bian, Vincent and Cai, Merrick and Follett, Christopher L.}, + month = mar, + year = {2023}, + note = {Number: 1 +Publisher: Nature Publishing Group}, + keywords = {Biogeography, Projection and prediction}, + pages = {1445}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/257L3PIB/Bian et al. - 2023 - Understanding opposing predictions of Prochlorococ.pdf:application/pdf}, +} + +@misc{winkens_contrastive_2020, + title = {Contrastive {Training} for {Improved} {Out}-of-{Distribution} {Detection}}, + url = {http://arxiv.org/abs/2007.05566}, + abstract = {Reliable detection of out-of-distribution (OOD) inputs is increasingly understood to be a precondition for deployment of machine learning systems. This paper proposes and investigates the use of contrastive training to boost OOD detection performance. Unlike leading methods for OOD detection, our approach does not require access to examples labeled explicitly as OOD, which can be difficult to collect in practice. We show in extensive experiments that contrastive training significantly helps OOD detection performance on a number of common benchmarks. By introducing and employing the Confusion Log Probability (CLP) score, which quantifies the difficulty of the OOD detection task by capturing the similarity of inlier and outlier datasets, we show that our method especially improves performance in the `near OOD' classes -- a particularly challenging setting for previous methods.}, + urldate = {2023-11-13}, + publisher = {arXiv}, + author = {Winkens, Jim and Bunel, Rudy and Roy, Abhijit Guha and Stanforth, Robert and Natarajan, Vivek and Ledsam, Joseph R. and MacWilliams, Patricia and Kohli, Pushmeet and Karthikesalingam, Alan and Kohl, Simon and Cemgil, Taylan and Eslami, S. M. Ali and Ronneberger, Olaf}, + month = jul, + year = {2020}, + note = {arXiv:2007.05566 [cs, stat]}, + keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/ZW3DG7B6/2007.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/5M6W3QNV/Winkens et al. - 2020 - Contrastive Training for Improved Out-of-Distribut.pdf:application/pdf}, +} + +@inproceedings{zhou_contrastive_2021, + title = {Contrastive {Out}-of-{Distribution} {Detection} for {Pretrained} {Transformers}}, + url = {http://arxiv.org/abs/2104.08812}, + doi = {10.18653/v1/2021.emnlp-main.84}, + abstract = {Pretrained Transformers achieve remarkable performance when training and test data are from the same distribution. However, in real-world scenarios, the model often faces out-of-distribution (OOD) instances that can cause severe semantic shift problems at inference time. Therefore, in practice, a reliable model should identify such instances, and then either reject them during inference or pass them over to models that handle another distribution. In this paper, we develop an unsupervised OOD detection method, in which only the in-distribution (ID) data are used in training. We propose to fine-tune the Transformers with a contrastive loss, which improves the compactness of representations, such that OOD instances can be better differentiated from ID ones. These OOD instances can then be accurately detected using the Mahalanobis distance in the model's penultimate layer. We experiment with comprehensive settings and achieve near-perfect OOD detection performance, outperforming baselines drastically. We further investigate the rationales behind the improvement, finding that more compact representations through margin-based contrastive learning bring the improvement. We release our code to the community for future research.}, + urldate = {2023-11-13}, + booktitle = {Proceedings of the 2021 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}}, + author = {Zhou, Wenxuan and Liu, Fangyu and Chen, Muhao}, + year = {2021}, + note = {arXiv:2104.08812 [cs]}, + keywords = {Computer Science - Computation and Language}, + pages = {1100--1111}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/IB8D8VD8/2104.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/XFIETIDR/Zhou et al. - 2021 - Contrastive Out-of-Distribution Detection for Pret.pdf:application/pdf}, +} + +@article{beery_lecture_nodate-2, + title = {Lecture 20: {Transfer} {Learning} {II}}, + language = {en}, + author = {Beery, Sara}, + file = {20_transfer_learning_2_SB.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/20_transfer_learning_2_SB.pdf:application/pdf}, +} + +@misc{kingma_auto-encoding_2022, + title = {Auto-{Encoding} {Variational} {Bayes}}, + url = {http://arxiv.org/abs/1312.6114}, + abstract = {How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions are two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.}, + urldate = {2023-11-27}, + publisher = {arXiv}, + author = {Kingma, Diederik P. and Welling, Max}, + month = dec, + year = {2022}, + note = {arXiv:1312.6114 [cs, stat]}, + keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/H7BQXHMR/1312.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/QR4A6NNA/Kingma and Welling - 2022 - Auto-Encoding Variational Bayes.pdf:application/pdf}, +} + +@article{sonnewald_elucidating_2020, + title = {Elucidating ecological complexity: {Unsupervised} learning determines global marine eco-provinces}, + volume = {6}, + shorttitle = {Elucidating ecological complexity}, + url = {https://www.science.org/doi/full/10.1126/sciadv.aay4740}, + doi = {10.1126/sciadv.aay4740}, + abstract = {An unsupervised learning method is presented for determining global marine ecological provinces (eco-provinces) from plankton community structure and nutrient flux data. The systematic aggregated eco-province (SAGE) method identifies eco-provinces within a highly nonlinear ecosystem model. To accommodate the non-Gaussian covariance of the data, SAGE uses t-stochastic neighbor embedding (t-SNE) to reduce dimensionality. Over a hundred eco-provinces are identified with the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Using a connectivity graph with ecological dissimilarity as the distance metric, robust aggregated eco-provinces (AEPs) are objectively defined by nesting the eco-provinces. Using the AEPs, the control of nutrient supply rates on community structure is explored. Eco-provinces and AEPs are unique and aid model interpretation. They could facilitate model intercomparison and potentially improve understanding and monitoring of marine ecosystems.}, + number = {22}, + urldate = {2023-11-28}, + journal = {Science Advances}, + author = {Sonnewald, Maike and Dutkiewicz, Stephanie and Hill, Christopher and Forget, Gael}, + month = may, + year = {2020}, + note = {Publisher: American Association for the Advancement of Science}, + pages = {eaay4740}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/FSUVU9LS/Sonnewald et al. - 2020 - Elucidating ecological complexity Unsupervised le.pdf:application/pdf}, +} + +@misc{ho_denoising_2020, + title = {Denoising {Diffusion} {Probabilistic} {Models}}, + url = {http://arxiv.org/abs/2006.11239}, + doi = {10.48550/arXiv.2006.11239}, + abstract = {We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at https://github.com/hojonathanho/diffusion}, + urldate = {2023-11-30}, + publisher = {arXiv}, + author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter}, + month = dec, + year = {2020}, + note = {arXiv:2006.11239 [cs, stat]}, + keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, + file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/A854EPRR/Ho et al. - 2020 - Denoising Diffusion Probabilistic Models.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/R4HY8MGJ/2006.html:text/html}, +} + +@inproceedings{wang_partial_2022, + title = {Partial and {Asymmetric} {Contrastive} {Learning} for {Out}-of-{Distribution} {Detection} in {Long}-{Tailed} {Recognition}}, + url = {https://proceedings.mlr.press/v162/wang22aq.html}, + abstract = {Existing out-of-distribution (OOD) detection methods are typically benchmarked on training sets with balanced class distributions. However, in real-world applications, it is common for the training sets to have long-tailed distributions. In this work, we first demonstrate that existing OOD detection methods commonly suffer from significant performance degradation when the training set is long-tail distributed. Through analysis, we posit that this is because the models struggle to distinguish the minority tail-class in-distribution samples, from the true OOD samples, making the tail classes more prone to be falsely detected as OOD. To solve this problem, we propose Partial and Asymmetric Supervised Contrastive Learning (PASCL), which explicitly encourages the model to distinguish between tail-class in-distribution samples and OOD samples. To further boost in-distribution classification accuracy, we propose Auxiliary Branch Finetuning, which uses two separate branches of BN and classification layers for anomaly detection and in-distribution classification, respectively. The intuition is that in-distribution and OOD anomaly data have different underlying distributions. Our method outperforms previous state-of-the-art method by \$1.29\%\$, \$1.45\%\$, \$0.69\%\$ anomaly detection false positive rate (FPR) and \$3.24\%\$, \$4.06\%\$, \$7.89\%\$ in-distribution classification accuracy on CIFAR10-LT, CIFAR100-LT, and ImageNet-LT, respectively. Code and pre-trained models are available at https://github.com/amazon-research/long-tailed-ood-detection.}, + language = {en}, + urldate = {2023-12-04}, + booktitle = {Proceedings of the 39th {International} {Conference} on {Machine} {Learning}}, + publisher = {PMLR}, + author = {Wang, Haotao and Zhang, Aston and Zhu, Yi and Zheng, Shuai and Li, Mu and Smola, Alex J. and Wang, Zhangyang}, + month = jun, + year = {2022}, + note = {ISSN: 2640-3498}, + pages = {23446--23458}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/Y2THTBUA/Wang et al. - 2022 - Partial and Asymmetric Contrastive Learning for Ou.pdf:application/pdf}, +} + +@article{higgins_-vae_2017, + title = {β-{VAE}: {LEARNING} {BASIC} {VISUAL} {CONCEPTS} {WITH} {A} {CONSTRAINED} {VARIATIONAL} {FRAMEWORK}}, + abstract = {Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to learn and reason in the same way that humans do. We introduce β-VAE, a new state-of-the-art framework for automated discovery of interpretable factorised latent representations from raw image data in a completely unsupervised manner. Our approach is a modification of the variational autoencoder (VAE) framework. We introduce an adjustable hyperparameter β that balances latent channel capacity and independence constraints with reconstruction accuracy. We demonstrate that β-VAE with appropriately tuned β {\textgreater} 1 qualitatively outperforms VAE (β = 1), as well as state of the art unsupervised (InfoGAN) and semi-supervised (DC-IGN) approaches to disentangled factor learning on a variety of datasets (celebA, faces and chairs). Furthermore, we devise a protocol to quantitatively compare the degree of disentanglement learnt by different models, and show that our approach also significantly outperforms all baselines quantitatively. Unlike InfoGAN, β-VAE is stable to train, makes few assumptions about the data and relies on tuning a single hyperparameter β, which can be directly optimised through a hyperparameter search using weakly labelled data or through heuristic visual inspection for purely unsupervised data.}, + language = {en}, + author = {Higgins, Irina and Matthey, Loic and Pal, Arka and Burgess, Christopher and Glorot, Xavier and Botvinick, Matthew and Mohamed, Shakir and Lerchner, Alexander}, + year = {2017}, + file = {Higgins et al. - 2017 - β-VAE LEARNING BASIC VISUAL CONCEPTS WITH A CONST.pdf:/Users/birdy/Zotero/storage/9VGRX2XM/Higgins et al. - 2017 - β-VAE LEARNING BASIC VISUAL CONCEPTS WITH A CONST.pdf:application/pdf}, +} + +@article{schmarje_fuzzy_2021, + title = {Fuzzy {Overclustering}: {Semi}-{Supervised} {Classification} of {Fuzzy} {Labels} with {Overclustering} and {Inverse} {Cross}-{Entropy}}, + volume = {21}, + copyright = {http://creativecommons.org/licenses/by/3.0/}, + issn = {1424-8220}, + shorttitle = {Fuzzy {Overclustering}}, + url = {https://www.mdpi.com/1424-8220/21/19/6661}, + doi = {10.3390/s21196661}, + abstract = {Deep learning has been successfully applied to many classification problems including underwater challenges. However, a long-standing issue with deep learning is the need for large and consistently labeled datasets. Although current approaches in semi-supervised learning can decrease the required amount of annotated data by a factor of 10 or even more, this line of research still uses distinct classes. For underwater classification, and uncurated real-world datasets in general, clean class boundaries can often not be given due to a limited information content in the images and transitional stages of the depicted objects. This leads to different experts having different opinions and thus producing fuzzy labels which could also be considered ambiguous or divergent. We propose a novel framework for handling semi-supervised classifications of such fuzzy labels. It is based on the idea of overclustering to detect substructures in these fuzzy labels. We propose a novel loss to improve the overclustering capability of our framework and show the benefit of overclustering for fuzzy labels. We show that our framework is superior to previous state-of-the-art semi-supervised methods when applied to real-world plankton data with fuzzy labels. Moreover, we acquire 5 to 10\% more consistent predictions of substructures.}, + language = {en}, + number = {19}, + urldate = {2023-12-04}, + journal = {Sensors}, + author = {Schmarje, Lars and Brünger, Johannes and Santarossa, Monty and Schröder, Simon-Martin and Kiko, Rainer and Koch, Reinhard}, + month = jan, + year = {2021}, + note = {Number: 19 +Publisher: Multidisciplinary Digital Publishing Institute}, + keywords = {deep learning, fuzzy, marine, noisy, plankton, real-world, semi-supervised}, + pages = {6661}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/WILN3PSB/Schmarje et al. - 2021 - Fuzzy Overclustering Semi-Supervised Classificati.pdf:application/pdf}, +} + +@inproceedings{liu_good-d_2023, + address = {New York, NY, USA}, + series = {{WSDM} '23}, + title = {{GOOD}-{D}: {On} {Unsupervised} {Graph} {Out}-{Of}-{Distribution} {Detection}}, + isbn = {978-1-4503-9407-9}, + shorttitle = {{GOOD}-{D}}, + url = {https://dl.acm.org/doi/10.1145/3539597.3570446}, + doi = {10.1145/3539597.3570446}, + abstract = {Most existing deep learning models are trained based on the closed-world assumption, where the test data is assumed to be drawn i.i.d. from the same distribution as the training data, known as in-distribution (ID). However, when models are deployed in an open-world scenario, test samples can be out-of-distribution (OOD) and therefore should be handled with caution. To detect such OOD samples drawn from unknown distribution, OOD detection has received increasing attention lately. However, current endeavors mostly focus on grid-structured data and its application for graph-structured data remains under-explored. Considering the fact that data labeling on graphs is commonly time-expensive and labor-intensive, in this work we study the problem of unsupervised graph OOD detection, aiming at detecting OOD graphs solely based on unlabeled ID data. To achieve this goal, we develop a new graph contrastive learning framework GOOD-D for detecting OOD graphs without using any ground-truth labels. By performing hierarchical contrastive learning on the augmented graphs generated by our perturbation-free graph data augmentation method, GOOD-D is able to capture the latent ID patterns and accurately detect OOD graphs based on the semantic inconsistency in different granularities (i.e., node-level, graph-level, and group-level). As a pioneering work in unsupervised graph-level OOD detection, we build a comprehensive benchmark to compare our proposed approach with different state-of-the-art methods. The experiment results demonstrate the superiority of our approach over different methods on various datasets.}, + urldate = {2023-12-04}, + booktitle = {Proceedings of the {Sixteenth} {ACM} {International} {Conference} on {Web} {Search} and {Data} {Mining}}, + publisher = {Association for Computing Machinery}, + author = {Liu, Yixin and Ding, Kaize and Liu, Huan and Pan, Shirui}, + month = feb, + year = {2023}, + keywords = {contrastive learning, graph neural networks, out-of-distribution detection}, + pages = {339--347}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/I8T5QYSC/Liu et al. - 2023 - GOOD-D On Unsupervised Graph Out-Of-Distribution .pdf:application/pdf}, +} + +@misc{wang_understanding_2022, + title = {Understanding {Contrastive} {Representation} {Learning} through {Alignment} and {Uniformity} on the {Hypersphere}}, + url = {http://arxiv.org/abs/2005.10242}, + abstract = {Contrastive representation learning has been outstandingly successful in practice. In this work, we identify two key properties related to the contrastive loss: (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere. We prove that, asymptotically, the contrastive loss optimizes these properties, and analyze their positive effects on downstream tasks. Empirically, we introduce an optimizable metric to quantify each property. Extensive experiments on standard vision and language datasets confirm the strong agreement between both metrics and downstream task performance. Remarkably, directly optimizing for these two metrics leads to representations with comparable or better performance at downstream tasks than contrastive learning. Project Page: https://tongzhouwang.info/hypersphere Code: https://github.com/SsnL/align\_uniform , https://github.com/SsnL/moco\_align\_uniform}, + urldate = {2023-12-04}, + publisher = {arXiv}, + author = {Wang, Tongzhou and Isola, Phillip}, + month = aug, + year = {2022}, + note = {arXiv:2005.10242 [cs, stat]}, + keywords = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/WFW3P94A/2005.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/KJZGVSLC/Wang and Isola - 2022 - Understanding Contrastive Representation Learning .pdf:application/pdf}, +} + +@inproceedings{chen_atom_2021, + address = {Cham}, + series = {Lecture {Notes} in {Computer} {Science}}, + title = {{ATOM}: {Robustifying} {Out}-of-{Distribution} {Detection} {Using} {Outlier} {Mining}}, + isbn = {978-3-030-86523-8}, + shorttitle = {{ATOM}}, + doi = {10.1007/978-3-030-86523-8_26}, + abstract = {Detecting out-of-distribution (OOD) inputs is critical for safely deploying deep learning models in an open-world setting. However, existing OOD detection solutions can be brittle in the open world, facing various types of adversarial OOD inputs. While methods leveraging auxiliary OOD data have emerged, our analysis on illuminative examples reveals a key insight that the majority of auxiliary OOD examples may not meaningfully improve or even hurt the decision boundary of the OOD detector, which is also observed in empirical results on real data. In this paper, we provide a theoretically motivated method, Adversarial Training with informative Outlier Mining (ATOM), which improves the robustness of OOD detection. We show that, by mining informative auxiliary OOD data, one can significantly improve OOD detection performance, and somewhat surprisingly, generalize to unseen adversarial attacks. ATOM achieves state-of-the-art performance under a broad family of classic and adversarial OOD evaluation tasks. For example, on the CIFAR-10 in-distribution dataset, ATOM reduces the FPR (at TPR 95\%) by up to 57.99\% under adversarial OOD inputs, surpassing the previous best baseline by a large margin.}, + language = {en}, + booktitle = {Machine {Learning} and {Knowledge} {Discovery} in {Databases}. {Research} {Track}}, + publisher = {Springer International Publishing}, + author = {Chen, Jiefeng and Li, Yixuan and Wu, Xi and Liang, Yingyu and Jha, Somesh}, + editor = {Oliver, Nuria and Pérez-Cruz, Fernando and Kramer, Stefan and Read, Jesse and Lozano, Jose A.}, + year = {2021}, + keywords = {Out-of-distribution detection, Outlier mining, Robustness}, + pages = {430--445}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/5PNM3HAW/Chen et al. - 2021 - ATOM Robustifying Out-of-Distribution Detection U.pdf:application/pdf}, +} + +@misc{chen_simple_2020, + title = {A {Simple} {Framework} for {Contrastive} {Learning} of {Visual} {Representations}}, + url = {http://arxiv.org/abs/2002.05709}, + abstract = {This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5\% top-1 accuracy, which is a 7\% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1\% of the labels, we achieve 85.8\% top-5 accuracy, outperforming AlexNet with 100X fewer labels.}, + urldate = {2024-01-04}, + publisher = {arXiv}, + author = {Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey}, + month = jun, + year = {2020}, + note = {arXiv:2002.05709 [cs, stat]}, + keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/QSFZN5RI/2002.html:text/html;Chen et al. - 2020 - A Simple Framework for Contrastive Learning of Vis.pdf:/Users/birdy/Zotero/storage/UA8HJXR5/Chen et al. - 2020 - A Simple Framework for Contrastive Learning of Vis.pdf:application/pdf}, +} + +@misc{pang_rethinking_2020, + title = {Rethinking {Softmax} {Cross}-{Entropy} {Loss} for {Adversarial} {Robustness}}, + url = {http://arxiv.org/abs/1905.10626}, + abstract = {Previous work shows that adversarially robust generalization requires larger sample complexity, and the same dataset, e.g., CIFAR-10, which enables good standard accuracy may not suffice to train robust models. Since collecting new training data could be costly, we focus on better utilizing the given data by inducing the regions with high sample density in the feature space, which could lead to locally sufficient samples for robust learning. We first formally show that the softmax cross-entropy (SCE) loss and its variants convey inappropriate supervisory signals, which encourage the learned feature points to spread over the space sparsely in training. This inspires us to propose the Max-Mahalanobis center (MMC) loss to explicitly induce dense feature regions in order to benefit robustness. Namely, the MMC loss encourages the model to concentrate on learning ordered and compact representations, which gather around the preset optimal centers for different classes. We empirically demonstrate that applying the MMC loss can significantly improve robustness even under strong adaptive attacks, while keeping state-of-the-art accuracy on clean inputs with little extra computation compared to the SCE loss.}, + urldate = {2024-01-06}, + publisher = {arXiv}, + author = {Pang, Tianyu and Xu, Kun and Dong, Yinpeng and Du, Chao and Chen, Ning and Zhu, Jun}, + month = feb, + year = {2020}, + note = {arXiv:1905.10626 [cs, stat]}, + keywords = {Computer Science - Cryptography and Security, Computer Science - Machine Learning, Statistics - Machine Learning}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/ZQWMNTQ4/1905.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/L6RK8EVE/Pang et al. - 2020 - Rethinking Softmax Cross-Entropy Loss for Adversar.pdf:application/pdf}, +} + +@inproceedings{zhai_s4l_2019, + address = {Seoul, Korea (South)}, + title = {{S4L}: {Self}-{Supervised} {Semi}-{Supervised} {Learning}}, + isbn = {978-1-72814-803-8}, + shorttitle = {{S4L}}, + url = {https://ieeexplore.ieee.org/document/9010283/}, + doi = {10.1109/ICCV.2019.00156}, + abstract = {This work tackles the problem of semi-supervised learning of image classifiers. Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual representation learning. Unifying these two approaches, we propose the framework of self-supervised semi-supervised learning (S4L) and use it to derive two novel semi-supervised image classification methods. We demonstrate the effectiveness of these methods in comparison to both carefully tuned baselines, and existing semi-supervised learning methods. We then show that S4L and existing semi-supervised methods can be jointly trained, yielding a new state-of-the-art result on semi-supervised ILSVRC-2012 with 10\% of labels.}, + language = {en}, + urldate = {2024-01-06}, + booktitle = {2019 {IEEE}/{CVF} {International} {Conference} on {Computer} {Vision} ({ICCV})}, + publisher = {IEEE}, + author = {Zhai, Xiaohua and Oliver, Avital and Kolesnikov, Alexander and Beyer, Lucas}, + month = oct, + year = {2019}, + pages = {1476--1485}, + file = {Zhai et al. - 2019 - S4L Self-Supervised Semi-Supervised Learning.pdf:/Users/birdy/Zotero/storage/8NNHKTJM/Zhai et al. - 2019 - S4L Self-Supervised Semi-Supervised Learning.pdf:application/pdf}, +} + +@misc{duckworth_dar1_2023, + title = {{DAR1} - {An} {Easy} to {Use}, {Single}-{Box}, {Configuration} of a {Global} {Scale} {Biogeochemical} {Ecosystem} {Model}}, + copyright = {© 2023, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/}, + url = {https://www.biorxiv.org/content/10.1101/2023.11.23.568480v1}, + doi = {10.1101/2023.11.23.568480}, + abstract = {Global scale biogeochemical ocean models are key to understanding the mechanisms which drive elemental cycles and their links to marine ecosystems. However, these models are rarely straightforward to use and their complexity can make them difficult to interpret. Here, we aim to address this problem by developing DAR1 – a single box configuration of the MIT Darwin model, which is itself a global scale biogeochemical ecosystem model designed for understanding the emergent patterns of global plankton distributions. Our configuration is packaged inside Docker and implemented in the Julia programming language. This helps make it both computationally efficient and straightforward to implement on any local machine, independent of hardware and operating system. It allows for the replication of field and laboratory experiments in a model ocean and the rapid comparison of global runs with local predictions. We provide three case studies: a framework for nutrient amendment bottle experiments; a test of steady state assumptions on plankton community structure at the global scale; and an exploration of the niche space of nitrogen-fixing plankton. Our method has the potential to facilitate broader direct testing of global model assumptions in the context of small-scale measurements. DAR1 will also allow observational and laboratory oceanographers to directly test their results against model predictions in a straightforward, easy to use, manner.}, + language = {en}, + urldate = {2024-01-06}, + publisher = {bioRxiv}, + author = {Duckworth, Barbara and Dutkiewicz, Stephanie and Jahn, Oliver and Hill, Christopher N. and Follett, Christopher L.}, + month = nov, + year = {2023}, + note = {Pages: 2023.11.23.568480 +Section: New Results}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/3376RTZG/Duckworth et al. - 2023 - DAR1 - An Easy to Use, Single-Box, Configuration o.pdf:application/pdf}, +} + +@article{falkowski_role_1994, + title = {The role of phytoplankton photosynthesis in global biogeochemical cycles}, + volume = {39}, + issn = {1573-5079}, + url = {https://doi.org/10.1007/BF00014586}, + doi = {10.1007/BF00014586}, + abstract = {Phytoplankton biomass in the world's oceans amounts to only ∽1–2\% of the total global plant carbon, yet these organisms fix between 30 and 50 billion metric tons of carbon annually, which is about 40\% of the total. On geological time scales there is profound evidence of the importance of phytoplankton photosynthesis in biogeochemical cycles. It is generally assumed that present phytoplankton productivity is in a quasi steady-state (on the time scale of decades). However, in a global context, the stability of oceanic photosynthetic processes is dependent on the physical circulation of the upper ocean and is therefore strongly influenced by the atmosphere. The net flux of atmospheric radiation is critical to determining the depth of the upper mixed layer and the vertical fluxes of nutrients. These latter two parameters are keys to determining the intensity, and spatial and temporal distributions of phytoplankton blooms. Atmospheric radiation budgets are not in steady-state. Driven largely by anthropogenic activities in the 20th century, increased levels of IR- absorbing gases such as CO2, CH4 and CFC's and NOx will potentially increase atmospheric temperatures on a global scale. The atmospheric radiation budget can affect phytoplankton photosynthesis directly and indirectly. Increased temperature differences between the continents and oceans have been implicated in higher wind stresses at the ocean margins. Increased wind speeds can lead to higher nutrient fluxes. Throughout most of the central oceans, nitrate concentrations are sub-micromolar and there is strong evidence that the quantum efficiency of Photosystem II is impaired by nutrient stress. Higher nutrient fluxes would lead to both an increase in phytoplankton biomass and higher biomass-specific rates of carbon fixation. However, in the center of the ocean gyres, increased radiative heating could reduce the vertical flux of nutrients to the euphotic zone, and hence lead to a reduction in phytoplankton carbon fixation. Increased desertification in terrestrial ecosystems can lead to increased aeolean loadings of essential micronutrients, such as iron. An increased flux of aeolean micronutrients could fertilize nutrient-replete areas of the open ocean with limiting trace elements, thereby stimulating photosynthetic rates. The factors which limit phytoplankton biomass and photosynthesis are discussed and examined with regard to potential changes in the Earth climate system which can lead the oceans away from steady-state. While it is difficult to confidently deduce changes in either phytoplankton biomass or photosynthetic rates on decadal time scales, time-series analysis of ocean transparency data suggest long-term trends have occurred in the North Pacific Ocean in the 20th century. However, calculations of net carbon uptake by the oceans resulting from phytoplankton photosynthesis suggest that without a supply of nutrients external to the ocean, carbon fixation in the open ocean is not presently a significant sink for excess atmospheric CO2.}, + language = {en}, + number = {3}, + urldate = {2024-01-06}, + journal = {Photosynthesis Research}, + author = {Falkowski, Paul G.}, + month = mar, + year = {1994}, + keywords = {biogeochemical cycles, oceans, photoacclimation, Photosystem II nutrient limitation, phytoplankton quantum efficiency of photosynthesis}, + pages = {235--258}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/6ECKTMBR/Falkowski - 1994 - The role of phytoplankton photosynthesis in global.pdf:application/pdf}, +} + +@article{treguer_influence_2018, + title = {Influence of diatom diversity on the ocean biological carbon pump}, + volume = {11}, + copyright = {2017 Springer Nature Limited}, + issn = {1752-0908}, + url = {https://www.nature.com/articles/s41561-017-0028-x}, + doi = {10.1038/s41561-017-0028-x}, + abstract = {Diatoms sustain the marine food web and contribute to the export of carbon from the surface ocean to depth. They account for about 40\% of marine primary productivity and particulate carbon exported to depth as part of the biological pump. Diatoms have long been known to be abundant in turbulent, nutrient-rich waters, but observations and simulations indicate that they are dominant also in meso- and submesoscale structures such as fronts and filaments, and in the deep chlorophyll maximum. Diatoms vary widely in size, morphology and elemental composition, all of which control the quality, quantity and sinking speed of biogenic matter to depth. In particular, their silica shells provide ballast to marine snow and faecal pellets, and can help transport carbon to both the mesopelagic layer and deep ocean. Herein we show that the extent to which diatoms contribute to the export of carbon varies by diatom type, with carbon transfer modulated by the Si/C ratio of diatom cells, the thickness of the shells and their life strategies; for instance, the tendency to form aggregates or resting spores. Model simulations project a decline in the contribution of diatoms to primary production everywhere outside of the Southern Ocean. We argue that we need to understand changes in diatom diversity, life cycle and plankton interactions in a warmer and more acidic ocean in much more detail to fully assess any changes in their contribution to the biological pump.}, + language = {en}, + number = {1}, + urldate = {2024-01-06}, + journal = {Nature Geoscience}, + author = {Tréguer, Paul and Bowler, Chris and Moriceau, Brivaela and Dutkiewicz, Stephanie and Gehlen, Marion and Aumont, Olivier and Bittner, Lucie and Dugdale, Richard and Finkel, Zoe and Iudicone, Daniele and Jahn, Oliver and Guidi, Lionel and Lasbleiz, Marine and Leblanc, Karine and Levy, Marina and Pondaven, Philippe}, + month = jan, + year = {2018}, + note = {Number: 1 +Publisher: Nature Publishing Group}, + keywords = {Biogeochemistry, Carbon cycle, Marine biology, Ocean sciences}, + pages = {27--37}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/VLLSAC7H/Tréguer et al. - 2018 - Influence of diatom diversity on the ocean biologi.pdf:application/pdf}, +} + +@misc{bengio_representation_2014, + title = {Representation {Learning}: {A} {Review} and {New} {Perspectives}}, + shorttitle = {Representation {Learning}}, + url = {http://arxiv.org/abs/1206.5538}, + abstract = {The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning.}, + urldate = {2024-01-06}, + publisher = {arXiv}, + author = {Bengio, Yoshua and Courville, Aaron and Vincent, Pascal}, + month = apr, + year = {2014}, + note = {arXiv:1206.5538 [cs]}, + keywords = {Computer Science - Machine Learning}, + file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/EYA2B52H/1206.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/2CFSH6MT/Bengio et al. - 2014 - Representation Learning A Review and New Perspect.pdf:application/pdf}, +} + +@misc{white_gradients2-mgl1704-ifcb-abundance_2020-04-01_v10_2020, + title = {Gradients2-{MGL1704}-{IFCB}-{Abundance}\_2020-04-01\_v1.0}, + url = {https://zenodo.org/records/4267140}, + doi = {10.5281/zenodo.4267140}, + abstract = {Cruise: Gradients 2, MGL1704 Project Name: Simons Foundation, Gradient NPSG Dataset Description: The Imaging FlowCytoBot (IFCB) is an in situ automated imaging flow cytometer that generates images of particles suspended in seawater, in this case from the underway uncontaminated seawater system aboard the R/V Langseth (intake 5m).   The IFCB uses a recycled sheath fluid (0.2 µm filtered seawater) to align and drive particles individually towards a light source (red laser, 4.5 mW) in order to detect and identify single or colonial cells using a combination of optical properties (red fluorescence and light scattering intensities) and high resolution images (3.2 pixels per micron) by a mounted camera. Both optical properties are used to trigger targeted image acquisition of suspended particles in the size range {\textless}4 to 100 μm. The instrument continuously samples (few seconds) from  {\textasciitilde}5 ml aliquots from the intake, and processes all particles contained in that volume for the next 20 mins.  Images corresponding to the "Sample" variable are available on the cruise's dashboard (http://ifcb-data.soest.hawaii.edu/IFCB\_NPTZ).  This dataset is for the abundance of imaged cells by genus.  For each sample, the total number of cells classified to the genus-level by a random forest algorithm (Sosik and Olson, 2007 doi:10.4319/lom.2007.5.204) is counted and divided by the corresponding volume analyzed ({\textasciitilde}5 mL). Note that we used all the images collected during Gradients 2.0 to train the random forest algorithm and that classification is therefore highly accurate for this dataset. Using 7 µm calibration beads, we estimated that the error of cell concentration due to cell detection during sample acquisition averages 11 ± 10 \%, independently of concentrations in the range 1-10000 cell/mL.}, + urldate = {2024-01-06}, + publisher = {Zenodo}, + author = {White, Angelicque}, + month = apr, + year = {2020}, + file = {Zenodo Snapshot:/Users/birdy/Zotero/storage/456EVCSQ/4267140.html:text/html}, +} + +@article{kanungo_efficient_2002, + title = {An efficient k-means clustering algorithm: analysis and implementation}, + volume = {24}, + issn = {1939-3539}, + shorttitle = {An efficient k-means clustering algorithm}, + url = {https://ieeexplore.ieee.org/document/1017616}, + doi = {10.1109/TPAMI.2002.1017616}, + abstract = {In k-means clustering, we are given a set of n data points in d-dimensional space R/sup d/ and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd's (1982) algorithm. We present a simple and efficient implementation of Lloyd's k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm's running time, which shows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in color quantization, data compression, and image segmentation.}, + number = {7}, + urldate = {2024-01-06}, + journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, + author = {Kanungo, T. and Mount, D.M. and Netanyahu, N.S. and Piatko, C.D. and Silverman, R. and Wu, A.Y.}, + month = jul, + year = {2002}, + note = {Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence}, + pages = {881--892}, + file = {IEEE Xplore Abstract Record:/Users/birdy/Zotero/storage/RC5LGQ6V/1017616.html:text/html;IEEE Xplore Full Text PDF:/Users/birdy/Zotero/storage/NUTPDLG4/Kanungo et al. - 2002 - An efficient k-means clustering algorithm analysi.pdf:application/pdf}, +} + +@inproceedings{he_deep_2016, + title = {Deep {Residual} {Learning} for {Image} {Recognition}}, + url = {https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html}, + urldate = {2024-01-06}, + author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, + year = {2016}, + pages = {770--778}, + file = {Full Text PDF:/Users/birdy/Zotero/storage/RYXX2LD8/He et al. - 2016 - Deep Residual Learning for Image Recognition.pdf:application/pdf}, +} diff --git a/assets/img/2023-11-11-plankton-representation/fig-IFCB-examples.png b/assets/img/2023-11-11-plankton-representation/fig-IFCB-examples.png new file mode 100644 index 0000000000000000000000000000000000000000..e76daf7bab763abea386c6d296626a35041da5f7 GIT binary patch literal 1015316 zcmeFZWmH^E(Bn*KNBsjs{-4h%JcXtS|g9U=S1P>MfY7WR$aZnsHw=}ZK7L z9;IOnbaf7N_I38=4(4X085pd+c(R86{wGSldRL>J*sPedE~)w4qWrPd2a;K(XDw%O zyN-kTu1Z%7pWf2d^O4Dcs2i7CLEoNYVf8(u)^BbrKL*UG@JMX9dY@J}D* zvFJ*k(@`(+!*XjFxP#jtRVLh~6fM}~F?x62SWwS5sV4LnuUWRmdSf%dGxK#wI5 z%;_e3vA|9C0%-(-;NR83p^wpEZj|~y&dcw-2}Xa 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Barbie Date: Sat, 6 Jan 2024 17:34:13 -0500 Subject: [PATCH 3/4] removed unnecessary file --- assets/bibliography/My Library.bib | 2462 ----------------- .../bibliography/PlanktonRepresentation.bib | 2462 ----------------- 2 files changed, 4924 deletions(-) delete mode 100644 assets/bibliography/My Library.bib delete mode 100644 assets/bibliography/PlanktonRepresentation.bib diff --git a/assets/bibliography/My Library.bib b/assets/bibliography/My Library.bib deleted file mode 100644 index b201fa10..00000000 --- a/assets/bibliography/My Library.bib +++ /dev/null @@ -1,2462 +0,0 @@ - -@article{araujo_validation_2005, - title = {Validation of species–climate impact models under climate change}, - volume = {11}, - issn = {1365-2486}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-2486.2005.01000.x}, - doi = {10.1111/j.1365-2486.2005.01000.x}, - abstract = {Increasing concern over the implications of climate change for biodiversity has led to the use of species–climate envelope models to project species extinction risk under climate-change scenarios. However, recent studies have demonstrated significant variability in model predictions and there remains a pressing need to validate models and to reduce uncertainties. Model validation is problematic as predictions are made for events that have not yet occurred. Resubstituition and data partitioning of present-day data sets are, therefore, commonly used to test the predictive performance of models. However, these approaches suffer from the problems of spatial and temporal autocorrelation in the calibration and validation sets. Using observed distribution shifts among 116 British breeding-bird species over the past ∼20 years, we are able to provide a first independent validation of four envelope modelling techniques under climate change. Results showed good to fair predictive performance on independent validation, although rules used to assess model performance are difficult to interpret in a decision-planning context. We also showed that measures of performance on nonindependent data provided optimistic estimates of models' predictive ability on independent data. Artificial neural networks and generalized additive models provided generally more accurate predictions of species range shifts than generalized linear models or classification tree analysis. Data for independent model validation and replication of this study are rare and we argue that perfect validation may not in fact be conceptually possible. We also note that usefulness of models is contingent on both the questions being asked and the techniques used. Implementations of species–climate envelope models for testing hypotheses and predicting future events may prove wrong, while being potentially useful if put into appropriate context.}, - language = {en}, - number = {9}, - urldate = {2021-12-15}, - journal = {Global Change Biology}, - author = {Araújo, Miguel B. and Pearson, Richard G. and Thuiller, Wilfried and Erhard, Markus}, - year = {2005}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2486.2005.01000.x}, - keywords = {bioclimatic-envelope models, breeding birds, Britain, climate change, model accuracy, uncertainty, validation, unread, not printed}, - pages = {1504--1513}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/6EVK36UH/Araújo et al. - 2005 - Validation of species–climate impact models under .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/C68Y6MBK/j.1365-2486.2005.01000.html:text/html}, -} - -@incollection{kolter_unveiling_2012, - title = {Unveiling {Prochlorococcus}}, - isbn = {978-1-55581-540-0 978-1-55581-847-0}, - url = {http://www.asmscience.org/content/book/10.1128/9781555818470.chap23}, - language = {en}, - urldate = {2021-12-15}, - booktitle = {Microbes and {Evolution}}, - publisher = {American Society of Microbiology}, - author = {Chisholm, Sallie W.}, - editor = {{Kolter} and {Maloy}}, - month = jan, - year = {2012}, - doi = {10.1128/9781555818470.ch23}, - keywords = {read, printed}, - pages = {165--171}, - file = {Chisholm - 2012 - Unveiling Prochlorococcus.pdf:/Users/birdy/Zotero/storage/D2MZS4I5/Chisholm - 2012 - Unveiling Prochlorococcus.pdf:application/pdf}, -} - -@article{follett_moving_2021, - title = {Moving ecological and biogeochemical transitions across the {North} {Pacific}}, - volume = {66}, - issn = {1939-5590}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lno.11763}, - doi = {10.1002/lno.11763}, - abstract = {In the North Pacific Ocean, nutrient rich surface waters flow south from the subpolar gyre through a transitional region and into the subtropics. Along the way, nutrients are used, recycled, and exported, leading to lower biomass and a commensurate change in ecosystem structure moving southward. We focus on the region between the two gyres (the Transition Zone) using a coupled biophysical ocean model, remote sensing, floats, and cruise data to explore the nature of the physical, biogeochemical, and ecological fields in this region. Nonlinear interactions between biological processes and the meridional gradient in nutrient supply lead to sharp shifts across this zone. These transitions between a southern region with more uniform biological and biogeochemical properties and steep meridional gradients to the north are diagnosed from extrema in the first derivative of the properties with latitude. Some transitions like that for chlorophyll a (the transition zone chlorophyll front [TZCF]) experience large seasonal excursions while the location of the transitions in other properties moves very little. The seasonal shifts are not caused by changes in the horizontal flow field, but rather by the interaction of seasonal, depth related, forcing with the mean latitudinal gradients. Focusing on the TZCF as a case study, we express its phase velocity in terms of vertical nutrient flux and internal ecosystem processes, demonstrating their nearly equal influence on its motion. This framework of propagating biogeochemical transitions can be systematically expanded to better understand the processes that structure ecosystems and biogeochemistry in the North Pacific and beyond.}, - language = {en}, - number = {6}, - urldate = {2021-12-15}, - journal = {Limnology and Oceanography}, - author = {Follett, Christopher L. and Dutkiewicz, Stephanie and Forget, Gael and Cael, B. B. and Follows, Michael J.}, - year = {2021}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lno.11763}, - keywords = {read, darwin, printed}, - pages = {2442--2454}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/XUKLUSS4/Follett et al. - 2021 - Moving ecological and biogeochemical transitions a.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/MASWCC2S/lno.html:text/html}, -} - -@article{guisan_predictive_2000, - title = {Predictive habitat distribution models in ecology}, - volume = {135}, - issn = {03043800}, - url = {https://linkinghub.elsevier.com/retrieve/pii/S0304380000003549}, - doi = {10.1016/S0304-3800(00)00354-9}, - abstract = {With the rise of new powerful statistical techniques and GIS tools, the development of predictive habitat distribution models has rapidly increased in ecology. Such models are static and probabilistic in nature, since they statistically relate the geographical distribution of species or communities to their present environment. A wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate change research, and habitat or species management. In this paper, we present a review of predictive habitat distribution modeling. The variety of statistical techniques used is growing. Ordinary multiple regression and its generalized form (GLM) are very popular and are often used for modeling species distributions. Other methods include neural networks, ordination and classification methods, Bayesian models, locally weighted approaches (e.g. GAM), environmental envelopes or even combinations of these models. The selection of an appropriate method should not depend solely on statistical considerations. Some models are better suited to reflect theoretical findings on the shape and nature of the species’ response (or realized niche). Conceptual considerations include e.g. the trade-off between optimizing accuracy versus optimizing generality. In the field of static distribution modeling, the latter is mostly related to selecting appropriate predictor variables and to designing an appropriate procedure for model selection. New methods, including threshold-independent measures (e.g. receiver operating characteristic (ROC)-plots) and resampling techniques (e.g. bootstrap, cross-validation) have been introduced in ecology for testing the accuracy of predictive models. The choice of an evaluation measure should be driven primarily by the goals of the study. This may possibly lead to the attribution of different weights to the various types of prediction errors (e.g. omission, commission or confusion). Testing the model in a wider range of situations (in space and time) will permit one to define the range of applications for which the model predictions are suitable. In turn, the qualification of the model depends primarily on the goals of the study that define the qualification criteria and on the usability of the model, rather than on statistics alone. © 2000 Elsevier Science B.V. All rights reserved.}, - language = {en}, - number = {2-3}, - urldate = {2021-12-15}, - journal = {Ecological Modelling}, - author = {Guisan, Antoine and Zimmermann, Niklaus E.}, - month = dec, - year = {2000}, - keywords = {unread, sdm, printed}, - pages = {147--186}, - file = {Guisan and Zimmermann - 2000 - Predictive habitat distribution models in ecology.pdf:/Users/birdy/Zotero/storage/SGKKM6TL/Guisan and Zimmermann - 2000 - Predictive habitat distribution models in ecology.pdf:application/pdf}, -} - -@article{bardon_testing_2021, - title = {Testing the {Skill} of a {Species} {Distribution} {Model} {Using} a 21st {Century} {Virtual} {Ecosystem}}, - volume = {48}, - issn = {1944-8007}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2021GL093455}, - doi = {10.1029/2021GL093455}, - abstract = {Plankton communities play an important role in marine food webs, in biogeochemical cycling, and in Earth's climate; yet observations are sparse, and predictions of how they might respond to climate change vary. Correlative species distribution models (SDM's) have been applied to predicting biogeography based on relationships to observed environmental variables. To investigate sources of uncertainty, we use a correlative SDM to predict the plankton biogeography of a 21st century marine ecosystem model (Darwin). Darwin output is sampled to mimic historical ocean observations, and the SDM is trained using generalized additive models. We find that predictive skill varies across test cases, and between functional groups, with errors that are more attributable to spatiotemporal sampling bias than sample size. End-of-century predictions are poor, limited by changes in target-predictor relationships over time. Our findings illustrate the fundamental challenges faced by empirical models in using limited observational data to predict complex, dynamic systems.}, - language = {en}, - number = {22}, - urldate = {2021-12-15}, - journal = {Geophysical Research Letters}, - author = {Bardon, L. R. and Ward, B. A. and Dutkiewicz, S. and Cael, B. B.}, - year = {2021}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021GL093455}, - keywords = {climate change, unread, darwin, sdm, complex systems, numerical models, oceanography, plankton ecology, statistical learning, printed}, - pages = {e2021GL093455}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/RKQ6X8MD/Bardon et al. - 2021 - Testing the Skill of a Species Distribution Model .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/53EFUGZ7/2021GL093455.html:text/html}, -} - -@article{elith_species_2009, - title = {Species {Distribution} {Models}: {Ecological} {Explanation} and {Prediction} {Across} {Space} and {Time}}, - volume = {40}, - shorttitle = {Species {Distribution} {Models}}, - url = {https://doi.org/10.1146/annurev.ecolsys.110308.120159}, - doi = {10.1146/annurev.ecolsys.110308.120159}, - abstract = {Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in “established use.” Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.}, - number = {1}, - urldate = {2021-12-15}, - journal = {Annual Review of Ecology, Evolution, and Systematics}, - author = {Elith, Jane and Leathwick, John R.}, - year = {2009}, - note = {\_eprint: https://doi.org/10.1146/annurev.ecolsys.110308.120159}, - keywords = {read, sdm, printed}, - pages = {677--697}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/94HVWZRY/Elith and Leathwick - 2009 - Species Distribution Models Ecological Explanatio.pdf:application/pdf}, -} - -@article{henson_future_2021, - title = {Future phytoplankton diversity in a changing climate}, - volume = {12}, - issn = {2041-1723}, - url = {https://www.nature.com/articles/s41467-021-25699-w}, - doi = {10.1038/s41467-021-25699-w}, - abstract = {Abstract - The future response of marine ecosystem diversity to continued anthropogenic forcing is poorly constrained. Phytoplankton are a diverse set of organisms that form the base of the marine ecosystem. Currently, ocean biogeochemistry and ecosystem models used for climate change projections typically include only 2−3 phytoplankton types and are, therefore, too simple to adequately assess the potential for changes in plankton community structure. Here, we analyse a complex ecosystem model with 35 phytoplankton types to evaluate the changes in phytoplankton community composition, turnover and size structure over the 21st century. We find that the rate of turnover in the phytoplankton community becomes faster during this century, that is, the community structure becomes increasingly unstable in response to climate change. Combined with alterations to phytoplankton diversity, our results imply a loss of ecological resilience with likely knock-on effects on the productivity and functioning of the marine environment.}, - language = {en}, - number = {1}, - urldate = {2021-12-15}, - journal = {Nature Communications}, - author = {Henson, Stephanie A. and Cael, B. B. and Allen, Stephanie R. and Dutkiewicz, Stephanie}, - month = dec, - year = {2021}, - keywords = {unread, not printed}, - pages = {5372}, - file = {Henson et al. - 2021 - Future phytoplankton diversity in a changing clima.pdf:/Users/birdy/Zotero/storage/GLZDZK9S/Henson et al. - 2021 - Future phytoplankton diversity in a changing clima.pdf:application/pdf}, -} - -@article{wu_modeling_2021, - title = {Modeling {Photosynthesis} and {Exudation} in {Subtropical} {Oceans}}, - volume = {35}, - issn = {1944-9224}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2021GB006941}, - doi = {10.1029/2021GB006941}, - abstract = {Marine phytoplankton contributes nearly half of the total primary production on Earth through photosynthesis. Parameterizations of algal photosynthesis commonly employed in global biogeochemical simulations generally fail to capture the observed vertical structure of primary production. Here we examined the consequences of decoupling photosynthesis (carbon fixation) and biosynthesis (biomass building) with accumulation or exudation of excess photosynthate under energy rich conditions in both regional and global models. The results show that the decoupling of these two processes improved the simulated vertical profile of primary production, increased modeled primary production over 30\% globally and over 40\% in subtropical oceans, improved simulated meridional patterns of particulate C:N:P and increased modeled surface pool of labile/semi-labile dissolved organic carbon. More generally, these results highlight the importance of exudation, which results from the decoupling of photosynthesis and biosynthesis, as a major physiological process affecting ocean biogeochemistry.}, - language = {en}, - number = {9}, - urldate = {2021-12-15}, - journal = {Global Biogeochemical Cycles}, - author = {Wu, Zhen and Dutkiewicz, Stephanie and Jahn, Oliver and Sher, Daniel and White, Angelicque and Follows, Michael J.}, - year = {2021}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021GB006941}, - keywords = {unread, not printed, biogeochemical model, exudation, photosynthesis, phytoplankton, primary production, subtropical gyres, EAPS}, - pages = {e2021GB006941}, -} - -@article{ser-giacomi_lagrangian_2021, - title = {Lagrangian betweenness as a measure of bottlenecks in dynamical systems with oceanographic examples}, - volume = {12}, - issn = {2041-1723}, - url = {https://www.nature.com/articles/s41467-021-25155-9}, - doi = {10.1038/s41467-021-25155-9}, - abstract = {Abstract - The study of connectivity patterns in networks has brought novel insights across diverse fields ranging from neurosciences to epidemic spreading or climate. In this context, betweenness centrality has demonstrated to be a very effective measure to identify nodes that act as focus of congestion, or bottlenecks, in the network. However, there is not a way to define betweenness outside the network framework. By analytically linking dynamical systems and network theory, we provide a trajectory-based formulation of betweenness, called Lagrangian betweenness, as a function of Lyapunov exponents. This extends the concept of betweenness beyond the context of network theory relating hyperbolic points and heteroclinic connections in any dynamical system to the structural bottlenecks of the network associated with it. Using modeled and observational velocity fields, we show that such bottlenecks are present and surprisingly persistent in the oceanic circulation across different spatio-temporal scales and we illustrate the role of these areas in driving fluid transport over vast oceanic regions. Analyzing plankton abundance data from the Kuroshio region of the Pacific Ocean, we find significant spatial correlations between measures of diversity and betweenness, suggesting promise for ecological applications.}, - language = {en}, - number = {1}, - urldate = {2021-12-15}, - journal = {Nature Communications}, - author = {Ser-Giacomi, Enrico and Baudena, Alberto and Rossi, Vincent and Follows, Mick and Clayton, Sophie and Vasile, Ruggero and López, Cristóbal and Hernández-García, Emilio}, - month = dec, - year = {2021}, - keywords = {unread, not printed, EAPS}, - pages = {4935}, - file = {Ser-Giacomi et al. - 2021 - Lagrangian betweenness as a measure of bottlenecks.pdf:/Users/birdy/Zotero/storage/ENVD6A4Z/Ser-Giacomi et al. - 2021 - Lagrangian betweenness as a measure of bottlenecks.pdf:application/pdf}, -} - -@article{juranek_importance_2020, - title = {The {Importance} of the {Phytoplankton} “{Middle} {Class}” to {Ocean} {Net} {Community} {Production}}, - volume = {34}, - issn = {1944-9224}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2020GB006702}, - doi = {10.1029/2020GB006702}, - abstract = {The net balance between photosynthesis and respiration in the surface ocean is a key regulator of ocean-atmosphere carbon dioxide (CO2) partitioning, and by extension, Earth's climate. The slight excess of photosynthesis over community respiration in sunlit waters, known as net community production (NCP), sets the upper bound on the sequestration of carbon via biologically mediated export. Prevailing paradigms suggest a high/low binary where net primary production (NPP), NCP, and export are highest in ecosystems characterized by microplankton ({\textgreater}20 μm) and lowest in ecosystems dominated by picoplankton ({\textless}2 μm). This bifurcation model neglects the potential importance of nanoplankton (2–20 μm)—i.e., the “middle” size class—toward global biological pump functioning. Here, we show a relationship between the biomass of nanoplankton and oxygen-based estimates of NCP across natural ecological gradients in the North Pacific Ocean. Using a suite of high-resolution optical imaging approaches including SeaFlow, Imaging FlowCytobot, and laser-based scattering, nanoplankton dynamics are observed to dominate the particle size distribution throughout an 1,000 km transition between the subtropical and subpolar North Pacific, where NCP rates are threefold to fivefold higher than subtropical values. Based on ecological theory applied to the Darwin size-based ecosystem model, we hypothesize that intermediate size class organisms are capable of high rates of production via an optimization of bottom-up and top-down control inherent to the “middle class.” More broadly, the model indicates the global importance of nanoplankton for ocean biological production.}, - language = {en}, - number = {12}, - urldate = {2021-12-15}, - journal = {Global Biogeochemical Cycles}, - author = {Juranek, Lauren W. and White, Angelicque E. and Dugenne, Mathilde and Henderikx Freitas, Fernanda and Dutkiewicz, Stephanie and Ribalet, Francois and Ferrón, Sara and Armbrust, E. Virginia and Karl, David M.}, - year = {2020}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020GB006702}, - keywords = {read, not printed, EAPS}, - pages = {e2020GB006702}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/FI7EY7HA/Juranek et al. - 2020 - The Importance of the Phytoplankton “Middle Class”.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/48CTG827/2020GB006702.html:text/html}, -} - -@article{kempes_growth_2012, - title = {Growth, metabolic partitioning, and the size of microorganisms}, - volume = {109}, - copyright = {© . Freely available online through the PNAS open access option.}, - issn = {0027-8424, 1091-6490}, - url = {https://www.pnas.org/content/109/2/495}, - doi = {10.1073/pnas.1115585109}, - abstract = {Population growth rate is a fundamental ecological and evolutionary characteristic of living organisms, but individuals must balance the metabolism devoted to biosynthesis and reproduction against the maintenance of existing structure and other functionality. Here we present a mathematical model that relates metabolic partitioning to the form of growth. The model captures the observed growth trajectory of single cells and individuals for a variety of species and taxa spanning prokaryotes, unicellular eukaryotes, and small multicellular eukaryotes. Our analysis suggests that the per-unit costs of biosynthesis and maintenance are conserved across prokaryotes and eukaryotes. However, the relative metabolic expenditure on growth and maintenance of whole organisms clearly differentiates taxa: prokaryotes spend an increasing fraction of their entire metabolism on growth with increasing cell size, whereas eukaryotes devote a diminishing fraction. These differences allow us to predict the minimum and maximum size for each taxonomic group, anticipating observed evolutionary life-history transitions. The framework provides energetic insights into taxonomic tradeoffs related to growth and metabolism and constrains traits that are important for size-structured modeling of microbial communities and their ecological and biogeochemical effects.}, - language = {en}, - number = {2}, - urldate = {2022-01-28}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Kempes, Christopher P. and Dutkiewicz, Stephanie and Follows, Michael J.}, - month = jan, - year = {2012}, - pmid = {22203990}, - note = {Publisher: National Academy of Sciences -Section: Biological Sciences}, - keywords = {unread, evolutionary transitions, maintenance metabolism, metabolic ecology, ontogenetic growth, single cell growth}, - pages = {495--500}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/72H8W27A/Kempes et al. - 2012 - Growth, metabolic partitioning, and the size of mi.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/4LRVBPN7/495.html:text/html}, -} - -@article{dutkiewicz_dimensions_2020, - title = {Dimensions of marine phytoplankton diversity}, - volume = {17}, - issn = {1726-4170}, - url = {https://bg.copernicus.org/articles/17/609/2020/}, - doi = {10.5194/bg-17-609-2020}, - abstract = {{\textless}p{\textgreater}{\textless}strong class="journal-contentHeaderColor"{\textgreater}Abstract.{\textless}/strong{\textgreater} Biodiversity of phytoplankton is important for ecosystem stability and marine biogeochemistry. However, the large-scale patterns of diversity are not well understood and are often poorly characterized in terms of statistical relationships with factors such as latitude, temperature and productivity. Here we use ecological theory and a global trait-based ecosystem model to provide mechanistic understanding of patterns of phytoplankton diversity. Our study suggests that phytoplankton diversity across three dimensions of trait space (size, biogeochemical function and thermal tolerance) is controlled by disparate combinations of drivers: the supply rate of the limiting resource, the imbalance in different resource supplies relative to competing phytoplankton demands, size-selective grazing and transport by the moving ocean. Using sensitivity studies we show that each dimension of diversity is controlled by different drivers. Models including only one (or two) of the trait dimensions will have different patterns of diversity than one which incorporates another trait dimension. We use the results of our model exploration to infer the controls on the diversity patterns derived from field observations along meridional transects in the Atlantic and to explain why different taxa and size classes have differing patterns.{\textless}/p{\textgreater}}, - language = {English}, - number = {3}, - urldate = {2022-02-02}, - journal = {Biogeosciences}, - author = {Dutkiewicz, Stephanie and Cermeno, Pedro and Jahn, Oliver and Follows, Michael J. and Hickman, Anna E. and Taniguchi, Darcy A. A. and Ward, Ben A.}, - month = feb, - year = {2020}, - note = {Publisher: Copernicus GmbH}, - keywords = {read}, - pages = {609--634}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/GDPB2N66/Dutkiewicz et al. - 2020 - Dimensions of marine phytoplankton diversity.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/6J7BBRZ4/2020.html:text/html}, -} - -@article{armstrong_grazing_1994, - title = {Grazing limitation and nutrient limitation in marine ecosystems: {Steady} state solutions of an ecosystem model with multiple food chains}, - volume = {39}, - issn = {1939-5590}, - shorttitle = {Grazing limitation and nutrient limitation in marine ecosystems}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lo.1994.39.3.0597}, - doi = {10.4319/lo.1994.39.3.0597}, - abstract = {Here I develop a model with multiple phytoplankton-zooplankton food chains where each food chain is based on a different size class of algae. Model parameters were chosen to allow food chains based on smaller algal size classes to dominate under oligotrophic conditions, with larger size classes being added sequentially with increased nutrient loading. The model makes use of allometric relations among size classes to minimize the number of free parameters, facilitating numerical exploration of its steady state behavior. The model is simple, yet it is complex enough to allow simultaneous predator limitation of algal size classes (or species) and nutrient limitation of total phytoplankton biomass. This perspective, not obtainable from models with single phytoplankton and zooplankton size classes, is applied to the particular case of elevated nitrate levels in the equatorial Pacific. The model shows clearly that it is possible for each phytoplankton size class to be limited by its herbivores, while at the same time micronutrient (notably iron) deficiency may limit the number of size classes that can exist in the community and hence the total phytoplankton biomass that can be supported.}, - language = {en}, - number = {3}, - urldate = {2022-02-07}, - journal = {Limnology and Oceanography}, - author = {Armstrong, Robert A.}, - year = {1994}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lo.1994.39.3.0597}, - keywords = {unread}, - pages = {597--608}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/62GDW2AR/Armstrong - 1994 - Grazing limitation and nutrient limitation in mari.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/FJKQ3LLF/lo.1994.39.3.html:text/html}, -} - -@article{dutkiewicz_modeling_2009, - title = {Modeling the coupling of ocean ecology and biogeochemistry}, - volume = {23}, - issn = {1944-9224}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2008GB003405}, - doi = {10.1029/2008GB003405}, - abstract = {We examine the interplay between ecology and biogeochemical cycles in the context of a global three-dimensional ocean model where self-assembling phytoplankton communities emerge from a wide set of potentially viable cell types. We consider the complex model solutions in the light of resource competition theory. The emergent community structures and ecological regimes vary across different physical environments in the model ocean: Strongly seasonal, high-nutrient regions are dominated by fast growing bloom specialists, while stable, low-seasonality regions are dominated by organisms that can grow at low nutrient concentrations and are suited to oligotrophic conditions. In the latter regions, the framework of resource competition theory provides a useful qualitative and quantitative diagnostic tool with which to interpret the outcome of competition between model organisms, their regulation of the resource environment, and the sensitivity of the system to changes in key physiological characteristics of the cells.}, - language = {en}, - number = {4}, - urldate = {2022-02-14}, - journal = {Global Biogeochemical Cycles}, - author = {Dutkiewicz, S. and Follows, M. J. and Bragg, J. G.}, - year = {2009}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2008GB003405}, - keywords = {unread, ecology, ocean biogeochemistry, resource competition}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/ME3K4FVT/Dutkiewicz et al. - 2009 - Modeling the coupling of ocean ecology and biogeoc.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/JWID8TMI/2008GB003405.html:text/html}, -} - -@article{maranon_resource_2015, - title = {Resource supply alone explains the variability of marine phytoplankton size structure}, - volume = {60}, - issn = {1939-5590}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lno.10138}, - doi = {10.1002/lno.10138}, - abstract = {Due to the covariation between temperature and resource availability in the surface ocean, a correct assessment of resource supply is crucial to determine if temperature has a direct effect on phytoplankton size structure. To remove the effect of resources, López-Urrutia and Morán analyzed data subsets with narrow ranges of variation in Chlorophyll a (Chl a) concentration and found that temperature is correlated with Chl a partitioning among size classes, from which they concluded that temperature is an important variable to explain the variability of phytoplankton size structure. Our analysis, however, shows that resource supply varies widely also within these subsets and, importantly, that it is inversely correlated with temperature. Therefore, the relationship between temperature and size structure reflects instead the effect of resources. When groups of samples with similar resource supply conditions are considered, no correlation between temperature and phytoplankton size structure is observed, which invalidates the conclusion of López-Urrutia and Morán. Even within restricted ranges of variation for phytoplankton biomass and production, changes in resource supply alone are sufficient to explain the variability of phytoplankton size structure in the sea.}, - language = {en}, - number = {5}, - urldate = {2022-02-16}, - journal = {Limnology and Oceanography}, - author = {Marañón, Emilio and Cermeño, Pedro and Latasa, Mikel and Tadonléké, Rémy D.}, - year = {2015}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lno.10138}, - keywords = {unread, prosynref}, - pages = {1848--1854}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/PFJ4GLDA/Marañón et al. - 2015 - Resource supply alone explains the variability of .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/HSLF3VCJ/lno.html:text/html}, -} - -@article{follett_trophic_2022, - title = {Trophic interactions with heterotrophic bacteria limit the range of \textit{{Prochlorococcus}}}, - volume = {119}, - issn = {0027-8424, 1091-6490}, - url = {http://www.pnas.org/lookup/doi/10.1073/pnas.2110993118}, - doi = {10.1073/pnas.2110993118}, - abstract = {Prochlorococcus - is both the smallest and numerically most abundant photosynthesizing organism on the planet. While thriving in the warm oligotrophic gyres, - Prochlorococcus - concentrations drop rapidly in higher-latitude regions. Transect data from the North Pacific show the collapse occurring at a wide range of temperatures and latitudes (temperature is often hypothesized to cause this shift), suggesting an ecological mechanism may be at play. An often used size-based theory of phytoplankton community structure that has been incorporated into computational models correctly predicts the dominance of - Prochlorococcus - in the gyres, and the relative dominance of larger cells at high latitudes. However, both theory and computational models fail to explain the poleward collapse. When heterotrophic bacteria and predators that prey nonspecifically on both - Prochlorococcus - and bacteria are included in the theoretical framework, the collapse of - Prochlorococcus - occurs with increasing nutrient supplies. The poleward collapse of - Prochlorococcus - populations then naturally emerges when this mechanism of “shared predation” is implemented in a complex global ecosystem model. Additionally, the theory correctly predicts trends in both the abundance and mean size of the heterotrophic bacteria. These results suggest that ecological controls need to be considered to understand the biogeography of - Prochlorococcus - and predict its changes under future ocean conditions. Indirect interactions within a microbial network can be essential in setting community structure.}, - language = {en}, - number = {2}, - urldate = {2022-02-23}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Follett, Christopher L. and Dutkiewicz, Stephanie and Ribalet, François and Zakem, Emily and Caron, David and Armbrust, E. Virginia and Follows, Michael J.}, - month = jan, - year = {2022}, - pages = {e2110993118}, - file = {Follett et al. - 2022 - Trophic interactions with heterotrophic bacteria l.pdf:/Users/birdy/Zotero/storage/PJMSAXIG/Follett et al. - 2022 - Trophic interactions with heterotrophic bacteria l.pdf:application/pdf}, -} - -@article{muratore_complex_2022, - title = {Complex marine microbial communities partition metabolism of scarce resources over the diel cycle}, - volume = {6}, - copyright = {2022 The Author(s), under exclusive licence to Springer Nature Limited}, - issn = {2397-334X}, - url = {https://www.nature.com/articles/s41559-021-01606-w}, - doi = {10.1038/s41559-021-01606-w}, - abstract = {Complex assemblages of microbes in the surface ocean are responsible for approximately half of global carbon fixation. The persistence of high taxonomic diversity despite competition for a small suite of relatively homogeneously distributed nutrients, that is, ‘the paradox of the plankton’, represents a long-standing challenge for ecological theory. Here we find evidence consistent with temporal niche partitioning of nitrogen assimilation processes over a diel cycle in the North Pacific Subtropical Gyre. We jointly analysed transcript abundances, lipids and metabolites and discovered that a small number of diel archetypes can explain pervasive periodic dynamics. Metabolic pathway analysis of identified diel signals revealed asynchronous timing in the transcription of nitrogen uptake and assimilation genes among different microbial groups—cyanobacteria, heterotrophic bacteria and eukaryotes. This temporal niche partitioning of nitrogen uptake emerged despite synchronous transcription of photosynthesis and central carbon metabolism genes and associated macromolecular abundances. Temporal niche partitioning may be a mechanism by which microorganisms in the open ocean mitigate competition for scarce resources, supporting community coexistence.}, - language = {en}, - number = {2}, - urldate = {2022-02-25}, - journal = {Nature Ecology \& Evolution}, - author = {Muratore, Daniel and Boysen, Angela K. and Harke, Matthew J. and Becker, Kevin W. and Casey, John R. and Coesel, Sacha N. and Mende, Daniel R. and Wilson, Samuel T. and Aylward, Frank O. and Eppley, John M. and Vislova, Alice and Peng, Shengyun and Rodriguez-Gonzalez, Rogelio A. and Beckett, Stephen J. and Virginia Armbrust, E. and DeLong, Edward F. and Karl, David M. and White, Angelicque E. and Zehr, Jonathan P. and Van Mooy, Benjamin A. S. and Dyhrman, Sonya T. and Ingalls, Anitra E. and Weitz, Joshua S.}, - month = feb, - year = {2022}, - note = {Number: 2 -Publisher: Nature Publishing Group}, - keywords = {unread, Microbial biooceanography, Microbial ecology}, - pages = {218--229}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/T58JCF6Q/Muratore et al. - 2022 - Complex marine microbial communities partition met.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/HC4GW85C/s41559-021-01606-w.html:text/html}, -} - -@article{mangolte_plankton_2022, - title = {Plankton community response to fronts : winners and losers}, - shorttitle = {Plankton community response to fronts}, - url = {https://hal.archives-ouvertes.fr/hal-03607937}, - doi = {10.1093/plankt/fbac010}, - abstract = {Fronts are particularly productive regions of the ocean, and biodiversity hotspots for many marine species. Here we use an ocean-ecosystem model to investigate the effect of fronts on plankton ecology. We focus on energetic fronts in Western Boundary Current systems that efficiently inject nutrients into the euphotic layer and which are physical boundaries between productive and oligotrophic provinces. We found that the fronts form an environment distinct from both provinces, favorable to some plankton groups (diatoms, dinoflagellates and large carnivorous zooplankton) and less favorable to others (pico-phytoplankton, coccolithophores, small grazers), and with an overall larger diversity. In agreement with previous understanding, we find that bottom-up abiotic processes (nutrient enrichment) explain the prevalence of groups with fast growing rates (the "winners"). Importantly, our results also show that biotic interactions within the ecosystem may play a larger role than previously thought. We show that the winners can have a negative impact on other plankton species (the "losers") through two indirect competitive processes: community shading (modification of the light environment by the plankton community leading to light-limitation of some plankton groups) and shared predation (where an increase in one functional group leads to increased grazing by a shared predator on another functional group).}, - urldate = {2022-03-23}, - journal = {Journal of Plankton Research}, - author = {Mangolte, Inès and Lévy, Marina and Dutkiewicz, Stephanie and Clayton, Sophie and Jahn, Oliver}, - month = mar, - year = {2022}, - note = {Publisher: Oxford University Press (OUP)}, - keywords = {unread, phytoplankton, biodiversity, fronts, marine ecology, modelling}, - file = {HAL PDF Full Text:/Users/birdy/Zotero/storage/65HVTJEW/Mangolte et al. - 2022 - Plankton community response to fronts winners an.pdf:application/pdf}, -} - -@article{shang_oxidative_2022, - title = {Oxidative metabolisms catalyzed {Earth}’s oxygenation}, - volume = {13}, - copyright = {2022 The Author(s)}, - issn = {2041-1723}, - url = {https://www.nature.com/articles/s41467-022-28996-0}, - doi = {10.1038/s41467-022-28996-0}, - abstract = {The burial of organic carbon, which prevents its remineralization via oxygen-consuming processes, is considered one of the causes of Earth’s oxygenation. Yet, higher levels of oxygen are thought to inhibit burial. Here we propose a resolution of this conundrum, wherein Earth’s initial oxygenation is favored by oxidative metabolisms generating partially oxidized organic matter (POOM), increasing burial via interaction with minerals in sediments. First, we introduce the POOM hypothesis via a mathematical argument. Second, we reconstruct the evolutionary history of one key enzyme family, flavin-dependent Baeyer–Villiger monooxygenases, that generates POOM, and show the temporal consistency of its diversification with the Proterozoic and Phanerozoic atmospheric oxygenation. Finally, we propose that the expansion of oxidative metabolisms instigated a positive feedback, which was amplified by the chemical changes to minerals on Earth’s surface. Collectively, these results suggest that Earth’s oxygenation is an autocatalytic transition induced by a combination of biological innovations and geological changes.}, - language = {en}, - number = {1}, - urldate = {2022-04-29}, - journal = {Nature Communications}, - author = {Shang, Haitao and Rothman, Daniel H. and Fournier, Gregory P.}, - month = mar, - year = {2022}, - note = {Number: 1 -Publisher: Nature Publishing Group}, - keywords = {unread, Coevolution, Element cycles}, - pages = {1328}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/JAG35KDC/Shang et al. - 2022 - Oxidative metabolisms catalyzed Earth’s oxygenatio.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/28GUMIIB/s41467-022-28996-0.html:text/html}, -} - -@article{ganopolski_climber-2_2001, - title = {{CLIMBER}-2: a climate system model of intermediate complexity. {Part} {II}: model sensitivity}, - volume = {17}, - issn = {0930-7575, 1432-0894}, - shorttitle = {{CLIMBER}-2}, - url = {http://link.springer.com/10.1007/s003820000144}, - doi = {10.1007/s003820000144}, - language = {en}, - number = {10}, - urldate = {2022-05-02}, - journal = {Climate Dynamics}, - author = {Ganopolski, A. and Petoukhov, V. and Rahmstorf, S. and Brovkin, V. and Claussen, M. and Eliseev, A. and Kubatzki, C.}, - month = jul, - year = {2001}, - keywords = {read}, - pages = {735--751}, - file = {Ganopolski et al. - 2001 - CLIMBER-2 a climate system model of intermediate .pdf:/Users/birdy/Zotero/storage/X4G6F5HW/Ganopolski et al. - 2001 - CLIMBER-2 a climate system model of intermediate .pdf:application/pdf}, -} - -@article{bhatia_its_2022, - chapter = {The Upshot}, - title = {‘{It}’s {Super} {Spectacular}.’ {See} {How} the {Tonga} {Volcano} {Unleashed} a {Once}-in-a-{Century} {Shockwave}.}, - issn = {0362-4331}, - url = {https://www.nytimes.com/interactive/2022/04/14/upshot/tonga-pressure-wave.html}, - abstract = {A new simulation gives a detailed look at a shockwave that circled the planet for days.}, - language = {en-US}, - urldate = {2022-05-06}, - journal = {The New York Times}, - author = {Bhatia, Aatish and Catangui, Sean}, - month = apr, - year = {2022}, - keywords = {Hunga Tonga-Hunga Ha'apai (Tonga), Tidal Waves and Tsunamis, Tonga, Volcanoes}, - file = {Snapshot:/Users/birdy/Zotero/storage/K9I8BQD2/tonga-pressure-wave.html:text/html}, -} - -@article{lee_improved_2018, - title = {Improved {Prediction} of {Harmful} {Algal} {Blooms} in {Four} {Major} {South} {Korea}’s {Rivers} {Using} {Deep} {Learning} {Models}}, - volume = {15}, - issn = {1661-7827}, - url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6069434/}, - doi = {10.3390/ijerph15071322}, - abstract = {Harmful algal blooms are an annual phenomenon that cause environmental damage, economic losses, and disease outbreaks. A fundamental solution to this problem is still lacking, thus, the best option for counteracting the effects of algal blooms is to improve advance warnings (predictions). However, existing physical prediction models have difficulties setting a clear coefficient indicating the relationship between each factor when predicting algal blooms, and many variable data sources are required for the analysis. These limitations are accompanied by high time and economic costs. Meanwhile, artificial intelligence and deep learning methods have become increasingly common in scientific research; attempts to apply the long short-term memory (LSTM) model to environmental research problems are increasing because the LSTM model exhibits good performance for time-series data prediction. However, few studies have applied deep learning models or LSTM to algal bloom prediction, especially in South Korea, where algal blooms occur annually. Therefore, we employed the LSTM model for algal bloom prediction in four major rivers of South Korea. We conducted short-term (one week) predictions by employing regression analysis and deep learning techniques on a newly constructed water quality and quantity dataset drawn from 16 dammed pools on the rivers. Three deep learning models (multilayer perceptron, MLP; recurrent neural network, RNN; and long short-term memory, LSTM) were used to predict chlorophyll-a, a recognized proxy for algal activity. The results were compared to those from OLS (ordinary least square) regression analysis and actual data based on the root mean square error (RSME). The LSTM model showed the highest prediction rate for harmful algal blooms and all deep learning models out-performed the OLS regression analysis. Our results reveal the potential for predicting algal blooms using LSTM and deep learning.}, - number = {7}, - urldate = {2022-05-19}, - journal = {International Journal of Environmental Research and Public Health}, - author = {Lee, Sangmok and Lee, Donghyun}, - month = jul, - year = {2018}, - pmid = {29937531}, - pmcid = {PMC6069434}, - pages = {1322}, - file = {Full Text:/Users/birdy/Zotero/storage/AA2K29UM/Lee and Lee - 2018 - Improved Prediction of Harmful Algal Blooms in Fou.pdf:application/pdf}, -} - -@article{yu_predicting_2021, - title = {Predicting coastal algal blooms with environmental factors by machine learning methods}, - volume = {123}, - issn = {1470-160X}, - url = {https://www.sciencedirect.com/science/article/pii/S1470160X20312760}, - doi = {10.1016/j.ecolind.2020.107334}, - abstract = {Harmful algal blooms are a major type of marine disaster that endangers the marine ecological environment and human health. Since the algal bloom is a complex nonlinear process with time characteristics, traditional statistical methods often cannot provide good predictions. In this paper, we propose a method based on machine learning with the aim to predict the occurrence of algal blooms by environmental parameters. The features related to algal bloom growth have been experimented for achieving a good prediction of algal concentrations by a combination strategy. We validate the prediction performance on two real datasets from two locations in US and China, i.e., Scripps Pier, California and Sishili Bay, Shandong, respectively. The models and feature subsets have been selected to complete the missing data and predict the phytoplankton concentration. The results demonstrate the efficiency of our method in the short-term prediction of concentrations by selecting appropriate features. The comparison studies prove the advantage of our developed machine learning method. The importance of every features for the prediction performance reveals crucial factors for the outbreak of harmful algal blooms.}, - language = {en}, - urldate = {2022-05-24}, - journal = {Ecological Indicators}, - author = {Yu, Peixuan and Gao, Rui and Zhang, Dezhen and Liu, Zhi-Ping}, - month = apr, - year = {2021}, - keywords = {unread, Feature importance, Feature selection, GBDT, Harmful algal bloom, Machine learning}, - pages = {107334}, - file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/XICJL7KH/S1470160X20312760.html:text/html}, -} - -@article{dutkiewicz_capturing_2015, - title = {Capturing optically important constituents and properties in a marine biogeochemical and ecosystem model}, - volume = {12}, - issn = {1726-4189}, - url = {https://bg.copernicus.org/articles/12/4447/2015/}, - doi = {10.5194/bg-12-4447-2015}, - abstract = {Abstract. We present a numerical model of the ocean that couples a three-stream radiative transfer component with a marine biogeochemical–ecosystem component in a dynamic three-dimensional physical framework. The radiative transfer component resolves the penetration of spectral irradiance as it is absorbed and scattered within the water column. We explicitly include the effect of several optically important water constituents (different phytoplankton functional types; detrital particles; and coloured dissolved organic matter, CDOM). The model is evaluated against in situ-observed and satellite-derived products. In particular we compare to concurrently measured biogeochemical, ecosystem, and optical data along a meridional transect of the Atlantic Ocean. The simulation captures the patterns and magnitudes of these data, and estimates surface upwelling irradiance analogous to that observed by ocean colour satellite instruments. We find that incorporating the different optically important constituents explicitly and including spectral irradiance was crucial to capture the variability in the depth of the subsurface chlorophyll a (Chl a) maximum. We conduct a series of sensitivity experiments to demonstrate, globally, the relative importance of each of the water constituents, as well as the crucial feedbacks between the light field, the relative fitness of phytoplankton types, and the biogeochemistry of the ocean. CDOM has proportionally more importance at attenuating light at short wavelengths and in more productive waters, phytoplankton absorption is relatively more important at the subsurface Chl a maximum, and water molecules have the greatest contribution when concentrations of other constituents are low, such as in the oligotrophic gyres. Scattering had less effect on attenuation, but since it is important for the amount and type of upwelling irradiance, it is crucial for setting sea surface reflectance. Strikingly, sensitivity experiments in which absorption by any of the optical constituents was increased led to a decrease in the size of the oligotrophic regions of the subtropical gyres: lateral nutrient supplies were enhanced as a result of decreasing high-latitude productivity. This new model that captures bio-optical feedbacks will be important for improving our understanding of the role of light and optical constituents on ocean biogeochemistry, especially in a changing environment. Further, resolving surface upwelling irradiance will make it easier to connect to satellite-derived products in the future.}, - language = {en}, - number = {14}, - urldate = {2022-08-15}, - journal = {Biogeosciences}, - author = {Dutkiewicz, S. and Hickman, A. E. and Jahn, O. and Gregg, W. W. and Mouw, C. B. and Follows, M. J.}, - month = jul, - year = {2015}, - pages = {4447--4481}, - file = {Dutkiewicz et al. - 2015 - Capturing optically important constituents and pro.pdf:/Users/birdy/Zotero/storage/DEFKPKCD/Dutkiewicz et al. - 2015 - Capturing optically important constituents and pro.pdf:application/pdf}, -} - -@article{han_solving_2018, - title = {Solving high-dimensional partial differential equations using deep learning}, - volume = {115}, - url = {https://www.pnas.org/doi/abs/10.1073/pnas.1718942115}, - doi = {10.1073/pnas.1718942115}, - abstract = {Developing algorithms for solving high-dimensional partial differential equations -(PDEs) has been an exceedingly difficult task for a long time, du...}, - language = {EN}, - number = {34}, - urldate = {2022-08-31}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Han, Jiequn and Jentzen, Arnulf and E, Weinan}, - month = aug, - year = {2018}, - note = {Company: National Academy of Sciences -Distributor: National Academy of Sciences -Institution: National Academy of Sciences -Label: National Academy of Sciences -Publisher: Proceedings of the National Academy of Sciences}, - keywords = {ML}, - pages = {8505--8510}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/PCZ84KRN/Han et al. - 2018 - Solving high-dimensional partial differential equa.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/KSCJDJ7T/pnas.html:text/html}, -} - -@article{follows_emergent_2007, - title = {Emergent {Biogeography} of {Microbial} {Communities} in a {Model} {Ocean}}, - volume = {315}, - issn = {0036-8075, 1095-9203}, - url = {https://www.science.org/doi/10.1126/science.1138544}, - doi = {10.1126/science.1138544}, - abstract = {A marine ecosystem model seeded with many phytoplankton types, whose physiological traits were randomly assigned from ranges defined by field and laboratory data, generated an emergent community structure and biogeography consistent with observed global phytoplankton distributions. The modeled organisms included types analogous to the marine cyanobacterium - Prochlorococcus - . Their emergent global distributions and physiological properties simultaneously correspond to observations. This flexible representation of community structure can be used to explore relations between ecosystems, biogeochemical cycles, and climate change.}, - language = {en}, - number = {5820}, - urldate = {2022-08-31}, - journal = {Science}, - author = {Follows, Michael J. and Dutkiewicz, Stephanie and Grant, Scott and Chisholm, Sallie W.}, - month = mar, - year = {2007}, - pages = {1843--1846}, - file = {Follows et al. - 2007 - Emergent Biogeography of Microbial Communities in .pdf:/Users/birdy/Zotero/storage/5N8359IB/Follows et al. - 2007 - Emergent Biogeography of Microbial Communities in .pdf:application/pdf}, -} - -@article{duyck_sloop_2015, - title = {Sloop: {A} pattern retrieval engine for individual animal identification}, - volume = {48}, - issn = {0031-3203}, - shorttitle = {Sloop}, - url = {https://www.sciencedirect.com/science/article/pii/S0031320314002763}, - doi = {10.1016/j.patcog.2014.07.017}, - abstract = {Identifying individuals in photographs of animals collected over time is a non-invasive approach for ecological monitoring and conservation. This paper describes the design and use of Sloop, the first image retrieval system for individual animal identification incorporating crowd-sourced relevance feedback. Sloop׳s iterative retrieval strategy using hierarchical and aggregated matching and relevance feedback consistently improves deformation and correspondence-based approaches for individual identification across several species. Its crowdsourcing strategy is successful in utilizing relevance feedback on a large scale. Sloop is in operational use. The user experience and results are presented here to facilitate the creation of a community-based individual identification system for conservation planning.}, - language = {en}, - number = {4}, - urldate = {2022-10-01}, - journal = {Pattern Recognition}, - author = {Duyck, James and Finn, Chelsea and Hutcheon, Andy and Vera, Pablo and Salas, Joaquin and Ravela, Sai}, - month = apr, - year = {2015}, - keywords = {Animal biometrics, Conservation, Crowdsourcing, Gecko, Hybrid shape contexts, Individual identification, Local features, Photo-identification, Relevance feedback, Salamander, Scale-cascaded alignment, Skink, Whale shark}, - pages = {1059--1073}, - file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/EVXJTLMW/S0031320314002763.html:text/html}, -} - -@article{taylor_shutdown_2011, - title = {Shutdown of turbulent convection as a new criterion for the onset of spring phytoplankton blooms}, - volume = {56}, - issn = {1939-5590}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lo.2011.56.6.2293}, - doi = {10.4319/lo.2011.56.6.2293}, - abstract = {The onset of phytoplankton blooms in late winter, early spring has been traditionally associated with the shoaling of the mixed layer above a critical depth. Here we show that the onset of a bloom can also be triggered by a reduction in air–sea fluxes at the end of winter. When net cooling subsides at the end of winter, turbulent mixing becomes weak, thereby increasing the residence time of phytoplankton cells in the euphotic layer and allowing a bloom to develop. The necessary change in the air–sea flux generally precedes mixed-layer shoaling, and may provide a better indicator for the onset of the spring bloom than the mixed-layer depth alone. Our hypothesis is supported by numerical simulations and remote sensing data.}, - language = {en}, - number = {6}, - urldate = {2022-10-05}, - journal = {Limnology and Oceanography}, - author = {Taylor, John R. and Ferrari, Raffaele}, - year = {2011}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lo.2011.56.6.2293}, - pages = {2293--2307}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/X7EVVVGL/Taylor and Ferrari - 2011 - Shutdown of turbulent convection as a new criterio.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/SJS6MACH/lo.2011.56.6.html:text/html}, -} - -@article{regos_effects_2019, - title = {Effects of species traits and environmental predictors on performance and transferability of ecological niche models}, - volume = {9}, - issn = {20452322}, - url = {https://www.mendeley.com/catalogue/30ca2198-448a-30e7-bb65-8f00f0bc94ed/?ref=raven&dgcid=raven_md_suggest_email&dgcid=raven_md_suggest_mie_email}, - doi = {10.1038/s41598-019-40766-5}, - abstract = {(2019) Regos et al. Scientific Reports. The ability of ecological niche models (ENMs) to produce robust predictions for different time frames (i.e. temporal transferability) may be hindered by a la...}, - language = {en-GB}, - number = {1}, - urldate = {2022-10-11}, - journal = {Scientific Reports}, - author = {Regos, Adrián and Gagne, Laura and Alcaraz-Segura, Domingo and Honrado, João P. and Domínguez, Jesús}, - year = {2019}, - note = {Number: 1}, - pages = {undefined--undefined}, - file = {Full Text:/Users/birdy/Zotero/storage/83W8A9G9/Regos et al. - 2019 - Effects of species traits and environmental predic.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/CSIQ6CV6/30ca2198-448a-30e7-bb65-8f00f0bc94ed.html:text/html}, -} - -@misc{beery_species_2021, - title = {Species {Distribution} {Modeling} for {Machine} {Learning} {Practitioners}: {A} {Review}}, - shorttitle = {Species {Distribution} {Modeling} for {Machine} {Learning} {Practitioners}}, - url = {http://arxiv.org/abs/2107.10400}, - abstract = {Conservation science depends on an accurate understanding of what's happening in a given ecosystem. How many species live there? What is the makeup of the population? How is that changing over time? Species Distribution Modeling (SDM) seeks to predict the spatial (and sometimes temporal) patterns of species occurrence, i.e. where a species is likely to be found. The last few years have seen a surge of interest in applying powerful machine learning tools to challenging problems in ecology. Despite its considerable importance, SDM has received relatively little attention from the computer science community. Our goal in this work is to provide computer scientists with the necessary background to read the SDM literature and develop ecologically useful ML-based SDM algorithms. In particular, we introduce key SDM concepts and terminology, review standard models, discuss data availability, and highlight technical challenges and pitfalls.}, - urldate = {2022-10-11}, - publisher = {arXiv}, - author = {Beery, Sara and Cole, Elijah and Parker, Joseph and Perona, Pietro and Winner, Kevin}, - month = jul, - year = {2021}, - note = {arXiv:2107.10400 [cs, stat]}, - keywords = {Computer Science - Machine Learning, Statistics - Applications}, - file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/5D6ZSJQL/Beery et al. - 2021 - Species Distribution Modeling for Machine Learning.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/UNSJQTFB/2107.html:text/html}, -} - -@article{beery_scaling_2021, - title = {Scaling biodiversity monitoring for the data age}, - volume = {27}, - issn = {1528-4972, 1528-4980}, - url = {https://dl.acm.org/doi/10.1145/3466857}, - doi = {10.1145/3466857}, - abstract = {Technological advances have made it possible to collect massive amounts of biodiversity data. How can analysis efforts keep up?}, - language = {en}, - number = {4}, - urldate = {2022-10-11}, - journal = {XRDS: Crossroads, The ACM Magazine for Students}, - author = {Beery, Sara}, - month = jun, - year = {2021}, - pages = {14--18}, - file = {Beery - 2021 - Scaling biodiversity monitoring for the data age.pdf:/Users/birdy/Zotero/storage/N366LVCR/Beery - 2021 - Scaling biodiversity monitoring for the data age.pdf:application/pdf}, -} - -@article{olson_submersible_2007, - title = {A submersible imaging-in-flow instrument to analyze nano-and microplankton: {Imaging} {FlowCytobot}}, - volume = {5}, - issn = {1541-5856}, - shorttitle = {A submersible imaging-in-flow instrument to analyze nano-and microplankton}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lom.2007.5.195}, - doi = {10.4319/lom.2007.5.195}, - abstract = {A fundamental understanding of the interaction between physical and biological factors that regulate plankton species composition requires, first of all, detailed and sustained observations. Only now is it becoming possible to acquire these types of observations, as we develop and deploy instruments that can continuously monitor individual organisms in the ocean. Our research group can measure and count the smallest phytoplankton cells using a submersible flow cytometer (FlowCytobot), in which optical properties of individual suspended cells are recorded as they pass through a focused laser beam. However, FlowCytobot cannot efficiently sample or identify the much larger cells (10 to {\textgreater}100 µm) that often dominate the plankton in coastal waters. Because these larger cells often have recognizable morphologies, we have developed a second submersible flow cytometer, with imaging capability and increased water sampling rate (typically, 5 mL seawater analyzed every 20 min), to characterize these nano- and microplankton. Like the original, Imaging FlowCytobot can operate unattended for months at a time; it obtains power from and communicates with a shore laboratory, so we can monitor results and modify sampling procedures when needed. Imaging FlowCytobot was successfully tested for 2 months in Woods Hole Harbor and is presently deployed alongside FlowCytobot at the Martha's Vineyard Coastal Observatory. These combined approaches will allow continuous long-term observations of plankton community structure over a wide range of cell sizes and types, and help to elucidate the processes and interactions that control the life cycles of individual species.}, - language = {en}, - number = {6}, - urldate = {2022-10-20}, - journal = {Limnology and Oceanography: Methods}, - author = {Olson, Robert J. and Sosik, Heidi M.}, - year = {2007}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lom.2007.5.195}, - pages = {195--203}, - file = {Full Text:/Users/birdy/Zotero/storage/JP4F24FI/Olson and Sosik - 2007 - A submersible imaging-in-flow instrument to analyz.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/9E2FX4K7/lom.2007.5.html:text/html}, -} - -@article{sosik_automated_2007, - title = {Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry}, - volume = {5}, - issn = {1541-5856}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lom.2007.5.204}, - doi = {10.4319/lom.2007.5.204}, - abstract = {High-resolution photomicrographs of phytoplankton cells and chains can now be acquired with imaging-in-flow systems at rates that make manual identification impractical for many applications. To address the challenge for automated taxonomic identification of images generated by our custom-built submersible Imaging FlowCytobot, we developed an approach that relies on extraction of image features, which are then presented to a machine learning algorithm for classification. Our approach uses a combination of image feature types including size, shape, symmetry, and texture characteristics, plus orientation invariant moments, diffraction pattern sampling, and co-occurrence matrix statistics. Some of these features required preprocessing with image analysis techniques including edge detection after phase congruency calculations, morphological operations, boundary representation and simplification, and rotation. For the machine learning strategy, we developed an approach that combines a feature selection algorithm and use of a support vector machine specified with a rigorous parameter selection and training approach. After training, a 22-category classifier provides 88\% overall accuracy for an independent test set, with individual category accuracies ranging from 68\% to 99\%. We demonstrate application of this classifier to a nearly uninterrupted 2-month time series of images acquired in Woods Hole Harbor, including use of statistical error correction to derive quantitative concentration estimates, which are shown to be unbiased with respect to manual estimates for random subsamples. Our approach, which provides taxonomically resolved estimates of phytoplankton abundance with fine temporal resolution (hours for many species), permits access to scales of variability from tidal to seasonal and longer.}, - language = {en}, - number = {6}, - urldate = {2022-10-20}, - journal = {Limnology and Oceanography: Methods}, - author = {Sosik, Heidi M. and Olson, Robert J.}, - year = {2007}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lom.2007.5.204}, - pages = {204--216}, - file = {Full Text:/Users/birdy/Zotero/storage/6E2WBCLM/Sosik and Olson - 2007 - Automated taxonomic classification of phytoplankto.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/UIZ3PRI8/lom.2007.5.html:text/html}, -} - -@article{besson_towards_nodate, - title = {Towards the fully automated monitoring of ecological communities}, - volume = {n/a}, - issn = {1461-0248}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ele.14123}, - doi = {10.1111/ele.14123}, - abstract = {High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components—for example, individual behaviours and traits, and species abundance and distribution—is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.}, - language = {en}, - number = {n/a}, - urldate = {2022-10-24}, - journal = {Ecology Letters}, - author = {Besson, Marc and Alison, Jamie and Bjerge, Kim and Gorochowski, Thomas E. and Høye, Toke T. and Jucker, Tommaso and Mann, Hjalte M. R. and Clements, Christopher F.}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ele.14123}, - keywords = {community ecology, computer vision, deep learning, high-resolution monitoring, remote sensing}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/BBZAT6LU/Besson et al. - Towards the fully automated monitoring of ecologic.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/GEGIFAZV/ele.html:text/html}, -} - -@article{pierella_karlusich_coupling_2022, - title = {Coupling {Imaging} and {Omics} in {Plankton} {Surveys}: {State}-of-the-{Art}, {Challenges}, and {Future} {Directions}}, - volume = {9}, - issn = {2296-7745}, - shorttitle = {Coupling {Imaging} and {Omics} in {Plankton} {Surveys}}, - url = {https://www.frontiersin.org/articles/10.3389/fmars.2022.878803}, - abstract = {A major challenge in characterizing plankton communities is the collection, identification and quantification of samples in a time-efficient way. The classical manual microscopy counts are gradually being replaced by high throughput imaging and nucleic acid sequencing. DNA sequencing allows deep taxonomic resolution (including cryptic species) as well as high detection power (detecting rare species), while RNA provides insights on function and potential activity. However, these methods are affected by database limitations, PCR bias, and copy number variability across taxa. Recent developments in high-throughput imaging applied in situ or on collected samples (high-throughput microscopy, Underwater Vision Profiler, FlowCam, ZooScan, etc) has enabled a rapid enumeration of morphologically-distinguished plankton populations, estimates of biovolume/biomass, and provides additional valuable phenotypic information. Although machine learning classifiers generate encouraging results to classify marine plankton images in a time efficient way, there is still a need for large training datasets of manually annotated images. Here we provide workflow examples that couple nucleic acid sequencing with high-throughput imaging for a more complete and robust analysis of microbial communities. We also describe the publicly available and collaborative web application EcoTaxa, which offers tools for the rapid validation of plankton by specialists with the help of automatic recognition algorithms. Finally, we describe how the field is moving with citizen science programs, unmanned autonomous platforms with in situ sensors, and sequencing and digitalization of historical plankton samples.}, - urldate = {2022-10-27}, - journal = {Frontiers in Marine Science}, - author = {Pierella Karlusich, Juan José and Lombard, Fabien and Irisson, Jean-Olivier and Bowler, Chris and Foster, Rachel A.}, - year = {2022}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/7HJUM6PQ/Pierella Karlusich et al. - 2022 - Coupling Imaging and Omics in Plankton Surveys St.pdf:application/pdf}, -} - -@article{pierella_karlusich_global_2021, - title = {Global distribution patterns of marine nitrogen-fixers by imaging and molecular methods}, - volume = {12}, - copyright = {2021 The Author(s)}, - issn = {2041-1723}, - url = {https://www.nature.com/articles/s41467-021-24299-y}, - doi = {10.1038/s41467-021-24299-y}, - abstract = {Nitrogen fixation has a critical role in marine primary production, yet our understanding of marine nitrogen-fixers (diazotrophs) is hindered by limited observations. Here, we report a quantitative image analysis pipeline combined with mapping of molecular markers for mining {\textgreater}2,000,000 images and {\textgreater}1300 metagenomes from surface, deep chlorophyll maximum and mesopelagic seawater samples across 6 size fractions ({\textless}0.2–2000 μm). We use this approach to characterise the diversity, abundance, biovolume and distribution of symbiotic, colony-forming and particle-associated diazotrophs at a global scale. We show that imaging and PCR-free molecular data are congruent. Sequence reads indicate diazotrophs are detected from the ultrasmall bacterioplankton ({\textless}0.2 μm) to mesoplankton (180–2000 μm) communities, while images predict numerous symbiotic and colony-forming diazotrophs ({\textgreater}20 µm). Using imaging and molecular data, we estimate that polyploidy can substantially affect gene abundances of symbiotic versus colony-forming diazotrophs. Our results support the canonical view that larger diazotrophs ({\textgreater}10 μm) dominate the tropical belts, while unicellular cyanobacterial and non-cyanobacterial diazotrophs are globally distributed in surface and mesopelagic layers. We describe co-occurring diazotrophic lineages of different lifestyles and identify high-density regions of diazotrophs in the global ocean. Overall, we provide an update of marine diazotroph biogeographical diversity and present a new bioimaging-bioinformatic workflow.}, - language = {en}, - number = {1}, - urldate = {2022-10-28}, - journal = {Nature Communications}, - author = {Pierella Karlusich, Juan José and Pelletier, Eric and Lombard, Fabien and Carsique, Madeline and Dvorak, Etienne and Colin, Sébastien and Picheral, Marc and Cornejo-Castillo, Francisco M. and Acinas, Silvia G. and Pepperkok, Rainer and Karsenti, Eric and de Vargas, Colomban and Wincker, Patrick and Bowler, Chris and Foster, Rachel A.}, - month = jul, - year = {2021}, - note = {Number: 1 -Publisher: Nature Publishing Group}, - keywords = {Microbial biooceanography, Element cycles, Environmental microbiology, Marine biology}, - pages = {4160}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/6QTE42Z7/Pierella Karlusich et al. - 2021 - Global distribution patterns of marine nitrogen-fi.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/FZ492JKD/s41467-021-24299-y.html:text/html}, -} - -@misc{feichtenhofer_large-scale_2021, - title = {A {Large}-{Scale} {Study} on {Unsupervised} {Spatiotemporal} {Representation} {Learning}}, - url = {http://arxiv.org/abs/2104.14558}, - abstract = {We present a large-scale study on unsupervised spatiotemporal representation learning from videos. With a unified perspective on four recent image-based frameworks, we study a simple objective that can easily generalize all these methods to space-time. Our objective encourages temporally-persistent features in the same video, and in spite of its simplicity, it works surprisingly well across: (i) different unsupervised frameworks, (ii) pre-training datasets, (iii) downstream datasets, and (iv) backbone architectures. We draw a series of intriguing observations from this study, e.g., we discover that encouraging long-spanned persistency can be effective even if the timespan is 60 seconds. In addition to state-of-the-art results in multiple benchmarks, we report a few promising cases in which unsupervised pre-training can outperform its supervised counterpart. Code is made available at https://github.com/facebookresearch/SlowFast}, - urldate = {2022-10-29}, - publisher = {arXiv}, - author = {Feichtenhofer, Christoph and Fan, Haoqi and Xiong, Bo and Girshick, Ross and He, Kaiming}, - month = apr, - year = {2021}, - note = {arXiv:2104.14558 [cs]}, - keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition}, - file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/P4P752JB/Feichtenhofer et al. - 2021 - A Large-Scale Study on Unsupervised Spatiotemporal.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/LV4YRCJQ/2104.html:text/html}, -} - -@article{lambert_dynamic_2022, - title = {The dynamic trophic architecture of open-ocean protist communities revealed through machine-guided metatranscriptomics}, - volume = {119}, - copyright = {Copyright © 2022 the Author(s). Published by PNAS.}, - url = {https://www.pnas.org/doi/abs/10.1073/pnas.2100916119}, - doi = {10.1073/pnas.2100916119}, - abstract = {Intricate networks of single-celled eukaryotes (protists) dominate carbon flow in -the ocean. Their growth, demise, and interactions with other micr...}, - language = {EN}, - number = {7}, - urldate = {2022-11-01}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Lambert, Bennett S. and Groussman, Ryan D. and Schatz, Megan J. and Coesel, Sacha N. and Durham, Bryndan P. and Alverson, Andrew J. and White, Angelicque E. and Armbrust, E. Virginia}, - month = feb, - year = {2022}, - note = {Company: National Academy of Sciences -Distributor: National Academy of Sciences -Institution: National Academy of Sciences -Label: National Academy of Sciences -Publisher: Proceedings of the National Academy of Sciences}, - pages = {e2100916119}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/U3RTSWWR/Lambert et al. - 2022 - The dynamic trophic architecture of open-ocean pro.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/W4FGUHV8/pnas.html:text/html}, -} - -@article{bourne_insights_2016, - title = {Insights into the {Coral} {Microbiome}: {Underpinning} the {Health} and {Resilience} of {Reef} {Ecosystems}}, - volume = {70}, - issn = {0066-4227, 1545-3251}, - shorttitle = {Insights into the {Coral} {Microbiome}}, - url = {https://www.annualreviews.org/doi/10.1146/annurev-micro-102215-095440}, - doi = {10.1146/annurev-micro-102215-095440}, - abstract = {Corals are fundamental ecosystem engineers, creating large, intricate reefs that support diverse and abundant marine life. At the core of a healthy coral animal is a dynamic relationship with microorganisms, including a mutually beneficial symbiosis with photosynthetic dinoflagellates (Symbiodinium spp.) and enduring partnerships with an array of bacterial, archaeal, fungal, protistan, and viral associates, collectively termed the coral holobiont. The combined genomes of this coral holobiont form a coral hologenome, and genomic interactions within the hologenome ultimately define the coral phenotype. Here we integrate contemporary scientific knowledge regarding the ecological, host-specific, and environmental forces shaping the diversity, specificity, and distribution of microbial symbionts within the coral holobiont, explore physiological pathways that contribute to holobiont fitness, and describe potential mechanisms for holobiont homeostasis. Understanding the role of the microbiome in coral resilience, acclimation, and environmental adaptation is a new frontier in reef science that will require large-scale collaborative research efforts.}, - language = {en}, - number = {1}, - urldate = {2022-11-02}, - journal = {Annual Review of Microbiology}, - author = {Bourne, David G. and Morrow, Kathleen M. and Webster, Nicole S.}, - month = sep, - year = {2016}, - pages = {317--340}, - file = {Bourne et al. - 2016 - Insights into the Coral Microbiome Underpinning t.pdf:/Users/birdy/Zotero/storage/AE37AHKP/Bourne et al. - 2016 - Insights into the Coral Microbiome Underpinning t.pdf:application/pdf}, -} - -@article{zhang_microbially_2019, - title = {Microbially mediated nutrient cycles in marine sponges}, - volume = {95}, - issn = {0168-6496}, - url = {https://doi.org/10.1093/femsec/fiz155}, - doi = {10.1093/femsec/fiz155}, - abstract = {Efficient nutrient cycles mediated by symbiotic microorganisms with their hosts are vital to support the high productivity of coral reef ecosystems. In these ecosystems, marine sponges are important habitat-forming organisms in the benthic community and harbor abundant microbial symbionts. However, few studies have reviewed the critical microbially mediated nutrient cycling processes in marine sponges. To bridge this gap, in this review article, we summarize existing knowledge and recent advances in understanding microbially mediated carbon (C), nitrogen (N), phosphorus (P) and sulfur (S) cycles in sponges, propose a conceptual model that describes potential interactions and constraints in the major nutrient cycles, and suggest that shifting redox state induced by animal behavior like sponge pumping can exert great influence on the activities of symbiotic microbial communities. Constraints include the lack of knowledge on spatial and temporal variations and host behavior; more studies are needed in these areas. Sponge microbiomes may have a significant impact on the nutrient cycles in the world’s coral reef ecosystems.}, - number = {11}, - urldate = {2022-11-02}, - journal = {FEMS Microbiology Ecology}, - author = {Zhang, Fan and Jonas, Lauren and Lin, Hanzhi and Hill, Russell T}, - month = nov, - year = {2019}, - keywords = {EMO}, - pages = {fiz155}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/372EJVV6/Zhang et al. - 2019 - Microbially mediated nutrient cycles in marine spo.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/AN3DK4P7/5582607.html:text/html}, -} - -@article{ceh_nutrient_2013, - title = {Nutrient cycling in early coral life stages: {Pocillopora} damicornis larvae provide their algal symbiont ({Symbiodinium}) with nitrogen acquired from bacterial associates}, - volume = {3}, - issn = {2045-7758}, - shorttitle = {Nutrient cycling in early coral life stages}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.642}, - doi = {10.1002/ece3.642}, - abstract = {The waters surrounding coral reef ecosystems are generally poor in nutrients, yet their levels of primary production are comparable with those reported from tropical rain forests. One explanation of this paradox is the efficient cycling of nutrients between the coral host, its endosymbiotic alga Symbiodinium and a wide array of microorganisms. Despite their importance for the animals' fitness, the cycling of nutrients in early coral life stages and the initial establishment of partnerships with the microbes involved in these processes has received little scrutiny to date. Nitrogen is an essential but limited nutrient in coral reef ecosystems. In order to assess the early nutrient exchange between bacteria and corals, coral larvae of the species Pocillopora damicornis were incubated with two coral-associated bacteria (Alteromonas sp., or Vibrio alginolyticus), prelabeled with the stable nitrogen isotope 15N. The incorporation and translocation of nitrogen from Vibrio- and Alteromonas bacteria into P. damicornis coral larvae and specifically into the coral-symbiotic Symbiodinium were detected by nanoscale secondary ion mass spectrometry (NanoSIMS). A significant increase in the amount of enriched 15N (two to threefold compared to natural abundance) was observed in P. damicornis larvae within 8 h of incubation for both bacterial treatments (one-way ANOVA, F5,53 = 18.03, P = 0.004 for Alteromonas sp. and F5,53 = 18.03, P = 0.0001 for V. alginolyticus). These findings reveal that coral larvae acquire nutrients previously taken up from the environment by bacteria. The additional nitrogen may increase the survival rate and fitness of the developing coral and therefore contribute to the successful maintenance of coral reefs.}, - language = {en}, - number = {8}, - urldate = {2022-11-02}, - journal = {Ecology and Evolution}, - author = {Ceh, Janja and Kilburn, Matt R. and Cliff, John B. and Raina, Jean-Baptiste and van Keulen, Mike and Bourne, David G.}, - year = {2013}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.642}, - keywords = {EMO, Bacteria, coral larvae, nanoSIMS, nitrogen}, - pages = {2393--2400}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/8PI4Q2QV/Ceh et al. - 2013 - Nutrient cycling in early coral life stages Pocil.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/L6GYJYMJ/ece3.html:text/html}, -} - -@article{radecker_heat_2022, - title = {Heat stress reduces the contribution of diazotrophs to coral holobiont nitrogen cycling}, - volume = {16}, - copyright = {2021 The Author(s)}, - issn = {1751-7370}, - url = {https://www.nature.com/articles/s41396-021-01158-8}, - doi = {10.1038/s41396-021-01158-8}, - abstract = {Efficient nutrient cycling in the coral-algal symbiosis requires constant but limited nitrogen availability. Coral-associated diazotrophs, i.e., prokaryotes capable of fixing dinitrogen, may thus support productivity in a stable coral-algal symbiosis but could contribute to its breakdown when overstimulated. However, the effects of environmental conditions on diazotroph communities and their interaction with other members of the coral holobiont remain poorly understood. Here we assessed the effects of heat stress on diazotroph diversity and their contribution to holobiont nutrient cycling in the reef-building coral Stylophora pistillata from the central Red Sea. In a stable symbiotic state, we found that nitrogen fixation by coral-associated diazotrophs constitutes a source of nitrogen to the algal symbionts. Heat stress caused an increase in nitrogen fixation concomitant with a change in diazotroph communities. Yet, this additional fixed nitrogen was not assimilated by the coral tissue or the algal symbionts. We conclude that although diazotrophs may support coral holobiont functioning under low nitrogen availability, altered nutrient cycling during heat stress abates the dependence of the coral host and its algal symbionts on diazotroph-derived nitrogen. Consequently, the role of nitrogen fixation in the coral holobiont is strongly dependent on its nutritional status and varies dynamically with environmental conditions.}, - language = {en}, - number = {4}, - urldate = {2022-11-02}, - journal = {The ISME Journal}, - author = {Rädecker, Nils and Pogoreutz, Claudia and Gegner, Hagen M. and Cárdenas, Anny and Perna, Gabriela and Geißler, Laura and Roth, Florian and Bougoure, Jeremy and Guagliardo, Paul and Struck, Ulrich and Wild, Christian and Pernice, Mathieu and Raina, Jean-Baptiste and Meibom, Anders and Voolstra, Christian R.}, - month = apr, - year = {2022}, - note = {Number: 4 -Publisher: Nature Publishing Group}, - keywords = {Microbial ecology, EMO, Climate-change ecology}, - pages = {1110--1118}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/XMDXIJE7/Rädecker et al. - 2022 - Heat stress reduces the contribution of diazotroph.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/3RSSVZZL/s41396-021-01158-8.html:text/html}, -} - -@article{radecker_nitrogen_2015, - title = {Nitrogen cycling in corals: the key to understanding holobiont functioning?}, - volume = {23}, - issn = {0966-842X}, - shorttitle = {Nitrogen cycling in corals}, - url = {https://www.sciencedirect.com/science/article/pii/S0966842X1500075X}, - doi = {10.1016/j.tim.2015.03.008}, - abstract = {Corals are animals that form close mutualistic associations with endosymbiotic photosynthetic algae of the genus Symbiodinium. Together they provide the calcium carbonate framework of coral reef ecosystems. The importance of the microbiome (i.e., bacteria, archaea, fungi, and viruses) to holobiont functioning has only recently been recognized. Given that growth and density of Symbiodinium within the coral host is highly dependent on nitrogen availability, nitrogen-cycling microbes may be of fundamental importance to the stability of the coral–algae symbiosis and holobiont functioning, in particular under nutrient-enriched and -depleted scenarios. We summarize what is known about nitrogen cycling in corals and conclude that disturbance of microbial nitrogen cycling may be tightly linked to coral bleaching and disease.}, - language = {en}, - number = {8}, - urldate = {2022-11-02}, - journal = {Trends in Microbiology}, - author = {Rädecker, Nils and Pogoreutz, Claudia and Voolstra, Christian R. and Wiedenmann, Jörg and Wild, Christian}, - month = aug, - year = {2015}, - keywords = {EMO, coral bleaching, coral disease, coral holobiont, nutrient limitation, symbiosis}, - pages = {490--497}, - file = {ScienceDirect Full Text PDF:/Users/birdy/Zotero/storage/QY245ZPK/Rädecker et al. - 2015 - Nitrogen cycling in corals the key to understandi.pdf:application/pdf;ScienceDirect Snapshot:/Users/birdy/Zotero/storage/I62WKSM8/S0966842X1500075X.html:text/html}, -} - -@article{zhang_multi-task_nodate, - title = {Multi-task few-shot learning with composed data augmentation for image classification}, - volume = {n/a}, - issn = {1751-9640}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1049/cvi2.12150}, - doi = {10.1049/cvi2.12150}, - abstract = {Few-shot learning (FSL) attempts to learn and optimise the model from a few examples on image classification, which is still threatened by data scarcity. To generate more data as supplements, data augmentation is considered as a powerful and popular technique to enhance the robustness of few-shot models. However, there are still some weaknesses in applying augmentation methods. For example, all augmented samples have similar semantic information with respect to different augmented transformations, which makes these traditional augmentation methods incapable of learning the property being varied. To address this challenge, we introduce multi-task learning to learn a primary few-shot classification task and an auxiliary self-supervised task, simultaneously. The self-supervised task can learn transformation property as auxiliary self-supervision signals to improve the performance of the primary few-shot classification task. Additionally, we propose a simple, flexible, and effective mechanism for decision fusion to further improve the reliability of the classifier, named model-agnostic ensemble inference (MAEI). Specifically, the MAEI mechanism can eliminate the influence of outliers for FSL using non-maximum suppression. Extensive experiment results demonstrate that our method can outperform other state-of-the-art methods by large margins.}, - language = {en}, - number = {n/a}, - urldate = {2022-11-03}, - journal = {IET Computer Vision}, - author = {Zhang, Rui and Yang, Yixin and Li, Yang and Wang, Jiabao and Li, Hang and Miao, Zhuang}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1049/cvi2.12150}, - keywords = {data augmentation, few-shot learning, multi-task learning, non-maximum suppression, self-supervised learning}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/QWAG8GIJ/Zhang et al. - Multi-task few-shot learning with composed data au.pdf:application/pdf}, -} - -@article{mngadi_estimating_2022, - title = {Estimating aboveground net primary productivity of reforested trees in an urban landscape using biophysical variables and remotely sensed data}, - volume = {802}, - issn = {00489697}, - url = {https://linkinghub.elsevier.com/retrieve/pii/S0048969721050336}, - doi = {10.1016/j.scitotenv.2021.149958}, - language = {en}, - urldate = {2022-11-21}, - journal = {Science of The Total Environment}, - author = {Mngadi, Mthembeni and Odindi, John and Mutanga, Onisimo and Sibanda, Mbulisi}, - month = jan, - year = {2022}, - pages = {149958}, -} - -@article{henderikx-freitas_diel_2022, - title = {Diel variations in the estimated refractive index of bulk oceanic particles}, - volume = {30}, - copyright = {\&\#169; 2022 Optica Publishing Group}, - issn = {1094-4087}, - url = {https://opg.optica.org/oe/abstract.cfm?uri=oe-30-24-44141}, - doi = {10.1364/OE.469565}, - abstract = {The index of refraction (n) of particles is an important parameter in optical models that aims to extract particle size and carbon concentrations from light scattering measurements. An inadequate choice of n can critically affect the characterization and interpretation of optically-derived parameters, including those from satellite-based models which provide the current view of how biogeochemical processes vary over the global ocean. Yet, little is known about how n varies over time and space to inform such models. Particularly, in situ estimates of n for bulk water samples and at diel-resolving time scales are rare. Here, we demonstrate a method to estimate n using simultaneously and independently collected particulate beam attenuation coefficients, particle size distribution data, and a Mie theory model. We apply this method to surface waters of the North Pacific Subtropical Gyre (NPSG) at hourly resolution. Clear diel cycles in n were observed, marked by minima around local sunrise and maxima around sunset, qualitatively consistent with several laboratory-based estimates of n for specific phytoplankton species. A sensitivity analysis showed that the daily oscillation in n amplitude was somewhat insensitive to broad variations in method assumptions, ranging from 11.3\&\#x2009;\&\#x00B1;\&\#x2009;4.3\&\#x0025; to 16.9\&\#x2009;\&\#x00B1;\&\#x2009;2.9\&\#x0025;. Such estimates are crucial for improvement of algorithms that extract the particle size and production from bulk optical measurements, and could potentially help establish a link between n variations and changes in cellular composition of in situ particles.}, - language = {EN}, - number = {24}, - urldate = {2022-11-22}, - journal = {Optics Express}, - author = {Henderikx-Freitas, Fernanda and Allen, James G. and Lansdorp, Bob M. and White, Angelicque E.}, - month = nov, - year = {2022}, - note = {Publisher: Optica Publishing Group}, - pages = {44141--44159}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/HPPVHGUH/Henderikx-Freitas et al. - 2022 - Diel variations in the estimated refractive index .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/YVISH8DF/fulltext.html:text/html}, -} - -@misc{noauthor_accelerated_nodate, - title = {Accelerated modern human–induced species losses: {Entering} the sixth mass extinction}, - shorttitle = {Accelerated modern human–induced species losses}, - url = {https://www.science.org/doi/10.1126/sciadv.1400253}, - language = {en}, - urldate = {2022-12-01}, - doi = {10.1126/sciadv.1400253}, - file = {Full Text:/Users/birdy/Zotero/storage/Z7BVSY72/Accelerated modern human–induced species losses E.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/AC954VKH/sciadv.html:text/html}, -} - -@article{koning_network_2020, - title = {A network of grassroots reserves protects tropical river fish diversity}, - volume = {588}, - copyright = {2020 The Author(s), under exclusive licence to Springer Nature Limited}, - issn = {1476-4687}, - url = {https://www.nature.com/articles/s41586-020-2944-y}, - doi = {10.1038/s41586-020-2944-y}, - abstract = {Intensive fisheries have reduced fish biodiversity and abundance in aquatic ecosystems worldwide1–3. ‘No-take’ marine reserves have become a cornerstone of marine ecosystem-based fisheries management4–6, and their benefits for adjacent fisheries are maximized when reserve design fosters synergies among nearby reserves7,8. The applicability of this marine reserve network paradigm to riverine biodiversity and inland fisheries remains largely untested. Here we show that reserves created by 23 separate communities in Thailand’s Salween basin have markedly increased fish richness, density, and biomass relative to adjacent areas. Moreover, key correlates of the success of protected areas in marine ecosystems—particularly reserve size and enforcement—predict differences in ecological benefits among riverine reserves. Occupying a central position in the network confers additional gains, underscoring the importance of connectivity within dendritic river systems. The emergence of network-based benefits is remarkable given that these reserves are young (less than 25 years old) and arose without formal coordination. Freshwater ecosystems are under-represented among the world’s protected areas9, and our findings suggest that networks of small, community-based reserves offer a generalizable model for protecting biodiversity and augmenting fisheries as the world’s rivers face unprecedented pressures10,11.}, - language = {en}, - number = {7839}, - urldate = {2022-12-01}, - journal = {Nature}, - author = {Koning, Aaron A. and Perales, K. Martin and Fluet-Chouinard, Etienne and McIntyre, Peter B.}, - month = dec, - year = {2020}, - note = {Number: 7839 -Publisher: Nature Publishing Group}, - keywords = {Conservation biology, Freshwater ecology}, - pages = {631--635}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/U38SBSD4/Koning et al. - 2020 - A network of grassroots reserves protects tropical.pdf:application/pdf}, -} - -@article{robbins_lawns_2001, - title = {Lawns and {Toxins}: {An} {Ecology} of the {City}}, - volume = {18}, - issn = {0264-2751}, - shorttitle = {Lawns and {Toxins}}, - url = {https://www.sciencedirect.com/science/article/pii/S0264275101000294}, - doi = {10.1016/S0264-2751(01)00029-4}, - abstract = {This paper surveys the problems of contemporary urban ecology through the lens of lawn chemical usage, exploring the difficulty of explaining and managing urban ecological dilemmas that, though built from the disaggregated choices of individuals, aggregate into large and serious issues. Introductory discussion surveys the seriousness of lawn chemicals as urban non-point pollution sources and suggests why the issue, and problems like it, it understudied. Analysis proceeds with a case study from the United States city of Columbus, Ohio, utilizing formal survey techniques and aanlysis of county assessor's data. The results suggest lawns and lawn care chemicals are expanding with urban sprewl and that users of high-input lawn chemical systems are more likely to be wealthy, well-educated, and knowledgeable about the negative environmental impacts of the actions than non-users. Further investigation demonstrates the instrumental logics of homeowners in pursuit of property values but also points to the moral and community-oriented institutions that enforce and propel high chemical use. The conclusions point to policy options for dealing with the lawn chemical dilemma but suggest the difficulties of circumventing the deeply structured roots of the problem.}, - language = {en}, - number = {6}, - urldate = {2022-12-01}, - journal = {Cities}, - author = {Robbins, Paul and Polderman, Annemarie and Birkenholtz, Trevor}, - month = dec, - year = {2001}, - keywords = {consumption, political ecology, urban ecology}, - pages = {369--380}, - file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/PAWPGVRF/S0264275101000294.html:text/html}, -} - -@misc{orenstein_whoi-plankton-_2015, - title = {{WHOI}-{Plankton}- {A} {Large} {Scale} {Fine} {Grained} {Visual} {Recognition} {Benchmark} {Dataset} for {Plankton} {Classification}}, - url = {http://arxiv.org/abs/1510.00745}, - abstract = {Planktonic organisms are of fundamental importance to marine ecosystems: they form the basis of the food web, provide the link between the atmosphere and the deep ocean, and influence global-scale biogeochemical cycles. Scientists are increasingly using imaging-based technologies to study these creatures in their natural habit. Images from such systems provide an unique opportunity to model and understand plankton ecosystems, but the collected datasets can be enormous. The Imaging FlowCytobot (IFCB) at Woods Hole Oceanographic Institution, for example, is an {\textbackslash}emph\{in situ\} system that has been continuously imaging plankton since 2006. To date, it has generated more than 700 million samples. Manual classification of such a vast image collection is impractical due to the size of the data set. In addition, the annotation task is challenging due to the large space of relevant classes, intra-class variability, and inter-class similarity. Methods for automated classification exist, but the accuracy is often below that of human experts. Here we introduce WHOI-Plankton: a large scale, fine-grained visual recognition dataset for plankton classification, which comprises over 3.4 million expert-labeled images across 70 classes. The labeled image set is complied from over 8 years of near continuous data collection with the IFCB at the Martha's Vineyard Coastal Observatory (MVCO). We discuss relevant metrics for evaluation of classification performance and provide results for a traditional method based on hand-engineered features and two methods based on convolutional neural networks.}, - urldate = {2023-01-10}, - publisher = {arXiv}, - author = {Orenstein, Eric C. and Beijbom, Oscar and Peacock, Emily E. and Sosik, Heidi M.}, - month = oct, - year = {2015}, - note = {arXiv:1510.00745 [cs]}, - keywords = {Computer Science - Computer Vision and Pattern Recognition}, - file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/V4XB9956/Orenstein et al. - 2015 - WHOI-Plankton- A Large Scale Fine Grained Visual R.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/UKCDAVKQ/1510.html:text/html}, -} - -@article{jones_matthew_b_gries_corinna_proceedings_2011, - title = {Proceedings of the {Environmental} {Information} {Management} {Conference} 2011}, - url = {https://eim.ecoinformatics.org/eim2011/eim-proceedings-2011/at_download/file}, - doi = {10.5060/D2NC5Z4X}, - abstract = {Lack of observations that span the wide range of critical space and time scales continues to limit many aspects of oceanography. As ocean observatories and observing networks mature, the role for optical technologies and approaches in helping to overcome this limitation continues to grow. As a result the quantity and complexity of data produced is increasing at a pace that threatens to overwhelm the capacity of individual researchers who must cope with large high-resolution datasets, complex, multistage analyses, and the challenges of preserving sufficient metadata and provenance information to ensure reproducibility and avoid costly reprocessing or data loss. We have developed approaches to address these new challenges in the context of a case study involving very large numbers ({\textasciitilde}1 billion) of images collected at coastal observatories by Imaging FlowCytobot, an automated submersible flow cytometer that produces high resolution images of plankton and other microscopic particles at rates up to 10 Hz for months to years. By developing partnerships amongst oceanographers generating and using such data and computer scientists focused on improving science outcomes, we have prototyped a replicable system. It provides simple and ubiquitous access to observational data and products via web services in standard formats; accelerates image processing by enabling algorithms developed with desktop applications to be rapidly deployed and evaluated on shared, high-performance servers; and improves data integrity by replacing error-prone manual data management processes with generalized, automated services. The informatics system is currently in operation for multiple Imaging FlowCytobot datasets and being tested with other types of ocean imagery.}, - language = {en}, - urldate = {2023-01-10}, - author = {Jones, Matthew B.; Gries, Corinna}, - year = {2011}, - note = {Publisher: University of California}, - file = {Jones, Matthew B.\; Gries, Corinna - 2011 - Proceedings of the Environmental Information Manag.pdf:/Users/birdy/Zotero/storage/8LGGH6BH/Jones, Matthew B.\; Gries, Corinna - 2011 - Proceedings of the Environmental Information Manag.pdf:application/pdf}, -} - -@misc{noauthor_creative_nodate, - title = {Creative {Team} {\textbar} {Pulse} at {Omega} {Mart}}, - url = {https://credits.meowwolf.com/omega-mart/factory/pulse}, - abstract = {Discover the artists and creative teams behind Meow Wolf's exhibitions. Works include sculpture, painting, fabrication, digital art, writing, film, and more.}, - urldate = {2023-01-13}, - journal = {Credits {\textbar} Meow Wolf}, - file = {Snapshot:/Users/birdy/Zotero/storage/8C2WZPAI/pulse.html:text/html}, -} - -@article{mahaffey_phytoplankton_2012, - title = {Phytoplankton response to deep seawater nutrient addition in the {North} {Pacific} {Subtropical} {Gyre}}, - volume = {460}, - issn = {0171-8630, 1616-1599}, - url = {http://www.int-res.com/abstracts/meps/v460/p13-34/}, - doi = {10.3354/meps09699}, - abstract = {We examined the phytoplankton response to the addition of nutrient-enriched deep seawater (DSW) and of nitrate only during shipboard experiments performed between July 2004 and May 2007 in the North Pacific Subtropical Gyre (NPSG). Chlorophyll a (chl a), community size structure and composition, carbon fixation (CF) rates, and nutrient concentrations were measured daily for 5 to 7 d under simulated in situ conditions. Despite the fact that the NPSG is a permanently stratified, oligotrophic biome, there was a seasonal response to the addition of DSW and nitrate. In summertime experiments, chl a and CF rates increased up to 18- and 22-fold, respectively, relative to unamended controls after an incubation period of 5 to 6 d. Nutrients were assimilated to below control concentrations. A shift from a Prochlorococcus-dominated to a Synechococcus- and diatom-dominated community, and an 8- to 12-fold increase in the chl a content of the {\textgreater} 2 µm size fraction were observed. Addition of nitrate only increased chl a and CF rates 10- and 7-fold, respectively. In wintertime experiments, chl a and CF rates increased up to 4- and 9-fold, respectively, after an incubation period of up to 7 d. Only 50\% of nutrients were assimilated and nitrate alone stimulated a 2-fold increase in chl a and CF rates. We explore the role of nutrient limitation, community composition, grazing, and light in explaining our observations. Findings from this seasonal and multi-year field experiment demonstrate how little we know about bloom development in the NPSG and highlight the potential risk in extrapolating the response of phytoplankton to natural or artificial fertilization from short-term studies.}, - language = {en}, - urldate = {2023-02-22}, - journal = {Marine Ecology Progress Series}, - author = {Mahaffey, C and Björkman, Km and Karl, Dm}, - month = jul, - year = {2012}, - pages = {13--34}, - file = {Mahaffey et al. - 2012 - Phytoplankton response to deep seawater nutrient a.pdf:/Users/birdy/Zotero/storage/BZYUHQ9I/Mahaffey et al. - 2012 - Phytoplankton response to deep seawater nutrient a.pdf:application/pdf}, -} - -@article{tilman_invasions_1984, - title = {Invasions of equilibria: tests of resource competition using two species of algae}, - volume = {61}, - issn = {1432-1939}, - shorttitle = {Invasions of equilibria}, - url = {https://doi.org/10.1007/BF00396760}, - doi = {10.1007/BF00396760}, - abstract = {Single-species, steady-state chemostat cultures of two freshwater diatoms showed that Fragilaria crotonensis had a lower equilibrial requirement for silicate than did Tabellaria fenestratra. Using this information, resource competition theory predicts that Fragilaria should be able to invade silicate-limited equilibrium populations of Tabellaria, but Tabellaria should not be able to invade silicatelimited Fragilaria. This prediction was supported by a series of invasion experiments. The two species did not have detectable differences in their phosphate requirements. Invasion experiments showed that Fragilaria could invade Tabellaria cultures, but that Tabellaria could not invade Fragilaria cultures under phosphate-limited conditions. These results are consistent with predictions based on previous studies of the phosphate physiology of these genera.}, - language = {en}, - number = {2}, - urldate = {2023-02-22}, - journal = {Oecologia}, - author = {Tilman, David and Sterner, Robert W.}, - month = feb, - year = {1984}, - pages = {197--200}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/7S64RFJG/Tilman and Sterner - 1984 - Invasions of equilibria tests of resource competi.pdf:application/pdf}, -} - -@article{tilman_resource_1977, - title = {Resource {Competition} between {Plankton} {Algae}: {An} {Experimental} and {Theoretical} {Approach}}, - volume = {58}, - issn = {00129658}, - shorttitle = {Resource {Competition} between {Plankton} {Algae}}, - url = {http://doi.wiley.com/10.2307/1935608}, - doi = {10.2307/1935608}, - language = {en}, - number = {2}, - urldate = {2023-02-22}, - journal = {Ecology}, - author = {Tilman, David}, - month = mar, - year = {1977}, - pages = {338--348}, - file = {Tilman - 1977 - Resource Competition between Plankton Algae An Ex.pdf:/Users/birdy/Zotero/storage/E6YRP87L/Tilman - 1977 - Resource Competition between Plankton Algae An Ex.pdf:application/pdf}, -} - -@article{burghardt_impact_2009, - title = {Impact of {Native} {Plants} on {Bird} and {Butterfly} {Biodiversity} in {Suburban} {Landscapes}}, - volume = {23}, - issn = {1523-1739}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1523-1739.2008.01076.x}, - doi = {10.1111/j.1523-1739.2008.01076.x}, - abstract = {Abstract: Managed landscapes in which non-native ornamental plants are favored over native vegetation now dominate the United States, particularly east of the Mississippi River. We measured how landscaping with native plants affects the avian and lepidopteran communities on 6 pairs of suburban properties in southeastern Pennsylvania. One property in each pair was landscaped entirely with native plants and the other exhibited a more conventional suburban mixture of plants—a native canopy with non-native groundcover and shrubs. Vegetation sampling confirmed that total plant cover and plant diversity did not differ between treatments, but non-native plant cover was greater on the conventional sites and native plant cover was greater on the native sites. Several avian (abundance, species richness, biomass, and breeding-bird abundance) and larval lepidopteran (abundance and species richness) community parameters were measured from June 2006 to August 2006. Native properties supported significantly more caterpillars and caterpillar species and significantly greater bird abundance, diversity, species richness, biomass, and breeding pairs of native species. Of particular importance is that bird species of regional conservation concern were 8 times more abundant and significantly more diverse on native properties. In our study area, native landscaping positively influenced the avian and lepidopteran carrying capacity of suburbia and provided a mechanism for reducing biodiversity losses in human-dominated landscapes.}, - language = {en}, - number = {1}, - urldate = {2023-02-23}, - journal = {Conservation Biology}, - author = {Burghardt, Karin T. and Tallamy, Douglas W. and Gregory Shriver, W.}, - year = {2009}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1523-1739.2008.01076.x}, - keywords = {biodiversity, aves, biodiversidad, birds, ecosistemas bajo manejo, Lepidoptera, Lepidóptera, managed ecosystems, native plants, non-native plants, paisaje suburbano, plantas nativas, plantas no nativas, suburban landscape}, - pages = {219--224}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/9IP46YJ7/Burghardt et al. - 2009 - Impact of Native Plants on Bird and Butterfly Biod.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/2M776SVN/j.1523-1739.2008.01076.html:text/html}, -} - -@misc{noauthor_pnas2211758120pdf_nodate, - title = {pnas.2211758120.pdf: 1.873 {Systems} {Ecology} in {Theory}}, - url = {https://canvas.mit.edu/courses/18350/files/3301971?module_item_id=856541}, - urldate = {2023-05-02}, - file = {pnas.2211758120.pdf\: 1.873 Systems Ecology in Theory:/Users/birdy/Zotero/storage/FR2ZNW6N/3301971.html:text/html}, -} - -@article{munch_constraining_2023, - title = {Constraining nonlinear time series modeling with the metabolic theory of ecology}, - volume = {120}, - issn = {0027-8424, 1091-6490}, - url = {https://pnas.org/doi/10.1073/pnas.2211758120}, - doi = {10.1073/pnas.2211758120}, - abstract = {Forecasting the response of ecological systems to environmental change is a critical challenge for sustainable management. The metabolic theory of ecology (MTE) posits scaling of biological rates with temperature, but it has had limited application to population dynamic forecasting. Here we use the temperature dependence of the MTE to constrain empirical dynamic modeling (EDM), an equation-free nonlinear machine learning approach for forecasting. By rescaling time with temperature and modeling dynamics on a “metabolic time step,” our method (MTE-EDM) improved forecast accuracy in 18 of 19 empirical ectotherm time series (by 19\% on average), with the largest gains in more seasonal environments. MTE-EDM assumes that temperature affects only the rate, rather than the form, of population dynamics, and that interacting species have approximately similar temperature dependence. A review of laboratory studies suggests these assumptions are reasonable, at least approximately, though not for all ecological systems. Our approach highlights how to combine modern data-driven forecasting techniques with ecological theory and mechanistic understanding to predict the response of complex ecosystems to temperature variability and trends.}, - language = {en}, - number = {12}, - urldate = {2023-05-02}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Munch, Stephan B. and Rogers, Tanya L. and Symons, Celia C. and Anderson, David and Pennekamp, Frank}, - month = mar, - year = {2023}, - pages = {e2211758120}, - file = {pnas.2211758120.pdf:/Users/birdy/Downloads/pnas.2211758120.pdf:application/pdf}, -} - -@article{sugihara_nonlinear_1990, - title = {Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series}, - volume = {344}, - copyright = {1990 Nature Publishing Group}, - issn = {1476-4687}, - url = {https://www.nature.com/articles/344734a0}, - doi = {10.1038/344734a0}, - abstract = {An approach is presented for making short-term predictions about the trajectories of chaotic dynamical systems. The method is applied to data on measles, chickenpox, and marine phytoplankton populations, to show how apparent noise associated with deterministic chaos can be distinguished from sampling error and other sources of externally induced environmental noise.}, - language = {en}, - number = {6268}, - urldate = {2023-05-02}, - journal = {Nature}, - author = {Sugihara, George and May, Robert M.}, - month = apr, - year = {1990}, - note = {Number: 6268 -Publisher: Nature Publishing Group}, - keywords = {Humanities and Social Sciences, multidisciplinary, Science}, - pages = {734--741}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/LDYABKT4/Sugihara and May - 1990 - Nonlinear forecasting as a way of distinguishing c.pdf:application/pdf}, -} - -@article{fujita_alternative_2023, - title = {Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics}, - volume = {11}, - issn = {2049-2618}, - url = {https://doi.org/10.1186/s40168-023-01474-5}, - doi = {10.1186/s40168-023-01474-5}, - abstract = {Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in human microbiomes.}, - language = {en}, - number = {1}, - urldate = {2023-05-02}, - journal = {Microbiome}, - author = {Fujita, Hiroaki and Ushio, Masayuki and Suzuki, Kenta and Abe, Masato S. and Yamamichi, Masato and Iwayama, Koji and Canarini, Alberto and Hayashi, Ibuki and Fukushima, Keitaro and Fukuda, Shinji and Kiers, E. Toby and Toju, Hirokazu}, - month = mar, - year = {2023}, - keywords = {Alternative stable states, Biodiversity, Biological communities, Chaos, Community collapse, Community stability, Dysbiosis, Empirical dynamic modeling, Microbiome dynamics, Non-linear dynamics}, - pages = {63}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/LJZAZT65/Fujita et al. - 2023 - Alternative stable states, nonlinear behavior, and.pdf:application/pdf}, -} - -@inproceedings{kay_caltech_2022, - address = {Cham}, - series = {Lecture {Notes} in {Computer} {Science}}, - title = {The {Caltech} {Fish} {Counting} {Dataset}: {A} {Benchmark} for {Multiple}-{Object} {Tracking} and {Counting}}, - isbn = {978-3-031-20074-8}, - shorttitle = {The {Caltech} {Fish} {Counting} {Dataset}}, - doi = {10.1007/978-3-031-20074-8_17}, - abstract = {We present the Caltech Fish Counting Dataset (CFC ), a large-scale dataset for detecting, tracking, and counting fish in sonar videos. We identify sonar videos as a rich source of data for advancing low signal-to-noise computer vision applications and tackling domain generalization in multiple-object tracking (MOT) and counting. In comparison to existing MOT and counting datasets, which are largely restricted to videos of people and vehicles in cities, CFC is sourced from a natural-world domain where targets are not easily resolvable and appearance features cannot be easily leveraged for target re-identification. With over half a million annotations in over 1,500 videos sourced from seven different sonar cameras, CFC allows researchers to train MOT and counting algorithms and evaluate generalization performance at unseen test locations. We perform extensive baseline experiments and identify key challenges and opportunities for advancing the state of the art in generalization in MOT and counting.}, - language = {en}, - booktitle = {Computer {Vision} – {ECCV} 2022}, - publisher = {Springer Nature Switzerland}, - author = {Kay, Justin and Kulits, Peter and Stathatos, Suzanne and Deng, Siqi and Young, Erik and Beery, Sara and Van Horn, Grant and Perona, Pietro}, - editor = {Avidan, Shai and Brostow, Gabriel and Cissé, Moustapha and Farinella, Giovanni Maria and Hassner, Tal}, - year = {2022}, - keywords = {Counting, Detection, Tracking, Video dataset}, - pages = {290--311}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/M2APBWFQ/Kay et al. - 2022 - The Caltech Fish Counting Dataset A Benchmark for.pdf:application/pdf}, -} - -@article{chang_engineering_2021, - title = {Engineering complex communities by directed evolution}, - volume = {5}, - copyright = {2021 The Author(s), under exclusive licence to Springer Nature Limited}, - issn = {2397-334X}, - url = {https://www.nature.com/articles/s41559-021-01457-5}, - doi = {10.1038/s41559-021-01457-5}, - abstract = {Directed evolution has been used for decades to engineer biological systems at or below the organismal level. Above the organismal level, a small number of studies have attempted to artificially select microbial ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. Here, we have developed a flexible modelling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions. By artificially selecting hundreds of in silico microbial metacommunities under identical conditions, we first show that the main breeding methods used to date, which do not necessarily let communities reach their ecological equilibrium, are outperformed by a simple screen of sufficiently mature communities. We then identify a range of alternative directed evolution strategies that, particularly when applied in combination, are well suited for the top-down engineering of large, diverse and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure–function landscape to be navigated in search of dynamically stable and ecologically resilient communities with desired quantitative attributes.}, - language = {en}, - number = {7}, - urldate = {2023-05-11}, - journal = {Nature Ecology \& Evolution}, - author = {Chang, Chang-Yu and Vila, Jean C. C. and Bender, Madeline and Li, Richard and Mankowski, Madeleine C. and Bassette, Molly and Borden, Julia and Golfier, Stefan and Sanchez, Paul Gerald L. and Waymack, Rachel and Zhu, Xinwen and Diaz-Colunga, Juan and Estrela, Sylvie and Rebolleda-Gomez, Maria and Sanchez, Alvaro}, - month = jul, - year = {2021}, - note = {Number: 7 -Publisher: Nature Publishing Group}, - keywords = {Community ecology, Evolutionary theory}, - pages = {1011--1023}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/28DAPUW2/Chang et al. - 2021 - Engineering complex communities by directed evolut.pdf:application/pdf}, -} - -@article{lazebnik_can_2002, - title = {Can a biologist fix a radio?—{Or}, what {I} learned while studying apoptosis}, - volume = {2}, - issn = {1535-6108, 1878-3686}, - shorttitle = {Can a biologist fix a radio?}, - url = {https://www.cell.com/cancer-cell/abstract/S1535-6108(02)00133-2}, - doi = {10.1016/S1535-6108(02)00133-2}, - language = {English}, - number = {3}, - urldate = {2023-05-11}, - journal = {Cancer Cell}, - author = {Lazebnik, Yuri}, - month = sep, - year = {2002}, - pmid = {12242150}, - note = {Publisher: Elsevier}, - pages = {179--182}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/MM347IX4/Lazebnik - 2002 - Can a biologist fix a radio—Or, what I learned wh.pdf:application/pdf}, -} - -@article{deyle_tracking_2016, - title = {Tracking and forecasting ecosystem interactions in real time}, - volume = {283}, - url = {https://royalsocietypublishing.org/doi/full/10.1098/rspb.2015.2258}, - doi = {10.1098/rspb.2015.2258}, - abstract = {Evidence shows that species interactions are not constant but change as the ecosystem shifts to new states. Although controlled experiments and model investigations demonstrate how nonlinear interactions can arise in principle, empirical tools to track and predict them in nature are lacking. Here we present a practical method, using available time-series data, to measure and forecast changing interactions in real systems, and identify the underlying mechanisms. The method is illustrated with model data from a marine mesocosm experiment and limnologic field data from Sparkling Lake, WI, USA. From simple to complex, these examples demonstrate the feasibility of quantifying, predicting and understanding state-dependent, nonlinear interactions as they occur in situ and in real time—a requirement for managing resources in a nonlinear, non-equilibrium world.}, - number = {1822}, - urldate = {2023-05-16}, - journal = {Proceedings of the Royal Society B: Biological Sciences}, - author = {Deyle, Ethan R. and May, Robert M. and Munch, Stephan B. and Sugihara, George}, - month = jan, - year = {2016}, - note = {Publisher: Royal Society}, - keywords = {changing interaction strength, community matrix, empirical dynamics, nonlinear, S-map, state space reconstruction}, - pages = {20152258}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/GAGG2JHP/Deyle et al. - 2016 - Tracking and forecasting ecosystem interactions in.pdf:application/pdf}, -} - -@article{gonzalez_automatic_2019, - title = {Automatic plankton quantification using deep features}, - volume = {41}, - issn = {0142-7873}, - url = {https://doi.org/10.1093/plankt/fbz023}, - doi = {10.1093/plankt/fbz023}, - abstract = {The study of marine plankton data is vital to monitor the health of the world’s oceans. In recent decades, automatic plankton recognition systems have proved useful to address the vast amount of data collected by specially engineered in situ digital imaging systems. At the beginning, these systems were developed and put into operation using traditional automatic classification techniques, which were fed with hand-designed local image descriptors (such as Fourier features), obtaining quite successful results. In the past few years, there have been many advances in the computer vision community with the rebirth of neural networks. In this paper, we leverage how descriptors computed using convolutional neural networks trained with out-of-domain data are useful to replace hand-designed descriptors in the task of estimating the prevalence of each plankton class in a water sample. To achieve this goal, we have designed a broad set of experiments that show how effective these deep features are when working in combination with state-of-the-art quantification algorithms.}, - number = {4}, - urldate = {2023-05-18}, - journal = {Journal of Plankton Research}, - author = {González, Pablo and Castaño, Alberto and Peacock, Emily E and Díez, Jorge and Del Coz, Juan José and Sosik, Heidi M}, - month = jul, - year = {2019}, - pages = {449--463}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/YS4AGBKW/González et al. - 2019 - Automatic plankton quantification using deep featu.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/M6LWQPI2/5537667.html:text/html}, -} - -@inproceedings{walker_improving_2021, - address = {Montreal, BC, Canada}, - title = {Improving {Rare}-{Class} {Recognition} of {Marine} {Plankton} with {Hard} {Negative} {Mining}}, - isbn = {978-1-66540-191-3}, - url = {https://ieeexplore.ieee.org/document/9607849/}, - doi = {10.1109/ICCVW54120.2021.00410}, - abstract = {Biological oceanographers are increasingly adopting machine learning techniques to conduct quantitative assessments of marine plankton. Most supervised plankton classifiers are trained on labeled image datasets annotated by domain experts under the closed world assumption: all object classes and their priors are the same during both training and deployment. This assumption, however, is hard to satisfy in the actual ocean where data is subject to dataset shift due to shifting populations and from the introduction of object categories not seen during training. Here we present an alternative approach for training and evaluating plankton classifiers under the more realistic open world scenario. We specifically address the problems of out-of-distribution detection and dataset shift under the class imbalance setting where downsampling is needed to reliably detect and classify relatively rare target classes. We apply a hard negative mining approach called Background Resampling to perform downsampling and compare it to other strategies. We show that Background Resampling improves detection of novel particle classes while simultaneously providing competitive classification performance under dataset shift.}, - language = {en}, - urldate = {2023-05-18}, - booktitle = {2021 {IEEE}/{CVF} {International} {Conference} on {Computer} {Vision} {Workshops} ({ICCVW})}, - publisher = {IEEE}, - author = {Walker, Joseph L. and Orenstein, Eric C.}, - month = oct, - year = {2021}, - pages = {3665--3675}, - file = {Walker and Orenstein - 2021 - Improving Rare-Class Recognition of Marine Plankto.pdf:/Users/birdy/Zotero/storage/9IBBTBGB/Walker and Orenstein - 2021 - Improving Rare-Class Recognition of Marine Plankto.pdf:application/pdf}, -} - -@article{kerr_collaborative_2020, - title = {Collaborative {Deep} {Learning} {Models} to {Handle} {Class} {Imbalance} in {FlowCam} {Plankton} {Imagery}}, - volume = {8}, - issn = {2169-3536}, - doi = {10.1109/ACCESS.2020.3022242}, - abstract = {Using automated imaging technologies, it is now possible to generate previously unprecedented volumes of plankton image data which can be used to study the composition of plankton assemblages. However, the current need to manually classify individual images introduces a bottleneck into processing chains. Although Machine Learning techniques have been used to try and address this issue, past efforts have suffered from accuracy limitations, especially in minority classes. Here we use state-of-the-art methods in Deep Learning to investigate suitable architectures for training an automated plankton classification system which achieves high efficacy for both abundant and rare taxa. We collected live plankton from Station L4 in the Western English Channel and imaged 11,371 particles covering 104 taxonomic groups using the automated plankton imaging system FlowCam. The image set contained a severe class imbalance, with some taxa represented by {\textgreater} 600 images while other, rarer taxa were represented by just 14. We demonstrate that by allowing multiple Deep Learning models to collaborate in a single classification system, classification accuracy improves for minority classes when compared with the best individual model. The top collaborative model achieved a 6 \% improvement in F1 accuracy over the best individual model, while overall accuracy improved by 3.2 \%. This resulted in a 97.4 \% overall accuracy score and a 96.2 \% F1 macro score on a separate holdout test set containing 104 taxonomic groups. Based on a survey of similar studies in the literature, we believe collaborative deep learning models can significantly improve the accuracy of existing automated plankton classification systems.}, - journal = {IEEE Access}, - author = {Kerr, Thomas and Clark, James R. and Fileman, Elaine S. and Widdicombe, Claire E. and Pugeault, Nicolas}, - year = {2020}, - note = {Conference Name: IEEE Access}, - keywords = {Machine learning, deep learning, Automated plankton identification, Biological neural networks, Collaboration, Computational modeling, Convolution, convolutional neural networks, Feature extraction, FlowCam, multi-layer perceptron model, Station L4, Training}, - pages = {170013--170032}, - file = {IEEE Xplore Abstract Record:/Users/birdy/Zotero/storage/E9MSTETK/stamp.html:text/html;IEEE Xplore Full Text PDF:/Users/birdy/Zotero/storage/P2XN56XI/Kerr et al. - 2020 - Collaborative Deep Learning Models to Handle Class.pdf:application/pdf}, -} - -@article{ellen_improving_2019, - title = {Improving plankton image classification using context metadata}, - volume = {17}, - issn = {1541-5856}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lom3.10324}, - doi = {10.1002/lom3.10324}, - abstract = {Advances in both hardware and software are enabling rapid proliferation of in situ plankton imaging methods, requiring more effective machine learning approaches to image classification. Deep Learning methods, such as convolutional neural networks (CNNs), show marked improvement over traditional feature-based supervised machine learning algorithms, but require careful optimization of hyperparameters and adequate training sets. Here, we document some best practices in applying CNNs to zooplankton and marine snow images and note where our results differ from contemporary Deep Learning findings in other domains. We boost the performance of CNN classifiers by incorporating metadata of different types and illustrate how to assimilate metadata beyond simple concatenation. We utilize both geotemporal (e.g., sample depth, location, time of day) and hydrographic (e.g., temperature, salinity, chlorophyll a) metadata and show that either type by itself, or both combined, can substantially reduce error rates. Incorporation of context metadata also boosts performance of the feature-based classifiers we evaluated: Random Forest, Extremely Randomized Trees, Gradient Boosted Classifier, Support Vector Machines, and Multilayer Perceptron. For our assessments, we use an original data set of 350,000 in situ images (roughly 50\% marine snow and 50\% non-snow sorted into 26 categories) from a novel in situ Zooglider. We document asymptotically increasing performance with more computationally intensive techniques, such as substantially deeper networks and artificially augmented data sets. Our best model achieves 92.3\% accuracy with our 27-class data set. We provide guidance for further refinements that are likely to provide additional gains in classifier accuracy.}, - language = {en}, - number = {8}, - urldate = {2023-05-18}, - journal = {Limnology and Oceanography: Methods}, - author = {Ellen, Jeffrey S. and Graff, Casey A. and Ohman, Mark D.}, - year = {2019}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10324}, - pages = {439--461}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/3BP4JKLG/Ellen et al. - 2019 - Improving plankton image classification using cont.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/H2X6CN9U/lom3.html:text/html}, -} - -@article{wilchek_human---loop_2023, - title = {Human-in-the-loop for computer vision assurance: {A} survey}, - volume = {123}, - issn = {0952-1976}, - shorttitle = {Human-in-the-loop for computer vision assurance}, - url = {https://www.sciencedirect.com/science/article/pii/S0952197623005602}, - doi = {10.1016/j.engappai.2023.106376}, - abstract = {Human-in-the-loop (HITL), a key branch of Human–Computer Interaction (HCI), is increasingly proposed in the research literature as a key assurance method for automated analyses and predictive application designs. As the need increases to improve methods in Artificial Intelligence (AI) model training, optimize systems performance, provide AI explainability, and monitor AI system operations, the concept of HITL is gaining traction due to its value in solving these challenges. This survey of existing works on HITL from a computer vision system design perspective focuses on the following AI assurance principles: (1) improved data assurance, such as data preparation or automated data labeling; (2) algorithmic assurance, such as managing uncertainty and AI trustworthiness; and (3) critical limitations and capabilities introduced by HITL into a system’s operational efficiency. We survey prior work within these foci, including technical strengths and weaknesses of novel approaches and ongoing research. This review of the state of the art in HITL computer vision research supports an informed discussion of considerations and future opportunities in this critical space.}, - language = {en}, - urldate = {2023-05-19}, - journal = {Engineering Applications of Artificial Intelligence}, - author = {Wilchek, Matthew and Hanley, Will and Lim, Jude and Luther, Kurt and Batarseh, Feras A.}, - month = aug, - year = {2023}, - keywords = {AI assurance, Computer vision, Human-in-the-loop, Human–computer interaction, Learning systems, Object detection}, - pages = {106376}, - file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/XUK4ZLLN/S0952197623005602.html:text/html}, -} - -@article{bian_understanding_2023, - title = {Understanding opposing predictions of {Prochlorococcus} in a changing climate}, - volume = {14}, - copyright = {2023 The Author(s)}, - issn = {2041-1723}, - url = {https://www.nature.com/articles/s41467-023-36928-9}, - doi = {10.1038/s41467-023-36928-9}, - abstract = {Statistically derived species distribution models (SDMs) are increasingly used to predict ecological changes on a warming planet. For Prochlorococcus, the most abundant phytoplankton, an established statistical prediction conflicts with dynamical models as they predict large, opposite, changes in abundance. We probe the SDM at various spatial-temporal scales, showing that light and temperature fail to explain both temporal fluctuations and sharp spatial transitions. Strong correlations between changes in temperature and population emerge only at very large spatial scales, as transects pass through transitions between regions of high and low abundance. Furthermore, a two-state model based on a temperature threshold matches the original SDM in the surface ocean. We conclude that the original SDM has little power to predict changes when Prochlorococcus is already abundant, which resolves the conflict with dynamical models. Our conclusion suggests that SDMs should prove efficacy across multiple spatial-temporal scales before being trusted in a changing ocean.}, - language = {en}, - number = {1}, - urldate = {2023-06-05}, - journal = {Nature Communications}, - author = {Bian, Vincent and Cai, Merrick and Follett, Christopher L.}, - month = mar, - year = {2023}, - note = {Number: 1 -Publisher: Nature Publishing Group}, - keywords = {Biogeography, Projection and prediction, DAR1}, - pages = {1445}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/PQNIBYHE/Bian et al. - 2023 - Understanding opposing predictions of Prochlorococ.pdf:application/pdf}, -} - -@misc{noauthor_ocean_nodate, - title = {Ocean mover’s distance: using optimal transport for analysing oceanographic data}, - shorttitle = {Ocean mover’s distance}, - url = {https://royalsocietypublishing.org/doi/epdf/10.1098/rspa.2021.0875}, - language = {en}, - urldate = {2023-06-05}, - doi = {10.1098/rspa.2021.0875}, - file = {Full Text:/Users/birdy/Zotero/storage/YPAJVFL3/Ocean mover’s distance using optimal transport fo.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/MVL45IZE/rspa.2021.html:text/html}, -} - -@article{irisson_machine_2022, - title = {Machine {Learning} for the {Study} of {Plankton} and {Marine} {Snow} from {Images}}, - volume = {14}, - url = {https://doi.org/10.1146/annurev-marine-041921-013023}, - doi = {10.1146/annurev-marine-041921-013023}, - abstract = {Quantitative imaging instruments produce a large number of images of plankton and marine snow, acquired in a controlled manner, from which the visual characteristics of individual objects and their in situ concentrations can be computed. To exploit this wealth of information, machine learning is necessary to automate tasks such as taxonomic classification. Through a review of the literature, we highlight the progress of those machine classifiers and what they can and still cannot be trusted for. Several examples showcase how the combination of quantitative imaging with machine learning has brought insights on pelagic ecology. They also highlight what is still missing and how images could be exploited further through trait-based approaches. In the future, we suggest deeper interactions with the computer sciences community, the adoption of data standards, and the more systematic sharing of databases to build a global community of pelagic image providers and users.}, - number = {1}, - urldate = {2023-07-14}, - journal = {Annual Review of Marine Science}, - author = {Irisson, Jean-Olivier and Ayata, Sakina-Dorothée and Lindsay, Dhugal J. and Karp-Boss, Lee and Stemmann, Lars}, - year = {2022}, - pmid = {34460314}, - note = {\_eprint: https://doi.org/10.1146/annurev-marine-041921-013023}, - pages = {277--301}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/IWC2B69M/Irisson et al. - 2022 - Machine Learning for the Study of Plankton and Mar.pdf:application/pdf}, -} - -@article{macneil_plankton_2021, - title = {Plankton classification with high-throughput submersible holographic microscopy and transfer learning}, - volume = {21}, - issn = {2730-7182}, - url = {https://doi.org/10.1186/s12862-021-01839-0}, - doi = {10.1186/s12862-021-01839-0}, - abstract = {Plankton are foundational to marine food webs and an important feature for characterizing ocean health. Recent developments in quantitative imaging devices provide in-flow high-throughput sampling from bulk volumes—opening new ecological challenges exploring microbial eukaryotic variation and diversity, alongside technical hurdles to automate classification from large datasets. However, a limited number of deployable imaging instruments have been coupled with the most prominent classification algorithms—effectively limiting the extraction of curated observations from field deployments. Holography offers relatively simple coherent microscopy designs with non-intrusive 3-D image information, and rapid frame rates that support data-driven plankton imaging tasks. Classification benchmarks across different domains have been set with transfer learning approaches, focused on repurposing pre-trained, state-of-the-art deep learning models as classifiers to learn new image features without protracted model training times. Combining the data production of holography, digital image processing, and computer vision could improve in-situ monitoring of plankton communities and contribute to sampling the diversity of microbial eukaryotes.}, - number = {1}, - urldate = {2023-07-14}, - journal = {BMC Ecology and Evolution}, - author = {MacNeil, Liam and Missan, Sergey and Luo, Junliang and Trappenberg, Thomas and LaRoche, Julie}, - month = jun, - year = {2021}, - keywords = {Classification workflow, Convolutional neural networks, Deep learning, Deployable microscope, High-throughput imaging, Holographic microscopy, Plankton}, - pages = {123}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/983GMGI8/MacNeil et al. - 2021 - Plankton classification with high-throughput subme.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/SAXVJK45/s12862-021-01839-0.html:text/html}, -} - -@article{scotti_ecosystem-based_2022, - title = {Ecosystem-based fisheries management increases catch and carbon sequestration through recovery of exploited stocks: {The} western {Baltic} {Sea} case study}, - volume = {9}, - issn = {2296-7745}, - shorttitle = {Ecosystem-based fisheries management increases catch and carbon sequestration through recovery of exploited stocks}, - url = {https://www.frontiersin.org/articles/10.3389/fmars.2022.879998}, - abstract = {Legal requirement in Europe asks for Ecosystem-Based Fisheries Management (EBFM) in European seas, including consideration of trophic interactions and minimization of negative impacts of fishing on food webs and ecosystem functioning. This study presents the first mass-balanced ecosystem model focused on the western Baltic Sea (WBS). Results show that heavy fishing pressure exerted on the WBS has forced top predators such as harbour porpoise and cod to cover their dietary needs by shifting from forage fish to other prey or find food outside of the model area. The model was then developed to explore the dynamics of four future fishery scenarios: (1) business as usual (BAU), (2) maximum sustainable fishing (F = FMSY), (3) half of FMSY, and (4) EBFM with F = 0.5 FMSY for forage fish and F = 0.8 FMSY for other fish. Simulations show that BAU would perpetuate low catches from depleted stocks with a high risk of extinction for harbour porpoise. In contrast, the EBFM scenario would allow the recovery of harbour porpoise, forage fish and cod with increases in catch of herring and cod. EBFM promotes ecosystem resilience to eutrophication and ocean warming, and through the rebuilding of commercial stocks increases by more than three times carbon sequestration compared to BAU. The model provides an interrelated assessment of trophic guilds in the WBS, as required by European law to assess whether European seas are in good environmental status.}, - urldate = {2023-07-14}, - journal = {Frontiers in Marine Science}, - author = {Scotti, Marco and Opitz, Silvia and MacNeil, Liam and Kreutle, Axel and Pusch, Christian and Froese, Rainer}, - year = {2022}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/DFDLU7N4/Scotti et al. - 2022 - Ecosystem-based fisheries management increases cat.pdf:application/pdf}, -} - -@article{tsakalakis_diel_2022, - title = {Diel light cycles affect phytoplankton competition in the global ocean}, - volume = {31}, - copyright = {© 2022 The Authors. Global Ecology and Biogeography published by John Wiley \& Sons Ltd.}, - issn = {1466-8238}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/geb.13562}, - doi = {10.1111/geb.13562}, - abstract = {Aim Light, essential for photosynthesis, is present in two periodic cycles in nature: seasonal and diel. Although seasonality of light is typically resolved in ocean biogeochemical–ecosystem models because of its significance for seasonal succession and biogeography of phytoplankton, the diel light cycle is generally not resolved. The goal of this study is to demonstrate the impact of diel light cycles on phytoplankton competition and biogeography in the global ocean. Location Global ocean. Major taxa studied Phytoplankton. Methods We use a three-dimensional global ocean model and compare simulations of high temporal resolution with and without diel light cycles. The model simulates 15 phytoplankton types with different cell sizes, encompassing two broad ecological strategies: small cells with high nutrient affinity (gleaners) and larger cells with high maximal growth rate (opportunists). Both are grazed by zooplankton and limited by nitrogen, phosphorus and iron. Results Simulations show that diel cycles of light induce diel cycles in limiting nutrients in the global ocean. Diel nutrient cycles are associated with higher concentrations of limiting nutrients, by 100\% at low latitudes (−40° to 40°), a process that increases the relative abundance of opportunists over gleaners. Size classes with the highest maximal growth rates from both gleaner and opportunist groups are favoured by diel light cycles. This mechanism weakens as latitude increases, because the effects of the seasonal cycle dominate over those of the diel cycle. Main conclusions Understanding the mechanisms that govern phytoplankton biogeography is crucial for predicting ocean ecosystem functioning and biogeochemical cycles. We show that the diel light cycle has a significant impact on phytoplankton competition and biogeography, indicating the need for understanding the role of diel processes in shaping macroecological patterns in the global ocean.}, - language = {en}, - number = {9}, - urldate = {2023-08-04}, - journal = {Global Ecology and Biogeography}, - author = {Tsakalakis, Ioannis and Follows, Michael J. and Dutkiewicz, Stephanie and Follett, Christopher L. and Vallino, Joseph J.}, - year = {2022}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/geb.13562}, - keywords = {phytoplankton, resource competition, modelling, biogeography, diel light cycle, global ocean, nutrient cycles, diel}, - pages = {1838--1849}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/4IRAUGAH/Tsakalakis et al. - 2022 - Diel light cycles affect phytoplankton competition.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/QKKTIJ3P/geb.html:text/html}, -} - -@article{tsakalakis_diel_2018, - title = {Diel light cycle as a key factor for modelling phytoplankton biogeography and diversity}, - volume = {384}, - issn = {0304-3800}, - url = {https://www.sciencedirect.com/science/article/pii/S0304380018302278}, - doi = {10.1016/j.ecolmodel.2018.06.022}, - abstract = {Understanding the mechanisms driving species biogeography and biodiversity remains a major challenge in phytoplankton ecology. Using a model of two phytoplankton species with a gleaner-opportunist trade-off and competing for light and a limiting nutrient, we show that the diel light cycle may be an essential factor to explain large-scale ecological patterns. When only the seasonal light cycle is considered (control scenario) the model predicts that, independently of the nutrient supply, gleaners should dominate across all latitudes and opportunists can obtain a temporal niche only at high latitudes. However, the diel light cycle makes the competition outcome also a function of nutrient supply by affecting the amplitude of diel nutrient oscillations, with gleaners dominating when nutrient supply is low, opportunists when nutrient supply is high, and both species coexisting at intermediate levels of nutrient supply. The combined effects of seasonal and diel light cycles (diel scenario) shape a latitudinal diversity gradient with decreasing diversity towards higher latitudes and a unimodal dependence of diversity on nutrient supply and, therefore, on ecosystem productivity. The proposed mechanism can help interpret the biogeography of major phytoplankton functional groups in the global ocean and link them with large-scale biodiversity patterns.}, - language = {en}, - urldate = {2023-08-04}, - journal = {Ecological Modelling}, - author = {Tsakalakis, Ioannis and Pahlow, Markus and Oschlies, Andreas and Blasius, Bernd and Ryabov, Alexey B.}, - month = sep, - year = {2018}, - keywords = {Biodiversity, Biogeography, diel, Diel light cycle, Nutrient oscillations, Phytoplankton, Resource competition}, - pages = {241--248}, - file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/IRUYQ5WD/S0304380018302278.html:text/html;Tsakalakis et al. - 2018 - Diel light cycle as a key factor for modelling phy.pdf:/Users/birdy/Zotero/storage/EW5BGQ4D/Tsakalakis et al. - 2018 - Diel light cycle as a key factor for modelling phy.pdf:application/pdf}, -} - -@article{grilli_macroecological_2020, - title = {Macroecological laws describe variation and diversity in microbial communities}, - volume = {11}, - copyright = {2020 The Author(s)}, - issn = {2041-1723}, - url = {https://www.nature.com/articles/s41467-020-18529-y}, - doi = {10.1038/s41467-020-18529-y}, - abstract = {How the coexistence of many species is maintained is a fundamental and unresolved question in ecology. Coexistence is a puzzle because we lack a mechanistic understanding of the variation in species presence and abundance. Whether variation in ecological communities is driven by deterministic or random processes is one of the most controversial issues in ecology. Here, I study the variation of species presence and abundance in microbial communities from a macroecological standpoint. I identify three macroecological laws that quantitatively characterize the fluctuation of species abundance across communities and over time. Using these three laws, one can predict species’ presence and absence, diversity, and commonly studied macroecological patterns. I show that a mathematical model based on environmental stochasticity, the stochastic logistic model, quantitatively predicts the three macroecological laws, as well as non-stationary properties of community dynamics.}, - language = {en}, - number = {1}, - urldate = {2023-08-04}, - journal = {Nature Communications}, - author = {Grilli, Jacopo}, - month = sep, - year = {2020}, - note = {Number: 1 -Publisher: Nature Publishing Group}, - keywords = {Microbial ecology, Biodiversity, Community ecology, Ecological modelling, Macroecology, microbes}, - pages = {4743}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/XTDE7UWR/Grilli - 2020 - Macroecological laws describe variation and divers.pdf:application/pdf}, -} - -@article{williamson_beneficial_2001, - title = {Beneficial and {Detrimental} {Effects} of {Uv} on {Aquatic} {Organisms}: {Implications} of {Spectral} {Variation}}, - volume = {11}, - copyright = {© 2001 by the Ecological Society of America}, - issn = {1939-5582}, - shorttitle = {Beneficial and {Detrimental} {Effects} of {Uv} on {Aquatic} {Organisms}}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1890/1051-0761%282001%29011%5B1843%3ABADEOU%5D2.0.CO%3B2}, - doi = {10.1890/1051-0761(2001)011[1843:BADEOU]2.0.CO;2}, - abstract = {Solar ultraviolet radiation (UVR) may have beneficial as well as detrimental effects on living systems. For example, UV-B radiation (280–320 nm) is generally damaging, while UV-A radiation (320–400 nm) may cause damage or stimulate beneficial photorepair of UV-B damage. The nature of both direct and indirect effects of UVR in nature depends on both the photon flux density and the spectral composition of the radiation incident on aquatic organisms across environmental UVR gradients in space (depth, transparency, elevation) and time (diel, seasonal, interannual). Here we use the common and widespread freshwater cladoceran Daphnia pulicaria as a model organism to demonstrate the potential importance of these wavelength-specific effects of UVR to the ecology of aquatic organisms. UVR-exposure experiments are used to manipulate both natural solar and artificial UVR sources to examine the beneficial as well as detrimental effects of different wavelengths of UVR. Changes in the spectral composition of solar radiation are also examined along several natural environmental gradients including diel gradients, depth gradients, and dissolved organic carbon (DOC) gradients. The implications of variation in the spectral composition of UVR for aquatic organisms are discussed. The first biological weighting function (BWF) for a freshwater cladoceran is presented here. It demonstrates that the shortest UV-B wavelengths in sunlight are potentially the most damaging per photon. However, due to the greater photon flux density of longer wavelength UVR in sunlight, the net potential damage to Daphnia in nature is greatest for the longer wavelength UV-B and shorter wavelength UV-A radiation in the 305–322 nm range. Overall the contribution of UV-B to the total mortality response of Daphnia exposed to full-spectrum solar radiation for 7 h on a sunny summer day is 64\% while UV-A contributes 36\%. The BWF for Daphnia is used with the transmission spectrum for Mylar D to demonstrate that Mylar D cuts out only about half of the damaging UVR in sunlight. Following exposure to damaging UV-B, Daphnia exhibits a dramatic increase in survival in the presence of longer wavelength UV-A and visible radiation due to the stimulation of photoenzymatic repair. We present data that demonstrate the importance of both atmospheric ozone and DOC in creating strong environmental gradients in the intensity (irradiance) and spectral composition of solar UVR in nature. The light-absorbing component of DOC, chromophoric dissolved organic matter (CDOM), is particularly important in creating depth refugia from damaging UV-B in freshwater ecosystems. CDOM may also cause intense variations in the ratio of potentially beneficial UV-A to detrimental UV-B radiation to which aquatic organisms are exposed. In addition to changes in atmospheric ozone, future changes in CDOM related to climate change or other environmental disturbances may substantially alter the underwater exposure of a variety of aquatic organisms to different wavelengths of solar UVR.}, - language = {en}, - number = {6}, - urldate = {2023-08-04}, - journal = {Ecological Applications}, - author = {Williamson, Craig E. and Neale, Patrick J. and Grad, Gabriella and De Lange, Hendrika J. and Hargreaves, Bruce R.}, - year = {2001}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1890/1051-0761\%282001\%29011\%5B1843\%3ABADEOU\%5D2.0.CO\%3B2}, - keywords = {diel, biological weighting function, Daphnia pulicaria, dissolved organic carbon, environmental gradients, lake optics, ozone depletion, relative damage to aquatic organisms, spectral composition of solar radiation, tolerance of UV radiation, ultraviolet radiation, UV-A and UV-B, zooplankton}, - pages = {1843--1857}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/C65AT69Z/Williamson et al. - 2001 - Beneficial and Detrimental Effects of Uv on Aquati.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/AQL7Y8NU/1051-0761(2001)011[1843BADEOU]2.0.html:text/html}, -} - -@incollection{fasham_phytoplankton_2003, - address = {Berlin, Heidelberg}, - title = {Phytoplankton and {Their} {Role} in {Primary}, {New}, and {Export} {Production}}, - isbn = {978-3-642-62691-3 978-3-642-55844-3}, - url = {http://link.springer.com/10.1007/978-3-642-55844-3_5}, - language = {en}, - urldate = {2023-08-15}, - booktitle = {Ocean {Biogeochemistry}}, - publisher = {Springer Berlin Heidelberg}, - author = {Falkowski, Paul G. and Laws, Edward A. and Barber, Richard T. and Murray, James W.}, - editor = {Fasham, Michael J. R.}, - year = {2003}, - doi = {10.1007/978-3-642-55844-3_5}, - keywords = {DAR1}, - pages = {99--121}, - file = {Falkowski et al. - 2003 - Phytoplankton and Their Role in Primary, New, and .pdf:/Users/birdy/Zotero/storage/W6TWZWCA/Falkowski et al. - 2003 - Phytoplankton and Their Role in Primary, New, and .pdf:application/pdf}, -} - -@article{tilman_resource_1977-1, - title = {Resource {Competition} between {Plankton} {Algae}: {An} {Experimental} and {Theoretical} {Approach}}, - volume = {58}, - issn = {0012-9658}, - shorttitle = {Resource {Competition} between {Plankton} {Algae}}, - url = {https://www.jstor.org/stable/1935608}, - doi = {10.2307/1935608}, - abstract = {The results of 76 long-term competition experiments between two species of freshwater algae (Asterionella formosa and Cyclotella meneghiniana) grown along a resource gradient agree with the predictions of two different models of resource competition. Both models are based on the functional resource-utilization response of each species to limiting resources. The Monod and the Variable Internal Stores model of competition made similar predictions. Asterionella was observed to be competitively dominant when both species were phosphate limited; Cyclotella was dominant when both species were silicate limited; and both species stably coexisted when each species was growth-rate limited by a different resource. Almost 75\% of the variance in the relative abundances of these two species along a natural silicate-phosphate gradient in Lake Michigan is explained by the Monod model.}, - number = {2}, - urldate = {2023-08-24}, - journal = {Ecology}, - author = {Tilman, David}, - year = {1977}, - note = {Publisher: Ecological Society of America}, - pages = {338--348}, -} - -@article{dallolmo_inferring_2011, - title = {Inferring phytoplankton carbon and eco-physiological rates from diel cycles of spectral particulate beam-attenuation coefficient}, - volume = {8}, - issn = {1726-4170}, - url = {https://bg.copernicus.org/articles/8/3423/2011/bg-8-3423-2011.html}, - doi = {10.5194/bg-8-3423-2011}, - abstract = {The diurnal fluctuations in solar irradiance impose a fundamental frequency on ocean biogeochemistry. Observations of the ocean carbon cycle at these frequencies are rare, but could be considerably expanded by measuring and interpreting the inherent optical properties. A method is presented to analyze diel cycles in particulate beam-attenuation coefficient (cp) measured at multiple wavelengths. The method is based on fitting observations with a size-structured population model coupled to an optical model to infer the particle size distribution and physiologically relevant parameters of the cells responsible for the measured diel cycle in cp. Results show that the information related to size and contained in the spectral data can be exploited to independently estimate growth and loss rates during the day and night. In addition, the model can characterize the population of particles affecting the diel variability in cp. Application of this method to spectral cp measured at a station in the oligotrophic Mediterranean Sea suggests that most of the observed variations in cp can be ascribed to a synchronized population of cells with an equivalent spherical diameter around 4.6±1.5 μm. The inferred carbon biomass of these cells was about 5.2–6.0 mg m−3 and accounted for approximately 10\% of the total particulate organic carbon. If successfully validated, this method may improve our in situ estimates of primary productivity.}, - language = {English}, - number = {11}, - urldate = {2023-08-25}, - journal = {Biogeosciences}, - author = {Dall'Olmo, G. and Boss, E. and Behrenfeld, M. J. and Westberry, T. K. and Courties, C. and Prieur, L. and Pujo-Pay, M. and Hardman-Mountford, N. and Moutin, T.}, - month = nov, - year = {2011}, - note = {Publisher: Copernicus GmbH}, - pages = {3423--3439}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/C6C247B3/Dall'Olmo et al. - 2011 - Inferring phytoplankton carbon and eco-physiologic.pdf:application/pdf}, -} - -@misc{noauthor_ai_nodate, - title = {The {AI} gig economy is coming for you}, - file = {[Hao] The AI gig economy is coming for you _ MIT Technology Review.pdf:/Users/birdy/Downloads/[Hao] The AI gig economy is coming for you _ MIT Technology Review.pdf:application/pdf}, -} - -@article{noauthor_architecture_nodate, - title = {Architecture of a {Database} {System}}, - file = {Architecture of a Database System.pdf:/Users/birdy/Zotero/storage/2DKEVTBM/Architecture of a Database System.pdf:application/pdf}, -} - -@article{isola_lecture_nodate, - title = {Lecture 4: {Architectures} for {Grids}}, - language = {en}, - author = {Isola, Phillip}, - file = {4_grid_archs.pdf:/Users/birdy/Documents/Meng Classes/Deep Learning/4_grid_archs.pdf:application/pdf}, -} - -@article{isola_lecture_nodate-1, - title = {Lecture 5: {Graph} {Neural} {Networks}}, - language = {en}, - author = {Isola, Phillip}, - file = {5_graph_nn.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/5_graph_nn.pdf:application/pdf}, -} - -@misc{noauthor_fast_nodate, - title = {Fast {Forward}: {MIT}'s {Climate} {Action} {Plan} for the {Decade}}, - shorttitle = {Fast {Forward}}, - url = {https://climate.mit.edu/climateaction/fastforward}, - language = {en}, - urldate = {2023-09-22}, - journal = {MIT Climate Portal}, - file = {Snapshot:/Users/birdy/Zotero/storage/E37ZPUKM/fastforward.html:text/html}, -} - -@article{bernstein_scaling_nodate, - title = {Scaling rules for optimisation}, - language = {en}, - author = {Bernstein, Jeremy}, - file = {6_scaling_JB.pdf:/Users/birdy/Documents/Meng Classes/Deep Learning/6_scaling_JB.pdf:application/pdf}, -} - -@article{isola_lecture_nodate-2, - title = {Lecture 3: {Approximation} {Theory}}, - language = {en}, - author = {Isola, Phillip}, - file = {3_approximation_PI.pdf:/Users/birdy/Documents/Meng Classes/Deep Learning/3_approximation_PI.pdf:application/pdf}, -} - -@misc{noauthor_6s898_nodate, - title = {6.{S898} {Lecture} on 9/12/2023 ({Tue})}, - url = {https://mit.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=885feb13-1128-46a6-94fa-b077010e681c}, - abstract = {Special subject in Computer Science}, - language = {en}, - urldate = {2023-09-30}, - file = {Snapshot:/Users/birdy/Zotero/storage/8KZHLHN6/Viewer.html:text/html}, -} - -@inproceedings{andreas_neural_2016, - address = {Las Vegas, NV, USA}, - title = {Neural {Module} {Networks}}, - isbn = {978-1-4673-8851-1}, - url = {http://ieeexplore.ieee.org/document/7780381/}, - doi = {10.1109/CVPR.2016.12}, - abstract = {Visual question answering is fundamentally compositional in nature—a question like where is the dog? shares substructure with questions like what color is the dog? and where is the cat? This paper seeks to simultaneously exploit the representational capacity of deep networks and the compositional linguistic structure of questions. We describe a procedure for constructing and learning neural module networks, which compose collections of jointly-trained neural “modules” into deep networks for question answering. Our approach decomposes questions into their linguistic substructures, and uses these structures to dynamically instantiate modular networks (with reusable components for recognizing dogs, classifying colors, etc.). The resulting compound networks are jointly trained. We evaluate our approach on two challenging datasets for visual question answering, achieving state-of-the-art results on both the VQA natural image dataset and a new dataset of complex questions about abstract shapes.}, - language = {en}, - urldate = {2023-09-30}, - booktitle = {2016 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})}, - publisher = {IEEE}, - author = {Andreas, Jacob and Rohrbach, Marcus and Darrell, Trevor and Klein, Dan}, - month = jun, - year = {2016}, - pages = {39--48}, - file = {Andreas et al. - 2016 - Neural Module Networks.pdf:/Users/birdy/Zotero/storage/YK4HIHHM/Andreas et al. - 2016 - Neural Module Networks.pdf:application/pdf}, -} - -@article{beery_lecture_nodate, - title = {Lecture 2: {How} to train a neural net}, - language = {en}, - author = {Beery, Sara}, - file = {2_backprop_SB.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/2_backprop_SB.pdf:application/pdf}, -} - -@misc{ruswurm_geographic_2023, - title = {Geographic {Location} {Encoding} with {Spherical} {Harmonics} and {Sinusoidal} {Representation} {Networks}}, - url = {http://arxiv.org/abs/2310.06743}, - abstract = {Learning feature representations of geographical space is vital for any machine learning model that integrates geolocated data, spanning application domains such as remote sensing, ecology, or epidemiology. Recent work mostly embeds coordinates using sine and cosine projections based on Double Fourier Sphere (DFS) features -- these embeddings assume a rectangular data domain even on global data, which can lead to artifacts, especially at the poles. At the same time, relatively little attention has been paid to the exact design of the neural network architectures these functional embeddings are combined with. This work proposes a novel location encoder for globally distributed geographic data that combines spherical harmonic basis functions, natively defined on spherical surfaces, with sinusoidal representation networks (SirenNets) that can be interpreted as learned Double Fourier Sphere embedding. We systematically evaluate the cross-product of positional embeddings and neural network architectures across various classification and regression benchmarks and synthetic evaluation datasets. In contrast to previous approaches that require the combination of both positional encoding and neural networks to learn meaningful representations, we show that both spherical harmonics and sinusoidal representation networks are competitive on their own but set state-of-the-art performances across tasks when combined. We provide source code at www.github.com/marccoru/locationencoder}, - urldate = {2023-10-12}, - publisher = {arXiv}, - author = {Rußwurm, Marc and Klemmer, Konstantin and Rolf, Esther and Zbinden, Robin and Tuia, Devis}, - month = oct, - year = {2023}, - note = {arXiv:2310.06743 [cs]}, - keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/N6J75A2A/2310.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/XSF9TGN7/Rußwurm et al. - 2023 - Geographic Location Encoding with Spherical Harmon.pdf:application/pdf}, -} - -@misc{eerola_survey_2023, - title = {Survey of {Automatic} {Plankton} {Image} {Recognition}: {Challenges}, {Existing} {Solutions} and {Future} {Perspectives}}, - shorttitle = {Survey of {Automatic} {Plankton} {Image} {Recognition}}, - url = {http://arxiv.org/abs/2305.11739}, - abstract = {Planktonic organisms are key components of aquatic ecosystems and respond quickly to changes in the environment, therefore their monitoring is vital to understand the changes in the environment. Yet, monitoring plankton at appropriate scales still remains a challenge, limiting our understanding of functioning of aquatic systems and their response to changes. Modern plankton imaging instruments can be utilized to sample at high frequencies, enabling novel possibilities to study plankton populations. However, manual analysis of the data is costly, time consuming and expert based, making such approach unsuitable for large-scale application and urging for automatic solutions. The key problem related to the utilization of plankton datasets through image analysis is plankton recognition. Despite the large amount of research done, automatic methods have not been widely adopted for operational use. In this paper, a comprehensive survey on existing solutions for automatic plankton recognition is presented. First, we identify the most notable challenges that that make the development of plankton recognition systems difficult. Then, we provide a detailed description of solutions for these challenges proposed in plankton recognition literature. Finally, we propose a workflow to identify the specific challenges in new datasets and the recommended approaches to address them. For many of the challenges, applicable solutions exist. However, important challenges remain unsolved: 1) the domain shift between the datasets hindering the development of a general plankton recognition system that would work across different imaging instruments, 2) the difficulty to identify and process the images of previously unseen classes, and 3) the uncertainty in expert annotations that affects the training of the machine learning models for recognition. These challenges should be addressed in the future research.}, - urldate = {2023-10-16}, - publisher = {arXiv}, - author = {Eerola, Tuomas and Batrakhanov, Daniel and Barazandeh, Nastaran Vatankhah and Kraft, Kaisa and Haraguchi, Lumi and Lensu, Lasse and Suikkanen, Sanna and Seppälä, Jukka and Tamminen, Timo and Kälviäinen, Heikki}, - month = may, - year = {2023}, - note = {arXiv:2305.11739 [cs]}, - keywords = {Computer Science - Computer Vision and Pattern Recognition}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/FG4ZBSVN/2305.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/NZX46TSK/Eerola et al. - 2023 - Survey of Automatic Plankton Image Recognition Ch.pdf:application/pdf}, -} - -@article{gonzalez_review_2018, - title = {A {Review} on {Quantification} {Learning}}, - volume = {50}, - issn = {0360-0300, 1557-7341}, - url = {https://dl.acm.org/doi/10.1145/3117807}, - doi = {10.1145/3117807}, - abstract = {The task of quantification consists in providing an aggregate estimation (e.g. the class distribution in a classification problem) for unseen test sets, applying a model that is trained using a training set with a different data distribution. Several real-world applications demand this kind of methods that do not require predictions for individual examples and just focus on obtaining accurate estimates at an aggregate level. During the past few years, several quantification methods have been proposed from different perspectives and with different goals. This paper presents a unified review of the main approaches with the aim of serving as an introductory tutorial for newcomers in the field.}, - language = {en}, - number = {5}, - urldate = {2023-10-16}, - journal = {ACM Computing Surveys}, - author = {González, Pablo and Castaño, Alberto and Chawla, Nitesh V. and Coz, Juan José Del}, - month = sep, - year = {2018}, - pages = {1--40}, - file = {González et al. - 2018 - A Review on Quantification Learning.pdf:/Users/birdy/Zotero/storage/76A4J8ML/quant-review-grAcc.pdf:application/pdf}, -} - -@article{sosa-trejo_vision-based_2023, - title = {Vision-based techniques for automatic marine plankton classification}, - volume = {56}, - issn = {1573-7462}, - url = {https://doi.org/10.1007/s10462-023-10456-w}, - doi = {10.1007/s10462-023-10456-w}, - abstract = {Plankton are an important component of life on Earth. Since the 19th century, scientists have attempted to quantify species distributions using many techniques, such as direct counting, sizing, and classification with microscopes. Since then, extraordinary work has been performed regarding the development of plankton imaging systems, producing a massive backlog of images that await classification. Automatic image processing and classification approaches are opening new avenues for avoiding time-consuming manual procedures. While some algorithms have been adapted from many other applications for use with plankton, other exciting techniques have been developed exclusively for this issue. Achieving higher accuracy than that of human taxonomists is not yet possible, but an expeditious analysis is essential for discovering the world beyond plankton. Recent studies have shown the imminent development of real-time, in situ plankton image classification systems, which have only been slowed down by the complex implementations of algorithms on low-power processing hardware. This article compiles the techniques that have been proposed for classifying marine plankton, focusing on automatic methods that utilize image processing, from the beginnings of this field to the present day.}, - language = {en}, - number = {11}, - urldate = {2023-10-16}, - journal = {Artificial Intelligence Review}, - author = {Sosa-Trejo, David and Bandera, Antonio and González, Martín and Hernández-León, Santiago}, - month = nov, - year = {2023}, - keywords = {Image processing, Marine plankton, Pattern recognition, Plankton classification}, - pages = {12853--12884}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/U7RIA6IX/Sosa-Trejo et al. - 2023 - Vision-based techniques for automatic marine plank.pdf:application/pdf}, -} - -@article{kerr_collaborative_2020-1, - title = {Collaborative {Deep} {Learning} {Models} to {Handle} {Class} {Imbalance} in {FlowCam} {Plankton} {Imagery}}, - volume = {8}, - issn = {2169-3536}, - url = {https://ieeexplore.ieee.org/abstract/document/9187202/authors#authors}, - doi = {10.1109/ACCESS.2020.3022242}, - abstract = {Using automated imaging technologies, it is now possible to generate previously unprecedented volumes of plankton image data which can be used to study the composition of plankton assemblages. However, the current need to manually classify individual images introduces a bottleneck into processing chains. Although Machine Learning techniques have been used to try and address this issue, past efforts have suffered from accuracy limitations, especially in minority classes. Here we use state-of-the-art methods in Deep Learning to investigate suitable architectures for training an automated plankton classification system which achieves high efficacy for both abundant and rare taxa. We collected live plankton from Station L4 in the Western English Channel and imaged 11,371 particles covering 104 taxonomic groups using the automated plankton imaging system FlowCam. The image set contained a severe class imbalance, with some taxa represented by {\textgreater} 600 images while other, rarer taxa were represented by just 14. We demonstrate that by allowing multiple Deep Learning models to collaborate in a single classification system, classification accuracy improves for minority classes when compared with the best individual model. The top collaborative model achieved a 6 \% improvement in F1 accuracy over the best individual model, while overall accuracy improved by 3.2 \%. This resulted in a 97.4 \% overall accuracy score and a 96.2 \% F1 macro score on a separate holdout test set containing 104 taxonomic groups. Based on a survey of similar studies in the literature, we believe collaborative deep learning models can significantly improve the accuracy of existing automated plankton classification systems.}, - urldate = {2023-10-16}, - journal = {IEEE Access}, - author = {Kerr, Thomas and Clark, James R. and Fileman, Elaine S. and Widdicombe, Claire E. and Pugeault, Nicolas}, - year = {2020}, - note = {Conference Name: IEEE Access}, - pages = {170013--170032}, - file = {IEEE Xplore Abstract Record:/Users/birdy/Zotero/storage/WPTTWRTB/authors.html:text/html;IEEE Xplore Full Text PDF:/Users/birdy/Zotero/storage/VLJWVUVD/Kerr et al. - 2020 - Collaborative Deep Learning Models to Handle Class.pdf:application/pdf}, -} - -@article{conradt_automated_2022, - title = {Automated {Plankton} {Classification} {With} a {Dynamic} {Optimization} and {Adaptation} {Cycle}}, - volume = {9}, - issn = {2296-7745}, - url = {https://www.frontiersin.org/articles/10.3389/fmars.2022.868420}, - abstract = {With recent advances in Machine Learning techniques based on Deep Neural Networks (DNNs), automated plankton image classification is becoming increasingly popular within the marine ecological sciences. Yet, while the most advanced methods can achieve human-level performance on the classification of everyday images, plankton image data possess properties that frequently require a final manual validation step. On the one hand, this is due to morphological properties manifesting in high intra-class and low inter-class variability, and, on the other hand is due to spatial-temporal changes in the composition and structure of the plankton community. Composition changes enforce a frequent updating of the classifier model via training with new user-generated training datasets. Here, we present a Dynamic Optimization Cycle (DOC), a processing pipeline that systematizes and streamlines the model adaptation process via an automatic updating of the training dataset based on manual-validation results. We find that frequent adaptation using the DOC pipeline yields strong maintenance of performance with respect to precision, recall and prediction of community composition, compared to more limited adaptation schemes. The DOC is therefore particularly useful when analyzing plankton at novel locations or time periods, where community differences are likely to occur. In order to enable an easy implementation of the DOC pipeline, we provide an end-to-end application with graphical user interface, as well as an initial dataset of training images. The DOC pipeline thus allows for high-throughput plankton classification and quick and systematized model adaptation, thus providing the means for highly-accelerated plankton analysis.}, - urldate = {2023-10-16}, - journal = {Frontiers in Marine Science}, - author = {Conradt, Jan and Börner, Gregor and López-Urrutia, Ángel and Möllmann, Christian and Moyano, Marta}, - year = {2022}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/YBEYVF4R/Conradt et al. - 2022 - Automated Plankton Classification With a Dynamic O.pdf:application/pdf}, -} - -@misc{ic_du_toit_automated_nodate, - title = {Automated machine learning plankton taxonomy pipeline}, - url = {http://vital.seals.ac.za:8080/vital/access/manager/PdfViewer/vital:59003/SOURCE1?viewPdfInternal=1}, - urldate = {2023-10-16}, - author = {I.C. du Toit}, - file = {document.pdf:/Users/birdy/Downloads/document.pdf:application/pdf;SOURCE1:/Users/birdy/Zotero/storage/8EZC4XHT/SOURCE1.html:text/html}, -} - -@article{gonzalez_validation_2017, - title = {Validation methods for plankton image classification systems}, - volume = {15}, - copyright = {© 2016 Association for the Sciences of Limnology and Oceanography}, - issn = {1541-5856}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lom3.10151}, - doi = {10.1002/lom3.10151}, - abstract = {In recent decades, the automatic study and analysis of plankton communities using imaging techniques has advanced significantly. The effectiveness of these automated systems appears to have improved, reaching acceptable levels of accuracy. However, plankton ecologists often find that classification systems do not work as well as expected when applied to new samples. This paper proposes a methodology to assess the efficacy of learned models which takes into account the fact that the data distribution (the plankton composition of the sample) can vary between the model building phase and the production phase. As opposed to most validation methods that consider the individual organism as the unit of validation, our approach uses a validation-by-sample, which is more appropriate when the objective is to estimate the abundance of different morphological groups. We argue that, in these cases, the base unit to correctly estimate the error is the sample, not the individual. Thus, model assessment processes require groups of samples with sufficient variability in order to provide precise error estimates.}, - language = {en}, - number = {3}, - urldate = {2023-10-16}, - journal = {Limnology and Oceanography: Methods}, - author = {González, Pablo and Álvarez, Eva and Díez, Jorge and López-Urrutia, Ángel and del Coz, Juan José}, - year = {2017}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10151}, - pages = {221--237}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/SW5FNZDQ/González et al. - 2017 - Validation methods for plankton image classificati.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/UB2XMFYB/lom3.html:text/html}, -} - -@article{isola_lecture_nodate-3, - title = {Lecture 11: {Representation} {Learning} {I}}, - language = {en}, - author = {Isola, Phillip}, - file = {11-rep_learning_1.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/11-rep_learning_1.pdf:application/pdf}, -} - -@article{beery_lecture_nodate-1, - title = {Lecture 12: {Similarity}-based {Representation} {Learning}}, - language = {en}, - author = {Beery, Sara}, - file = {12_similarity_based_rep_learning-SB.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/12_similarity_based_rep_learning-SB.pdf:application/pdf}, -} - -@inproceedings{van_horn_benchmarking_2021, - address = {Nashville, TN, USA}, - title = {Benchmarking {Representation} {Learning} for {Natural} {World} {Image} {Collections}}, - isbn = {978-1-66544-509-2}, - url = {https://ieeexplore.ieee.org/document/9577285/}, - doi = {10.1109/CVPR46437.2021.01269}, - abstract = {Recent progress in self-supervised learning has resulted in models that are capable of extracting rich representations from image collections without requiring any explicit label supervision. However, to date the vast majority of these approaches have restricted themselves to training on standard benchmark datasets such as ImageNet. We argue that fine-grained visual categorization problems, such as plant and animal species classification, provide an informative testbed for self-supervised learning. In order to facilitate progress in this area we present two new natural world visual classification datasets, iNat2021 and NeWT. The former consists of 2.7M images from 10k different species uploaded by users of the citizen science application iNaturalist. We designed the latter, NeWT, in collaboration with domain experts with the aim of benchmarking the performance of representation learning algorithms on a suite of challenging natural world binary classification tasks that go beyond standard species classification. These two new datasets allow us to explore questions related to large-scale representation and transfer learning in the context of finegrained categories. We provide a comprehensive analysis of feature extractors trained with and without supervision on ImageNet and iNat2021, shedding light on the strengths and weaknesses of different learned features across a diverse set of tasks. We find that features produced by standard supervised methods still outperform those produced by self-supervised approaches such as SimCLR. However, improved self-supervised learning methods are constantly being released and the iNat2021 and NeWT datasets are a valuable resource for tracking their progress.}, - language = {en}, - urldate = {2023-10-24}, - booktitle = {2021 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})}, - publisher = {IEEE}, - author = {Van Horn, Grant and Cole, Elijah and Beery, Sara and Wilber, Kimberly and Belongie, Serge and MacAodha, Oisin}, - month = jun, - year = {2021}, - pages = {12879--12888}, - file = {Van Horn et al. - 2021 - Benchmarking Representation Learning for Natural W.pdf:/Users/birdy/Zotero/storage/PYLIPA9T/Van Horn et al. - 2021 - Benchmarking Representation Learning for Natural W.pdf:application/pdf}, -} - -@article{orenstein_machine_2022, - title = {Machine learning techniques to characterize functional traits of plankton from image data}, - volume = {67}, - issn = {0024-3590, 1939-5590}, - url = {https://aslopubs.onlinelibrary.wiley.com/doi/10.1002/lno.12101}, - doi = {10.1002/lno.12101}, - abstract = {Plankton imaging systems supported by automated classification and analysis have improved ecologists’ ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.}, - language = {en}, - number = {8}, - urldate = {2023-10-25}, - journal = {Limnology and Oceanography}, - author = {Orenstein, Eric C. and Ayata, Sakina‐Dorothée and Maps, Frédéric and Becker, Érica C. and Benedetti, Fabio and Biard, Tristan and De Garidel‐Thoron, Thibault and Ellen, Jeffrey S. and Ferrario, Filippo and Giering, Sarah L. C. and Guy‐Haim, Tamar and Hoebeke, Laura and Iversen, Morten Hvitfeldt and Kiørboe, Thomas and Lalonde, Jean‐François and Lana, Arancha and Laviale, Martin and Lombard, Fabien and Lorimer, Tom and Martini, Séverine and Meyer, Albin and Möller, Klas Ove and Niehoff, Barbara and Ohman, Mark D. and Pradalier, Cédric and Romagnan, Jean‐Baptiste and Schröder, Simon‐Martin and Sonnet, Virginie and Sosik, Heidi M. and Stemmann, Lars S. and Stock, Michiel and Terbiyik‐Kurt, Tuba and Valcárcel‐Pérez, Nerea and Vilgrain, Laure and Wacquet, Guillaume and Waite, Anya M. and Irisson, Jean‐Olivier}, - month = aug, - year = {2022}, - pages = {1647--1669}, - file = {Orensteinal._2022.pdf:/Users/birdy/Documents/mit-meng-thesis/papers/Orensteinal._2022.pdf:application/pdf}, -} - -@article{schanz_robust_2023, - title = {Robust detection of marine life with label-free image feature learning and probability calibration}, - volume = {4}, - issn = {2632-2153}, - url = {https://iopscience.iop.org/article/10.1088/2632-2153/ace417}, - doi = {10.1088/2632-2153/ace417}, - abstract = {Abstract - - Advances in - in situ - marine life imaging have significantly increased the size and quality of available datasets, but automatic image analysis has not kept pace. Machine learning has shown promise for image processing, but its effectiveness is limited by several open challenges: the requirement for large expert-labeled training datasets, disagreement among experts, under-representation of various species and unreliable or overconfident predictions. To overcome these obstacles for automated underwater imaging, we combine and test recent developments in deep classifier networks and self-supervised feature learning. We use unlabeled images for pretraining deep neural networks to extract task-relevant image features, allowing learning algorithms to cope with scarcity in expert labels, and carefully evaluate performance in subsequent label-based tasks. Performance on rare classes is improved by applying data rebalancing together with a Bayesian correction to avoid biasing inferred - in situ - class frequencies. A divergence-based loss allows training on multiple, conflicting labels for the same image, leading to better estimates of uncertainty which we quantify with a novel accuracy measure. Together, these techniques can reduce the required label counts ∼100-fold while maintaining the accuracy of standard supervised training, shorten training time, cope with expert disagreement and reduce overconfidence.}, - language = {en}, - number = {3}, - urldate = {2023-10-25}, - journal = {Machine Learning: Science and Technology}, - author = {Schanz, Tobias and Ove Möller, Klas and Rühl, Saskia and Greenberg, David S}, - month = sep, - year = {2023}, - pages = {035007}, - file = {Schanz et al. - 2023 - Robust detection of marine life with label-free im.pdf:/Users/birdy/Zotero/storage/KBQL5Q2G/Schanz et al. - 2023 - Robust detection of marine life with label-free im.pdf:application/pdf}, -} - -@article{catlett_temperature_2023, - title = {Temperature dependence of parasitoid infection and abundance of a diatom revealed by automated imaging and classification}, - volume = {120}, - issn = {0027-8424, 1091-6490}, - url = {https://pnas.org/doi/10.1073/pnas.2303356120}, - doi = {10.1073/pnas.2303356120}, - abstract = {Diatoms are a group of phytoplankton that contribute disproportionately to global primary production. Traditional paradigms that suggest diatoms are consumed primarily by larger zooplankton are challenged by sporadic parasitic “epidemics” within diatom populations. However, our understanding of diatom parasitism is limited by difficulties in quantifying these interactions. Here, we observe the dynamics of - Cryothecomonas aestivalis - (a protist) infection of an important diatom on the Northeast U.S. Shelf (NES), - Guinardia delicatula - , with a combination of automated imaging-in-flow cytometry and a convolutional neural network image classifier. Application of the classifier to {\textgreater}1 billion images from a nearshore time series and {\textgreater}20 survey cruises across the broader NES reveals the spatiotemporal gradients and temperature dependence of - G. delicatula - abundance and infection dynamics. Suppression of parasitoid infection at temperatures {\textless}4 °C drives annual cycles in both - G. delicatula - infection and abundance, with an annual maximum in infection observed in the fall-winter preceding an annual maximum in host abundance in the winter-spring. This annual cycle likely varies spatially across the NES in response to variable annual cycles in water temperature. We show that infection remains suppressed for {\textasciitilde}2 mo following cold periods, possibly due to temperature-induced local extinctions of the - C. aestivalis - strain(s) that infect - G. delicatula - . These findings have implications for predicting impacts of a warming NES surface ocean on - G. delicatula - abundance and infection dynamics and demonstrate the potential of automated plankton imaging and classification to quantify phytoplankton parasitism in nature across unprecedented spatiotemporal scales.}, - language = {en}, - number = {28}, - urldate = {2023-10-25}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Catlett, Dylan and Peacock, Emily E. and Crockford, E. Taylor and Futrelle, Joe and Batchelder, Sidney and Stevens, Bethany L. F. and Gast, Rebecca J. and Zhang, Weifeng G. and Sosik, Heidi M.}, - month = jul, - year = {2023}, - pages = {e2303356120}, - file = {catlett-et-al-2023-temperature-dependence-of-parasitoid-infection-and-abundance-of-a-diatom-revealed-by-automated.pdf:/Users/birdy/Documents/mit-meng-thesis/papers/catlett-et-al-2023-temperature-dependence-of-parasitoid-infection-and-abundance-of-a-diatom-revealed-by-automated.pdf:application/pdf}, -} - -@article{culverhouse_empirical_2014, - title = {An empirical assessment of the consistency of taxonomic identifications}, - volume = {10}, - issn = {1745-1000}, - url = {https://doi.org/10.1080/17451000.2013.810762}, - doi = {10.1080/17451000.2013.810762}, - abstract = {Plankton counting and analysis is essential in ecological study, yet scant literature exists as to the reliability of those counts and the consistency of the experts who make the counts. To assess how variable expert taxonomic identifications are, a set of six archived mesozooplankton samples from a series of Longhurst Hardy Plankton Recorder net hauls were counted by expert zooplankton analysts located at six marine laboratories. Sample identifications were repeated on two separate days with over 700 target specimens counted and identified on each day across the samples. Twenty percent of the analysts returned counts that varied by more than 10\%. Thirty-three percent of analysts exhibited low identification consistencies, returning Intraclass Correlation Coefficient scores of less than 0.80. Statistical analyses of these data suggest that over 83\% of the observed categorical count variance can be attributed to inconsistencies within analysts. We suggest this is the root cause of variation in expert specimen labelling consistency.}, - number = {1}, - urldate = {2023-10-25}, - journal = {Marine Biology Research}, - author = {Culverhouse, Philip F. and Macleod, Norman and Williams, Robert and Benfield, Mark C. and Lopes, Rubens M. and Picheral, Marc}, - month = jan, - year = {2014}, - note = {Publisher: Taylor \& Francis -\_eprint: https://doi.org/10.1080/17451000.2013.810762}, - keywords = {expert performance, Human factors, mesozooplankton identification, root cause of variation}, - pages = {73--84}, -} - -@article{schroder_morphocluster_2020, - title = {{MorphoCluster}: {Efficient} {Annotation} of {Plankton} {Images} by {Clustering}}, - volume = {20}, - copyright = {http://creativecommons.org/licenses/by/3.0/}, - issn = {1424-8220}, - shorttitle = {{MorphoCluster}}, - url = {https://www.mdpi.com/1424-8220/20/11/3060}, - doi = {10.3390/s20113060}, - abstract = {In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue to increase in the coming years. Still, this data requires interpretation. MorphoCluster augments the human ability to discover patterns and perform object classification in large amounts of data by embedding unsupervised clustering in an interactive process. By aggregating similar images into clusters, our novel approach to image annotation increases consistency, multiplies the throughput of an annotator, and allows experts to adapt the granularity of their sorting scheme to the structure in the data. By sorting a set of 1.2 M objects into 280 data-driven classes in 71 h (16 k objects per hour), with 90\% of these classes having a precision of 0.889 or higher. This shows that MorphoCluster is at the same time fast, accurate, and consistent; provides a fine-grained and data-driven classification; and enables novelty detection.}, - language = {en}, - number = {11}, - urldate = {2023-10-26}, - journal = {Sensors}, - author = {Schröder, Simon-Martin and Kiko, Rainer and Koch, Reinhard}, - month = jan, - year = {2020}, - note = {Number: 11 -Publisher: Multidisciplinary Digital Publishing Institute}, - keywords = {deep learning, clustering, machine learning, marine image annotation, marine image recognition, plankton image classification}, - pages = {3060}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/ZHNATHDJ/Schröder et al. - 2020 - MorphoCluster Efficient Annotation of Plankton Im.pdf:application/pdf}, -} - -@article{henrichs_application_2021, - title = {Application of a convolutional neural network to improve automated early warning of harmful algal blooms}, - volume = {28}, - issn = {1614-7499}, - url = {https://doi.org/10.1007/s11356-021-12471-2}, - doi = {10.1007/s11356-021-12471-2}, - abstract = {Continuous monitoring and early warning together represent an important mitigation strategy for harmful algal blooms (HAB). The coast of Texas experiences periodic blooms of three HAB dinoflagellates: Karenia brevis, Dinophysis ovum, and Prorocentrum texanum. A plankton image data set acquired by an Imaging FlowCytobot over a decade of operation was used to train and evaluate two new automated image classifiers. A 112 class, random forest classifier (RF\_112) and a 112 class, convolutional neural network classifier (CNN\_112) were developed and compared with an existing, 54 class, random forest classifier (RF\_54) already in use as an early warning notification system. Both 112 class classifiers exhibited improved performance over the RF\_54 classifier when tested on three different HAB species with the CNN\_112 classifier producing fewer false positives and false negatives in most of the cases tested. For K. brevis and P. texanum, the current threshold of 2 cells.mL−1 was identified as the best threshold to minimize the number of false positives and false negatives. For D. ovum, a threshold of 1 cell.mL−1 was found to produce the best results with regard to the number of false positives/negatives. A lower threshold will result in earlier notification of an increase in cell concentration and will provide state health managers with increased lead time to prepare for an impending HAB.}, - language = {en}, - number = {22}, - urldate = {2023-10-26}, - journal = {Environmental Science and Pollution Research}, - author = {Henrichs, Darren W. and Anglès, Sílvia and Gaonkar, Chetan C. and Campbell, Lisa}, - month = jun, - year = {2021}, - keywords = {CNN, Dinophysis ovum, Gulf of Mexico, HAB, Imaging FlowCytobot, Karenia brevis, Prorocentrum texanum}, - pages = {28544--28555}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/HPYZHY65/Henrichs et al. - 2021 - Application of a convolutional neural network to i.pdf:application/pdf}, -} - -@inproceedings{vaswani_attention_2017, - title = {Attention is {All} you {Need}}, - volume = {30}, - url = {https://proceedings.neurips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html}, - abstract = {The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration. The best performing such models also connect the encoder and decoder through an attentionm echanisms. We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely.Experiments on two machine translation tasks show these models to be superiorin quality while being more parallelizable and requiring significantly less timeto train. Our single model with 165 million parameters, achieves 27.5 BLEU onEnglish-to-German translation, improving over the existing best ensemble result by over 1 BLEU. On English-to-French translation, we outperform the previoussingle state-of-the-art with model by 0.7 BLEU, achieving a BLEU score of 41.1.}, - urldate = {2023-10-28}, - booktitle = {Advances in {Neural} {Information} {Processing} {Systems}}, - publisher = {Curran Associates, Inc.}, - author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, Łukasz and Polosukhin, Illia}, - year = {2017}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/G3A56AFM/Vaswani et al. - 2017 - Attention is All you Need.pdf:application/pdf}, -} - -@article{isola_lecture_nodate-4, - title = {Lecture 15: {Deep} {Generative} {Models} {I}}, - language = {en}, - author = {Isola, Phillip}, - file = {15_gen_models_1.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/15_gen_models_1.pdf:application/pdf}, -} - -@article{isola_lecture_nodate-5, - title = {Lecture 16: {Deep} {Generative} {Models} {II}}, - language = {en}, - author = {Isola, Phillip}, - file = {16_gen_models_2.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/16_gen_models_2.pdf:application/pdf}, -} - -@article{isola_lecture_nodate-6, - title = {Lecture 17: {Deep} {Generative} {Models} {III}}, - language = {en}, - author = {Isola, Phillip}, - file = {17_gen_models_3.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/17_gen_models_3.pdf:application/pdf}, -} - -@misc{1, - title = {How {Neural} {Networks} {Extrapolate}: {From} {Feedforward} to {Graph} {Neural} {Networks}}, - shorttitle = {How {Neural} {Networks} {Extrapolate}}, - url = {http://arxiv.org/abs/2009.11848}, - doi = {10.48550/arXiv.2009.11848}, - abstract = {We study how neural networks trained by gradient descent extrapolate, i.e., what they learn outside the support of the training distribution. Previous works report mixed empirical results when extrapolating with neural networks: while feedforward neural networks, a.k.a. multilayer perceptrons (MLPs), do not extrapolate well in certain simple tasks, Graph Neural Networks (GNNs) -- structured networks with MLP modules -- have shown some success in more complex tasks. Working towards a theoretical explanation, we identify conditions under which MLPs and GNNs extrapolate well. First, we quantify the observation that ReLU MLPs quickly converge to linear functions along any direction from the origin, which implies that ReLU MLPs do not extrapolate most nonlinear functions. But, they can provably learn a linear target function when the training distribution is sufficiently "diverse". Second, in connection to analyzing the successes and limitations of GNNs, these results suggest a hypothesis for which we provide theoretical and empirical evidence: the success of GNNs in extrapolating algorithmic tasks to new data (e.g., larger graphs or edge weights) relies on encoding task-specific non-linearities in the architecture or features. Our theoretical analysis builds on a connection of over-parameterized networks to the neural tangent kernel. Empirically, our theory holds across different training settings.}, - urldate = {2023-11-12}, - publisher = {arXiv}, - author = {Xu, Keyulu and Zhang, Mozhi and Li, Jingling and Du, Simon S. and Kawarabayashi, Ken-ichi and Jegelka, Stefanie}, - month = mar, - year = {2021}, - note = {arXiv:2009.11848 [cs, stat]}, - keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/MAMEYIFL/2009.html:text/html;Xu et al. - 2021 - How Neural Networks Extrapolate From Feedforward .pdf:/Users/birdy/Zotero/storage/XUU8Z7E2/Xu et al. - 2021 - How Neural Networks Extrapolate From Feedforward .pdf:application/pdf}, -} - -@misc{xiao_noise_2020, - title = {Noise or {Signal}: {The} {Role} of {Image} {Backgrounds} in {Object} {Recognition}}, - shorttitle = {Noise or {Signal}}, - url = {http://arxiv.org/abs/2006.09994}, - abstract = {We assess the tendency of state-of-the-art object recognition models to depend on signals from image backgrounds. We create a toolkit for disentangling foreground and background signal on ImageNet images, and find that (a) models can achieve non-trivial accuracy by relying on the background alone, (b) models often misclassify images even in the presence of correctly classified foregrounds--up to 87.5\% of the time with adversarially chosen backgrounds, and (c) more accurate models tend to depend on backgrounds less. Our analysis of backgrounds brings us closer to understanding which correlations machine learning models use, and how they determine models' out of distribution performance.}, - urldate = {2023-11-12}, - publisher = {arXiv}, - author = {Xiao, Kai and Engstrom, Logan and Ilyas, Andrew and Madry, Aleksander}, - month = jun, - year = {2020}, - note = {arXiv:2006.09994 [cs]}, - keywords = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/W2NV28Q2/2006.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/MZIZ22HW/Xiao et al. - 2020 - Noise or Signal The Role of Image Backgrounds in .pdf:application/pdf}, -} - -@misc{yang_generalized_2022, - title = {Generalized {Out}-of-{Distribution} {Detection}: {A} {Survey}}, - shorttitle = {Generalized {Out}-of-{Distribution} {Detection}}, - url = {http://arxiv.org/abs/2110.11334}, - doi = {10.48550/arXiv.2110.11334}, - abstract = {Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of machine learning systems. For instance, in autonomous driving, we would like the driving system to issue an alert and hand over the control to humans when it detects unusual scenes or objects that it has never seen during training time and cannot make a safe decision. The term, OOD detection, first emerged in 2017 and since then has received increasing attention from the research community, leading to a plethora of methods developed, ranging from classification-based to density-based to distance-based ones. Meanwhile, several other problems, including anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD), are closely related to OOD detection in terms of motivation and methodology. Despite common goals, these topics develop in isolation, and their subtle differences in definition and problem setting often confuse readers and practitioners. In this survey, we first present a unified framework called generalized OOD detection, which encompasses the five aforementioned problems, i.e., AD, ND, OSR, OOD detection, and OD. Under our framework, these five problems can be seen as special cases or sub-tasks, and are easier to distinguish. We then review each of these five areas by summarizing their recent technical developments, with a special focus on OOD detection methodologies. We conclude this survey with open challenges and potential research directions.}, - urldate = {2023-11-12}, - publisher = {arXiv}, - author = {Yang, Jingkang and Zhou, Kaiyang and Li, Yixuan and Liu, Ziwei}, - month = aug, - year = {2022}, - note = {arXiv:2110.11334 [cs]}, - keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition}, - file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/MA6JB7TD/Yang et al. - 2022 - Generalized Out-of-Distribution Detection A Surve.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/BH3RP2BX/2110.html:text/html}, -} - -@article{cui_out--distribution_2022, - title = {Out-of-{Distribution} ({OOD}) {Detection} {Based} on {Deep} {Learning}: {A} {Review}}, - volume = {11}, - copyright = {http://creativecommons.org/licenses/by/3.0/}, - issn = {2079-9292}, - shorttitle = {Out-of-{Distribution} ({OOD}) {Detection} {Based} on {Deep} {Learning}}, - url = {https://www.mdpi.com/2079-9292/11/21/3500}, - doi = {10.3390/electronics11213500}, - abstract = {Out-of-Distribution (OOD) detection separates ID (In-Distribution) data and OOD data from input data through a model. This problem has attracted increasing attention in the area of machine learning. OOD detection has achieved good intrusion detection, fraud detection, system health monitoring, sensor network event detection, and ecosystem interference detection. The method based on deep learning is the most studied in OOD detection. In this paper , related basic information on OOD detection based on deep learning is described, and we categorize methods according to the training data. OOD detection is divided into supervised, semisupervised, and unsupervised. Where supervised data are used, the methods are categorized according to technical means: model-based, distance-based, and density-based. Each classification is introduced with background, examples, and applications. In addition, we present the latest applications of OOD detection based on deep learning and the problems and expectations in this field.}, - language = {en}, - number = {21}, - urldate = {2023-11-12}, - journal = {Electronics}, - author = {Cui, Peng and Wang, Jinjia}, - month = jan, - year = {2022}, - note = {Number: 21 -Publisher: Multidisciplinary Digital Publishing Institute}, - keywords = {deep learning, anomaly detection, neural networks, novelty detection, Out-of-Distribution detection, outlier detection}, - pages = {3500}, - file = {Cui and Wang - 2022 - Out-of-Distribution (OOD) Detection Based on Deep .pdf:/Users/birdy/Zotero/storage/MK3M7WBW/Cui and Wang - 2022 - Out-of-Distribution (OOD) Detection Based on Deep .pdf:application/pdf}, -} - -@misc{ghassemi_comprehensive_2022, - title = {A {Comprehensive} {Review} of {Trends}, {Applications} and {Challenges} {In} {Out}-of-{Distribution} {Detection}}, - url = {http://arxiv.org/abs/2209.12935}, - abstract = {With recent advancements in artificial intelligence, its applications can be seen in every aspect of humans' daily life. From voice assistants to mobile healthcare and autonomous driving, we rely on the performance of AI methods for many critical tasks; therefore, it is essential to assert the performance of models in proper means to prevent damage. One of the shortfalls of AI models in general, and deep machine learning in particular, is a drop in performance when faced with shifts in the distribution of data. Nonetheless, these shifts are always expected in real-world applications; thus, a field of study has emerged, focusing on detecting out-of-distribution data subsets and enabling a more comprehensive generalization. Furthermore, as many deep learning based models have achieved near-perfect results on benchmark datasets, the need to evaluate these models' reliability and trustworthiness for pushing towards real-world applications is felt more strongly than ever. This has given rise to a growing number of studies in the field of out-of-distribution detection and domain generalization, which begs the need for surveys that compare these studies from various perspectives and highlight their straightens and weaknesses. This paper presents a survey that, in addition to reviewing more than 70 papers in this field, presents challenges and directions for future works and offers a unifying look into various types of data shifts and solutions for better generalization.}, - urldate = {2023-11-12}, - publisher = {arXiv}, - author = {Ghassemi, Navid and Fazl-Ersi, Ehsan}, - month = sep, - year = {2022}, - note = {arXiv:2209.12935 [cs]}, - keywords = {Computer Science - Machine Learning}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/GJU4ZCYR/2209.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/TWZAVEUQ/Ghassemi and Fazl-Ersi - 2022 - A Comprehensive Review of Trends, Applications and.pdf:application/pdf}, -} - -@book{quinonero-candela_dataset_2009, - address = {Cambridge, Mass}, - series = {Neural information processing series}, - title = {Dataset shift in machine learning}, - isbn = {978-0-262-17005-5 978-0-262-54587-7}, - language = {en}, - publisher = {MIT Press}, - editor = {Quiñonero-Candela, Joaquin}, - year = {2009}, - note = {OCLC: ocn227205909}, - keywords = {Machine learning}, - file = {Quiñonero-Candela - 2009 - Dataset shift in machine learning.pdf:/Users/birdy/Zotero/storage/JJT973SR/Quiñonero-Candela - 2009 - Dataset shift in machine learning.pdf:application/pdf}, -} - -@article{may_will_1972, - title = {Will a {Large} {Complex} {System} be {Stable}?}, - volume = {238}, - copyright = {1972 Springer Nature Limited}, - issn = {1476-4687}, - url = {https://www.nature.com/articles/238413a0}, - doi = {10.1038/238413a0}, - abstract = {Gardner and Ashby1 have suggested that large complex systems which are assembled (connected) at random may be expected to be stable up to a certain critical level of connectance, and then, as this increases, to suddenly become unstable. Their conclusions were based on the trend of computer studies of systems with 4, 7 and 10 variables.}, - language = {en}, - number = {5364}, - urldate = {2023-11-13}, - journal = {Nature}, - author = {May, Robert M.}, - month = aug, - year = {1972}, - note = {Number: 5364 -Publisher: Nature Publishing Group}, - keywords = {Humanities and Social Sciences, multidisciplinary, Science}, - pages = {413--414}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/K3ZKQCSD/May - 1972 - Will a Large Complex System be Stable.pdf:application/pdf}, -} - -@article{armstrong_stable_1999, - title = {Stable model structures for representing biogeochemical diversity and size spectra in plankton communities}, - volume = {21}, - issn = {0142-7873}, - url = {https://doi.org/10.1093/plankt/21.3.445}, - doi = {10.1093/plankt/21.3.445}, - abstract = {There is a fundamental duality in the way we view planktonic communities. On one hand, we see these communities as composed of a diverse mixture of taxonomically and biogeochemically distinct species; pelagic ecosystem models capable of predicting the ocean's role in the global carbon cycle will need to resolve these multiple plankton taxa. However, we have also come to appreciate that superimposed on this diversity are regular patterns of size structure, where plots of abundance within size classes typically show a power-law dependence on size. A truly complete model description of planktonic community structure must reflect both aspects of this duality. Here I examine two classes of ecosystem models that are capable of representing this duality. The first structure is based on multiple (\<it\>n\</it\>) couplets of phytoplankton -(\<it\>P\</it\>) and zooplankton (\<it\>Z\</it\>) [an '\<it\>n\</it\>(\<it\>PZ\</it\>)' model]. Within certain regions of parameter space, this model structure can produce anomalous oscillatory behaviors, the mathematical origin of which is explored in detail. I then examine an alternative food web structure in which total -herbivore abundance is represented by a single state variable,\<it\>G\</it\>, and where grazing pressure is distributed among -taxa in proportion to their abundances. This '(\<it\>nP)G\</it\>' -system is stabilized by the redistribution of grazing intensity among taxa -in response to changes in their densities. This 'distributed grazing' model -also naturally produces size spectra of plankton abundance; this last -observation argues that the model with distributed grazing can help in -uncovering and representing the mechanistic basis for the genesis and -maintenance of planktonic size spectra of phytoplankton and bacteria.}, - number = {3}, - urldate = {2023-11-13}, - journal = {Journal of Plankton Research}, - author = {Armstrong, Robert A.}, - month = mar, - year = {1999}, - pages = {445--464}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/LP2EYBJD/1999 - Stable model structures for representing biogeoche.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/MN45F6HS/1395820.html:text/html}, -} - -@article{ashkezari_simons_2021, - title = {Simons {Collaborative} {Marine} {Atlas} {Project} ({Simons} {CMAP}): {An} open-source portal to share, visualize, and analyze ocean data}, - volume = {19}, - copyright = {© 2021 The Authors. Limnology and Oceanography: Methods published by Wiley Periodicals LLC on behalf of Association for the Sciences of Limnology and Oceanography.}, - issn = {1541-5856}, - shorttitle = {Simons {Collaborative} {Marine} {Atlas} {Project} ({Simons} {CMAP})}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lom3.10439}, - doi = {10.1002/lom3.10439}, - abstract = {Simons Collaborative Marine Atlas Project (Simons CMAP) is an open-source data portal that interconnects large, complex, and diverse public data sets currently dispersed in different formats across different Oceanography discipline-specific databases. Simons CMAP is designed to streamline the retrieval of custom subsets of data, the generation of data visualizations, and the analyses of diverse data, thus expanding the power of these potentially underutilized data sets for cross-disciplinary studies of ocean processes. We describe a unified architecture that allows numerical model outputs, satellite products, and field observations to be readily shared, mined, and integrated regardless of data set size or resolution. A current focus of Simons CMAP is integration of physical, chemical, and biological data sets essential for characterizing the biogeography of key marine microbes across ocean basins and seasonal cycles. Using a practical example, we demonstrate how our unifying data harmonization plans significantly simplifies and allows for systematic data integration across all Simons CMAP data sets.}, - language = {en}, - number = {7}, - urldate = {2023-11-13}, - journal = {Limnology and Oceanography: Methods}, - author = {Ashkezari, Mohammad D. and Hagen, Norland R. and Denholtz, Michael and Neang, Andrew and Burns, Tansy C. and Morales, Rhonda L. and Lee, Charlotte P. and Hill, Christopher N. and Armbrust, E. Virginia}, - year = {2021}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10439}, - pages = {488--496}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/DUNQVLE5/Ashkezari et al. - 2021 - Simons Collaborative Marine Atlas Project (Simons .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/CPQTRWWV/lom3.html:text/html}, -} - -@article{aumont_pisces-v2_2015, - title = {{PISCES}-v2: an ocean biogeochemical model for carbon and ecosystem studies}, - volume = {8}, - url = {https://gmd.copernicus.org/articles/8/2465/2015/}, - doi = {10.5194/gmd-8-2465-2015}, - number = {8}, - journal = {Geoscientific Model Development}, - author = {Aumont, O. and Ethé, C. and Tagliabue, A. and Bopp, L. and Gehlen, M.}, - year = {2015}, - pages = {2465--2513}, -} - -@article{bardon_testing_2021-1, - title = {Testing the {Skill} of a {Species} {Distribution} {Model} {Using} a 21st {Century} {Virtual} {Ecosystem}}, - volume = {48}, - copyright = {© 2021. The Authors.}, - issn = {1944-8007}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2021GL093455}, - doi = {10.1029/2021GL093455}, - abstract = {Plankton communities play an important role in marine food webs, in biogeochemical cycling, and in Earth's climate; yet observations are sparse, and predictions of how they might respond to climate change vary. Correlative species distribution models (SDM's) have been applied to predicting biogeography based on relationships to observed environmental variables. To investigate sources of uncertainty, we use a correlative SDM to predict the plankton biogeography of a 21st century marine ecosystem model (Darwin). Darwin output is sampled to mimic historical ocean observations, and the SDM is trained using generalized additive models. We find that predictive skill varies across test cases, and between functional groups, with errors that are more attributable to spatiotemporal sampling bias than sample size. End-of-century predictions are poor, limited by changes in target-predictor relationships over time. Our findings illustrate the fundamental challenges faced by empirical models in using limited observational data to predict complex, dynamic systems.}, - language = {en}, - number = {22}, - urldate = {2023-11-13}, - journal = {Geophysical Research Letters}, - author = {Bardon, L. R. and Ward, B. A. and Dutkiewicz, S. and Cael, B. B.}, - year = {2021}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021GL093455}, - keywords = {climate change, complex systems, numerical models, oceanography, plankton ecology, statistical learning}, - pages = {e2021GL093455}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/CD5IBV65/Bardon et al. - 2021 - Testing the Skill of a Species Distribution Model .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/DK86CFJT/2021GL093455.html:text/html}, -} - -@article{bian_understanding_2023-1, - title = {Understanding opposing predictions of {Prochlorococcus} in a changing climate}, - volume = {14}, - copyright = {2023 The Author(s)}, - issn = {2041-1723}, - url = {https://www.nature.com/articles/s41467-023-36928-9}, - doi = {10.1038/s41467-023-36928-9}, - abstract = {Statistically derived species distribution models (SDMs) are increasingly used to predict ecological changes on a warming planet. For Prochlorococcus, the most abundant phytoplankton, an established statistical prediction conflicts with dynamical models as they predict large, opposite, changes in abundance. We probe the SDM at various spatial-temporal scales, showing that light and temperature fail to explain both temporal fluctuations and sharp spatial transitions. Strong correlations between changes in temperature and population emerge only at very large spatial scales, as transects pass through transitions between regions of high and low abundance. Furthermore, a two-state model based on a temperature threshold matches the original SDM in the surface ocean. We conclude that the original SDM has little power to predict changes when Prochlorococcus is already abundant, which resolves the conflict with dynamical models. Our conclusion suggests that SDMs should prove efficacy across multiple spatial-temporal scales before being trusted in a changing ocean.}, - language = {en}, - number = {1}, - urldate = {2023-11-13}, - journal = {Nature Communications}, - author = {Bian, Vincent and Cai, Merrick and Follett, Christopher L.}, - month = mar, - year = {2023}, - note = {Number: 1 -Publisher: Nature Publishing Group}, - keywords = {Biogeography, Projection and prediction}, - pages = {1445}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/257L3PIB/Bian et al. - 2023 - Understanding opposing predictions of Prochlorococ.pdf:application/pdf}, -} - -@misc{winkens_contrastive_2020, - title = {Contrastive {Training} for {Improved} {Out}-of-{Distribution} {Detection}}, - url = {http://arxiv.org/abs/2007.05566}, - abstract = {Reliable detection of out-of-distribution (OOD) inputs is increasingly understood to be a precondition for deployment of machine learning systems. This paper proposes and investigates the use of contrastive training to boost OOD detection performance. Unlike leading methods for OOD detection, our approach does not require access to examples labeled explicitly as OOD, which can be difficult to collect in practice. We show in extensive experiments that contrastive training significantly helps OOD detection performance on a number of common benchmarks. By introducing and employing the Confusion Log Probability (CLP) score, which quantifies the difficulty of the OOD detection task by capturing the similarity of inlier and outlier datasets, we show that our method especially improves performance in the `near OOD' classes -- a particularly challenging setting for previous methods.}, - urldate = {2023-11-13}, - publisher = {arXiv}, - author = {Winkens, Jim and Bunel, Rudy and Roy, Abhijit Guha and Stanforth, Robert and Natarajan, Vivek and Ledsam, Joseph R. and MacWilliams, Patricia and Kohli, Pushmeet and Karthikesalingam, Alan and Kohl, Simon and Cemgil, Taylan and Eslami, S. M. Ali and Ronneberger, Olaf}, - month = jul, - year = {2020}, - note = {arXiv:2007.05566 [cs, stat]}, - keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/ZW3DG7B6/2007.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/5M6W3QNV/Winkens et al. - 2020 - Contrastive Training for Improved Out-of-Distribut.pdf:application/pdf}, -} - -@inproceedings{zhou_contrastive_2021, - title = {Contrastive {Out}-of-{Distribution} {Detection} for {Pretrained} {Transformers}}, - url = {http://arxiv.org/abs/2104.08812}, - doi = {10.18653/v1/2021.emnlp-main.84}, - abstract = {Pretrained Transformers achieve remarkable performance when training and test data are from the same distribution. However, in real-world scenarios, the model often faces out-of-distribution (OOD) instances that can cause severe semantic shift problems at inference time. Therefore, in practice, a reliable model should identify such instances, and then either reject them during inference or pass them over to models that handle another distribution. In this paper, we develop an unsupervised OOD detection method, in which only the in-distribution (ID) data are used in training. We propose to fine-tune the Transformers with a contrastive loss, which improves the compactness of representations, such that OOD instances can be better differentiated from ID ones. These OOD instances can then be accurately detected using the Mahalanobis distance in the model's penultimate layer. We experiment with comprehensive settings and achieve near-perfect OOD detection performance, outperforming baselines drastically. We further investigate the rationales behind the improvement, finding that more compact representations through margin-based contrastive learning bring the improvement. We release our code to the community for future research.}, - urldate = {2023-11-13}, - booktitle = {Proceedings of the 2021 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}}, - author = {Zhou, Wenxuan and Liu, Fangyu and Chen, Muhao}, - year = {2021}, - note = {arXiv:2104.08812 [cs]}, - keywords = {Computer Science - Computation and Language}, - pages = {1100--1111}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/IB8D8VD8/2104.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/XFIETIDR/Zhou et al. - 2021 - Contrastive Out-of-Distribution Detection for Pret.pdf:application/pdf}, -} - -@article{beery_lecture_nodate-2, - title = {Lecture 20: {Transfer} {Learning} {II}}, - language = {en}, - author = {Beery, Sara}, - file = {20_transfer_learning_2_SB.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/20_transfer_learning_2_SB.pdf:application/pdf}, -} - -@misc{kingma_auto-encoding_2022, - title = {Auto-{Encoding} {Variational} {Bayes}}, - url = {http://arxiv.org/abs/1312.6114}, - abstract = {How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions are two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.}, - urldate = {2023-11-27}, - publisher = {arXiv}, - author = {Kingma, Diederik P. and Welling, Max}, - month = dec, - year = {2022}, - note = {arXiv:1312.6114 [cs, stat]}, - keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/H7BQXHMR/1312.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/QR4A6NNA/Kingma and Welling - 2022 - Auto-Encoding Variational Bayes.pdf:application/pdf}, -} - -@article{sonnewald_elucidating_2020, - title = {Elucidating ecological complexity: {Unsupervised} learning determines global marine eco-provinces}, - volume = {6}, - shorttitle = {Elucidating ecological complexity}, - url = {https://www.science.org/doi/full/10.1126/sciadv.aay4740}, - doi = {10.1126/sciadv.aay4740}, - abstract = {An unsupervised learning method is presented for determining global marine ecological provinces (eco-provinces) from plankton community structure and nutrient flux data. The systematic aggregated eco-province (SAGE) method identifies eco-provinces within a highly nonlinear ecosystem model. To accommodate the non-Gaussian covariance of the data, SAGE uses t-stochastic neighbor embedding (t-SNE) to reduce dimensionality. Over a hundred eco-provinces are identified with the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Using a connectivity graph with ecological dissimilarity as the distance metric, robust aggregated eco-provinces (AEPs) are objectively defined by nesting the eco-provinces. Using the AEPs, the control of nutrient supply rates on community structure is explored. Eco-provinces and AEPs are unique and aid model interpretation. They could facilitate model intercomparison and potentially improve understanding and monitoring of marine ecosystems.}, - number = {22}, - urldate = {2023-11-28}, - journal = {Science Advances}, - author = {Sonnewald, Maike and Dutkiewicz, Stephanie and Hill, Christopher and Forget, Gael}, - month = may, - year = {2020}, - note = {Publisher: American Association for the Advancement of Science}, - pages = {eaay4740}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/FSUVU9LS/Sonnewald et al. - 2020 - Elucidating ecological complexity Unsupervised le.pdf:application/pdf}, -} - -@misc{ho_denoising_2020, - title = {Denoising {Diffusion} {Probabilistic} {Models}}, - url = {http://arxiv.org/abs/2006.11239}, - doi = {10.48550/arXiv.2006.11239}, - abstract = {We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at https://github.com/hojonathanho/diffusion}, - urldate = {2023-11-30}, - publisher = {arXiv}, - author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter}, - month = dec, - year = {2020}, - note = {arXiv:2006.11239 [cs, stat]}, - keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, - file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/A854EPRR/Ho et al. - 2020 - Denoising Diffusion Probabilistic Models.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/R4HY8MGJ/2006.html:text/html}, -} - -@inproceedings{wang_partial_2022, - title = {Partial and {Asymmetric} {Contrastive} {Learning} for {Out}-of-{Distribution} {Detection} in {Long}-{Tailed} {Recognition}}, - url = {https://proceedings.mlr.press/v162/wang22aq.html}, - abstract = {Existing out-of-distribution (OOD) detection methods are typically benchmarked on training sets with balanced class distributions. However, in real-world applications, it is common for the training sets to have long-tailed distributions. In this work, we first demonstrate that existing OOD detection methods commonly suffer from significant performance degradation when the training set is long-tail distributed. Through analysis, we posit that this is because the models struggle to distinguish the minority tail-class in-distribution samples, from the true OOD samples, making the tail classes more prone to be falsely detected as OOD. To solve this problem, we propose Partial and Asymmetric Supervised Contrastive Learning (PASCL), which explicitly encourages the model to distinguish between tail-class in-distribution samples and OOD samples. To further boost in-distribution classification accuracy, we propose Auxiliary Branch Finetuning, which uses two separate branches of BN and classification layers for anomaly detection and in-distribution classification, respectively. The intuition is that in-distribution and OOD anomaly data have different underlying distributions. Our method outperforms previous state-of-the-art method by \$1.29\%\$, \$1.45\%\$, \$0.69\%\$ anomaly detection false positive rate (FPR) and \$3.24\%\$, \$4.06\%\$, \$7.89\%\$ in-distribution classification accuracy on CIFAR10-LT, CIFAR100-LT, and ImageNet-LT, respectively. Code and pre-trained models are available at https://github.com/amazon-research/long-tailed-ood-detection.}, - language = {en}, - urldate = {2023-12-04}, - booktitle = {Proceedings of the 39th {International} {Conference} on {Machine} {Learning}}, - publisher = {PMLR}, - author = {Wang, Haotao and Zhang, Aston and Zhu, Yi and Zheng, Shuai and Li, Mu and Smola, Alex J. and Wang, Zhangyang}, - month = jun, - year = {2022}, - note = {ISSN: 2640-3498}, - pages = {23446--23458}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/Y2THTBUA/Wang et al. - 2022 - Partial and Asymmetric Contrastive Learning for Ou.pdf:application/pdf}, -} - -@article{higgins_-vae_2017, - title = {β-{VAE}: {LEARNING} {BASIC} {VISUAL} {CONCEPTS} {WITH} {A} {CONSTRAINED} {VARIATIONAL} {FRAMEWORK}}, - abstract = {Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to learn and reason in the same way that humans do. We introduce β-VAE, a new state-of-the-art framework for automated discovery of interpretable factorised latent representations from raw image data in a completely unsupervised manner. Our approach is a modification of the variational autoencoder (VAE) framework. We introduce an adjustable hyperparameter β that balances latent channel capacity and independence constraints with reconstruction accuracy. We demonstrate that β-VAE with appropriately tuned β {\textgreater} 1 qualitatively outperforms VAE (β = 1), as well as state of the art unsupervised (InfoGAN) and semi-supervised (DC-IGN) approaches to disentangled factor learning on a variety of datasets (celebA, faces and chairs). Furthermore, we devise a protocol to quantitatively compare the degree of disentanglement learnt by different models, and show that our approach also significantly outperforms all baselines quantitatively. Unlike InfoGAN, β-VAE is stable to train, makes few assumptions about the data and relies on tuning a single hyperparameter β, which can be directly optimised through a hyperparameter search using weakly labelled data or through heuristic visual inspection for purely unsupervised data.}, - language = {en}, - author = {Higgins, Irina and Matthey, Loic and Pal, Arka and Burgess, Christopher and Glorot, Xavier and Botvinick, Matthew and Mohamed, Shakir and Lerchner, Alexander}, - year = {2017}, - file = {Higgins et al. - 2017 - β-VAE LEARNING BASIC VISUAL CONCEPTS WITH A CONST.pdf:/Users/birdy/Zotero/storage/9VGRX2XM/Higgins et al. - 2017 - β-VAE LEARNING BASIC VISUAL CONCEPTS WITH A CONST.pdf:application/pdf}, -} - -@article{schmarje_fuzzy_2021, - title = {Fuzzy {Overclustering}: {Semi}-{Supervised} {Classification} of {Fuzzy} {Labels} with {Overclustering} and {Inverse} {Cross}-{Entropy}}, - volume = {21}, - copyright = {http://creativecommons.org/licenses/by/3.0/}, - issn = {1424-8220}, - shorttitle = {Fuzzy {Overclustering}}, - url = {https://www.mdpi.com/1424-8220/21/19/6661}, - doi = {10.3390/s21196661}, - abstract = {Deep learning has been successfully applied to many classification problems including underwater challenges. However, a long-standing issue with deep learning is the need for large and consistently labeled datasets. Although current approaches in semi-supervised learning can decrease the required amount of annotated data by a factor of 10 or even more, this line of research still uses distinct classes. For underwater classification, and uncurated real-world datasets in general, clean class boundaries can often not be given due to a limited information content in the images and transitional stages of the depicted objects. This leads to different experts having different opinions and thus producing fuzzy labels which could also be considered ambiguous or divergent. We propose a novel framework for handling semi-supervised classifications of such fuzzy labels. It is based on the idea of overclustering to detect substructures in these fuzzy labels. We propose a novel loss to improve the overclustering capability of our framework and show the benefit of overclustering for fuzzy labels. We show that our framework is superior to previous state-of-the-art semi-supervised methods when applied to real-world plankton data with fuzzy labels. Moreover, we acquire 5 to 10\% more consistent predictions of substructures.}, - language = {en}, - number = {19}, - urldate = {2023-12-04}, - journal = {Sensors}, - author = {Schmarje, Lars and Brünger, Johannes and Santarossa, Monty and Schröder, Simon-Martin and Kiko, Rainer and Koch, Reinhard}, - month = jan, - year = {2021}, - note = {Number: 19 -Publisher: Multidisciplinary Digital Publishing Institute}, - keywords = {deep learning, fuzzy, marine, noisy, plankton, real-world, semi-supervised}, - pages = {6661}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/WILN3PSB/Schmarje et al. - 2021 - Fuzzy Overclustering Semi-Supervised Classificati.pdf:application/pdf}, -} - -@inproceedings{liu_good-d_2023, - address = {New York, NY, USA}, - series = {{WSDM} '23}, - title = {{GOOD}-{D}: {On} {Unsupervised} {Graph} {Out}-{Of}-{Distribution} {Detection}}, - isbn = {978-1-4503-9407-9}, - shorttitle = {{GOOD}-{D}}, - url = {https://dl.acm.org/doi/10.1145/3539597.3570446}, - doi = {10.1145/3539597.3570446}, - abstract = {Most existing deep learning models are trained based on the closed-world assumption, where the test data is assumed to be drawn i.i.d. from the same distribution as the training data, known as in-distribution (ID). However, when models are deployed in an open-world scenario, test samples can be out-of-distribution (OOD) and therefore should be handled with caution. To detect such OOD samples drawn from unknown distribution, OOD detection has received increasing attention lately. However, current endeavors mostly focus on grid-structured data and its application for graph-structured data remains under-explored. Considering the fact that data labeling on graphs is commonly time-expensive and labor-intensive, in this work we study the problem of unsupervised graph OOD detection, aiming at detecting OOD graphs solely based on unlabeled ID data. To achieve this goal, we develop a new graph contrastive learning framework GOOD-D for detecting OOD graphs without using any ground-truth labels. By performing hierarchical contrastive learning on the augmented graphs generated by our perturbation-free graph data augmentation method, GOOD-D is able to capture the latent ID patterns and accurately detect OOD graphs based on the semantic inconsistency in different granularities (i.e., node-level, graph-level, and group-level). As a pioneering work in unsupervised graph-level OOD detection, we build a comprehensive benchmark to compare our proposed approach with different state-of-the-art methods. The experiment results demonstrate the superiority of our approach over different methods on various datasets.}, - urldate = {2023-12-04}, - booktitle = {Proceedings of the {Sixteenth} {ACM} {International} {Conference} on {Web} {Search} and {Data} {Mining}}, - publisher = {Association for Computing Machinery}, - author = {Liu, Yixin and Ding, Kaize and Liu, Huan and Pan, Shirui}, - month = feb, - year = {2023}, - keywords = {contrastive learning, graph neural networks, out-of-distribution detection}, - pages = {339--347}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/I8T5QYSC/Liu et al. - 2023 - GOOD-D On Unsupervised Graph Out-Of-Distribution .pdf:application/pdf}, -} - -@misc{wang_understanding_2022, - title = {Understanding {Contrastive} {Representation} {Learning} through {Alignment} and {Uniformity} on the {Hypersphere}}, - url = {http://arxiv.org/abs/2005.10242}, - abstract = {Contrastive representation learning has been outstandingly successful in practice. In this work, we identify two key properties related to the contrastive loss: (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere. We prove that, asymptotically, the contrastive loss optimizes these properties, and analyze their positive effects on downstream tasks. Empirically, we introduce an optimizable metric to quantify each property. Extensive experiments on standard vision and language datasets confirm the strong agreement between both metrics and downstream task performance. Remarkably, directly optimizing for these two metrics leads to representations with comparable or better performance at downstream tasks than contrastive learning. Project Page: https://tongzhouwang.info/hypersphere Code: https://github.com/SsnL/align\_uniform , https://github.com/SsnL/moco\_align\_uniform}, - urldate = {2023-12-04}, - publisher = {arXiv}, - author = {Wang, Tongzhou and Isola, Phillip}, - month = aug, - year = {2022}, - note = {arXiv:2005.10242 [cs, stat]}, - keywords = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/WFW3P94A/2005.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/KJZGVSLC/Wang and Isola - 2022 - Understanding Contrastive Representation Learning .pdf:application/pdf}, -} - -@inproceedings{chen_atom_2021, - address = {Cham}, - series = {Lecture {Notes} in {Computer} {Science}}, - title = {{ATOM}: {Robustifying} {Out}-of-{Distribution} {Detection} {Using} {Outlier} {Mining}}, - isbn = {978-3-030-86523-8}, - shorttitle = {{ATOM}}, - doi = {10.1007/978-3-030-86523-8_26}, - abstract = {Detecting out-of-distribution (OOD) inputs is critical for safely deploying deep learning models in an open-world setting. However, existing OOD detection solutions can be brittle in the open world, facing various types of adversarial OOD inputs. While methods leveraging auxiliary OOD data have emerged, our analysis on illuminative examples reveals a key insight that the majority of auxiliary OOD examples may not meaningfully improve or even hurt the decision boundary of the OOD detector, which is also observed in empirical results on real data. In this paper, we provide a theoretically motivated method, Adversarial Training with informative Outlier Mining (ATOM), which improves the robustness of OOD detection. We show that, by mining informative auxiliary OOD data, one can significantly improve OOD detection performance, and somewhat surprisingly, generalize to unseen adversarial attacks. ATOM achieves state-of-the-art performance under a broad family of classic and adversarial OOD evaluation tasks. For example, on the CIFAR-10 in-distribution dataset, ATOM reduces the FPR (at TPR 95\%) by up to 57.99\% under adversarial OOD inputs, surpassing the previous best baseline by a large margin.}, - language = {en}, - booktitle = {Machine {Learning} and {Knowledge} {Discovery} in {Databases}. {Research} {Track}}, - publisher = {Springer International Publishing}, - author = {Chen, Jiefeng and Li, Yixuan and Wu, Xi and Liang, Yingyu and Jha, Somesh}, - editor = {Oliver, Nuria and Pérez-Cruz, Fernando and Kramer, Stefan and Read, Jesse and Lozano, Jose A.}, - year = {2021}, - keywords = {Out-of-distribution detection, Outlier mining, Robustness}, - pages = {430--445}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/5PNM3HAW/Chen et al. - 2021 - ATOM Robustifying Out-of-Distribution Detection U.pdf:application/pdf}, -} - -@misc{chen_simple_2020, - title = {A {Simple} {Framework} for {Contrastive} {Learning} of {Visual} {Representations}}, - url = {http://arxiv.org/abs/2002.05709}, - abstract = {This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5\% top-1 accuracy, which is a 7\% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1\% of the labels, we achieve 85.8\% top-5 accuracy, outperforming AlexNet with 100X fewer labels.}, - urldate = {2024-01-04}, - publisher = {arXiv}, - author = {Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey}, - month = jun, - year = {2020}, - note = {arXiv:2002.05709 [cs, stat]}, - keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/QSFZN5RI/2002.html:text/html;Chen et al. - 2020 - A Simple Framework for Contrastive Learning of Vis.pdf:/Users/birdy/Zotero/storage/UA8HJXR5/Chen et al. - 2020 - A Simple Framework for Contrastive Learning of Vis.pdf:application/pdf}, -} - -@misc{pang_rethinking_2020, - title = {Rethinking {Softmax} {Cross}-{Entropy} {Loss} for {Adversarial} {Robustness}}, - url = {http://arxiv.org/abs/1905.10626}, - abstract = {Previous work shows that adversarially robust generalization requires larger sample complexity, and the same dataset, e.g., CIFAR-10, which enables good standard accuracy may not suffice to train robust models. Since collecting new training data could be costly, we focus on better utilizing the given data by inducing the regions with high sample density in the feature space, which could lead to locally sufficient samples for robust learning. We first formally show that the softmax cross-entropy (SCE) loss and its variants convey inappropriate supervisory signals, which encourage the learned feature points to spread over the space sparsely in training. This inspires us to propose the Max-Mahalanobis center (MMC) loss to explicitly induce dense feature regions in order to benefit robustness. Namely, the MMC loss encourages the model to concentrate on learning ordered and compact representations, which gather around the preset optimal centers for different classes. We empirically demonstrate that applying the MMC loss can significantly improve robustness even under strong adaptive attacks, while keeping state-of-the-art accuracy on clean inputs with little extra computation compared to the SCE loss.}, - urldate = {2024-01-06}, - publisher = {arXiv}, - author = {Pang, Tianyu and Xu, Kun and Dong, Yinpeng and Du, Chao and Chen, Ning and Zhu, Jun}, - month = feb, - year = {2020}, - note = {arXiv:1905.10626 [cs, stat]}, - keywords = {Computer Science - Cryptography and Security, Computer Science - Machine Learning, Statistics - Machine Learning}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/ZQWMNTQ4/1905.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/L6RK8EVE/Pang et al. - 2020 - Rethinking Softmax Cross-Entropy Loss for Adversar.pdf:application/pdf}, -} - -@inproceedings{zhai_s4l_2019, - address = {Seoul, Korea (South)}, - title = {{S4L}: {Self}-{Supervised} {Semi}-{Supervised} {Learning}}, - isbn = {978-1-72814-803-8}, - shorttitle = {{S4L}}, - url = {https://ieeexplore.ieee.org/document/9010283/}, - doi = {10.1109/ICCV.2019.00156}, - abstract = {This work tackles the problem of semi-supervised learning of image classifiers. Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual representation learning. Unifying these two approaches, we propose the framework of self-supervised semi-supervised learning (S4L) and use it to derive two novel semi-supervised image classification methods. We demonstrate the effectiveness of these methods in comparison to both carefully tuned baselines, and existing semi-supervised learning methods. We then show that S4L and existing semi-supervised methods can be jointly trained, yielding a new state-of-the-art result on semi-supervised ILSVRC-2012 with 10\% of labels.}, - language = {en}, - urldate = {2024-01-06}, - booktitle = {2019 {IEEE}/{CVF} {International} {Conference} on {Computer} {Vision} ({ICCV})}, - publisher = {IEEE}, - author = {Zhai, Xiaohua and Oliver, Avital and Kolesnikov, Alexander and Beyer, Lucas}, - month = oct, - year = {2019}, - pages = {1476--1485}, - file = {Zhai et al. - 2019 - S4L Self-Supervised Semi-Supervised Learning.pdf:/Users/birdy/Zotero/storage/8NNHKTJM/Zhai et al. - 2019 - S4L Self-Supervised Semi-Supervised Learning.pdf:application/pdf}, -} - -@misc{duckworth_dar1_2023, - title = {{DAR1} - {An} {Easy} to {Use}, {Single}-{Box}, {Configuration} of a {Global} {Scale} {Biogeochemical} {Ecosystem} {Model}}, - copyright = {© 2023, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/}, - url = {https://www.biorxiv.org/content/10.1101/2023.11.23.568480v1}, - doi = {10.1101/2023.11.23.568480}, - abstract = {Global scale biogeochemical ocean models are key to understanding the mechanisms which drive elemental cycles and their links to marine ecosystems. However, these models are rarely straightforward to use and their complexity can make them difficult to interpret. Here, we aim to address this problem by developing DAR1 – a single box configuration of the MIT Darwin model, which is itself a global scale biogeochemical ecosystem model designed for understanding the emergent patterns of global plankton distributions. Our configuration is packaged inside Docker and implemented in the Julia programming language. This helps make it both computationally efficient and straightforward to implement on any local machine, independent of hardware and operating system. It allows for the replication of field and laboratory experiments in a model ocean and the rapid comparison of global runs with local predictions. We provide three case studies: a framework for nutrient amendment bottle experiments; a test of steady state assumptions on plankton community structure at the global scale; and an exploration of the niche space of nitrogen-fixing plankton. Our method has the potential to facilitate broader direct testing of global model assumptions in the context of small-scale measurements. DAR1 will also allow observational and laboratory oceanographers to directly test their results against model predictions in a straightforward, easy to use, manner.}, - language = {en}, - urldate = {2024-01-06}, - publisher = {bioRxiv}, - author = {Duckworth, Barbara and Dutkiewicz, Stephanie and Jahn, Oliver and Hill, Christopher N. and Follett, Christopher L.}, - month = nov, - year = {2023}, - note = {Pages: 2023.11.23.568480 -Section: New Results}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/3376RTZG/Duckworth et al. - 2023 - DAR1 - An Easy to Use, Single-Box, Configuration o.pdf:application/pdf}, -} - -@article{falkowski_role_1994, - title = {The role of phytoplankton photosynthesis in global biogeochemical cycles}, - volume = {39}, - issn = {1573-5079}, - url = {https://doi.org/10.1007/BF00014586}, - doi = {10.1007/BF00014586}, - abstract = {Phytoplankton biomass in the world's oceans amounts to only ∽1–2\% of the total global plant carbon, yet these organisms fix between 30 and 50 billion metric tons of carbon annually, which is about 40\% of the total. On geological time scales there is profound evidence of the importance of phytoplankton photosynthesis in biogeochemical cycles. It is generally assumed that present phytoplankton productivity is in a quasi steady-state (on the time scale of decades). However, in a global context, the stability of oceanic photosynthetic processes is dependent on the physical circulation of the upper ocean and is therefore strongly influenced by the atmosphere. The net flux of atmospheric radiation is critical to determining the depth of the upper mixed layer and the vertical fluxes of nutrients. These latter two parameters are keys to determining the intensity, and spatial and temporal distributions of phytoplankton blooms. Atmospheric radiation budgets are not in steady-state. Driven largely by anthropogenic activities in the 20th century, increased levels of IR- absorbing gases such as CO2, CH4 and CFC's and NOx will potentially increase atmospheric temperatures on a global scale. The atmospheric radiation budget can affect phytoplankton photosynthesis directly and indirectly. Increased temperature differences between the continents and oceans have been implicated in higher wind stresses at the ocean margins. Increased wind speeds can lead to higher nutrient fluxes. Throughout most of the central oceans, nitrate concentrations are sub-micromolar and there is strong evidence that the quantum efficiency of Photosystem II is impaired by nutrient stress. Higher nutrient fluxes would lead to both an increase in phytoplankton biomass and higher biomass-specific rates of carbon fixation. However, in the center of the ocean gyres, increased radiative heating could reduce the vertical flux of nutrients to the euphotic zone, and hence lead to a reduction in phytoplankton carbon fixation. Increased desertification in terrestrial ecosystems can lead to increased aeolean loadings of essential micronutrients, such as iron. An increased flux of aeolean micronutrients could fertilize nutrient-replete areas of the open ocean with limiting trace elements, thereby stimulating photosynthetic rates. The factors which limit phytoplankton biomass and photosynthesis are discussed and examined with regard to potential changes in the Earth climate system which can lead the oceans away from steady-state. While it is difficult to confidently deduce changes in either phytoplankton biomass or photosynthetic rates on decadal time scales, time-series analysis of ocean transparency data suggest long-term trends have occurred in the North Pacific Ocean in the 20th century. However, calculations of net carbon uptake by the oceans resulting from phytoplankton photosynthesis suggest that without a supply of nutrients external to the ocean, carbon fixation in the open ocean is not presently a significant sink for excess atmospheric CO2.}, - language = {en}, - number = {3}, - urldate = {2024-01-06}, - journal = {Photosynthesis Research}, - author = {Falkowski, Paul G.}, - month = mar, - year = {1994}, - keywords = {biogeochemical cycles, oceans, photoacclimation, Photosystem II nutrient limitation, phytoplankton quantum efficiency of photosynthesis}, - pages = {235--258}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/6ECKTMBR/Falkowski - 1994 - The role of phytoplankton photosynthesis in global.pdf:application/pdf}, -} - -@article{treguer_influence_2018, - title = {Influence of diatom diversity on the ocean biological carbon pump}, - volume = {11}, - copyright = {2017 Springer Nature Limited}, - issn = {1752-0908}, - url = {https://www.nature.com/articles/s41561-017-0028-x}, - doi = {10.1038/s41561-017-0028-x}, - abstract = {Diatoms sustain the marine food web and contribute to the export of carbon from the surface ocean to depth. They account for about 40\% of marine primary productivity and particulate carbon exported to depth as part of the biological pump. Diatoms have long been known to be abundant in turbulent, nutrient-rich waters, but observations and simulations indicate that they are dominant also in meso- and submesoscale structures such as fronts and filaments, and in the deep chlorophyll maximum. Diatoms vary widely in size, morphology and elemental composition, all of which control the quality, quantity and sinking speed of biogenic matter to depth. In particular, their silica shells provide ballast to marine snow and faecal pellets, and can help transport carbon to both the mesopelagic layer and deep ocean. Herein we show that the extent to which diatoms contribute to the export of carbon varies by diatom type, with carbon transfer modulated by the Si/C ratio of diatom cells, the thickness of the shells and their life strategies; for instance, the tendency to form aggregates or resting spores. Model simulations project a decline in the contribution of diatoms to primary production everywhere outside of the Southern Ocean. We argue that we need to understand changes in diatom diversity, life cycle and plankton interactions in a warmer and more acidic ocean in much more detail to fully assess any changes in their contribution to the biological pump.}, - language = {en}, - number = {1}, - urldate = {2024-01-06}, - journal = {Nature Geoscience}, - author = {Tréguer, Paul and Bowler, Chris and Moriceau, Brivaela and Dutkiewicz, Stephanie and Gehlen, Marion and Aumont, Olivier and Bittner, Lucie and Dugdale, Richard and Finkel, Zoe and Iudicone, Daniele and Jahn, Oliver and Guidi, Lionel and Lasbleiz, Marine and Leblanc, Karine and Levy, Marina and Pondaven, Philippe}, - month = jan, - year = {2018}, - note = {Number: 1 -Publisher: Nature Publishing Group}, - keywords = {Biogeochemistry, Carbon cycle, Marine biology, Ocean sciences}, - pages = {27--37}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/VLLSAC7H/Tréguer et al. - 2018 - Influence of diatom diversity on the ocean biologi.pdf:application/pdf}, -} - -@misc{bengio_representation_2014, - title = {Representation {Learning}: {A} {Review} and {New} {Perspectives}}, - shorttitle = {Representation {Learning}}, - url = {http://arxiv.org/abs/1206.5538}, - abstract = {The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning.}, - urldate = {2024-01-06}, - publisher = {arXiv}, - author = {Bengio, Yoshua and Courville, Aaron and Vincent, Pascal}, - month = apr, - year = {2014}, - note = {arXiv:1206.5538 [cs]}, - keywords = {Computer Science - Machine Learning}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/EYA2B52H/1206.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/2CFSH6MT/Bengio et al. - 2014 - Representation Learning A Review and New Perspect.pdf:application/pdf}, -} - -@misc{white_gradients2-mgl1704-ifcb-abundance_2020-04-01_v10_2020, - title = {Gradients2-{MGL1704}-{IFCB}-{Abundance}\_2020-04-01\_v1.0}, - url = {https://zenodo.org/records/4267140}, - doi = {10.5281/zenodo.4267140}, - abstract = {Cruise: Gradients 2, MGL1704 Project Name: Simons Foundation, Gradient NPSG Dataset Description: The Imaging FlowCytoBot (IFCB) is an in situ automated imaging flow cytometer that generates images of particles suspended in seawater, in this case from the underway uncontaminated seawater system aboard the R/V Langseth (intake 5m).   The IFCB uses a recycled sheath fluid (0.2 µm filtered seawater) to align and drive particles individually towards a light source (red laser, 4.5 mW) in order to detect and identify single or colonial cells using a combination of optical properties (red fluorescence and light scattering intensities) and high resolution images (3.2 pixels per micron) by a mounted camera. Both optical properties are used to trigger targeted image acquisition of suspended particles in the size range {\textless}4 to 100 μm. The instrument continuously samples (few seconds) from  {\textasciitilde}5 ml aliquots from the intake, and processes all particles contained in that volume for the next 20 mins.  Images corresponding to the "Sample" variable are available on the cruise's dashboard (http://ifcb-data.soest.hawaii.edu/IFCB\_NPTZ).  This dataset is for the abundance of imaged cells by genus.  For each sample, the total number of cells classified to the genus-level by a random forest algorithm (Sosik and Olson, 2007 doi:10.4319/lom.2007.5.204) is counted and divided by the corresponding volume analyzed ({\textasciitilde}5 mL). Note that we used all the images collected during Gradients 2.0 to train the random forest algorithm and that classification is therefore highly accurate for this dataset. Using 7 µm calibration beads, we estimated that the error of cell concentration due to cell detection during sample acquisition averages 11 ± 10 \%, independently of concentrations in the range 1-10000 cell/mL.}, - urldate = {2024-01-06}, - publisher = {Zenodo}, - author = {White, Angelicque}, - month = apr, - year = {2020}, - file = {Zenodo Snapshot:/Users/birdy/Zotero/storage/456EVCSQ/4267140.html:text/html}, -} - -@article{kanungo_efficient_2002, - title = {An efficient k-means clustering algorithm: analysis and implementation}, - volume = {24}, - issn = {1939-3539}, - shorttitle = {An efficient k-means clustering algorithm}, - url = {https://ieeexplore.ieee.org/document/1017616}, - doi = {10.1109/TPAMI.2002.1017616}, - abstract = {In k-means clustering, we are given a set of n data points in d-dimensional space R/sup d/ and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd's (1982) algorithm. We present a simple and efficient implementation of Lloyd's k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm's running time, which shows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in color quantization, data compression, and image segmentation.}, - number = {7}, - urldate = {2024-01-06}, - journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, - author = {Kanungo, T. and Mount, D.M. and Netanyahu, N.S. and Piatko, C.D. and Silverman, R. and Wu, A.Y.}, - month = jul, - year = {2002}, - note = {Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence}, - pages = {881--892}, - file = {IEEE Xplore Abstract Record:/Users/birdy/Zotero/storage/RC5LGQ6V/1017616.html:text/html;IEEE Xplore Full Text PDF:/Users/birdy/Zotero/storage/NUTPDLG4/Kanungo et al. - 2002 - An efficient k-means clustering algorithm analysi.pdf:application/pdf}, -} - -@inproceedings{he_deep_2016, - title = {Deep {Residual} {Learning} for {Image} {Recognition}}, - url = {https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html}, - urldate = {2024-01-06}, - author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, - year = {2016}, - pages = {770--778}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/RYXX2LD8/He et al. - 2016 - Deep Residual Learning for Image Recognition.pdf:application/pdf}, -} diff --git a/assets/bibliography/PlanktonRepresentation.bib b/assets/bibliography/PlanktonRepresentation.bib deleted file mode 100644 index b201fa10..00000000 --- a/assets/bibliography/PlanktonRepresentation.bib +++ /dev/null @@ -1,2462 +0,0 @@ - -@article{araujo_validation_2005, - title = {Validation of species–climate impact models under climate change}, - volume = {11}, - issn = {1365-2486}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-2486.2005.01000.x}, - doi = {10.1111/j.1365-2486.2005.01000.x}, - abstract = {Increasing concern over the implications of climate change for biodiversity has led to the use of species–climate envelope models to project species extinction risk under climate-change scenarios. However, recent studies have demonstrated significant variability in model predictions and there remains a pressing need to validate models and to reduce uncertainties. Model validation is problematic as predictions are made for events that have not yet occurred. Resubstituition and data partitioning of present-day data sets are, therefore, commonly used to test the predictive performance of models. However, these approaches suffer from the problems of spatial and temporal autocorrelation in the calibration and validation sets. Using observed distribution shifts among 116 British breeding-bird species over the past ∼20 years, we are able to provide a first independent validation of four envelope modelling techniques under climate change. Results showed good to fair predictive performance on independent validation, although rules used to assess model performance are difficult to interpret in a decision-planning context. We also showed that measures of performance on nonindependent data provided optimistic estimates of models' predictive ability on independent data. Artificial neural networks and generalized additive models provided generally more accurate predictions of species range shifts than generalized linear models or classification tree analysis. Data for independent model validation and replication of this study are rare and we argue that perfect validation may not in fact be conceptually possible. We also note that usefulness of models is contingent on both the questions being asked and the techniques used. Implementations of species–climate envelope models for testing hypotheses and predicting future events may prove wrong, while being potentially useful if put into appropriate context.}, - language = {en}, - number = {9}, - urldate = {2021-12-15}, - journal = {Global Change Biology}, - author = {Araújo, Miguel B. and Pearson, Richard G. and Thuiller, Wilfried and Erhard, Markus}, - year = {2005}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2486.2005.01000.x}, - keywords = {bioclimatic-envelope models, breeding birds, Britain, climate change, model accuracy, uncertainty, validation, unread, not printed}, - pages = {1504--1513}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/6EVK36UH/Araújo et al. - 2005 - Validation of species–climate impact models under .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/C68Y6MBK/j.1365-2486.2005.01000.html:text/html}, -} - -@incollection{kolter_unveiling_2012, - title = {Unveiling {Prochlorococcus}}, - isbn = {978-1-55581-540-0 978-1-55581-847-0}, - url = {http://www.asmscience.org/content/book/10.1128/9781555818470.chap23}, - language = {en}, - urldate = {2021-12-15}, - booktitle = {Microbes and {Evolution}}, - publisher = {American Society of Microbiology}, - author = {Chisholm, Sallie W.}, - editor = {{Kolter} and {Maloy}}, - month = jan, - year = {2012}, - doi = {10.1128/9781555818470.ch23}, - keywords = {read, printed}, - pages = {165--171}, - file = {Chisholm - 2012 - Unveiling Prochlorococcus.pdf:/Users/birdy/Zotero/storage/D2MZS4I5/Chisholm - 2012 - Unveiling Prochlorococcus.pdf:application/pdf}, -} - -@article{follett_moving_2021, - title = {Moving ecological and biogeochemical transitions across the {North} {Pacific}}, - volume = {66}, - issn = {1939-5590}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lno.11763}, - doi = {10.1002/lno.11763}, - abstract = {In the North Pacific Ocean, nutrient rich surface waters flow south from the subpolar gyre through a transitional region and into the subtropics. Along the way, nutrients are used, recycled, and exported, leading to lower biomass and a commensurate change in ecosystem structure moving southward. We focus on the region between the two gyres (the Transition Zone) using a coupled biophysical ocean model, remote sensing, floats, and cruise data to explore the nature of the physical, biogeochemical, and ecological fields in this region. Nonlinear interactions between biological processes and the meridional gradient in nutrient supply lead to sharp shifts across this zone. These transitions between a southern region with more uniform biological and biogeochemical properties and steep meridional gradients to the north are diagnosed from extrema in the first derivative of the properties with latitude. Some transitions like that for chlorophyll a (the transition zone chlorophyll front [TZCF]) experience large seasonal excursions while the location of the transitions in other properties moves very little. The seasonal shifts are not caused by changes in the horizontal flow field, but rather by the interaction of seasonal, depth related, forcing with the mean latitudinal gradients. Focusing on the TZCF as a case study, we express its phase velocity in terms of vertical nutrient flux and internal ecosystem processes, demonstrating their nearly equal influence on its motion. This framework of propagating biogeochemical transitions can be systematically expanded to better understand the processes that structure ecosystems and biogeochemistry in the North Pacific and beyond.}, - language = {en}, - number = {6}, - urldate = {2021-12-15}, - journal = {Limnology and Oceanography}, - author = {Follett, Christopher L. and Dutkiewicz, Stephanie and Forget, Gael and Cael, B. B. and Follows, Michael J.}, - year = {2021}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lno.11763}, - keywords = {read, darwin, printed}, - pages = {2442--2454}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/XUKLUSS4/Follett et al. - 2021 - Moving ecological and biogeochemical transitions a.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/MASWCC2S/lno.html:text/html}, -} - -@article{guisan_predictive_2000, - title = {Predictive habitat distribution models in ecology}, - volume = {135}, - issn = {03043800}, - url = {https://linkinghub.elsevier.com/retrieve/pii/S0304380000003549}, - doi = {10.1016/S0304-3800(00)00354-9}, - abstract = {With the rise of new powerful statistical techniques and GIS tools, the development of predictive habitat distribution models has rapidly increased in ecology. Such models are static and probabilistic in nature, since they statistically relate the geographical distribution of species or communities to their present environment. A wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate change research, and habitat or species management. In this paper, we present a review of predictive habitat distribution modeling. The variety of statistical techniques used is growing. Ordinary multiple regression and its generalized form (GLM) are very popular and are often used for modeling species distributions. Other methods include neural networks, ordination and classification methods, Bayesian models, locally weighted approaches (e.g. GAM), environmental envelopes or even combinations of these models. The selection of an appropriate method should not depend solely on statistical considerations. Some models are better suited to reflect theoretical findings on the shape and nature of the species’ response (or realized niche). Conceptual considerations include e.g. the trade-off between optimizing accuracy versus optimizing generality. In the field of static distribution modeling, the latter is mostly related to selecting appropriate predictor variables and to designing an appropriate procedure for model selection. New methods, including threshold-independent measures (e.g. receiver operating characteristic (ROC)-plots) and resampling techniques (e.g. bootstrap, cross-validation) have been introduced in ecology for testing the accuracy of predictive models. The choice of an evaluation measure should be driven primarily by the goals of the study. This may possibly lead to the attribution of different weights to the various types of prediction errors (e.g. omission, commission or confusion). Testing the model in a wider range of situations (in space and time) will permit one to define the range of applications for which the model predictions are suitable. In turn, the qualification of the model depends primarily on the goals of the study that define the qualification criteria and on the usability of the model, rather than on statistics alone. © 2000 Elsevier Science B.V. All rights reserved.}, - language = {en}, - number = {2-3}, - urldate = {2021-12-15}, - journal = {Ecological Modelling}, - author = {Guisan, Antoine and Zimmermann, Niklaus E.}, - month = dec, - year = {2000}, - keywords = {unread, sdm, printed}, - pages = {147--186}, - file = {Guisan and Zimmermann - 2000 - Predictive habitat distribution models in ecology.pdf:/Users/birdy/Zotero/storage/SGKKM6TL/Guisan and Zimmermann - 2000 - Predictive habitat distribution models in ecology.pdf:application/pdf}, -} - -@article{bardon_testing_2021, - title = {Testing the {Skill} of a {Species} {Distribution} {Model} {Using} a 21st {Century} {Virtual} {Ecosystem}}, - volume = {48}, - issn = {1944-8007}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2021GL093455}, - doi = {10.1029/2021GL093455}, - abstract = {Plankton communities play an important role in marine food webs, in biogeochemical cycling, and in Earth's climate; yet observations are sparse, and predictions of how they might respond to climate change vary. Correlative species distribution models (SDM's) have been applied to predicting biogeography based on relationships to observed environmental variables. To investigate sources of uncertainty, we use a correlative SDM to predict the plankton biogeography of a 21st century marine ecosystem model (Darwin). Darwin output is sampled to mimic historical ocean observations, and the SDM is trained using generalized additive models. We find that predictive skill varies across test cases, and between functional groups, with errors that are more attributable to spatiotemporal sampling bias than sample size. End-of-century predictions are poor, limited by changes in target-predictor relationships over time. Our findings illustrate the fundamental challenges faced by empirical models in using limited observational data to predict complex, dynamic systems.}, - language = {en}, - number = {22}, - urldate = {2021-12-15}, - journal = {Geophysical Research Letters}, - author = {Bardon, L. R. and Ward, B. A. and Dutkiewicz, S. and Cael, B. B.}, - year = {2021}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021GL093455}, - keywords = {climate change, unread, darwin, sdm, complex systems, numerical models, oceanography, plankton ecology, statistical learning, printed}, - pages = {e2021GL093455}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/RKQ6X8MD/Bardon et al. - 2021 - Testing the Skill of a Species Distribution Model .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/53EFUGZ7/2021GL093455.html:text/html}, -} - -@article{elith_species_2009, - title = {Species {Distribution} {Models}: {Ecological} {Explanation} and {Prediction} {Across} {Space} and {Time}}, - volume = {40}, - shorttitle = {Species {Distribution} {Models}}, - url = {https://doi.org/10.1146/annurev.ecolsys.110308.120159}, - doi = {10.1146/annurev.ecolsys.110308.120159}, - abstract = {Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in “established use.” Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.}, - number = {1}, - urldate = {2021-12-15}, - journal = {Annual Review of Ecology, Evolution, and Systematics}, - author = {Elith, Jane and Leathwick, John R.}, - year = {2009}, - note = {\_eprint: https://doi.org/10.1146/annurev.ecolsys.110308.120159}, - keywords = {read, sdm, printed}, - pages = {677--697}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/94HVWZRY/Elith and Leathwick - 2009 - Species Distribution Models Ecological Explanatio.pdf:application/pdf}, -} - -@article{henson_future_2021, - title = {Future phytoplankton diversity in a changing climate}, - volume = {12}, - issn = {2041-1723}, - url = {https://www.nature.com/articles/s41467-021-25699-w}, - doi = {10.1038/s41467-021-25699-w}, - abstract = {Abstract - The future response of marine ecosystem diversity to continued anthropogenic forcing is poorly constrained. Phytoplankton are a diverse set of organisms that form the base of the marine ecosystem. Currently, ocean biogeochemistry and ecosystem models used for climate change projections typically include only 2−3 phytoplankton types and are, therefore, too simple to adequately assess the potential for changes in plankton community structure. Here, we analyse a complex ecosystem model with 35 phytoplankton types to evaluate the changes in phytoplankton community composition, turnover and size structure over the 21st century. We find that the rate of turnover in the phytoplankton community becomes faster during this century, that is, the community structure becomes increasingly unstable in response to climate change. Combined with alterations to phytoplankton diversity, our results imply a loss of ecological resilience with likely knock-on effects on the productivity and functioning of the marine environment.}, - language = {en}, - number = {1}, - urldate = {2021-12-15}, - journal = {Nature Communications}, - author = {Henson, Stephanie A. and Cael, B. B. and Allen, Stephanie R. and Dutkiewicz, Stephanie}, - month = dec, - year = {2021}, - keywords = {unread, not printed}, - pages = {5372}, - file = {Henson et al. - 2021 - Future phytoplankton diversity in a changing clima.pdf:/Users/birdy/Zotero/storage/GLZDZK9S/Henson et al. - 2021 - Future phytoplankton diversity in a changing clima.pdf:application/pdf}, -} - -@article{wu_modeling_2021, - title = {Modeling {Photosynthesis} and {Exudation} in {Subtropical} {Oceans}}, - volume = {35}, - issn = {1944-9224}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2021GB006941}, - doi = {10.1029/2021GB006941}, - abstract = {Marine phytoplankton contributes nearly half of the total primary production on Earth through photosynthesis. Parameterizations of algal photosynthesis commonly employed in global biogeochemical simulations generally fail to capture the observed vertical structure of primary production. Here we examined the consequences of decoupling photosynthesis (carbon fixation) and biosynthesis (biomass building) with accumulation or exudation of excess photosynthate under energy rich conditions in both regional and global models. The results show that the decoupling of these two processes improved the simulated vertical profile of primary production, increased modeled primary production over 30\% globally and over 40\% in subtropical oceans, improved simulated meridional patterns of particulate C:N:P and increased modeled surface pool of labile/semi-labile dissolved organic carbon. More generally, these results highlight the importance of exudation, which results from the decoupling of photosynthesis and biosynthesis, as a major physiological process affecting ocean biogeochemistry.}, - language = {en}, - number = {9}, - urldate = {2021-12-15}, - journal = {Global Biogeochemical Cycles}, - author = {Wu, Zhen and Dutkiewicz, Stephanie and Jahn, Oliver and Sher, Daniel and White, Angelicque and Follows, Michael J.}, - year = {2021}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021GB006941}, - keywords = {unread, not printed, biogeochemical model, exudation, photosynthesis, phytoplankton, primary production, subtropical gyres, EAPS}, - pages = {e2021GB006941}, -} - -@article{ser-giacomi_lagrangian_2021, - title = {Lagrangian betweenness as a measure of bottlenecks in dynamical systems with oceanographic examples}, - volume = {12}, - issn = {2041-1723}, - url = {https://www.nature.com/articles/s41467-021-25155-9}, - doi = {10.1038/s41467-021-25155-9}, - abstract = {Abstract - The study of connectivity patterns in networks has brought novel insights across diverse fields ranging from neurosciences to epidemic spreading or climate. In this context, betweenness centrality has demonstrated to be a very effective measure to identify nodes that act as focus of congestion, or bottlenecks, in the network. However, there is not a way to define betweenness outside the network framework. By analytically linking dynamical systems and network theory, we provide a trajectory-based formulation of betweenness, called Lagrangian betweenness, as a function of Lyapunov exponents. This extends the concept of betweenness beyond the context of network theory relating hyperbolic points and heteroclinic connections in any dynamical system to the structural bottlenecks of the network associated with it. Using modeled and observational velocity fields, we show that such bottlenecks are present and surprisingly persistent in the oceanic circulation across different spatio-temporal scales and we illustrate the role of these areas in driving fluid transport over vast oceanic regions. Analyzing plankton abundance data from the Kuroshio region of the Pacific Ocean, we find significant spatial correlations between measures of diversity and betweenness, suggesting promise for ecological applications.}, - language = {en}, - number = {1}, - urldate = {2021-12-15}, - journal = {Nature Communications}, - author = {Ser-Giacomi, Enrico and Baudena, Alberto and Rossi, Vincent and Follows, Mick and Clayton, Sophie and Vasile, Ruggero and López, Cristóbal and Hernández-García, Emilio}, - month = dec, - year = {2021}, - keywords = {unread, not printed, EAPS}, - pages = {4935}, - file = {Ser-Giacomi et al. - 2021 - Lagrangian betweenness as a measure of bottlenecks.pdf:/Users/birdy/Zotero/storage/ENVD6A4Z/Ser-Giacomi et al. - 2021 - Lagrangian betweenness as a measure of bottlenecks.pdf:application/pdf}, -} - -@article{juranek_importance_2020, - title = {The {Importance} of the {Phytoplankton} “{Middle} {Class}” to {Ocean} {Net} {Community} {Production}}, - volume = {34}, - issn = {1944-9224}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2020GB006702}, - doi = {10.1029/2020GB006702}, - abstract = {The net balance between photosynthesis and respiration in the surface ocean is a key regulator of ocean-atmosphere carbon dioxide (CO2) partitioning, and by extension, Earth's climate. The slight excess of photosynthesis over community respiration in sunlit waters, known as net community production (NCP), sets the upper bound on the sequestration of carbon via biologically mediated export. Prevailing paradigms suggest a high/low binary where net primary production (NPP), NCP, and export are highest in ecosystems characterized by microplankton ({\textgreater}20 μm) and lowest in ecosystems dominated by picoplankton ({\textless}2 μm). This bifurcation model neglects the potential importance of nanoplankton (2–20 μm)—i.e., the “middle” size class—toward global biological pump functioning. Here, we show a relationship between the biomass of nanoplankton and oxygen-based estimates of NCP across natural ecological gradients in the North Pacific Ocean. Using a suite of high-resolution optical imaging approaches including SeaFlow, Imaging FlowCytobot, and laser-based scattering, nanoplankton dynamics are observed to dominate the particle size distribution throughout an 1,000 km transition between the subtropical and subpolar North Pacific, where NCP rates are threefold to fivefold higher than subtropical values. Based on ecological theory applied to the Darwin size-based ecosystem model, we hypothesize that intermediate size class organisms are capable of high rates of production via an optimization of bottom-up and top-down control inherent to the “middle class.” More broadly, the model indicates the global importance of nanoplankton for ocean biological production.}, - language = {en}, - number = {12}, - urldate = {2021-12-15}, - journal = {Global Biogeochemical Cycles}, - author = {Juranek, Lauren W. and White, Angelicque E. and Dugenne, Mathilde and Henderikx Freitas, Fernanda and Dutkiewicz, Stephanie and Ribalet, Francois and Ferrón, Sara and Armbrust, E. Virginia and Karl, David M.}, - year = {2020}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020GB006702}, - keywords = {read, not printed, EAPS}, - pages = {e2020GB006702}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/FI7EY7HA/Juranek et al. - 2020 - The Importance of the Phytoplankton “Middle Class”.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/48CTG827/2020GB006702.html:text/html}, -} - -@article{kempes_growth_2012, - title = {Growth, metabolic partitioning, and the size of microorganisms}, - volume = {109}, - copyright = {© . Freely available online through the PNAS open access option.}, - issn = {0027-8424, 1091-6490}, - url = {https://www.pnas.org/content/109/2/495}, - doi = {10.1073/pnas.1115585109}, - abstract = {Population growth rate is a fundamental ecological and evolutionary characteristic of living organisms, but individuals must balance the metabolism devoted to biosynthesis and reproduction against the maintenance of existing structure and other functionality. Here we present a mathematical model that relates metabolic partitioning to the form of growth. The model captures the observed growth trajectory of single cells and individuals for a variety of species and taxa spanning prokaryotes, unicellular eukaryotes, and small multicellular eukaryotes. Our analysis suggests that the per-unit costs of biosynthesis and maintenance are conserved across prokaryotes and eukaryotes. However, the relative metabolic expenditure on growth and maintenance of whole organisms clearly differentiates taxa: prokaryotes spend an increasing fraction of their entire metabolism on growth with increasing cell size, whereas eukaryotes devote a diminishing fraction. These differences allow us to predict the minimum and maximum size for each taxonomic group, anticipating observed evolutionary life-history transitions. The framework provides energetic insights into taxonomic tradeoffs related to growth and metabolism and constrains traits that are important for size-structured modeling of microbial communities and their ecological and biogeochemical effects.}, - language = {en}, - number = {2}, - urldate = {2022-01-28}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Kempes, Christopher P. and Dutkiewicz, Stephanie and Follows, Michael J.}, - month = jan, - year = {2012}, - pmid = {22203990}, - note = {Publisher: National Academy of Sciences -Section: Biological Sciences}, - keywords = {unread, evolutionary transitions, maintenance metabolism, metabolic ecology, ontogenetic growth, single cell growth}, - pages = {495--500}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/72H8W27A/Kempes et al. - 2012 - Growth, metabolic partitioning, and the size of mi.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/4LRVBPN7/495.html:text/html}, -} - -@article{dutkiewicz_dimensions_2020, - title = {Dimensions of marine phytoplankton diversity}, - volume = {17}, - issn = {1726-4170}, - url = {https://bg.copernicus.org/articles/17/609/2020/}, - doi = {10.5194/bg-17-609-2020}, - abstract = {{\textless}p{\textgreater}{\textless}strong class="journal-contentHeaderColor"{\textgreater}Abstract.{\textless}/strong{\textgreater} Biodiversity of phytoplankton is important for ecosystem stability and marine biogeochemistry. However, the large-scale patterns of diversity are not well understood and are often poorly characterized in terms of statistical relationships with factors such as latitude, temperature and productivity. Here we use ecological theory and a global trait-based ecosystem model to provide mechanistic understanding of patterns of phytoplankton diversity. Our study suggests that phytoplankton diversity across three dimensions of trait space (size, biogeochemical function and thermal tolerance) is controlled by disparate combinations of drivers: the supply rate of the limiting resource, the imbalance in different resource supplies relative to competing phytoplankton demands, size-selective grazing and transport by the moving ocean. Using sensitivity studies we show that each dimension of diversity is controlled by different drivers. Models including only one (or two) of the trait dimensions will have different patterns of diversity than one which incorporates another trait dimension. We use the results of our model exploration to infer the controls on the diversity patterns derived from field observations along meridional transects in the Atlantic and to explain why different taxa and size classes have differing patterns.{\textless}/p{\textgreater}}, - language = {English}, - number = {3}, - urldate = {2022-02-02}, - journal = {Biogeosciences}, - author = {Dutkiewicz, Stephanie and Cermeno, Pedro and Jahn, Oliver and Follows, Michael J. and Hickman, Anna E. and Taniguchi, Darcy A. A. and Ward, Ben A.}, - month = feb, - year = {2020}, - note = {Publisher: Copernicus GmbH}, - keywords = {read}, - pages = {609--634}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/GDPB2N66/Dutkiewicz et al. - 2020 - Dimensions of marine phytoplankton diversity.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/6J7BBRZ4/2020.html:text/html}, -} - -@article{armstrong_grazing_1994, - title = {Grazing limitation and nutrient limitation in marine ecosystems: {Steady} state solutions of an ecosystem model with multiple food chains}, - volume = {39}, - issn = {1939-5590}, - shorttitle = {Grazing limitation and nutrient limitation in marine ecosystems}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lo.1994.39.3.0597}, - doi = {10.4319/lo.1994.39.3.0597}, - abstract = {Here I develop a model with multiple phytoplankton-zooplankton food chains where each food chain is based on a different size class of algae. Model parameters were chosen to allow food chains based on smaller algal size classes to dominate under oligotrophic conditions, with larger size classes being added sequentially with increased nutrient loading. The model makes use of allometric relations among size classes to minimize the number of free parameters, facilitating numerical exploration of its steady state behavior. The model is simple, yet it is complex enough to allow simultaneous predator limitation of algal size classes (or species) and nutrient limitation of total phytoplankton biomass. This perspective, not obtainable from models with single phytoplankton and zooplankton size classes, is applied to the particular case of elevated nitrate levels in the equatorial Pacific. The model shows clearly that it is possible for each phytoplankton size class to be limited by its herbivores, while at the same time micronutrient (notably iron) deficiency may limit the number of size classes that can exist in the community and hence the total phytoplankton biomass that can be supported.}, - language = {en}, - number = {3}, - urldate = {2022-02-07}, - journal = {Limnology and Oceanography}, - author = {Armstrong, Robert A.}, - year = {1994}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lo.1994.39.3.0597}, - keywords = {unread}, - pages = {597--608}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/62GDW2AR/Armstrong - 1994 - Grazing limitation and nutrient limitation in mari.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/FJKQ3LLF/lo.1994.39.3.html:text/html}, -} - -@article{dutkiewicz_modeling_2009, - title = {Modeling the coupling of ocean ecology and biogeochemistry}, - volume = {23}, - issn = {1944-9224}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2008GB003405}, - doi = {10.1029/2008GB003405}, - abstract = {We examine the interplay between ecology and biogeochemical cycles in the context of a global three-dimensional ocean model where self-assembling phytoplankton communities emerge from a wide set of potentially viable cell types. We consider the complex model solutions in the light of resource competition theory. The emergent community structures and ecological regimes vary across different physical environments in the model ocean: Strongly seasonal, high-nutrient regions are dominated by fast growing bloom specialists, while stable, low-seasonality regions are dominated by organisms that can grow at low nutrient concentrations and are suited to oligotrophic conditions. In the latter regions, the framework of resource competition theory provides a useful qualitative and quantitative diagnostic tool with which to interpret the outcome of competition between model organisms, their regulation of the resource environment, and the sensitivity of the system to changes in key physiological characteristics of the cells.}, - language = {en}, - number = {4}, - urldate = {2022-02-14}, - journal = {Global Biogeochemical Cycles}, - author = {Dutkiewicz, S. and Follows, M. J. and Bragg, J. G.}, - year = {2009}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2008GB003405}, - keywords = {unread, ecology, ocean biogeochemistry, resource competition}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/ME3K4FVT/Dutkiewicz et al. - 2009 - Modeling the coupling of ocean ecology and biogeoc.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/JWID8TMI/2008GB003405.html:text/html}, -} - -@article{maranon_resource_2015, - title = {Resource supply alone explains the variability of marine phytoplankton size structure}, - volume = {60}, - issn = {1939-5590}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lno.10138}, - doi = {10.1002/lno.10138}, - abstract = {Due to the covariation between temperature and resource availability in the surface ocean, a correct assessment of resource supply is crucial to determine if temperature has a direct effect on phytoplankton size structure. To remove the effect of resources, López-Urrutia and Morán analyzed data subsets with narrow ranges of variation in Chlorophyll a (Chl a) concentration and found that temperature is correlated with Chl a partitioning among size classes, from which they concluded that temperature is an important variable to explain the variability of phytoplankton size structure. Our analysis, however, shows that resource supply varies widely also within these subsets and, importantly, that it is inversely correlated with temperature. Therefore, the relationship between temperature and size structure reflects instead the effect of resources. When groups of samples with similar resource supply conditions are considered, no correlation between temperature and phytoplankton size structure is observed, which invalidates the conclusion of López-Urrutia and Morán. Even within restricted ranges of variation for phytoplankton biomass and production, changes in resource supply alone are sufficient to explain the variability of phytoplankton size structure in the sea.}, - language = {en}, - number = {5}, - urldate = {2022-02-16}, - journal = {Limnology and Oceanography}, - author = {Marañón, Emilio and Cermeño, Pedro and Latasa, Mikel and Tadonléké, Rémy D.}, - year = {2015}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lno.10138}, - keywords = {unread, prosynref}, - pages = {1848--1854}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/PFJ4GLDA/Marañón et al. - 2015 - Resource supply alone explains the variability of .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/HSLF3VCJ/lno.html:text/html}, -} - -@article{follett_trophic_2022, - title = {Trophic interactions with heterotrophic bacteria limit the range of \textit{{Prochlorococcus}}}, - volume = {119}, - issn = {0027-8424, 1091-6490}, - url = {http://www.pnas.org/lookup/doi/10.1073/pnas.2110993118}, - doi = {10.1073/pnas.2110993118}, - abstract = {Prochlorococcus - is both the smallest and numerically most abundant photosynthesizing organism on the planet. While thriving in the warm oligotrophic gyres, - Prochlorococcus - concentrations drop rapidly in higher-latitude regions. Transect data from the North Pacific show the collapse occurring at a wide range of temperatures and latitudes (temperature is often hypothesized to cause this shift), suggesting an ecological mechanism may be at play. An often used size-based theory of phytoplankton community structure that has been incorporated into computational models correctly predicts the dominance of - Prochlorococcus - in the gyres, and the relative dominance of larger cells at high latitudes. However, both theory and computational models fail to explain the poleward collapse. When heterotrophic bacteria and predators that prey nonspecifically on both - Prochlorococcus - and bacteria are included in the theoretical framework, the collapse of - Prochlorococcus - occurs with increasing nutrient supplies. The poleward collapse of - Prochlorococcus - populations then naturally emerges when this mechanism of “shared predation” is implemented in a complex global ecosystem model. Additionally, the theory correctly predicts trends in both the abundance and mean size of the heterotrophic bacteria. These results suggest that ecological controls need to be considered to understand the biogeography of - Prochlorococcus - and predict its changes under future ocean conditions. Indirect interactions within a microbial network can be essential in setting community structure.}, - language = {en}, - number = {2}, - urldate = {2022-02-23}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Follett, Christopher L. and Dutkiewicz, Stephanie and Ribalet, François and Zakem, Emily and Caron, David and Armbrust, E. Virginia and Follows, Michael J.}, - month = jan, - year = {2022}, - pages = {e2110993118}, - file = {Follett et al. - 2022 - Trophic interactions with heterotrophic bacteria l.pdf:/Users/birdy/Zotero/storage/PJMSAXIG/Follett et al. - 2022 - Trophic interactions with heterotrophic bacteria l.pdf:application/pdf}, -} - -@article{muratore_complex_2022, - title = {Complex marine microbial communities partition metabolism of scarce resources over the diel cycle}, - volume = {6}, - copyright = {2022 The Author(s), under exclusive licence to Springer Nature Limited}, - issn = {2397-334X}, - url = {https://www.nature.com/articles/s41559-021-01606-w}, - doi = {10.1038/s41559-021-01606-w}, - abstract = {Complex assemblages of microbes in the surface ocean are responsible for approximately half of global carbon fixation. The persistence of high taxonomic diversity despite competition for a small suite of relatively homogeneously distributed nutrients, that is, ‘the paradox of the plankton’, represents a long-standing challenge for ecological theory. Here we find evidence consistent with temporal niche partitioning of nitrogen assimilation processes over a diel cycle in the North Pacific Subtropical Gyre. We jointly analysed transcript abundances, lipids and metabolites and discovered that a small number of diel archetypes can explain pervasive periodic dynamics. Metabolic pathway analysis of identified diel signals revealed asynchronous timing in the transcription of nitrogen uptake and assimilation genes among different microbial groups—cyanobacteria, heterotrophic bacteria and eukaryotes. This temporal niche partitioning of nitrogen uptake emerged despite synchronous transcription of photosynthesis and central carbon metabolism genes and associated macromolecular abundances. Temporal niche partitioning may be a mechanism by which microorganisms in the open ocean mitigate competition for scarce resources, supporting community coexistence.}, - language = {en}, - number = {2}, - urldate = {2022-02-25}, - journal = {Nature Ecology \& Evolution}, - author = {Muratore, Daniel and Boysen, Angela K. and Harke, Matthew J. and Becker, Kevin W. and Casey, John R. and Coesel, Sacha N. and Mende, Daniel R. and Wilson, Samuel T. and Aylward, Frank O. and Eppley, John M. and Vislova, Alice and Peng, Shengyun and Rodriguez-Gonzalez, Rogelio A. and Beckett, Stephen J. and Virginia Armbrust, E. and DeLong, Edward F. and Karl, David M. and White, Angelicque E. and Zehr, Jonathan P. and Van Mooy, Benjamin A. S. and Dyhrman, Sonya T. and Ingalls, Anitra E. and Weitz, Joshua S.}, - month = feb, - year = {2022}, - note = {Number: 2 -Publisher: Nature Publishing Group}, - keywords = {unread, Microbial biooceanography, Microbial ecology}, - pages = {218--229}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/T58JCF6Q/Muratore et al. - 2022 - Complex marine microbial communities partition met.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/HC4GW85C/s41559-021-01606-w.html:text/html}, -} - -@article{mangolte_plankton_2022, - title = {Plankton community response to fronts : winners and losers}, - shorttitle = {Plankton community response to fronts}, - url = {https://hal.archives-ouvertes.fr/hal-03607937}, - doi = {10.1093/plankt/fbac010}, - abstract = {Fronts are particularly productive regions of the ocean, and biodiversity hotspots for many marine species. Here we use an ocean-ecosystem model to investigate the effect of fronts on plankton ecology. We focus on energetic fronts in Western Boundary Current systems that efficiently inject nutrients into the euphotic layer and which are physical boundaries between productive and oligotrophic provinces. We found that the fronts form an environment distinct from both provinces, favorable to some plankton groups (diatoms, dinoflagellates and large carnivorous zooplankton) and less favorable to others (pico-phytoplankton, coccolithophores, small grazers), and with an overall larger diversity. In agreement with previous understanding, we find that bottom-up abiotic processes (nutrient enrichment) explain the prevalence of groups with fast growing rates (the "winners"). Importantly, our results also show that biotic interactions within the ecosystem may play a larger role than previously thought. We show that the winners can have a negative impact on other plankton species (the "losers") through two indirect competitive processes: community shading (modification of the light environment by the plankton community leading to light-limitation of some plankton groups) and shared predation (where an increase in one functional group leads to increased grazing by a shared predator on another functional group).}, - urldate = {2022-03-23}, - journal = {Journal of Plankton Research}, - author = {Mangolte, Inès and Lévy, Marina and Dutkiewicz, Stephanie and Clayton, Sophie and Jahn, Oliver}, - month = mar, - year = {2022}, - note = {Publisher: Oxford University Press (OUP)}, - keywords = {unread, phytoplankton, biodiversity, fronts, marine ecology, modelling}, - file = {HAL PDF Full Text:/Users/birdy/Zotero/storage/65HVTJEW/Mangolte et al. - 2022 - Plankton community response to fronts winners an.pdf:application/pdf}, -} - -@article{shang_oxidative_2022, - title = {Oxidative metabolisms catalyzed {Earth}’s oxygenation}, - volume = {13}, - copyright = {2022 The Author(s)}, - issn = {2041-1723}, - url = {https://www.nature.com/articles/s41467-022-28996-0}, - doi = {10.1038/s41467-022-28996-0}, - abstract = {The burial of organic carbon, which prevents its remineralization via oxygen-consuming processes, is considered one of the causes of Earth’s oxygenation. Yet, higher levels of oxygen are thought to inhibit burial. Here we propose a resolution of this conundrum, wherein Earth’s initial oxygenation is favored by oxidative metabolisms generating partially oxidized organic matter (POOM), increasing burial via interaction with minerals in sediments. First, we introduce the POOM hypothesis via a mathematical argument. Second, we reconstruct the evolutionary history of one key enzyme family, flavin-dependent Baeyer–Villiger monooxygenases, that generates POOM, and show the temporal consistency of its diversification with the Proterozoic and Phanerozoic atmospheric oxygenation. Finally, we propose that the expansion of oxidative metabolisms instigated a positive feedback, which was amplified by the chemical changes to minerals on Earth’s surface. Collectively, these results suggest that Earth’s oxygenation is an autocatalytic transition induced by a combination of biological innovations and geological changes.}, - language = {en}, - number = {1}, - urldate = {2022-04-29}, - journal = {Nature Communications}, - author = {Shang, Haitao and Rothman, Daniel H. and Fournier, Gregory P.}, - month = mar, - year = {2022}, - note = {Number: 1 -Publisher: Nature Publishing Group}, - keywords = {unread, Coevolution, Element cycles}, - pages = {1328}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/JAG35KDC/Shang et al. - 2022 - Oxidative metabolisms catalyzed Earth’s oxygenatio.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/28GUMIIB/s41467-022-28996-0.html:text/html}, -} - -@article{ganopolski_climber-2_2001, - title = {{CLIMBER}-2: a climate system model of intermediate complexity. {Part} {II}: model sensitivity}, - volume = {17}, - issn = {0930-7575, 1432-0894}, - shorttitle = {{CLIMBER}-2}, - url = {http://link.springer.com/10.1007/s003820000144}, - doi = {10.1007/s003820000144}, - language = {en}, - number = {10}, - urldate = {2022-05-02}, - journal = {Climate Dynamics}, - author = {Ganopolski, A. and Petoukhov, V. and Rahmstorf, S. and Brovkin, V. and Claussen, M. and Eliseev, A. and Kubatzki, C.}, - month = jul, - year = {2001}, - keywords = {read}, - pages = {735--751}, - file = {Ganopolski et al. - 2001 - CLIMBER-2 a climate system model of intermediate .pdf:/Users/birdy/Zotero/storage/X4G6F5HW/Ganopolski et al. - 2001 - CLIMBER-2 a climate system model of intermediate .pdf:application/pdf}, -} - -@article{bhatia_its_2022, - chapter = {The Upshot}, - title = {‘{It}’s {Super} {Spectacular}.’ {See} {How} the {Tonga} {Volcano} {Unleashed} a {Once}-in-a-{Century} {Shockwave}.}, - issn = {0362-4331}, - url = {https://www.nytimes.com/interactive/2022/04/14/upshot/tonga-pressure-wave.html}, - abstract = {A new simulation gives a detailed look at a shockwave that circled the planet for days.}, - language = {en-US}, - urldate = {2022-05-06}, - journal = {The New York Times}, - author = {Bhatia, Aatish and Catangui, Sean}, - month = apr, - year = {2022}, - keywords = {Hunga Tonga-Hunga Ha'apai (Tonga), Tidal Waves and Tsunamis, Tonga, Volcanoes}, - file = {Snapshot:/Users/birdy/Zotero/storage/K9I8BQD2/tonga-pressure-wave.html:text/html}, -} - -@article{lee_improved_2018, - title = {Improved {Prediction} of {Harmful} {Algal} {Blooms} in {Four} {Major} {South} {Korea}’s {Rivers} {Using} {Deep} {Learning} {Models}}, - volume = {15}, - issn = {1661-7827}, - url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6069434/}, - doi = {10.3390/ijerph15071322}, - abstract = {Harmful algal blooms are an annual phenomenon that cause environmental damage, economic losses, and disease outbreaks. A fundamental solution to this problem is still lacking, thus, the best option for counteracting the effects of algal blooms is to improve advance warnings (predictions). However, existing physical prediction models have difficulties setting a clear coefficient indicating the relationship between each factor when predicting algal blooms, and many variable data sources are required for the analysis. These limitations are accompanied by high time and economic costs. Meanwhile, artificial intelligence and deep learning methods have become increasingly common in scientific research; attempts to apply the long short-term memory (LSTM) model to environmental research problems are increasing because the LSTM model exhibits good performance for time-series data prediction. However, few studies have applied deep learning models or LSTM to algal bloom prediction, especially in South Korea, where algal blooms occur annually. Therefore, we employed the LSTM model for algal bloom prediction in four major rivers of South Korea. We conducted short-term (one week) predictions by employing regression analysis and deep learning techniques on a newly constructed water quality and quantity dataset drawn from 16 dammed pools on the rivers. Three deep learning models (multilayer perceptron, MLP; recurrent neural network, RNN; and long short-term memory, LSTM) were used to predict chlorophyll-a, a recognized proxy for algal activity. The results were compared to those from OLS (ordinary least square) regression analysis and actual data based on the root mean square error (RSME). The LSTM model showed the highest prediction rate for harmful algal blooms and all deep learning models out-performed the OLS regression analysis. Our results reveal the potential for predicting algal blooms using LSTM and deep learning.}, - number = {7}, - urldate = {2022-05-19}, - journal = {International Journal of Environmental Research and Public Health}, - author = {Lee, Sangmok and Lee, Donghyun}, - month = jul, - year = {2018}, - pmid = {29937531}, - pmcid = {PMC6069434}, - pages = {1322}, - file = {Full Text:/Users/birdy/Zotero/storage/AA2K29UM/Lee and Lee - 2018 - Improved Prediction of Harmful Algal Blooms in Fou.pdf:application/pdf}, -} - -@article{yu_predicting_2021, - title = {Predicting coastal algal blooms with environmental factors by machine learning methods}, - volume = {123}, - issn = {1470-160X}, - url = {https://www.sciencedirect.com/science/article/pii/S1470160X20312760}, - doi = {10.1016/j.ecolind.2020.107334}, - abstract = {Harmful algal blooms are a major type of marine disaster that endangers the marine ecological environment and human health. Since the algal bloom is a complex nonlinear process with time characteristics, traditional statistical methods often cannot provide good predictions. In this paper, we propose a method based on machine learning with the aim to predict the occurrence of algal blooms by environmental parameters. The features related to algal bloom growth have been experimented for achieving a good prediction of algal concentrations by a combination strategy. We validate the prediction performance on two real datasets from two locations in US and China, i.e., Scripps Pier, California and Sishili Bay, Shandong, respectively. The models and feature subsets have been selected to complete the missing data and predict the phytoplankton concentration. The results demonstrate the efficiency of our method in the short-term prediction of concentrations by selecting appropriate features. The comparison studies prove the advantage of our developed machine learning method. The importance of every features for the prediction performance reveals crucial factors for the outbreak of harmful algal blooms.}, - language = {en}, - urldate = {2022-05-24}, - journal = {Ecological Indicators}, - author = {Yu, Peixuan and Gao, Rui and Zhang, Dezhen and Liu, Zhi-Ping}, - month = apr, - year = {2021}, - keywords = {unread, Feature importance, Feature selection, GBDT, Harmful algal bloom, Machine learning}, - pages = {107334}, - file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/XICJL7KH/S1470160X20312760.html:text/html}, -} - -@article{dutkiewicz_capturing_2015, - title = {Capturing optically important constituents and properties in a marine biogeochemical and ecosystem model}, - volume = {12}, - issn = {1726-4189}, - url = {https://bg.copernicus.org/articles/12/4447/2015/}, - doi = {10.5194/bg-12-4447-2015}, - abstract = {Abstract. We present a numerical model of the ocean that couples a three-stream radiative transfer component with a marine biogeochemical–ecosystem component in a dynamic three-dimensional physical framework. The radiative transfer component resolves the penetration of spectral irradiance as it is absorbed and scattered within the water column. We explicitly include the effect of several optically important water constituents (different phytoplankton functional types; detrital particles; and coloured dissolved organic matter, CDOM). The model is evaluated against in situ-observed and satellite-derived products. In particular we compare to concurrently measured biogeochemical, ecosystem, and optical data along a meridional transect of the Atlantic Ocean. The simulation captures the patterns and magnitudes of these data, and estimates surface upwelling irradiance analogous to that observed by ocean colour satellite instruments. We find that incorporating the different optically important constituents explicitly and including spectral irradiance was crucial to capture the variability in the depth of the subsurface chlorophyll a (Chl a) maximum. We conduct a series of sensitivity experiments to demonstrate, globally, the relative importance of each of the water constituents, as well as the crucial feedbacks between the light field, the relative fitness of phytoplankton types, and the biogeochemistry of the ocean. CDOM has proportionally more importance at attenuating light at short wavelengths and in more productive waters, phytoplankton absorption is relatively more important at the subsurface Chl a maximum, and water molecules have the greatest contribution when concentrations of other constituents are low, such as in the oligotrophic gyres. Scattering had less effect on attenuation, but since it is important for the amount and type of upwelling irradiance, it is crucial for setting sea surface reflectance. Strikingly, sensitivity experiments in which absorption by any of the optical constituents was increased led to a decrease in the size of the oligotrophic regions of the subtropical gyres: lateral nutrient supplies were enhanced as a result of decreasing high-latitude productivity. This new model that captures bio-optical feedbacks will be important for improving our understanding of the role of light and optical constituents on ocean biogeochemistry, especially in a changing environment. Further, resolving surface upwelling irradiance will make it easier to connect to satellite-derived products in the future.}, - language = {en}, - number = {14}, - urldate = {2022-08-15}, - journal = {Biogeosciences}, - author = {Dutkiewicz, S. and Hickman, A. E. and Jahn, O. and Gregg, W. W. and Mouw, C. B. and Follows, M. J.}, - month = jul, - year = {2015}, - pages = {4447--4481}, - file = {Dutkiewicz et al. - 2015 - Capturing optically important constituents and pro.pdf:/Users/birdy/Zotero/storage/DEFKPKCD/Dutkiewicz et al. - 2015 - Capturing optically important constituents and pro.pdf:application/pdf}, -} - -@article{han_solving_2018, - title = {Solving high-dimensional partial differential equations using deep learning}, - volume = {115}, - url = {https://www.pnas.org/doi/abs/10.1073/pnas.1718942115}, - doi = {10.1073/pnas.1718942115}, - abstract = {Developing algorithms for solving high-dimensional partial differential equations -(PDEs) has been an exceedingly difficult task for a long time, du...}, - language = {EN}, - number = {34}, - urldate = {2022-08-31}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Han, Jiequn and Jentzen, Arnulf and E, Weinan}, - month = aug, - year = {2018}, - note = {Company: National Academy of Sciences -Distributor: National Academy of Sciences -Institution: National Academy of Sciences -Label: National Academy of Sciences -Publisher: Proceedings of the National Academy of Sciences}, - keywords = {ML}, - pages = {8505--8510}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/PCZ84KRN/Han et al. - 2018 - Solving high-dimensional partial differential equa.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/KSCJDJ7T/pnas.html:text/html}, -} - -@article{follows_emergent_2007, - title = {Emergent {Biogeography} of {Microbial} {Communities} in a {Model} {Ocean}}, - volume = {315}, - issn = {0036-8075, 1095-9203}, - url = {https://www.science.org/doi/10.1126/science.1138544}, - doi = {10.1126/science.1138544}, - abstract = {A marine ecosystem model seeded with many phytoplankton types, whose physiological traits were randomly assigned from ranges defined by field and laboratory data, generated an emergent community structure and biogeography consistent with observed global phytoplankton distributions. The modeled organisms included types analogous to the marine cyanobacterium - Prochlorococcus - . Their emergent global distributions and physiological properties simultaneously correspond to observations. This flexible representation of community structure can be used to explore relations between ecosystems, biogeochemical cycles, and climate change.}, - language = {en}, - number = {5820}, - urldate = {2022-08-31}, - journal = {Science}, - author = {Follows, Michael J. and Dutkiewicz, Stephanie and Grant, Scott and Chisholm, Sallie W.}, - month = mar, - year = {2007}, - pages = {1843--1846}, - file = {Follows et al. - 2007 - Emergent Biogeography of Microbial Communities in .pdf:/Users/birdy/Zotero/storage/5N8359IB/Follows et al. - 2007 - Emergent Biogeography of Microbial Communities in .pdf:application/pdf}, -} - -@article{duyck_sloop_2015, - title = {Sloop: {A} pattern retrieval engine for individual animal identification}, - volume = {48}, - issn = {0031-3203}, - shorttitle = {Sloop}, - url = {https://www.sciencedirect.com/science/article/pii/S0031320314002763}, - doi = {10.1016/j.patcog.2014.07.017}, - abstract = {Identifying individuals in photographs of animals collected over time is a non-invasive approach for ecological monitoring and conservation. This paper describes the design and use of Sloop, the first image retrieval system for individual animal identification incorporating crowd-sourced relevance feedback. Sloop׳s iterative retrieval strategy using hierarchical and aggregated matching and relevance feedback consistently improves deformation and correspondence-based approaches for individual identification across several species. Its crowdsourcing strategy is successful in utilizing relevance feedback on a large scale. Sloop is in operational use. The user experience and results are presented here to facilitate the creation of a community-based individual identification system for conservation planning.}, - language = {en}, - number = {4}, - urldate = {2022-10-01}, - journal = {Pattern Recognition}, - author = {Duyck, James and Finn, Chelsea and Hutcheon, Andy and Vera, Pablo and Salas, Joaquin and Ravela, Sai}, - month = apr, - year = {2015}, - keywords = {Animal biometrics, Conservation, Crowdsourcing, Gecko, Hybrid shape contexts, Individual identification, Local features, Photo-identification, Relevance feedback, Salamander, Scale-cascaded alignment, Skink, Whale shark}, - pages = {1059--1073}, - file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/EVXJTLMW/S0031320314002763.html:text/html}, -} - -@article{taylor_shutdown_2011, - title = {Shutdown of turbulent convection as a new criterion for the onset of spring phytoplankton blooms}, - volume = {56}, - issn = {1939-5590}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lo.2011.56.6.2293}, - doi = {10.4319/lo.2011.56.6.2293}, - abstract = {The onset of phytoplankton blooms in late winter, early spring has been traditionally associated with the shoaling of the mixed layer above a critical depth. Here we show that the onset of a bloom can also be triggered by a reduction in air–sea fluxes at the end of winter. When net cooling subsides at the end of winter, turbulent mixing becomes weak, thereby increasing the residence time of phytoplankton cells in the euphotic layer and allowing a bloom to develop. The necessary change in the air–sea flux generally precedes mixed-layer shoaling, and may provide a better indicator for the onset of the spring bloom than the mixed-layer depth alone. Our hypothesis is supported by numerical simulations and remote sensing data.}, - language = {en}, - number = {6}, - urldate = {2022-10-05}, - journal = {Limnology and Oceanography}, - author = {Taylor, John R. and Ferrari, Raffaele}, - year = {2011}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lo.2011.56.6.2293}, - pages = {2293--2307}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/X7EVVVGL/Taylor and Ferrari - 2011 - Shutdown of turbulent convection as a new criterio.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/SJS6MACH/lo.2011.56.6.html:text/html}, -} - -@article{regos_effects_2019, - title = {Effects of species traits and environmental predictors on performance and transferability of ecological niche models}, - volume = {9}, - issn = {20452322}, - url = {https://www.mendeley.com/catalogue/30ca2198-448a-30e7-bb65-8f00f0bc94ed/?ref=raven&dgcid=raven_md_suggest_email&dgcid=raven_md_suggest_mie_email}, - doi = {10.1038/s41598-019-40766-5}, - abstract = {(2019) Regos et al. Scientific Reports. The ability of ecological niche models (ENMs) to produce robust predictions for different time frames (i.e. temporal transferability) may be hindered by a la...}, - language = {en-GB}, - number = {1}, - urldate = {2022-10-11}, - journal = {Scientific Reports}, - author = {Regos, Adrián and Gagne, Laura and Alcaraz-Segura, Domingo and Honrado, João P. and Domínguez, Jesús}, - year = {2019}, - note = {Number: 1}, - pages = {undefined--undefined}, - file = {Full Text:/Users/birdy/Zotero/storage/83W8A9G9/Regos et al. - 2019 - Effects of species traits and environmental predic.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/CSIQ6CV6/30ca2198-448a-30e7-bb65-8f00f0bc94ed.html:text/html}, -} - -@misc{beery_species_2021, - title = {Species {Distribution} {Modeling} for {Machine} {Learning} {Practitioners}: {A} {Review}}, - shorttitle = {Species {Distribution} {Modeling} for {Machine} {Learning} {Practitioners}}, - url = {http://arxiv.org/abs/2107.10400}, - abstract = {Conservation science depends on an accurate understanding of what's happening in a given ecosystem. How many species live there? What is the makeup of the population? How is that changing over time? Species Distribution Modeling (SDM) seeks to predict the spatial (and sometimes temporal) patterns of species occurrence, i.e. where a species is likely to be found. The last few years have seen a surge of interest in applying powerful machine learning tools to challenging problems in ecology. Despite its considerable importance, SDM has received relatively little attention from the computer science community. Our goal in this work is to provide computer scientists with the necessary background to read the SDM literature and develop ecologically useful ML-based SDM algorithms. In particular, we introduce key SDM concepts and terminology, review standard models, discuss data availability, and highlight technical challenges and pitfalls.}, - urldate = {2022-10-11}, - publisher = {arXiv}, - author = {Beery, Sara and Cole, Elijah and Parker, Joseph and Perona, Pietro and Winner, Kevin}, - month = jul, - year = {2021}, - note = {arXiv:2107.10400 [cs, stat]}, - keywords = {Computer Science - Machine Learning, Statistics - Applications}, - file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/5D6ZSJQL/Beery et al. - 2021 - Species Distribution Modeling for Machine Learning.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/UNSJQTFB/2107.html:text/html}, -} - -@article{beery_scaling_2021, - title = {Scaling biodiversity monitoring for the data age}, - volume = {27}, - issn = {1528-4972, 1528-4980}, - url = {https://dl.acm.org/doi/10.1145/3466857}, - doi = {10.1145/3466857}, - abstract = {Technological advances have made it possible to collect massive amounts of biodiversity data. How can analysis efforts keep up?}, - language = {en}, - number = {4}, - urldate = {2022-10-11}, - journal = {XRDS: Crossroads, The ACM Magazine for Students}, - author = {Beery, Sara}, - month = jun, - year = {2021}, - pages = {14--18}, - file = {Beery - 2021 - Scaling biodiversity monitoring for the data age.pdf:/Users/birdy/Zotero/storage/N366LVCR/Beery - 2021 - Scaling biodiversity monitoring for the data age.pdf:application/pdf}, -} - -@article{olson_submersible_2007, - title = {A submersible imaging-in-flow instrument to analyze nano-and microplankton: {Imaging} {FlowCytobot}}, - volume = {5}, - issn = {1541-5856}, - shorttitle = {A submersible imaging-in-flow instrument to analyze nano-and microplankton}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lom.2007.5.195}, - doi = {10.4319/lom.2007.5.195}, - abstract = {A fundamental understanding of the interaction between physical and biological factors that regulate plankton species composition requires, first of all, detailed and sustained observations. Only now is it becoming possible to acquire these types of observations, as we develop and deploy instruments that can continuously monitor individual organisms in the ocean. Our research group can measure and count the smallest phytoplankton cells using a submersible flow cytometer (FlowCytobot), in which optical properties of individual suspended cells are recorded as they pass through a focused laser beam. However, FlowCytobot cannot efficiently sample or identify the much larger cells (10 to {\textgreater}100 µm) that often dominate the plankton in coastal waters. Because these larger cells often have recognizable morphologies, we have developed a second submersible flow cytometer, with imaging capability and increased water sampling rate (typically, 5 mL seawater analyzed every 20 min), to characterize these nano- and microplankton. Like the original, Imaging FlowCytobot can operate unattended for months at a time; it obtains power from and communicates with a shore laboratory, so we can monitor results and modify sampling procedures when needed. Imaging FlowCytobot was successfully tested for 2 months in Woods Hole Harbor and is presently deployed alongside FlowCytobot at the Martha's Vineyard Coastal Observatory. These combined approaches will allow continuous long-term observations of plankton community structure over a wide range of cell sizes and types, and help to elucidate the processes and interactions that control the life cycles of individual species.}, - language = {en}, - number = {6}, - urldate = {2022-10-20}, - journal = {Limnology and Oceanography: Methods}, - author = {Olson, Robert J. and Sosik, Heidi M.}, - year = {2007}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lom.2007.5.195}, - pages = {195--203}, - file = {Full Text:/Users/birdy/Zotero/storage/JP4F24FI/Olson and Sosik - 2007 - A submersible imaging-in-flow instrument to analyz.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/9E2FX4K7/lom.2007.5.html:text/html}, -} - -@article{sosik_automated_2007, - title = {Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry}, - volume = {5}, - issn = {1541-5856}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lom.2007.5.204}, - doi = {10.4319/lom.2007.5.204}, - abstract = {High-resolution photomicrographs of phytoplankton cells and chains can now be acquired with imaging-in-flow systems at rates that make manual identification impractical for many applications. To address the challenge for automated taxonomic identification of images generated by our custom-built submersible Imaging FlowCytobot, we developed an approach that relies on extraction of image features, which are then presented to a machine learning algorithm for classification. Our approach uses a combination of image feature types including size, shape, symmetry, and texture characteristics, plus orientation invariant moments, diffraction pattern sampling, and co-occurrence matrix statistics. Some of these features required preprocessing with image analysis techniques including edge detection after phase congruency calculations, morphological operations, boundary representation and simplification, and rotation. For the machine learning strategy, we developed an approach that combines a feature selection algorithm and use of a support vector machine specified with a rigorous parameter selection and training approach. After training, a 22-category classifier provides 88\% overall accuracy for an independent test set, with individual category accuracies ranging from 68\% to 99\%. We demonstrate application of this classifier to a nearly uninterrupted 2-month time series of images acquired in Woods Hole Harbor, including use of statistical error correction to derive quantitative concentration estimates, which are shown to be unbiased with respect to manual estimates for random subsamples. Our approach, which provides taxonomically resolved estimates of phytoplankton abundance with fine temporal resolution (hours for many species), permits access to scales of variability from tidal to seasonal and longer.}, - language = {en}, - number = {6}, - urldate = {2022-10-20}, - journal = {Limnology and Oceanography: Methods}, - author = {Sosik, Heidi M. and Olson, Robert J.}, - year = {2007}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lom.2007.5.204}, - pages = {204--216}, - file = {Full Text:/Users/birdy/Zotero/storage/6E2WBCLM/Sosik and Olson - 2007 - Automated taxonomic classification of phytoplankto.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/UIZ3PRI8/lom.2007.5.html:text/html}, -} - -@article{besson_towards_nodate, - title = {Towards the fully automated monitoring of ecological communities}, - volume = {n/a}, - issn = {1461-0248}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ele.14123}, - doi = {10.1111/ele.14123}, - abstract = {High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components—for example, individual behaviours and traits, and species abundance and distribution—is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.}, - language = {en}, - number = {n/a}, - urldate = {2022-10-24}, - journal = {Ecology Letters}, - author = {Besson, Marc and Alison, Jamie and Bjerge, Kim and Gorochowski, Thomas E. and Høye, Toke T. and Jucker, Tommaso and Mann, Hjalte M. R. and Clements, Christopher F.}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ele.14123}, - keywords = {community ecology, computer vision, deep learning, high-resolution monitoring, remote sensing}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/BBZAT6LU/Besson et al. - Towards the fully automated monitoring of ecologic.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/GEGIFAZV/ele.html:text/html}, -} - -@article{pierella_karlusich_coupling_2022, - title = {Coupling {Imaging} and {Omics} in {Plankton} {Surveys}: {State}-of-the-{Art}, {Challenges}, and {Future} {Directions}}, - volume = {9}, - issn = {2296-7745}, - shorttitle = {Coupling {Imaging} and {Omics} in {Plankton} {Surveys}}, - url = {https://www.frontiersin.org/articles/10.3389/fmars.2022.878803}, - abstract = {A major challenge in characterizing plankton communities is the collection, identification and quantification of samples in a time-efficient way. The classical manual microscopy counts are gradually being replaced by high throughput imaging and nucleic acid sequencing. DNA sequencing allows deep taxonomic resolution (including cryptic species) as well as high detection power (detecting rare species), while RNA provides insights on function and potential activity. However, these methods are affected by database limitations, PCR bias, and copy number variability across taxa. Recent developments in high-throughput imaging applied in situ or on collected samples (high-throughput microscopy, Underwater Vision Profiler, FlowCam, ZooScan, etc) has enabled a rapid enumeration of morphologically-distinguished plankton populations, estimates of biovolume/biomass, and provides additional valuable phenotypic information. Although machine learning classifiers generate encouraging results to classify marine plankton images in a time efficient way, there is still a need for large training datasets of manually annotated images. Here we provide workflow examples that couple nucleic acid sequencing with high-throughput imaging for a more complete and robust analysis of microbial communities. We also describe the publicly available and collaborative web application EcoTaxa, which offers tools for the rapid validation of plankton by specialists with the help of automatic recognition algorithms. Finally, we describe how the field is moving with citizen science programs, unmanned autonomous platforms with in situ sensors, and sequencing and digitalization of historical plankton samples.}, - urldate = {2022-10-27}, - journal = {Frontiers in Marine Science}, - author = {Pierella Karlusich, Juan José and Lombard, Fabien and Irisson, Jean-Olivier and Bowler, Chris and Foster, Rachel A.}, - year = {2022}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/7HJUM6PQ/Pierella Karlusich et al. - 2022 - Coupling Imaging and Omics in Plankton Surveys St.pdf:application/pdf}, -} - -@article{pierella_karlusich_global_2021, - title = {Global distribution patterns of marine nitrogen-fixers by imaging and molecular methods}, - volume = {12}, - copyright = {2021 The Author(s)}, - issn = {2041-1723}, - url = {https://www.nature.com/articles/s41467-021-24299-y}, - doi = {10.1038/s41467-021-24299-y}, - abstract = {Nitrogen fixation has a critical role in marine primary production, yet our understanding of marine nitrogen-fixers (diazotrophs) is hindered by limited observations. Here, we report a quantitative image analysis pipeline combined with mapping of molecular markers for mining {\textgreater}2,000,000 images and {\textgreater}1300 metagenomes from surface, deep chlorophyll maximum and mesopelagic seawater samples across 6 size fractions ({\textless}0.2–2000 μm). We use this approach to characterise the diversity, abundance, biovolume and distribution of symbiotic, colony-forming and particle-associated diazotrophs at a global scale. We show that imaging and PCR-free molecular data are congruent. Sequence reads indicate diazotrophs are detected from the ultrasmall bacterioplankton ({\textless}0.2 μm) to mesoplankton (180–2000 μm) communities, while images predict numerous symbiotic and colony-forming diazotrophs ({\textgreater}20 µm). Using imaging and molecular data, we estimate that polyploidy can substantially affect gene abundances of symbiotic versus colony-forming diazotrophs. Our results support the canonical view that larger diazotrophs ({\textgreater}10 μm) dominate the tropical belts, while unicellular cyanobacterial and non-cyanobacterial diazotrophs are globally distributed in surface and mesopelagic layers. We describe co-occurring diazotrophic lineages of different lifestyles and identify high-density regions of diazotrophs in the global ocean. Overall, we provide an update of marine diazotroph biogeographical diversity and present a new bioimaging-bioinformatic workflow.}, - language = {en}, - number = {1}, - urldate = {2022-10-28}, - journal = {Nature Communications}, - author = {Pierella Karlusich, Juan José and Pelletier, Eric and Lombard, Fabien and Carsique, Madeline and Dvorak, Etienne and Colin, Sébastien and Picheral, Marc and Cornejo-Castillo, Francisco M. and Acinas, Silvia G. and Pepperkok, Rainer and Karsenti, Eric and de Vargas, Colomban and Wincker, Patrick and Bowler, Chris and Foster, Rachel A.}, - month = jul, - year = {2021}, - note = {Number: 1 -Publisher: Nature Publishing Group}, - keywords = {Microbial biooceanography, Element cycles, Environmental microbiology, Marine biology}, - pages = {4160}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/6QTE42Z7/Pierella Karlusich et al. - 2021 - Global distribution patterns of marine nitrogen-fi.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/FZ492JKD/s41467-021-24299-y.html:text/html}, -} - -@misc{feichtenhofer_large-scale_2021, - title = {A {Large}-{Scale} {Study} on {Unsupervised} {Spatiotemporal} {Representation} {Learning}}, - url = {http://arxiv.org/abs/2104.14558}, - abstract = {We present a large-scale study on unsupervised spatiotemporal representation learning from videos. With a unified perspective on four recent image-based frameworks, we study a simple objective that can easily generalize all these methods to space-time. Our objective encourages temporally-persistent features in the same video, and in spite of its simplicity, it works surprisingly well across: (i) different unsupervised frameworks, (ii) pre-training datasets, (iii) downstream datasets, and (iv) backbone architectures. We draw a series of intriguing observations from this study, e.g., we discover that encouraging long-spanned persistency can be effective even if the timespan is 60 seconds. In addition to state-of-the-art results in multiple benchmarks, we report a few promising cases in which unsupervised pre-training can outperform its supervised counterpart. Code is made available at https://github.com/facebookresearch/SlowFast}, - urldate = {2022-10-29}, - publisher = {arXiv}, - author = {Feichtenhofer, Christoph and Fan, Haoqi and Xiong, Bo and Girshick, Ross and He, Kaiming}, - month = apr, - year = {2021}, - note = {arXiv:2104.14558 [cs]}, - keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition}, - file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/P4P752JB/Feichtenhofer et al. - 2021 - A Large-Scale Study on Unsupervised Spatiotemporal.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/LV4YRCJQ/2104.html:text/html}, -} - -@article{lambert_dynamic_2022, - title = {The dynamic trophic architecture of open-ocean protist communities revealed through machine-guided metatranscriptomics}, - volume = {119}, - copyright = {Copyright © 2022 the Author(s). Published by PNAS.}, - url = {https://www.pnas.org/doi/abs/10.1073/pnas.2100916119}, - doi = {10.1073/pnas.2100916119}, - abstract = {Intricate networks of single-celled eukaryotes (protists) dominate carbon flow in -the ocean. Their growth, demise, and interactions with other micr...}, - language = {EN}, - number = {7}, - urldate = {2022-11-01}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Lambert, Bennett S. and Groussman, Ryan D. and Schatz, Megan J. and Coesel, Sacha N. and Durham, Bryndan P. and Alverson, Andrew J. and White, Angelicque E. and Armbrust, E. Virginia}, - month = feb, - year = {2022}, - note = {Company: National Academy of Sciences -Distributor: National Academy of Sciences -Institution: National Academy of Sciences -Label: National Academy of Sciences -Publisher: Proceedings of the National Academy of Sciences}, - pages = {e2100916119}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/U3RTSWWR/Lambert et al. - 2022 - The dynamic trophic architecture of open-ocean pro.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/W4FGUHV8/pnas.html:text/html}, -} - -@article{bourne_insights_2016, - title = {Insights into the {Coral} {Microbiome}: {Underpinning} the {Health} and {Resilience} of {Reef} {Ecosystems}}, - volume = {70}, - issn = {0066-4227, 1545-3251}, - shorttitle = {Insights into the {Coral} {Microbiome}}, - url = {https://www.annualreviews.org/doi/10.1146/annurev-micro-102215-095440}, - doi = {10.1146/annurev-micro-102215-095440}, - abstract = {Corals are fundamental ecosystem engineers, creating large, intricate reefs that support diverse and abundant marine life. At the core of a healthy coral animal is a dynamic relationship with microorganisms, including a mutually beneficial symbiosis with photosynthetic dinoflagellates (Symbiodinium spp.) and enduring partnerships with an array of bacterial, archaeal, fungal, protistan, and viral associates, collectively termed the coral holobiont. The combined genomes of this coral holobiont form a coral hologenome, and genomic interactions within the hologenome ultimately define the coral phenotype. Here we integrate contemporary scientific knowledge regarding the ecological, host-specific, and environmental forces shaping the diversity, specificity, and distribution of microbial symbionts within the coral holobiont, explore physiological pathways that contribute to holobiont fitness, and describe potential mechanisms for holobiont homeostasis. Understanding the role of the microbiome in coral resilience, acclimation, and environmental adaptation is a new frontier in reef science that will require large-scale collaborative research efforts.}, - language = {en}, - number = {1}, - urldate = {2022-11-02}, - journal = {Annual Review of Microbiology}, - author = {Bourne, David G. and Morrow, Kathleen M. and Webster, Nicole S.}, - month = sep, - year = {2016}, - pages = {317--340}, - file = {Bourne et al. - 2016 - Insights into the Coral Microbiome Underpinning t.pdf:/Users/birdy/Zotero/storage/AE37AHKP/Bourne et al. - 2016 - Insights into the Coral Microbiome Underpinning t.pdf:application/pdf}, -} - -@article{zhang_microbially_2019, - title = {Microbially mediated nutrient cycles in marine sponges}, - volume = {95}, - issn = {0168-6496}, - url = {https://doi.org/10.1093/femsec/fiz155}, - doi = {10.1093/femsec/fiz155}, - abstract = {Efficient nutrient cycles mediated by symbiotic microorganisms with their hosts are vital to support the high productivity of coral reef ecosystems. In these ecosystems, marine sponges are important habitat-forming organisms in the benthic community and harbor abundant microbial symbionts. However, few studies have reviewed the critical microbially mediated nutrient cycling processes in marine sponges. To bridge this gap, in this review article, we summarize existing knowledge and recent advances in understanding microbially mediated carbon (C), nitrogen (N), phosphorus (P) and sulfur (S) cycles in sponges, propose a conceptual model that describes potential interactions and constraints in the major nutrient cycles, and suggest that shifting redox state induced by animal behavior like sponge pumping can exert great influence on the activities of symbiotic microbial communities. Constraints include the lack of knowledge on spatial and temporal variations and host behavior; more studies are needed in these areas. Sponge microbiomes may have a significant impact on the nutrient cycles in the world’s coral reef ecosystems.}, - number = {11}, - urldate = {2022-11-02}, - journal = {FEMS Microbiology Ecology}, - author = {Zhang, Fan and Jonas, Lauren and Lin, Hanzhi and Hill, Russell T}, - month = nov, - year = {2019}, - keywords = {EMO}, - pages = {fiz155}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/372EJVV6/Zhang et al. - 2019 - Microbially mediated nutrient cycles in marine spo.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/AN3DK4P7/5582607.html:text/html}, -} - -@article{ceh_nutrient_2013, - title = {Nutrient cycling in early coral life stages: {Pocillopora} damicornis larvae provide their algal symbiont ({Symbiodinium}) with nitrogen acquired from bacterial associates}, - volume = {3}, - issn = {2045-7758}, - shorttitle = {Nutrient cycling in early coral life stages}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.642}, - doi = {10.1002/ece3.642}, - abstract = {The waters surrounding coral reef ecosystems are generally poor in nutrients, yet their levels of primary production are comparable with those reported from tropical rain forests. One explanation of this paradox is the efficient cycling of nutrients between the coral host, its endosymbiotic alga Symbiodinium and a wide array of microorganisms. Despite their importance for the animals' fitness, the cycling of nutrients in early coral life stages and the initial establishment of partnerships with the microbes involved in these processes has received little scrutiny to date. Nitrogen is an essential but limited nutrient in coral reef ecosystems. In order to assess the early nutrient exchange between bacteria and corals, coral larvae of the species Pocillopora damicornis were incubated with two coral-associated bacteria (Alteromonas sp., or Vibrio alginolyticus), prelabeled with the stable nitrogen isotope 15N. The incorporation and translocation of nitrogen from Vibrio- and Alteromonas bacteria into P. damicornis coral larvae and specifically into the coral-symbiotic Symbiodinium were detected by nanoscale secondary ion mass spectrometry (NanoSIMS). A significant increase in the amount of enriched 15N (two to threefold compared to natural abundance) was observed in P. damicornis larvae within 8 h of incubation for both bacterial treatments (one-way ANOVA, F5,53 = 18.03, P = 0.004 for Alteromonas sp. and F5,53 = 18.03, P = 0.0001 for V. alginolyticus). These findings reveal that coral larvae acquire nutrients previously taken up from the environment by bacteria. The additional nitrogen may increase the survival rate and fitness of the developing coral and therefore contribute to the successful maintenance of coral reefs.}, - language = {en}, - number = {8}, - urldate = {2022-11-02}, - journal = {Ecology and Evolution}, - author = {Ceh, Janja and Kilburn, Matt R. and Cliff, John B. and Raina, Jean-Baptiste and van Keulen, Mike and Bourne, David G.}, - year = {2013}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.642}, - keywords = {EMO, Bacteria, coral larvae, nanoSIMS, nitrogen}, - pages = {2393--2400}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/8PI4Q2QV/Ceh et al. - 2013 - Nutrient cycling in early coral life stages Pocil.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/L6GYJYMJ/ece3.html:text/html}, -} - -@article{radecker_heat_2022, - title = {Heat stress reduces the contribution of diazotrophs to coral holobiont nitrogen cycling}, - volume = {16}, - copyright = {2021 The Author(s)}, - issn = {1751-7370}, - url = {https://www.nature.com/articles/s41396-021-01158-8}, - doi = {10.1038/s41396-021-01158-8}, - abstract = {Efficient nutrient cycling in the coral-algal symbiosis requires constant but limited nitrogen availability. Coral-associated diazotrophs, i.e., prokaryotes capable of fixing dinitrogen, may thus support productivity in a stable coral-algal symbiosis but could contribute to its breakdown when overstimulated. However, the effects of environmental conditions on diazotroph communities and their interaction with other members of the coral holobiont remain poorly understood. Here we assessed the effects of heat stress on diazotroph diversity and their contribution to holobiont nutrient cycling in the reef-building coral Stylophora pistillata from the central Red Sea. In a stable symbiotic state, we found that nitrogen fixation by coral-associated diazotrophs constitutes a source of nitrogen to the algal symbionts. Heat stress caused an increase in nitrogen fixation concomitant with a change in diazotroph communities. Yet, this additional fixed nitrogen was not assimilated by the coral tissue or the algal symbionts. We conclude that although diazotrophs may support coral holobiont functioning under low nitrogen availability, altered nutrient cycling during heat stress abates the dependence of the coral host and its algal symbionts on diazotroph-derived nitrogen. Consequently, the role of nitrogen fixation in the coral holobiont is strongly dependent on its nutritional status and varies dynamically with environmental conditions.}, - language = {en}, - number = {4}, - urldate = {2022-11-02}, - journal = {The ISME Journal}, - author = {Rädecker, Nils and Pogoreutz, Claudia and Gegner, Hagen M. and Cárdenas, Anny and Perna, Gabriela and Geißler, Laura and Roth, Florian and Bougoure, Jeremy and Guagliardo, Paul and Struck, Ulrich and Wild, Christian and Pernice, Mathieu and Raina, Jean-Baptiste and Meibom, Anders and Voolstra, Christian R.}, - month = apr, - year = {2022}, - note = {Number: 4 -Publisher: Nature Publishing Group}, - keywords = {Microbial ecology, EMO, Climate-change ecology}, - pages = {1110--1118}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/XMDXIJE7/Rädecker et al. - 2022 - Heat stress reduces the contribution of diazotroph.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/3RSSVZZL/s41396-021-01158-8.html:text/html}, -} - -@article{radecker_nitrogen_2015, - title = {Nitrogen cycling in corals: the key to understanding holobiont functioning?}, - volume = {23}, - issn = {0966-842X}, - shorttitle = {Nitrogen cycling in corals}, - url = {https://www.sciencedirect.com/science/article/pii/S0966842X1500075X}, - doi = {10.1016/j.tim.2015.03.008}, - abstract = {Corals are animals that form close mutualistic associations with endosymbiotic photosynthetic algae of the genus Symbiodinium. Together they provide the calcium carbonate framework of coral reef ecosystems. The importance of the microbiome (i.e., bacteria, archaea, fungi, and viruses) to holobiont functioning has only recently been recognized. Given that growth and density of Symbiodinium within the coral host is highly dependent on nitrogen availability, nitrogen-cycling microbes may be of fundamental importance to the stability of the coral–algae symbiosis and holobiont functioning, in particular under nutrient-enriched and -depleted scenarios. We summarize what is known about nitrogen cycling in corals and conclude that disturbance of microbial nitrogen cycling may be tightly linked to coral bleaching and disease.}, - language = {en}, - number = {8}, - urldate = {2022-11-02}, - journal = {Trends in Microbiology}, - author = {Rädecker, Nils and Pogoreutz, Claudia and Voolstra, Christian R. and Wiedenmann, Jörg and Wild, Christian}, - month = aug, - year = {2015}, - keywords = {EMO, coral bleaching, coral disease, coral holobiont, nutrient limitation, symbiosis}, - pages = {490--497}, - file = {ScienceDirect Full Text PDF:/Users/birdy/Zotero/storage/QY245ZPK/Rädecker et al. - 2015 - Nitrogen cycling in corals the key to understandi.pdf:application/pdf;ScienceDirect Snapshot:/Users/birdy/Zotero/storage/I62WKSM8/S0966842X1500075X.html:text/html}, -} - -@article{zhang_multi-task_nodate, - title = {Multi-task few-shot learning with composed data augmentation for image classification}, - volume = {n/a}, - issn = {1751-9640}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1049/cvi2.12150}, - doi = {10.1049/cvi2.12150}, - abstract = {Few-shot learning (FSL) attempts to learn and optimise the model from a few examples on image classification, which is still threatened by data scarcity. To generate more data as supplements, data augmentation is considered as a powerful and popular technique to enhance the robustness of few-shot models. However, there are still some weaknesses in applying augmentation methods. For example, all augmented samples have similar semantic information with respect to different augmented transformations, which makes these traditional augmentation methods incapable of learning the property being varied. To address this challenge, we introduce multi-task learning to learn a primary few-shot classification task and an auxiliary self-supervised task, simultaneously. The self-supervised task can learn transformation property as auxiliary self-supervision signals to improve the performance of the primary few-shot classification task. Additionally, we propose a simple, flexible, and effective mechanism for decision fusion to further improve the reliability of the classifier, named model-agnostic ensemble inference (MAEI). Specifically, the MAEI mechanism can eliminate the influence of outliers for FSL using non-maximum suppression. Extensive experiment results demonstrate that our method can outperform other state-of-the-art methods by large margins.}, - language = {en}, - number = {n/a}, - urldate = {2022-11-03}, - journal = {IET Computer Vision}, - author = {Zhang, Rui and Yang, Yixin and Li, Yang and Wang, Jiabao and Li, Hang and Miao, Zhuang}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1049/cvi2.12150}, - keywords = {data augmentation, few-shot learning, multi-task learning, non-maximum suppression, self-supervised learning}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/QWAG8GIJ/Zhang et al. - Multi-task few-shot learning with composed data au.pdf:application/pdf}, -} - -@article{mngadi_estimating_2022, - title = {Estimating aboveground net primary productivity of reforested trees in an urban landscape using biophysical variables and remotely sensed data}, - volume = {802}, - issn = {00489697}, - url = {https://linkinghub.elsevier.com/retrieve/pii/S0048969721050336}, - doi = {10.1016/j.scitotenv.2021.149958}, - language = {en}, - urldate = {2022-11-21}, - journal = {Science of The Total Environment}, - author = {Mngadi, Mthembeni and Odindi, John and Mutanga, Onisimo and Sibanda, Mbulisi}, - month = jan, - year = {2022}, - pages = {149958}, -} - -@article{henderikx-freitas_diel_2022, - title = {Diel variations in the estimated refractive index of bulk oceanic particles}, - volume = {30}, - copyright = {\&\#169; 2022 Optica Publishing Group}, - issn = {1094-4087}, - url = {https://opg.optica.org/oe/abstract.cfm?uri=oe-30-24-44141}, - doi = {10.1364/OE.469565}, - abstract = {The index of refraction (n) of particles is an important parameter in optical models that aims to extract particle size and carbon concentrations from light scattering measurements. An inadequate choice of n can critically affect the characterization and interpretation of optically-derived parameters, including those from satellite-based models which provide the current view of how biogeochemical processes vary over the global ocean. Yet, little is known about how n varies over time and space to inform such models. Particularly, in situ estimates of n for bulk water samples and at diel-resolving time scales are rare. Here, we demonstrate a method to estimate n using simultaneously and independently collected particulate beam attenuation coefficients, particle size distribution data, and a Mie theory model. We apply this method to surface waters of the North Pacific Subtropical Gyre (NPSG) at hourly resolution. Clear diel cycles in n were observed, marked by minima around local sunrise and maxima around sunset, qualitatively consistent with several laboratory-based estimates of n for specific phytoplankton species. A sensitivity analysis showed that the daily oscillation in n amplitude was somewhat insensitive to broad variations in method assumptions, ranging from 11.3\&\#x2009;\&\#x00B1;\&\#x2009;4.3\&\#x0025; to 16.9\&\#x2009;\&\#x00B1;\&\#x2009;2.9\&\#x0025;. Such estimates are crucial for improvement of algorithms that extract the particle size and production from bulk optical measurements, and could potentially help establish a link between n variations and changes in cellular composition of in situ particles.}, - language = {EN}, - number = {24}, - urldate = {2022-11-22}, - journal = {Optics Express}, - author = {Henderikx-Freitas, Fernanda and Allen, James G. and Lansdorp, Bob M. and White, Angelicque E.}, - month = nov, - year = {2022}, - note = {Publisher: Optica Publishing Group}, - pages = {44141--44159}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/HPPVHGUH/Henderikx-Freitas et al. - 2022 - Diel variations in the estimated refractive index .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/YVISH8DF/fulltext.html:text/html}, -} - -@misc{noauthor_accelerated_nodate, - title = {Accelerated modern human–induced species losses: {Entering} the sixth mass extinction}, - shorttitle = {Accelerated modern human–induced species losses}, - url = {https://www.science.org/doi/10.1126/sciadv.1400253}, - language = {en}, - urldate = {2022-12-01}, - doi = {10.1126/sciadv.1400253}, - file = {Full Text:/Users/birdy/Zotero/storage/Z7BVSY72/Accelerated modern human–induced species losses E.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/AC954VKH/sciadv.html:text/html}, -} - -@article{koning_network_2020, - title = {A network of grassroots reserves protects tropical river fish diversity}, - volume = {588}, - copyright = {2020 The Author(s), under exclusive licence to Springer Nature Limited}, - issn = {1476-4687}, - url = {https://www.nature.com/articles/s41586-020-2944-y}, - doi = {10.1038/s41586-020-2944-y}, - abstract = {Intensive fisheries have reduced fish biodiversity and abundance in aquatic ecosystems worldwide1–3. ‘No-take’ marine reserves have become a cornerstone of marine ecosystem-based fisheries management4–6, and their benefits for adjacent fisheries are maximized when reserve design fosters synergies among nearby reserves7,8. The applicability of this marine reserve network paradigm to riverine biodiversity and inland fisheries remains largely untested. Here we show that reserves created by 23 separate communities in Thailand’s Salween basin have markedly increased fish richness, density, and biomass relative to adjacent areas. Moreover, key correlates of the success of protected areas in marine ecosystems—particularly reserve size and enforcement—predict differences in ecological benefits among riverine reserves. Occupying a central position in the network confers additional gains, underscoring the importance of connectivity within dendritic river systems. The emergence of network-based benefits is remarkable given that these reserves are young (less than 25 years old) and arose without formal coordination. Freshwater ecosystems are under-represented among the world’s protected areas9, and our findings suggest that networks of small, community-based reserves offer a generalizable model for protecting biodiversity and augmenting fisheries as the world’s rivers face unprecedented pressures10,11.}, - language = {en}, - number = {7839}, - urldate = {2022-12-01}, - journal = {Nature}, - author = {Koning, Aaron A. and Perales, K. Martin and Fluet-Chouinard, Etienne and McIntyre, Peter B.}, - month = dec, - year = {2020}, - note = {Number: 7839 -Publisher: Nature Publishing Group}, - keywords = {Conservation biology, Freshwater ecology}, - pages = {631--635}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/U38SBSD4/Koning et al. - 2020 - A network of grassroots reserves protects tropical.pdf:application/pdf}, -} - -@article{robbins_lawns_2001, - title = {Lawns and {Toxins}: {An} {Ecology} of the {City}}, - volume = {18}, - issn = {0264-2751}, - shorttitle = {Lawns and {Toxins}}, - url = {https://www.sciencedirect.com/science/article/pii/S0264275101000294}, - doi = {10.1016/S0264-2751(01)00029-4}, - abstract = {This paper surveys the problems of contemporary urban ecology through the lens of lawn chemical usage, exploring the difficulty of explaining and managing urban ecological dilemmas that, though built from the disaggregated choices of individuals, aggregate into large and serious issues. Introductory discussion surveys the seriousness of lawn chemicals as urban non-point pollution sources and suggests why the issue, and problems like it, it understudied. Analysis proceeds with a case study from the United States city of Columbus, Ohio, utilizing formal survey techniques and aanlysis of county assessor's data. The results suggest lawns and lawn care chemicals are expanding with urban sprewl and that users of high-input lawn chemical systems are more likely to be wealthy, well-educated, and knowledgeable about the negative environmental impacts of the actions than non-users. Further investigation demonstrates the instrumental logics of homeowners in pursuit of property values but also points to the moral and community-oriented institutions that enforce and propel high chemical use. The conclusions point to policy options for dealing with the lawn chemical dilemma but suggest the difficulties of circumventing the deeply structured roots of the problem.}, - language = {en}, - number = {6}, - urldate = {2022-12-01}, - journal = {Cities}, - author = {Robbins, Paul and Polderman, Annemarie and Birkenholtz, Trevor}, - month = dec, - year = {2001}, - keywords = {consumption, political ecology, urban ecology}, - pages = {369--380}, - file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/PAWPGVRF/S0264275101000294.html:text/html}, -} - -@misc{orenstein_whoi-plankton-_2015, - title = {{WHOI}-{Plankton}- {A} {Large} {Scale} {Fine} {Grained} {Visual} {Recognition} {Benchmark} {Dataset} for {Plankton} {Classification}}, - url = {http://arxiv.org/abs/1510.00745}, - abstract = {Planktonic organisms are of fundamental importance to marine ecosystems: they form the basis of the food web, provide the link between the atmosphere and the deep ocean, and influence global-scale biogeochemical cycles. Scientists are increasingly using imaging-based technologies to study these creatures in their natural habit. Images from such systems provide an unique opportunity to model and understand plankton ecosystems, but the collected datasets can be enormous. The Imaging FlowCytobot (IFCB) at Woods Hole Oceanographic Institution, for example, is an {\textbackslash}emph\{in situ\} system that has been continuously imaging plankton since 2006. To date, it has generated more than 700 million samples. Manual classification of such a vast image collection is impractical due to the size of the data set. In addition, the annotation task is challenging due to the large space of relevant classes, intra-class variability, and inter-class similarity. Methods for automated classification exist, but the accuracy is often below that of human experts. Here we introduce WHOI-Plankton: a large scale, fine-grained visual recognition dataset for plankton classification, which comprises over 3.4 million expert-labeled images across 70 classes. The labeled image set is complied from over 8 years of near continuous data collection with the IFCB at the Martha's Vineyard Coastal Observatory (MVCO). We discuss relevant metrics for evaluation of classification performance and provide results for a traditional method based on hand-engineered features and two methods based on convolutional neural networks.}, - urldate = {2023-01-10}, - publisher = {arXiv}, - author = {Orenstein, Eric C. and Beijbom, Oscar and Peacock, Emily E. and Sosik, Heidi M.}, - month = oct, - year = {2015}, - note = {arXiv:1510.00745 [cs]}, - keywords = {Computer Science - Computer Vision and Pattern Recognition}, - file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/V4XB9956/Orenstein et al. - 2015 - WHOI-Plankton- A Large Scale Fine Grained Visual R.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/UKCDAVKQ/1510.html:text/html}, -} - -@article{jones_matthew_b_gries_corinna_proceedings_2011, - title = {Proceedings of the {Environmental} {Information} {Management} {Conference} 2011}, - url = {https://eim.ecoinformatics.org/eim2011/eim-proceedings-2011/at_download/file}, - doi = {10.5060/D2NC5Z4X}, - abstract = {Lack of observations that span the wide range of critical space and time scales continues to limit many aspects of oceanography. As ocean observatories and observing networks mature, the role for optical technologies and approaches in helping to overcome this limitation continues to grow. As a result the quantity and complexity of data produced is increasing at a pace that threatens to overwhelm the capacity of individual researchers who must cope with large high-resolution datasets, complex, multistage analyses, and the challenges of preserving sufficient metadata and provenance information to ensure reproducibility and avoid costly reprocessing or data loss. We have developed approaches to address these new challenges in the context of a case study involving very large numbers ({\textasciitilde}1 billion) of images collected at coastal observatories by Imaging FlowCytobot, an automated submersible flow cytometer that produces high resolution images of plankton and other microscopic particles at rates up to 10 Hz for months to years. By developing partnerships amongst oceanographers generating and using such data and computer scientists focused on improving science outcomes, we have prototyped a replicable system. It provides simple and ubiquitous access to observational data and products via web services in standard formats; accelerates image processing by enabling algorithms developed with desktop applications to be rapidly deployed and evaluated on shared, high-performance servers; and improves data integrity by replacing error-prone manual data management processes with generalized, automated services. The informatics system is currently in operation for multiple Imaging FlowCytobot datasets and being tested with other types of ocean imagery.}, - language = {en}, - urldate = {2023-01-10}, - author = {Jones, Matthew B.; Gries, Corinna}, - year = {2011}, - note = {Publisher: University of California}, - file = {Jones, Matthew B.\; Gries, Corinna - 2011 - Proceedings of the Environmental Information Manag.pdf:/Users/birdy/Zotero/storage/8LGGH6BH/Jones, Matthew B.\; Gries, Corinna - 2011 - Proceedings of the Environmental Information Manag.pdf:application/pdf}, -} - -@misc{noauthor_creative_nodate, - title = {Creative {Team} {\textbar} {Pulse} at {Omega} {Mart}}, - url = {https://credits.meowwolf.com/omega-mart/factory/pulse}, - abstract = {Discover the artists and creative teams behind Meow Wolf's exhibitions. Works include sculpture, painting, fabrication, digital art, writing, film, and more.}, - urldate = {2023-01-13}, - journal = {Credits {\textbar} Meow Wolf}, - file = {Snapshot:/Users/birdy/Zotero/storage/8C2WZPAI/pulse.html:text/html}, -} - -@article{mahaffey_phytoplankton_2012, - title = {Phytoplankton response to deep seawater nutrient addition in the {North} {Pacific} {Subtropical} {Gyre}}, - volume = {460}, - issn = {0171-8630, 1616-1599}, - url = {http://www.int-res.com/abstracts/meps/v460/p13-34/}, - doi = {10.3354/meps09699}, - abstract = {We examined the phytoplankton response to the addition of nutrient-enriched deep seawater (DSW) and of nitrate only during shipboard experiments performed between July 2004 and May 2007 in the North Pacific Subtropical Gyre (NPSG). Chlorophyll a (chl a), community size structure and composition, carbon fixation (CF) rates, and nutrient concentrations were measured daily for 5 to 7 d under simulated in situ conditions. Despite the fact that the NPSG is a permanently stratified, oligotrophic biome, there was a seasonal response to the addition of DSW and nitrate. In summertime experiments, chl a and CF rates increased up to 18- and 22-fold, respectively, relative to unamended controls after an incubation period of 5 to 6 d. Nutrients were assimilated to below control concentrations. A shift from a Prochlorococcus-dominated to a Synechococcus- and diatom-dominated community, and an 8- to 12-fold increase in the chl a content of the {\textgreater} 2 µm size fraction were observed. Addition of nitrate only increased chl a and CF rates 10- and 7-fold, respectively. In wintertime experiments, chl a and CF rates increased up to 4- and 9-fold, respectively, after an incubation period of up to 7 d. Only 50\% of nutrients were assimilated and nitrate alone stimulated a 2-fold increase in chl a and CF rates. We explore the role of nutrient limitation, community composition, grazing, and light in explaining our observations. Findings from this seasonal and multi-year field experiment demonstrate how little we know about bloom development in the NPSG and highlight the potential risk in extrapolating the response of phytoplankton to natural or artificial fertilization from short-term studies.}, - language = {en}, - urldate = {2023-02-22}, - journal = {Marine Ecology Progress Series}, - author = {Mahaffey, C and Björkman, Km and Karl, Dm}, - month = jul, - year = {2012}, - pages = {13--34}, - file = {Mahaffey et al. - 2012 - Phytoplankton response to deep seawater nutrient a.pdf:/Users/birdy/Zotero/storage/BZYUHQ9I/Mahaffey et al. - 2012 - Phytoplankton response to deep seawater nutrient a.pdf:application/pdf}, -} - -@article{tilman_invasions_1984, - title = {Invasions of equilibria: tests of resource competition using two species of algae}, - volume = {61}, - issn = {1432-1939}, - shorttitle = {Invasions of equilibria}, - url = {https://doi.org/10.1007/BF00396760}, - doi = {10.1007/BF00396760}, - abstract = {Single-species, steady-state chemostat cultures of two freshwater diatoms showed that Fragilaria crotonensis had a lower equilibrial requirement for silicate than did Tabellaria fenestratra. Using this information, resource competition theory predicts that Fragilaria should be able to invade silicate-limited equilibrium populations of Tabellaria, but Tabellaria should not be able to invade silicatelimited Fragilaria. This prediction was supported by a series of invasion experiments. The two species did not have detectable differences in their phosphate requirements. Invasion experiments showed that Fragilaria could invade Tabellaria cultures, but that Tabellaria could not invade Fragilaria cultures under phosphate-limited conditions. These results are consistent with predictions based on previous studies of the phosphate physiology of these genera.}, - language = {en}, - number = {2}, - urldate = {2023-02-22}, - journal = {Oecologia}, - author = {Tilman, David and Sterner, Robert W.}, - month = feb, - year = {1984}, - pages = {197--200}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/7S64RFJG/Tilman and Sterner - 1984 - Invasions of equilibria tests of resource competi.pdf:application/pdf}, -} - -@article{tilman_resource_1977, - title = {Resource {Competition} between {Plankton} {Algae}: {An} {Experimental} and {Theoretical} {Approach}}, - volume = {58}, - issn = {00129658}, - shorttitle = {Resource {Competition} between {Plankton} {Algae}}, - url = {http://doi.wiley.com/10.2307/1935608}, - doi = {10.2307/1935608}, - language = {en}, - number = {2}, - urldate = {2023-02-22}, - journal = {Ecology}, - author = {Tilman, David}, - month = mar, - year = {1977}, - pages = {338--348}, - file = {Tilman - 1977 - Resource Competition between Plankton Algae An Ex.pdf:/Users/birdy/Zotero/storage/E6YRP87L/Tilman - 1977 - Resource Competition between Plankton Algae An Ex.pdf:application/pdf}, -} - -@article{burghardt_impact_2009, - title = {Impact of {Native} {Plants} on {Bird} and {Butterfly} {Biodiversity} in {Suburban} {Landscapes}}, - volume = {23}, - issn = {1523-1739}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1523-1739.2008.01076.x}, - doi = {10.1111/j.1523-1739.2008.01076.x}, - abstract = {Abstract: Managed landscapes in which non-native ornamental plants are favored over native vegetation now dominate the United States, particularly east of the Mississippi River. We measured how landscaping with native plants affects the avian and lepidopteran communities on 6 pairs of suburban properties in southeastern Pennsylvania. One property in each pair was landscaped entirely with native plants and the other exhibited a more conventional suburban mixture of plants—a native canopy with non-native groundcover and shrubs. Vegetation sampling confirmed that total plant cover and plant diversity did not differ between treatments, but non-native plant cover was greater on the conventional sites and native plant cover was greater on the native sites. Several avian (abundance, species richness, biomass, and breeding-bird abundance) and larval lepidopteran (abundance and species richness) community parameters were measured from June 2006 to August 2006. Native properties supported significantly more caterpillars and caterpillar species and significantly greater bird abundance, diversity, species richness, biomass, and breeding pairs of native species. Of particular importance is that bird species of regional conservation concern were 8 times more abundant and significantly more diverse on native properties. In our study area, native landscaping positively influenced the avian and lepidopteran carrying capacity of suburbia and provided a mechanism for reducing biodiversity losses in human-dominated landscapes.}, - language = {en}, - number = {1}, - urldate = {2023-02-23}, - journal = {Conservation Biology}, - author = {Burghardt, Karin T. and Tallamy, Douglas W. and Gregory Shriver, W.}, - year = {2009}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1523-1739.2008.01076.x}, - keywords = {biodiversity, aves, biodiversidad, birds, ecosistemas bajo manejo, Lepidoptera, Lepidóptera, managed ecosystems, native plants, non-native plants, paisaje suburbano, plantas nativas, plantas no nativas, suburban landscape}, - pages = {219--224}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/9IP46YJ7/Burghardt et al. - 2009 - Impact of Native Plants on Bird and Butterfly Biod.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/2M776SVN/j.1523-1739.2008.01076.html:text/html}, -} - -@misc{noauthor_pnas2211758120pdf_nodate, - title = {pnas.2211758120.pdf: 1.873 {Systems} {Ecology} in {Theory}}, - url = {https://canvas.mit.edu/courses/18350/files/3301971?module_item_id=856541}, - urldate = {2023-05-02}, - file = {pnas.2211758120.pdf\: 1.873 Systems Ecology in Theory:/Users/birdy/Zotero/storage/FR2ZNW6N/3301971.html:text/html}, -} - -@article{munch_constraining_2023, - title = {Constraining nonlinear time series modeling with the metabolic theory of ecology}, - volume = {120}, - issn = {0027-8424, 1091-6490}, - url = {https://pnas.org/doi/10.1073/pnas.2211758120}, - doi = {10.1073/pnas.2211758120}, - abstract = {Forecasting the response of ecological systems to environmental change is a critical challenge for sustainable management. The metabolic theory of ecology (MTE) posits scaling of biological rates with temperature, but it has had limited application to population dynamic forecasting. Here we use the temperature dependence of the MTE to constrain empirical dynamic modeling (EDM), an equation-free nonlinear machine learning approach for forecasting. By rescaling time with temperature and modeling dynamics on a “metabolic time step,” our method (MTE-EDM) improved forecast accuracy in 18 of 19 empirical ectotherm time series (by 19\% on average), with the largest gains in more seasonal environments. MTE-EDM assumes that temperature affects only the rate, rather than the form, of population dynamics, and that interacting species have approximately similar temperature dependence. A review of laboratory studies suggests these assumptions are reasonable, at least approximately, though not for all ecological systems. Our approach highlights how to combine modern data-driven forecasting techniques with ecological theory and mechanistic understanding to predict the response of complex ecosystems to temperature variability and trends.}, - language = {en}, - number = {12}, - urldate = {2023-05-02}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Munch, Stephan B. and Rogers, Tanya L. and Symons, Celia C. and Anderson, David and Pennekamp, Frank}, - month = mar, - year = {2023}, - pages = {e2211758120}, - file = {pnas.2211758120.pdf:/Users/birdy/Downloads/pnas.2211758120.pdf:application/pdf}, -} - -@article{sugihara_nonlinear_1990, - title = {Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series}, - volume = {344}, - copyright = {1990 Nature Publishing Group}, - issn = {1476-4687}, - url = {https://www.nature.com/articles/344734a0}, - doi = {10.1038/344734a0}, - abstract = {An approach is presented for making short-term predictions about the trajectories of chaotic dynamical systems. The method is applied to data on measles, chickenpox, and marine phytoplankton populations, to show how apparent noise associated with deterministic chaos can be distinguished from sampling error and other sources of externally induced environmental noise.}, - language = {en}, - number = {6268}, - urldate = {2023-05-02}, - journal = {Nature}, - author = {Sugihara, George and May, Robert M.}, - month = apr, - year = {1990}, - note = {Number: 6268 -Publisher: Nature Publishing Group}, - keywords = {Humanities and Social Sciences, multidisciplinary, Science}, - pages = {734--741}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/LDYABKT4/Sugihara and May - 1990 - Nonlinear forecasting as a way of distinguishing c.pdf:application/pdf}, -} - -@article{fujita_alternative_2023, - title = {Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics}, - volume = {11}, - issn = {2049-2618}, - url = {https://doi.org/10.1186/s40168-023-01474-5}, - doi = {10.1186/s40168-023-01474-5}, - abstract = {Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in human microbiomes.}, - language = {en}, - number = {1}, - urldate = {2023-05-02}, - journal = {Microbiome}, - author = {Fujita, Hiroaki and Ushio, Masayuki and Suzuki, Kenta and Abe, Masato S. and Yamamichi, Masato and Iwayama, Koji and Canarini, Alberto and Hayashi, Ibuki and Fukushima, Keitaro and Fukuda, Shinji and Kiers, E. Toby and Toju, Hirokazu}, - month = mar, - year = {2023}, - keywords = {Alternative stable states, Biodiversity, Biological communities, Chaos, Community collapse, Community stability, Dysbiosis, Empirical dynamic modeling, Microbiome dynamics, Non-linear dynamics}, - pages = {63}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/LJZAZT65/Fujita et al. - 2023 - Alternative stable states, nonlinear behavior, and.pdf:application/pdf}, -} - -@inproceedings{kay_caltech_2022, - address = {Cham}, - series = {Lecture {Notes} in {Computer} {Science}}, - title = {The {Caltech} {Fish} {Counting} {Dataset}: {A} {Benchmark} for {Multiple}-{Object} {Tracking} and {Counting}}, - isbn = {978-3-031-20074-8}, - shorttitle = {The {Caltech} {Fish} {Counting} {Dataset}}, - doi = {10.1007/978-3-031-20074-8_17}, - abstract = {We present the Caltech Fish Counting Dataset (CFC ), a large-scale dataset for detecting, tracking, and counting fish in sonar videos. We identify sonar videos as a rich source of data for advancing low signal-to-noise computer vision applications and tackling domain generalization in multiple-object tracking (MOT) and counting. In comparison to existing MOT and counting datasets, which are largely restricted to videos of people and vehicles in cities, CFC is sourced from a natural-world domain where targets are not easily resolvable and appearance features cannot be easily leveraged for target re-identification. With over half a million annotations in over 1,500 videos sourced from seven different sonar cameras, CFC allows researchers to train MOT and counting algorithms and evaluate generalization performance at unseen test locations. We perform extensive baseline experiments and identify key challenges and opportunities for advancing the state of the art in generalization in MOT and counting.}, - language = {en}, - booktitle = {Computer {Vision} – {ECCV} 2022}, - publisher = {Springer Nature Switzerland}, - author = {Kay, Justin and Kulits, Peter and Stathatos, Suzanne and Deng, Siqi and Young, Erik and Beery, Sara and Van Horn, Grant and Perona, Pietro}, - editor = {Avidan, Shai and Brostow, Gabriel and Cissé, Moustapha and Farinella, Giovanni Maria and Hassner, Tal}, - year = {2022}, - keywords = {Counting, Detection, Tracking, Video dataset}, - pages = {290--311}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/M2APBWFQ/Kay et al. - 2022 - The Caltech Fish Counting Dataset A Benchmark for.pdf:application/pdf}, -} - -@article{chang_engineering_2021, - title = {Engineering complex communities by directed evolution}, - volume = {5}, - copyright = {2021 The Author(s), under exclusive licence to Springer Nature Limited}, - issn = {2397-334X}, - url = {https://www.nature.com/articles/s41559-021-01457-5}, - doi = {10.1038/s41559-021-01457-5}, - abstract = {Directed evolution has been used for decades to engineer biological systems at or below the organismal level. Above the organismal level, a small number of studies have attempted to artificially select microbial ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. Here, we have developed a flexible modelling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions. By artificially selecting hundreds of in silico microbial metacommunities under identical conditions, we first show that the main breeding methods used to date, which do not necessarily let communities reach their ecological equilibrium, are outperformed by a simple screen of sufficiently mature communities. We then identify a range of alternative directed evolution strategies that, particularly when applied in combination, are well suited for the top-down engineering of large, diverse and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure–function landscape to be navigated in search of dynamically stable and ecologically resilient communities with desired quantitative attributes.}, - language = {en}, - number = {7}, - urldate = {2023-05-11}, - journal = {Nature Ecology \& Evolution}, - author = {Chang, Chang-Yu and Vila, Jean C. C. and Bender, Madeline and Li, Richard and Mankowski, Madeleine C. and Bassette, Molly and Borden, Julia and Golfier, Stefan and Sanchez, Paul Gerald L. and Waymack, Rachel and Zhu, Xinwen and Diaz-Colunga, Juan and Estrela, Sylvie and Rebolleda-Gomez, Maria and Sanchez, Alvaro}, - month = jul, - year = {2021}, - note = {Number: 7 -Publisher: Nature Publishing Group}, - keywords = {Community ecology, Evolutionary theory}, - pages = {1011--1023}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/28DAPUW2/Chang et al. - 2021 - Engineering complex communities by directed evolut.pdf:application/pdf}, -} - -@article{lazebnik_can_2002, - title = {Can a biologist fix a radio?—{Or}, what {I} learned while studying apoptosis}, - volume = {2}, - issn = {1535-6108, 1878-3686}, - shorttitle = {Can a biologist fix a radio?}, - url = {https://www.cell.com/cancer-cell/abstract/S1535-6108(02)00133-2}, - doi = {10.1016/S1535-6108(02)00133-2}, - language = {English}, - number = {3}, - urldate = {2023-05-11}, - journal = {Cancer Cell}, - author = {Lazebnik, Yuri}, - month = sep, - year = {2002}, - pmid = {12242150}, - note = {Publisher: Elsevier}, - pages = {179--182}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/MM347IX4/Lazebnik - 2002 - Can a biologist fix a radio—Or, what I learned wh.pdf:application/pdf}, -} - -@article{deyle_tracking_2016, - title = {Tracking and forecasting ecosystem interactions in real time}, - volume = {283}, - url = {https://royalsocietypublishing.org/doi/full/10.1098/rspb.2015.2258}, - doi = {10.1098/rspb.2015.2258}, - abstract = {Evidence shows that species interactions are not constant but change as the ecosystem shifts to new states. Although controlled experiments and model investigations demonstrate how nonlinear interactions can arise in principle, empirical tools to track and predict them in nature are lacking. Here we present a practical method, using available time-series data, to measure and forecast changing interactions in real systems, and identify the underlying mechanisms. The method is illustrated with model data from a marine mesocosm experiment and limnologic field data from Sparkling Lake, WI, USA. From simple to complex, these examples demonstrate the feasibility of quantifying, predicting and understanding state-dependent, nonlinear interactions as they occur in situ and in real time—a requirement for managing resources in a nonlinear, non-equilibrium world.}, - number = {1822}, - urldate = {2023-05-16}, - journal = {Proceedings of the Royal Society B: Biological Sciences}, - author = {Deyle, Ethan R. and May, Robert M. and Munch, Stephan B. and Sugihara, George}, - month = jan, - year = {2016}, - note = {Publisher: Royal Society}, - keywords = {changing interaction strength, community matrix, empirical dynamics, nonlinear, S-map, state space reconstruction}, - pages = {20152258}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/GAGG2JHP/Deyle et al. - 2016 - Tracking and forecasting ecosystem interactions in.pdf:application/pdf}, -} - -@article{gonzalez_automatic_2019, - title = {Automatic plankton quantification using deep features}, - volume = {41}, - issn = {0142-7873}, - url = {https://doi.org/10.1093/plankt/fbz023}, - doi = {10.1093/plankt/fbz023}, - abstract = {The study of marine plankton data is vital to monitor the health of the world’s oceans. In recent decades, automatic plankton recognition systems have proved useful to address the vast amount of data collected by specially engineered in situ digital imaging systems. At the beginning, these systems were developed and put into operation using traditional automatic classification techniques, which were fed with hand-designed local image descriptors (such as Fourier features), obtaining quite successful results. In the past few years, there have been many advances in the computer vision community with the rebirth of neural networks. In this paper, we leverage how descriptors computed using convolutional neural networks trained with out-of-domain data are useful to replace hand-designed descriptors in the task of estimating the prevalence of each plankton class in a water sample. To achieve this goal, we have designed a broad set of experiments that show how effective these deep features are when working in combination with state-of-the-art quantification algorithms.}, - number = {4}, - urldate = {2023-05-18}, - journal = {Journal of Plankton Research}, - author = {González, Pablo and Castaño, Alberto and Peacock, Emily E and Díez, Jorge and Del Coz, Juan José and Sosik, Heidi M}, - month = jul, - year = {2019}, - pages = {449--463}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/YS4AGBKW/González et al. - 2019 - Automatic plankton quantification using deep featu.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/M6LWQPI2/5537667.html:text/html}, -} - -@inproceedings{walker_improving_2021, - address = {Montreal, BC, Canada}, - title = {Improving {Rare}-{Class} {Recognition} of {Marine} {Plankton} with {Hard} {Negative} {Mining}}, - isbn = {978-1-66540-191-3}, - url = {https://ieeexplore.ieee.org/document/9607849/}, - doi = {10.1109/ICCVW54120.2021.00410}, - abstract = {Biological oceanographers are increasingly adopting machine learning techniques to conduct quantitative assessments of marine plankton. Most supervised plankton classifiers are trained on labeled image datasets annotated by domain experts under the closed world assumption: all object classes and their priors are the same during both training and deployment. This assumption, however, is hard to satisfy in the actual ocean where data is subject to dataset shift due to shifting populations and from the introduction of object categories not seen during training. Here we present an alternative approach for training and evaluating plankton classifiers under the more realistic open world scenario. We specifically address the problems of out-of-distribution detection and dataset shift under the class imbalance setting where downsampling is needed to reliably detect and classify relatively rare target classes. We apply a hard negative mining approach called Background Resampling to perform downsampling and compare it to other strategies. We show that Background Resampling improves detection of novel particle classes while simultaneously providing competitive classification performance under dataset shift.}, - language = {en}, - urldate = {2023-05-18}, - booktitle = {2021 {IEEE}/{CVF} {International} {Conference} on {Computer} {Vision} {Workshops} ({ICCVW})}, - publisher = {IEEE}, - author = {Walker, Joseph L. and Orenstein, Eric C.}, - month = oct, - year = {2021}, - pages = {3665--3675}, - file = {Walker and Orenstein - 2021 - Improving Rare-Class Recognition of Marine Plankto.pdf:/Users/birdy/Zotero/storage/9IBBTBGB/Walker and Orenstein - 2021 - Improving Rare-Class Recognition of Marine Plankto.pdf:application/pdf}, -} - -@article{kerr_collaborative_2020, - title = {Collaborative {Deep} {Learning} {Models} to {Handle} {Class} {Imbalance} in {FlowCam} {Plankton} {Imagery}}, - volume = {8}, - issn = {2169-3536}, - doi = {10.1109/ACCESS.2020.3022242}, - abstract = {Using automated imaging technologies, it is now possible to generate previously unprecedented volumes of plankton image data which can be used to study the composition of plankton assemblages. However, the current need to manually classify individual images introduces a bottleneck into processing chains. Although Machine Learning techniques have been used to try and address this issue, past efforts have suffered from accuracy limitations, especially in minority classes. Here we use state-of-the-art methods in Deep Learning to investigate suitable architectures for training an automated plankton classification system which achieves high efficacy for both abundant and rare taxa. We collected live plankton from Station L4 in the Western English Channel and imaged 11,371 particles covering 104 taxonomic groups using the automated plankton imaging system FlowCam. The image set contained a severe class imbalance, with some taxa represented by {\textgreater} 600 images while other, rarer taxa were represented by just 14. We demonstrate that by allowing multiple Deep Learning models to collaborate in a single classification system, classification accuracy improves for minority classes when compared with the best individual model. The top collaborative model achieved a 6 \% improvement in F1 accuracy over the best individual model, while overall accuracy improved by 3.2 \%. This resulted in a 97.4 \% overall accuracy score and a 96.2 \% F1 macro score on a separate holdout test set containing 104 taxonomic groups. Based on a survey of similar studies in the literature, we believe collaborative deep learning models can significantly improve the accuracy of existing automated plankton classification systems.}, - journal = {IEEE Access}, - author = {Kerr, Thomas and Clark, James R. and Fileman, Elaine S. and Widdicombe, Claire E. and Pugeault, Nicolas}, - year = {2020}, - note = {Conference Name: IEEE Access}, - keywords = {Machine learning, deep learning, Automated plankton identification, Biological neural networks, Collaboration, Computational modeling, Convolution, convolutional neural networks, Feature extraction, FlowCam, multi-layer perceptron model, Station L4, Training}, - pages = {170013--170032}, - file = {IEEE Xplore Abstract Record:/Users/birdy/Zotero/storage/E9MSTETK/stamp.html:text/html;IEEE Xplore Full Text PDF:/Users/birdy/Zotero/storage/P2XN56XI/Kerr et al. - 2020 - Collaborative Deep Learning Models to Handle Class.pdf:application/pdf}, -} - -@article{ellen_improving_2019, - title = {Improving plankton image classification using context metadata}, - volume = {17}, - issn = {1541-5856}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lom3.10324}, - doi = {10.1002/lom3.10324}, - abstract = {Advances in both hardware and software are enabling rapid proliferation of in situ plankton imaging methods, requiring more effective machine learning approaches to image classification. Deep Learning methods, such as convolutional neural networks (CNNs), show marked improvement over traditional feature-based supervised machine learning algorithms, but require careful optimization of hyperparameters and adequate training sets. Here, we document some best practices in applying CNNs to zooplankton and marine snow images and note where our results differ from contemporary Deep Learning findings in other domains. We boost the performance of CNN classifiers by incorporating metadata of different types and illustrate how to assimilate metadata beyond simple concatenation. We utilize both geotemporal (e.g., sample depth, location, time of day) and hydrographic (e.g., temperature, salinity, chlorophyll a) metadata and show that either type by itself, or both combined, can substantially reduce error rates. Incorporation of context metadata also boosts performance of the feature-based classifiers we evaluated: Random Forest, Extremely Randomized Trees, Gradient Boosted Classifier, Support Vector Machines, and Multilayer Perceptron. For our assessments, we use an original data set of 350,000 in situ images (roughly 50\% marine snow and 50\% non-snow sorted into 26 categories) from a novel in situ Zooglider. We document asymptotically increasing performance with more computationally intensive techniques, such as substantially deeper networks and artificially augmented data sets. Our best model achieves 92.3\% accuracy with our 27-class data set. We provide guidance for further refinements that are likely to provide additional gains in classifier accuracy.}, - language = {en}, - number = {8}, - urldate = {2023-05-18}, - journal = {Limnology and Oceanography: Methods}, - author = {Ellen, Jeffrey S. and Graff, Casey A. and Ohman, Mark D.}, - year = {2019}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10324}, - pages = {439--461}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/3BP4JKLG/Ellen et al. - 2019 - Improving plankton image classification using cont.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/H2X6CN9U/lom3.html:text/html}, -} - -@article{wilchek_human---loop_2023, - title = {Human-in-the-loop for computer vision assurance: {A} survey}, - volume = {123}, - issn = {0952-1976}, - shorttitle = {Human-in-the-loop for computer vision assurance}, - url = {https://www.sciencedirect.com/science/article/pii/S0952197623005602}, - doi = {10.1016/j.engappai.2023.106376}, - abstract = {Human-in-the-loop (HITL), a key branch of Human–Computer Interaction (HCI), is increasingly proposed in the research literature as a key assurance method for automated analyses and predictive application designs. As the need increases to improve methods in Artificial Intelligence (AI) model training, optimize systems performance, provide AI explainability, and monitor AI system operations, the concept of HITL is gaining traction due to its value in solving these challenges. This survey of existing works on HITL from a computer vision system design perspective focuses on the following AI assurance principles: (1) improved data assurance, such as data preparation or automated data labeling; (2) algorithmic assurance, such as managing uncertainty and AI trustworthiness; and (3) critical limitations and capabilities introduced by HITL into a system’s operational efficiency. We survey prior work within these foci, including technical strengths and weaknesses of novel approaches and ongoing research. This review of the state of the art in HITL computer vision research supports an informed discussion of considerations and future opportunities in this critical space.}, - language = {en}, - urldate = {2023-05-19}, - journal = {Engineering Applications of Artificial Intelligence}, - author = {Wilchek, Matthew and Hanley, Will and Lim, Jude and Luther, Kurt and Batarseh, Feras A.}, - month = aug, - year = {2023}, - keywords = {AI assurance, Computer vision, Human-in-the-loop, Human–computer interaction, Learning systems, Object detection}, - pages = {106376}, - file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/XUK4ZLLN/S0952197623005602.html:text/html}, -} - -@article{bian_understanding_2023, - title = {Understanding opposing predictions of {Prochlorococcus} in a changing climate}, - volume = {14}, - copyright = {2023 The Author(s)}, - issn = {2041-1723}, - url = {https://www.nature.com/articles/s41467-023-36928-9}, - doi = {10.1038/s41467-023-36928-9}, - abstract = {Statistically derived species distribution models (SDMs) are increasingly used to predict ecological changes on a warming planet. For Prochlorococcus, the most abundant phytoplankton, an established statistical prediction conflicts with dynamical models as they predict large, opposite, changes in abundance. We probe the SDM at various spatial-temporal scales, showing that light and temperature fail to explain both temporal fluctuations and sharp spatial transitions. Strong correlations between changes in temperature and population emerge only at very large spatial scales, as transects pass through transitions between regions of high and low abundance. Furthermore, a two-state model based on a temperature threshold matches the original SDM in the surface ocean. We conclude that the original SDM has little power to predict changes when Prochlorococcus is already abundant, which resolves the conflict with dynamical models. Our conclusion suggests that SDMs should prove efficacy across multiple spatial-temporal scales before being trusted in a changing ocean.}, - language = {en}, - number = {1}, - urldate = {2023-06-05}, - journal = {Nature Communications}, - author = {Bian, Vincent and Cai, Merrick and Follett, Christopher L.}, - month = mar, - year = {2023}, - note = {Number: 1 -Publisher: Nature Publishing Group}, - keywords = {Biogeography, Projection and prediction, DAR1}, - pages = {1445}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/PQNIBYHE/Bian et al. - 2023 - Understanding opposing predictions of Prochlorococ.pdf:application/pdf}, -} - -@misc{noauthor_ocean_nodate, - title = {Ocean mover’s distance: using optimal transport for analysing oceanographic data}, - shorttitle = {Ocean mover’s distance}, - url = {https://royalsocietypublishing.org/doi/epdf/10.1098/rspa.2021.0875}, - language = {en}, - urldate = {2023-06-05}, - doi = {10.1098/rspa.2021.0875}, - file = {Full Text:/Users/birdy/Zotero/storage/YPAJVFL3/Ocean mover’s distance using optimal transport fo.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/MVL45IZE/rspa.2021.html:text/html}, -} - -@article{irisson_machine_2022, - title = {Machine {Learning} for the {Study} of {Plankton} and {Marine} {Snow} from {Images}}, - volume = {14}, - url = {https://doi.org/10.1146/annurev-marine-041921-013023}, - doi = {10.1146/annurev-marine-041921-013023}, - abstract = {Quantitative imaging instruments produce a large number of images of plankton and marine snow, acquired in a controlled manner, from which the visual characteristics of individual objects and their in situ concentrations can be computed. To exploit this wealth of information, machine learning is necessary to automate tasks such as taxonomic classification. Through a review of the literature, we highlight the progress of those machine classifiers and what they can and still cannot be trusted for. Several examples showcase how the combination of quantitative imaging with machine learning has brought insights on pelagic ecology. They also highlight what is still missing and how images could be exploited further through trait-based approaches. In the future, we suggest deeper interactions with the computer sciences community, the adoption of data standards, and the more systematic sharing of databases to build a global community of pelagic image providers and users.}, - number = {1}, - urldate = {2023-07-14}, - journal = {Annual Review of Marine Science}, - author = {Irisson, Jean-Olivier and Ayata, Sakina-Dorothée and Lindsay, Dhugal J. and Karp-Boss, Lee and Stemmann, Lars}, - year = {2022}, - pmid = {34460314}, - note = {\_eprint: https://doi.org/10.1146/annurev-marine-041921-013023}, - pages = {277--301}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/IWC2B69M/Irisson et al. - 2022 - Machine Learning for the Study of Plankton and Mar.pdf:application/pdf}, -} - -@article{macneil_plankton_2021, - title = {Plankton classification with high-throughput submersible holographic microscopy and transfer learning}, - volume = {21}, - issn = {2730-7182}, - url = {https://doi.org/10.1186/s12862-021-01839-0}, - doi = {10.1186/s12862-021-01839-0}, - abstract = {Plankton are foundational to marine food webs and an important feature for characterizing ocean health. Recent developments in quantitative imaging devices provide in-flow high-throughput sampling from bulk volumes—opening new ecological challenges exploring microbial eukaryotic variation and diversity, alongside technical hurdles to automate classification from large datasets. However, a limited number of deployable imaging instruments have been coupled with the most prominent classification algorithms—effectively limiting the extraction of curated observations from field deployments. Holography offers relatively simple coherent microscopy designs with non-intrusive 3-D image information, and rapid frame rates that support data-driven plankton imaging tasks. Classification benchmarks across different domains have been set with transfer learning approaches, focused on repurposing pre-trained, state-of-the-art deep learning models as classifiers to learn new image features without protracted model training times. Combining the data production of holography, digital image processing, and computer vision could improve in-situ monitoring of plankton communities and contribute to sampling the diversity of microbial eukaryotes.}, - number = {1}, - urldate = {2023-07-14}, - journal = {BMC Ecology and Evolution}, - author = {MacNeil, Liam and Missan, Sergey and Luo, Junliang and Trappenberg, Thomas and LaRoche, Julie}, - month = jun, - year = {2021}, - keywords = {Classification workflow, Convolutional neural networks, Deep learning, Deployable microscope, High-throughput imaging, Holographic microscopy, Plankton}, - pages = {123}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/983GMGI8/MacNeil et al. - 2021 - Plankton classification with high-throughput subme.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/SAXVJK45/s12862-021-01839-0.html:text/html}, -} - -@article{scotti_ecosystem-based_2022, - title = {Ecosystem-based fisheries management increases catch and carbon sequestration through recovery of exploited stocks: {The} western {Baltic} {Sea} case study}, - volume = {9}, - issn = {2296-7745}, - shorttitle = {Ecosystem-based fisheries management increases catch and carbon sequestration through recovery of exploited stocks}, - url = {https://www.frontiersin.org/articles/10.3389/fmars.2022.879998}, - abstract = {Legal requirement in Europe asks for Ecosystem-Based Fisheries Management (EBFM) in European seas, including consideration of trophic interactions and minimization of negative impacts of fishing on food webs and ecosystem functioning. This study presents the first mass-balanced ecosystem model focused on the western Baltic Sea (WBS). Results show that heavy fishing pressure exerted on the WBS has forced top predators such as harbour porpoise and cod to cover their dietary needs by shifting from forage fish to other prey or find food outside of the model area. The model was then developed to explore the dynamics of four future fishery scenarios: (1) business as usual (BAU), (2) maximum sustainable fishing (F = FMSY), (3) half of FMSY, and (4) EBFM with F = 0.5 FMSY for forage fish and F = 0.8 FMSY for other fish. Simulations show that BAU would perpetuate low catches from depleted stocks with a high risk of extinction for harbour porpoise. In contrast, the EBFM scenario would allow the recovery of harbour porpoise, forage fish and cod with increases in catch of herring and cod. EBFM promotes ecosystem resilience to eutrophication and ocean warming, and through the rebuilding of commercial stocks increases by more than three times carbon sequestration compared to BAU. The model provides an interrelated assessment of trophic guilds in the WBS, as required by European law to assess whether European seas are in good environmental status.}, - urldate = {2023-07-14}, - journal = {Frontiers in Marine Science}, - author = {Scotti, Marco and Opitz, Silvia and MacNeil, Liam and Kreutle, Axel and Pusch, Christian and Froese, Rainer}, - year = {2022}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/DFDLU7N4/Scotti et al. - 2022 - Ecosystem-based fisheries management increases cat.pdf:application/pdf}, -} - -@article{tsakalakis_diel_2022, - title = {Diel light cycles affect phytoplankton competition in the global ocean}, - volume = {31}, - copyright = {© 2022 The Authors. Global Ecology and Biogeography published by John Wiley \& Sons Ltd.}, - issn = {1466-8238}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/geb.13562}, - doi = {10.1111/geb.13562}, - abstract = {Aim Light, essential for photosynthesis, is present in two periodic cycles in nature: seasonal and diel. Although seasonality of light is typically resolved in ocean biogeochemical–ecosystem models because of its significance for seasonal succession and biogeography of phytoplankton, the diel light cycle is generally not resolved. The goal of this study is to demonstrate the impact of diel light cycles on phytoplankton competition and biogeography in the global ocean. Location Global ocean. Major taxa studied Phytoplankton. Methods We use a three-dimensional global ocean model and compare simulations of high temporal resolution with and without diel light cycles. The model simulates 15 phytoplankton types with different cell sizes, encompassing two broad ecological strategies: small cells with high nutrient affinity (gleaners) and larger cells with high maximal growth rate (opportunists). Both are grazed by zooplankton and limited by nitrogen, phosphorus and iron. Results Simulations show that diel cycles of light induce diel cycles in limiting nutrients in the global ocean. Diel nutrient cycles are associated with higher concentrations of limiting nutrients, by 100\% at low latitudes (−40° to 40°), a process that increases the relative abundance of opportunists over gleaners. Size classes with the highest maximal growth rates from both gleaner and opportunist groups are favoured by diel light cycles. This mechanism weakens as latitude increases, because the effects of the seasonal cycle dominate over those of the diel cycle. Main conclusions Understanding the mechanisms that govern phytoplankton biogeography is crucial for predicting ocean ecosystem functioning and biogeochemical cycles. We show that the diel light cycle has a significant impact on phytoplankton competition and biogeography, indicating the need for understanding the role of diel processes in shaping macroecological patterns in the global ocean.}, - language = {en}, - number = {9}, - urldate = {2023-08-04}, - journal = {Global Ecology and Biogeography}, - author = {Tsakalakis, Ioannis and Follows, Michael J. and Dutkiewicz, Stephanie and Follett, Christopher L. and Vallino, Joseph J.}, - year = {2022}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/geb.13562}, - keywords = {phytoplankton, resource competition, modelling, biogeography, diel light cycle, global ocean, nutrient cycles, diel}, - pages = {1838--1849}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/4IRAUGAH/Tsakalakis et al. - 2022 - Diel light cycles affect phytoplankton competition.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/QKKTIJ3P/geb.html:text/html}, -} - -@article{tsakalakis_diel_2018, - title = {Diel light cycle as a key factor for modelling phytoplankton biogeography and diversity}, - volume = {384}, - issn = {0304-3800}, - url = {https://www.sciencedirect.com/science/article/pii/S0304380018302278}, - doi = {10.1016/j.ecolmodel.2018.06.022}, - abstract = {Understanding the mechanisms driving species biogeography and biodiversity remains a major challenge in phytoplankton ecology. Using a model of two phytoplankton species with a gleaner-opportunist trade-off and competing for light and a limiting nutrient, we show that the diel light cycle may be an essential factor to explain large-scale ecological patterns. When only the seasonal light cycle is considered (control scenario) the model predicts that, independently of the nutrient supply, gleaners should dominate across all latitudes and opportunists can obtain a temporal niche only at high latitudes. However, the diel light cycle makes the competition outcome also a function of nutrient supply by affecting the amplitude of diel nutrient oscillations, with gleaners dominating when nutrient supply is low, opportunists when nutrient supply is high, and both species coexisting at intermediate levels of nutrient supply. The combined effects of seasonal and diel light cycles (diel scenario) shape a latitudinal diversity gradient with decreasing diversity towards higher latitudes and a unimodal dependence of diversity on nutrient supply and, therefore, on ecosystem productivity. The proposed mechanism can help interpret the biogeography of major phytoplankton functional groups in the global ocean and link them with large-scale biodiversity patterns.}, - language = {en}, - urldate = {2023-08-04}, - journal = {Ecological Modelling}, - author = {Tsakalakis, Ioannis and Pahlow, Markus and Oschlies, Andreas and Blasius, Bernd and Ryabov, Alexey B.}, - month = sep, - year = {2018}, - keywords = {Biodiversity, Biogeography, diel, Diel light cycle, Nutrient oscillations, Phytoplankton, Resource competition}, - pages = {241--248}, - file = {ScienceDirect Snapshot:/Users/birdy/Zotero/storage/IRUYQ5WD/S0304380018302278.html:text/html;Tsakalakis et al. - 2018 - Diel light cycle as a key factor for modelling phy.pdf:/Users/birdy/Zotero/storage/EW5BGQ4D/Tsakalakis et al. - 2018 - Diel light cycle as a key factor for modelling phy.pdf:application/pdf}, -} - -@article{grilli_macroecological_2020, - title = {Macroecological laws describe variation and diversity in microbial communities}, - volume = {11}, - copyright = {2020 The Author(s)}, - issn = {2041-1723}, - url = {https://www.nature.com/articles/s41467-020-18529-y}, - doi = {10.1038/s41467-020-18529-y}, - abstract = {How the coexistence of many species is maintained is a fundamental and unresolved question in ecology. Coexistence is a puzzle because we lack a mechanistic understanding of the variation in species presence and abundance. Whether variation in ecological communities is driven by deterministic or random processes is one of the most controversial issues in ecology. Here, I study the variation of species presence and abundance in microbial communities from a macroecological standpoint. I identify three macroecological laws that quantitatively characterize the fluctuation of species abundance across communities and over time. Using these three laws, one can predict species’ presence and absence, diversity, and commonly studied macroecological patterns. I show that a mathematical model based on environmental stochasticity, the stochastic logistic model, quantitatively predicts the three macroecological laws, as well as non-stationary properties of community dynamics.}, - language = {en}, - number = {1}, - urldate = {2023-08-04}, - journal = {Nature Communications}, - author = {Grilli, Jacopo}, - month = sep, - year = {2020}, - note = {Number: 1 -Publisher: Nature Publishing Group}, - keywords = {Microbial ecology, Biodiversity, Community ecology, Ecological modelling, Macroecology, microbes}, - pages = {4743}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/XTDE7UWR/Grilli - 2020 - Macroecological laws describe variation and divers.pdf:application/pdf}, -} - -@article{williamson_beneficial_2001, - title = {Beneficial and {Detrimental} {Effects} of {Uv} on {Aquatic} {Organisms}: {Implications} of {Spectral} {Variation}}, - volume = {11}, - copyright = {© 2001 by the Ecological Society of America}, - issn = {1939-5582}, - shorttitle = {Beneficial and {Detrimental} {Effects} of {Uv} on {Aquatic} {Organisms}}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1890/1051-0761%282001%29011%5B1843%3ABADEOU%5D2.0.CO%3B2}, - doi = {10.1890/1051-0761(2001)011[1843:BADEOU]2.0.CO;2}, - abstract = {Solar ultraviolet radiation (UVR) may have beneficial as well as detrimental effects on living systems. For example, UV-B radiation (280–320 nm) is generally damaging, while UV-A radiation (320–400 nm) may cause damage or stimulate beneficial photorepair of UV-B damage. The nature of both direct and indirect effects of UVR in nature depends on both the photon flux density and the spectral composition of the radiation incident on aquatic organisms across environmental UVR gradients in space (depth, transparency, elevation) and time (diel, seasonal, interannual). Here we use the common and widespread freshwater cladoceran Daphnia pulicaria as a model organism to demonstrate the potential importance of these wavelength-specific effects of UVR to the ecology of aquatic organisms. UVR-exposure experiments are used to manipulate both natural solar and artificial UVR sources to examine the beneficial as well as detrimental effects of different wavelengths of UVR. Changes in the spectral composition of solar radiation are also examined along several natural environmental gradients including diel gradients, depth gradients, and dissolved organic carbon (DOC) gradients. The implications of variation in the spectral composition of UVR for aquatic organisms are discussed. The first biological weighting function (BWF) for a freshwater cladoceran is presented here. It demonstrates that the shortest UV-B wavelengths in sunlight are potentially the most damaging per photon. However, due to the greater photon flux density of longer wavelength UVR in sunlight, the net potential damage to Daphnia in nature is greatest for the longer wavelength UV-B and shorter wavelength UV-A radiation in the 305–322 nm range. Overall the contribution of UV-B to the total mortality response of Daphnia exposed to full-spectrum solar radiation for 7 h on a sunny summer day is 64\% while UV-A contributes 36\%. The BWF for Daphnia is used with the transmission spectrum for Mylar D to demonstrate that Mylar D cuts out only about half of the damaging UVR in sunlight. Following exposure to damaging UV-B, Daphnia exhibits a dramatic increase in survival in the presence of longer wavelength UV-A and visible radiation due to the stimulation of photoenzymatic repair. We present data that demonstrate the importance of both atmospheric ozone and DOC in creating strong environmental gradients in the intensity (irradiance) and spectral composition of solar UVR in nature. The light-absorbing component of DOC, chromophoric dissolved organic matter (CDOM), is particularly important in creating depth refugia from damaging UV-B in freshwater ecosystems. CDOM may also cause intense variations in the ratio of potentially beneficial UV-A to detrimental UV-B radiation to which aquatic organisms are exposed. In addition to changes in atmospheric ozone, future changes in CDOM related to climate change or other environmental disturbances may substantially alter the underwater exposure of a variety of aquatic organisms to different wavelengths of solar UVR.}, - language = {en}, - number = {6}, - urldate = {2023-08-04}, - journal = {Ecological Applications}, - author = {Williamson, Craig E. and Neale, Patrick J. and Grad, Gabriella and De Lange, Hendrika J. and Hargreaves, Bruce R.}, - year = {2001}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1890/1051-0761\%282001\%29011\%5B1843\%3ABADEOU\%5D2.0.CO\%3B2}, - keywords = {diel, biological weighting function, Daphnia pulicaria, dissolved organic carbon, environmental gradients, lake optics, ozone depletion, relative damage to aquatic organisms, spectral composition of solar radiation, tolerance of UV radiation, ultraviolet radiation, UV-A and UV-B, zooplankton}, - pages = {1843--1857}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/C65AT69Z/Williamson et al. - 2001 - Beneficial and Detrimental Effects of Uv on Aquati.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/AQL7Y8NU/1051-0761(2001)011[1843BADEOU]2.0.html:text/html}, -} - -@incollection{fasham_phytoplankton_2003, - address = {Berlin, Heidelberg}, - title = {Phytoplankton and {Their} {Role} in {Primary}, {New}, and {Export} {Production}}, - isbn = {978-3-642-62691-3 978-3-642-55844-3}, - url = {http://link.springer.com/10.1007/978-3-642-55844-3_5}, - language = {en}, - urldate = {2023-08-15}, - booktitle = {Ocean {Biogeochemistry}}, - publisher = {Springer Berlin Heidelberg}, - author = {Falkowski, Paul G. and Laws, Edward A. and Barber, Richard T. and Murray, James W.}, - editor = {Fasham, Michael J. R.}, - year = {2003}, - doi = {10.1007/978-3-642-55844-3_5}, - keywords = {DAR1}, - pages = {99--121}, - file = {Falkowski et al. - 2003 - Phytoplankton and Their Role in Primary, New, and .pdf:/Users/birdy/Zotero/storage/W6TWZWCA/Falkowski et al. - 2003 - Phytoplankton and Their Role in Primary, New, and .pdf:application/pdf}, -} - -@article{tilman_resource_1977-1, - title = {Resource {Competition} between {Plankton} {Algae}: {An} {Experimental} and {Theoretical} {Approach}}, - volume = {58}, - issn = {0012-9658}, - shorttitle = {Resource {Competition} between {Plankton} {Algae}}, - url = {https://www.jstor.org/stable/1935608}, - doi = {10.2307/1935608}, - abstract = {The results of 76 long-term competition experiments between two species of freshwater algae (Asterionella formosa and Cyclotella meneghiniana) grown along a resource gradient agree with the predictions of two different models of resource competition. Both models are based on the functional resource-utilization response of each species to limiting resources. The Monod and the Variable Internal Stores model of competition made similar predictions. Asterionella was observed to be competitively dominant when both species were phosphate limited; Cyclotella was dominant when both species were silicate limited; and both species stably coexisted when each species was growth-rate limited by a different resource. Almost 75\% of the variance in the relative abundances of these two species along a natural silicate-phosphate gradient in Lake Michigan is explained by the Monod model.}, - number = {2}, - urldate = {2023-08-24}, - journal = {Ecology}, - author = {Tilman, David}, - year = {1977}, - note = {Publisher: Ecological Society of America}, - pages = {338--348}, -} - -@article{dallolmo_inferring_2011, - title = {Inferring phytoplankton carbon and eco-physiological rates from diel cycles of spectral particulate beam-attenuation coefficient}, - volume = {8}, - issn = {1726-4170}, - url = {https://bg.copernicus.org/articles/8/3423/2011/bg-8-3423-2011.html}, - doi = {10.5194/bg-8-3423-2011}, - abstract = {The diurnal fluctuations in solar irradiance impose a fundamental frequency on ocean biogeochemistry. Observations of the ocean carbon cycle at these frequencies are rare, but could be considerably expanded by measuring and interpreting the inherent optical properties. A method is presented to analyze diel cycles in particulate beam-attenuation coefficient (cp) measured at multiple wavelengths. The method is based on fitting observations with a size-structured population model coupled to an optical model to infer the particle size distribution and physiologically relevant parameters of the cells responsible for the measured diel cycle in cp. Results show that the information related to size and contained in the spectral data can be exploited to independently estimate growth and loss rates during the day and night. In addition, the model can characterize the population of particles affecting the diel variability in cp. Application of this method to spectral cp measured at a station in the oligotrophic Mediterranean Sea suggests that most of the observed variations in cp can be ascribed to a synchronized population of cells with an equivalent spherical diameter around 4.6±1.5 μm. The inferred carbon biomass of these cells was about 5.2–6.0 mg m−3 and accounted for approximately 10\% of the total particulate organic carbon. If successfully validated, this method may improve our in situ estimates of primary productivity.}, - language = {English}, - number = {11}, - urldate = {2023-08-25}, - journal = {Biogeosciences}, - author = {Dall'Olmo, G. and Boss, E. and Behrenfeld, M. J. and Westberry, T. K. and Courties, C. and Prieur, L. and Pujo-Pay, M. and Hardman-Mountford, N. and Moutin, T.}, - month = nov, - year = {2011}, - note = {Publisher: Copernicus GmbH}, - pages = {3423--3439}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/C6C247B3/Dall'Olmo et al. - 2011 - Inferring phytoplankton carbon and eco-physiologic.pdf:application/pdf}, -} - -@misc{noauthor_ai_nodate, - title = {The {AI} gig economy is coming for you}, - file = {[Hao] The AI gig economy is coming for you _ MIT Technology Review.pdf:/Users/birdy/Downloads/[Hao] The AI gig economy is coming for you _ MIT Technology Review.pdf:application/pdf}, -} - -@article{noauthor_architecture_nodate, - title = {Architecture of a {Database} {System}}, - file = {Architecture of a Database System.pdf:/Users/birdy/Zotero/storage/2DKEVTBM/Architecture of a Database System.pdf:application/pdf}, -} - -@article{isola_lecture_nodate, - title = {Lecture 4: {Architectures} for {Grids}}, - language = {en}, - author = {Isola, Phillip}, - file = {4_grid_archs.pdf:/Users/birdy/Documents/Meng Classes/Deep Learning/4_grid_archs.pdf:application/pdf}, -} - -@article{isola_lecture_nodate-1, - title = {Lecture 5: {Graph} {Neural} {Networks}}, - language = {en}, - author = {Isola, Phillip}, - file = {5_graph_nn.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/5_graph_nn.pdf:application/pdf}, -} - -@misc{noauthor_fast_nodate, - title = {Fast {Forward}: {MIT}'s {Climate} {Action} {Plan} for the {Decade}}, - shorttitle = {Fast {Forward}}, - url = {https://climate.mit.edu/climateaction/fastforward}, - language = {en}, - urldate = {2023-09-22}, - journal = {MIT Climate Portal}, - file = {Snapshot:/Users/birdy/Zotero/storage/E37ZPUKM/fastforward.html:text/html}, -} - -@article{bernstein_scaling_nodate, - title = {Scaling rules for optimisation}, - language = {en}, - author = {Bernstein, Jeremy}, - file = {6_scaling_JB.pdf:/Users/birdy/Documents/Meng Classes/Deep Learning/6_scaling_JB.pdf:application/pdf}, -} - -@article{isola_lecture_nodate-2, - title = {Lecture 3: {Approximation} {Theory}}, - language = {en}, - author = {Isola, Phillip}, - file = {3_approximation_PI.pdf:/Users/birdy/Documents/Meng Classes/Deep Learning/3_approximation_PI.pdf:application/pdf}, -} - -@misc{noauthor_6s898_nodate, - title = {6.{S898} {Lecture} on 9/12/2023 ({Tue})}, - url = {https://mit.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=885feb13-1128-46a6-94fa-b077010e681c}, - abstract = {Special subject in Computer Science}, - language = {en}, - urldate = {2023-09-30}, - file = {Snapshot:/Users/birdy/Zotero/storage/8KZHLHN6/Viewer.html:text/html}, -} - -@inproceedings{andreas_neural_2016, - address = {Las Vegas, NV, USA}, - title = {Neural {Module} {Networks}}, - isbn = {978-1-4673-8851-1}, - url = {http://ieeexplore.ieee.org/document/7780381/}, - doi = {10.1109/CVPR.2016.12}, - abstract = {Visual question answering is fundamentally compositional in nature—a question like where is the dog? shares substructure with questions like what color is the dog? and where is the cat? This paper seeks to simultaneously exploit the representational capacity of deep networks and the compositional linguistic structure of questions. We describe a procedure for constructing and learning neural module networks, which compose collections of jointly-trained neural “modules” into deep networks for question answering. Our approach decomposes questions into their linguistic substructures, and uses these structures to dynamically instantiate modular networks (with reusable components for recognizing dogs, classifying colors, etc.). The resulting compound networks are jointly trained. We evaluate our approach on two challenging datasets for visual question answering, achieving state-of-the-art results on both the VQA natural image dataset and a new dataset of complex questions about abstract shapes.}, - language = {en}, - urldate = {2023-09-30}, - booktitle = {2016 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})}, - publisher = {IEEE}, - author = {Andreas, Jacob and Rohrbach, Marcus and Darrell, Trevor and Klein, Dan}, - month = jun, - year = {2016}, - pages = {39--48}, - file = {Andreas et al. - 2016 - Neural Module Networks.pdf:/Users/birdy/Zotero/storage/YK4HIHHM/Andreas et al. - 2016 - Neural Module Networks.pdf:application/pdf}, -} - -@article{beery_lecture_nodate, - title = {Lecture 2: {How} to train a neural net}, - language = {en}, - author = {Beery, Sara}, - file = {2_backprop_SB.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/2_backprop_SB.pdf:application/pdf}, -} - -@misc{ruswurm_geographic_2023, - title = {Geographic {Location} {Encoding} with {Spherical} {Harmonics} and {Sinusoidal} {Representation} {Networks}}, - url = {http://arxiv.org/abs/2310.06743}, - abstract = {Learning feature representations of geographical space is vital for any machine learning model that integrates geolocated data, spanning application domains such as remote sensing, ecology, or epidemiology. Recent work mostly embeds coordinates using sine and cosine projections based on Double Fourier Sphere (DFS) features -- these embeddings assume a rectangular data domain even on global data, which can lead to artifacts, especially at the poles. At the same time, relatively little attention has been paid to the exact design of the neural network architectures these functional embeddings are combined with. This work proposes a novel location encoder for globally distributed geographic data that combines spherical harmonic basis functions, natively defined on spherical surfaces, with sinusoidal representation networks (SirenNets) that can be interpreted as learned Double Fourier Sphere embedding. We systematically evaluate the cross-product of positional embeddings and neural network architectures across various classification and regression benchmarks and synthetic evaluation datasets. In contrast to previous approaches that require the combination of both positional encoding and neural networks to learn meaningful representations, we show that both spherical harmonics and sinusoidal representation networks are competitive on their own but set state-of-the-art performances across tasks when combined. We provide source code at www.github.com/marccoru/locationencoder}, - urldate = {2023-10-12}, - publisher = {arXiv}, - author = {Rußwurm, Marc and Klemmer, Konstantin and Rolf, Esther and Zbinden, Robin and Tuia, Devis}, - month = oct, - year = {2023}, - note = {arXiv:2310.06743 [cs]}, - keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/N6J75A2A/2310.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/XSF9TGN7/Rußwurm et al. - 2023 - Geographic Location Encoding with Spherical Harmon.pdf:application/pdf}, -} - -@misc{eerola_survey_2023, - title = {Survey of {Automatic} {Plankton} {Image} {Recognition}: {Challenges}, {Existing} {Solutions} and {Future} {Perspectives}}, - shorttitle = {Survey of {Automatic} {Plankton} {Image} {Recognition}}, - url = {http://arxiv.org/abs/2305.11739}, - abstract = {Planktonic organisms are key components of aquatic ecosystems and respond quickly to changes in the environment, therefore their monitoring is vital to understand the changes in the environment. Yet, monitoring plankton at appropriate scales still remains a challenge, limiting our understanding of functioning of aquatic systems and their response to changes. Modern plankton imaging instruments can be utilized to sample at high frequencies, enabling novel possibilities to study plankton populations. However, manual analysis of the data is costly, time consuming and expert based, making such approach unsuitable for large-scale application and urging for automatic solutions. The key problem related to the utilization of plankton datasets through image analysis is plankton recognition. Despite the large amount of research done, automatic methods have not been widely adopted for operational use. In this paper, a comprehensive survey on existing solutions for automatic plankton recognition is presented. First, we identify the most notable challenges that that make the development of plankton recognition systems difficult. Then, we provide a detailed description of solutions for these challenges proposed in plankton recognition literature. Finally, we propose a workflow to identify the specific challenges in new datasets and the recommended approaches to address them. For many of the challenges, applicable solutions exist. However, important challenges remain unsolved: 1) the domain shift between the datasets hindering the development of a general plankton recognition system that would work across different imaging instruments, 2) the difficulty to identify and process the images of previously unseen classes, and 3) the uncertainty in expert annotations that affects the training of the machine learning models for recognition. These challenges should be addressed in the future research.}, - urldate = {2023-10-16}, - publisher = {arXiv}, - author = {Eerola, Tuomas and Batrakhanov, Daniel and Barazandeh, Nastaran Vatankhah and Kraft, Kaisa and Haraguchi, Lumi and Lensu, Lasse and Suikkanen, Sanna and Seppälä, Jukka and Tamminen, Timo and Kälviäinen, Heikki}, - month = may, - year = {2023}, - note = {arXiv:2305.11739 [cs]}, - keywords = {Computer Science - Computer Vision and Pattern Recognition}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/FG4ZBSVN/2305.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/NZX46TSK/Eerola et al. - 2023 - Survey of Automatic Plankton Image Recognition Ch.pdf:application/pdf}, -} - -@article{gonzalez_review_2018, - title = {A {Review} on {Quantification} {Learning}}, - volume = {50}, - issn = {0360-0300, 1557-7341}, - url = {https://dl.acm.org/doi/10.1145/3117807}, - doi = {10.1145/3117807}, - abstract = {The task of quantification consists in providing an aggregate estimation (e.g. the class distribution in a classification problem) for unseen test sets, applying a model that is trained using a training set with a different data distribution. Several real-world applications demand this kind of methods that do not require predictions for individual examples and just focus on obtaining accurate estimates at an aggregate level. During the past few years, several quantification methods have been proposed from different perspectives and with different goals. This paper presents a unified review of the main approaches with the aim of serving as an introductory tutorial for newcomers in the field.}, - language = {en}, - number = {5}, - urldate = {2023-10-16}, - journal = {ACM Computing Surveys}, - author = {González, Pablo and Castaño, Alberto and Chawla, Nitesh V. and Coz, Juan José Del}, - month = sep, - year = {2018}, - pages = {1--40}, - file = {González et al. - 2018 - A Review on Quantification Learning.pdf:/Users/birdy/Zotero/storage/76A4J8ML/quant-review-grAcc.pdf:application/pdf}, -} - -@article{sosa-trejo_vision-based_2023, - title = {Vision-based techniques for automatic marine plankton classification}, - volume = {56}, - issn = {1573-7462}, - url = {https://doi.org/10.1007/s10462-023-10456-w}, - doi = {10.1007/s10462-023-10456-w}, - abstract = {Plankton are an important component of life on Earth. Since the 19th century, scientists have attempted to quantify species distributions using many techniques, such as direct counting, sizing, and classification with microscopes. Since then, extraordinary work has been performed regarding the development of plankton imaging systems, producing a massive backlog of images that await classification. Automatic image processing and classification approaches are opening new avenues for avoiding time-consuming manual procedures. While some algorithms have been adapted from many other applications for use with plankton, other exciting techniques have been developed exclusively for this issue. Achieving higher accuracy than that of human taxonomists is not yet possible, but an expeditious analysis is essential for discovering the world beyond plankton. Recent studies have shown the imminent development of real-time, in situ plankton image classification systems, which have only been slowed down by the complex implementations of algorithms on low-power processing hardware. This article compiles the techniques that have been proposed for classifying marine plankton, focusing on automatic methods that utilize image processing, from the beginnings of this field to the present day.}, - language = {en}, - number = {11}, - urldate = {2023-10-16}, - journal = {Artificial Intelligence Review}, - author = {Sosa-Trejo, David and Bandera, Antonio and González, Martín and Hernández-León, Santiago}, - month = nov, - year = {2023}, - keywords = {Image processing, Marine plankton, Pattern recognition, Plankton classification}, - pages = {12853--12884}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/U7RIA6IX/Sosa-Trejo et al. - 2023 - Vision-based techniques for automatic marine plank.pdf:application/pdf}, -} - -@article{kerr_collaborative_2020-1, - title = {Collaborative {Deep} {Learning} {Models} to {Handle} {Class} {Imbalance} in {FlowCam} {Plankton} {Imagery}}, - volume = {8}, - issn = {2169-3536}, - url = {https://ieeexplore.ieee.org/abstract/document/9187202/authors#authors}, - doi = {10.1109/ACCESS.2020.3022242}, - abstract = {Using automated imaging technologies, it is now possible to generate previously unprecedented volumes of plankton image data which can be used to study the composition of plankton assemblages. However, the current need to manually classify individual images introduces a bottleneck into processing chains. Although Machine Learning techniques have been used to try and address this issue, past efforts have suffered from accuracy limitations, especially in minority classes. Here we use state-of-the-art methods in Deep Learning to investigate suitable architectures for training an automated plankton classification system which achieves high efficacy for both abundant and rare taxa. We collected live plankton from Station L4 in the Western English Channel and imaged 11,371 particles covering 104 taxonomic groups using the automated plankton imaging system FlowCam. The image set contained a severe class imbalance, with some taxa represented by {\textgreater} 600 images while other, rarer taxa were represented by just 14. We demonstrate that by allowing multiple Deep Learning models to collaborate in a single classification system, classification accuracy improves for minority classes when compared with the best individual model. The top collaborative model achieved a 6 \% improvement in F1 accuracy over the best individual model, while overall accuracy improved by 3.2 \%. This resulted in a 97.4 \% overall accuracy score and a 96.2 \% F1 macro score on a separate holdout test set containing 104 taxonomic groups. Based on a survey of similar studies in the literature, we believe collaborative deep learning models can significantly improve the accuracy of existing automated plankton classification systems.}, - urldate = {2023-10-16}, - journal = {IEEE Access}, - author = {Kerr, Thomas and Clark, James R. and Fileman, Elaine S. and Widdicombe, Claire E. and Pugeault, Nicolas}, - year = {2020}, - note = {Conference Name: IEEE Access}, - pages = {170013--170032}, - file = {IEEE Xplore Abstract Record:/Users/birdy/Zotero/storage/WPTTWRTB/authors.html:text/html;IEEE Xplore Full Text PDF:/Users/birdy/Zotero/storage/VLJWVUVD/Kerr et al. - 2020 - Collaborative Deep Learning Models to Handle Class.pdf:application/pdf}, -} - -@article{conradt_automated_2022, - title = {Automated {Plankton} {Classification} {With} a {Dynamic} {Optimization} and {Adaptation} {Cycle}}, - volume = {9}, - issn = {2296-7745}, - url = {https://www.frontiersin.org/articles/10.3389/fmars.2022.868420}, - abstract = {With recent advances in Machine Learning techniques based on Deep Neural Networks (DNNs), automated plankton image classification is becoming increasingly popular within the marine ecological sciences. Yet, while the most advanced methods can achieve human-level performance on the classification of everyday images, plankton image data possess properties that frequently require a final manual validation step. On the one hand, this is due to morphological properties manifesting in high intra-class and low inter-class variability, and, on the other hand is due to spatial-temporal changes in the composition and structure of the plankton community. Composition changes enforce a frequent updating of the classifier model via training with new user-generated training datasets. Here, we present a Dynamic Optimization Cycle (DOC), a processing pipeline that systematizes and streamlines the model adaptation process via an automatic updating of the training dataset based on manual-validation results. We find that frequent adaptation using the DOC pipeline yields strong maintenance of performance with respect to precision, recall and prediction of community composition, compared to more limited adaptation schemes. The DOC is therefore particularly useful when analyzing plankton at novel locations or time periods, where community differences are likely to occur. In order to enable an easy implementation of the DOC pipeline, we provide an end-to-end application with graphical user interface, as well as an initial dataset of training images. The DOC pipeline thus allows for high-throughput plankton classification and quick and systematized model adaptation, thus providing the means for highly-accelerated plankton analysis.}, - urldate = {2023-10-16}, - journal = {Frontiers in Marine Science}, - author = {Conradt, Jan and Börner, Gregor and López-Urrutia, Ángel and Möllmann, Christian and Moyano, Marta}, - year = {2022}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/YBEYVF4R/Conradt et al. - 2022 - Automated Plankton Classification With a Dynamic O.pdf:application/pdf}, -} - -@misc{ic_du_toit_automated_nodate, - title = {Automated machine learning plankton taxonomy pipeline}, - url = {http://vital.seals.ac.za:8080/vital/access/manager/PdfViewer/vital:59003/SOURCE1?viewPdfInternal=1}, - urldate = {2023-10-16}, - author = {I.C. du Toit}, - file = {document.pdf:/Users/birdy/Downloads/document.pdf:application/pdf;SOURCE1:/Users/birdy/Zotero/storage/8EZC4XHT/SOURCE1.html:text/html}, -} - -@article{gonzalez_validation_2017, - title = {Validation methods for plankton image classification systems}, - volume = {15}, - copyright = {© 2016 Association for the Sciences of Limnology and Oceanography}, - issn = {1541-5856}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lom3.10151}, - doi = {10.1002/lom3.10151}, - abstract = {In recent decades, the automatic study and analysis of plankton communities using imaging techniques has advanced significantly. The effectiveness of these automated systems appears to have improved, reaching acceptable levels of accuracy. However, plankton ecologists often find that classification systems do not work as well as expected when applied to new samples. This paper proposes a methodology to assess the efficacy of learned models which takes into account the fact that the data distribution (the plankton composition of the sample) can vary between the model building phase and the production phase. As opposed to most validation methods that consider the individual organism as the unit of validation, our approach uses a validation-by-sample, which is more appropriate when the objective is to estimate the abundance of different morphological groups. We argue that, in these cases, the base unit to correctly estimate the error is the sample, not the individual. Thus, model assessment processes require groups of samples with sufficient variability in order to provide precise error estimates.}, - language = {en}, - number = {3}, - urldate = {2023-10-16}, - journal = {Limnology and Oceanography: Methods}, - author = {González, Pablo and Álvarez, Eva and Díez, Jorge and López-Urrutia, Ángel and del Coz, Juan José}, - year = {2017}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10151}, - pages = {221--237}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/SW5FNZDQ/González et al. - 2017 - Validation methods for plankton image classificati.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/UB2XMFYB/lom3.html:text/html}, -} - -@article{isola_lecture_nodate-3, - title = {Lecture 11: {Representation} {Learning} {I}}, - language = {en}, - author = {Isola, Phillip}, - file = {11-rep_learning_1.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/11-rep_learning_1.pdf:application/pdf}, -} - -@article{beery_lecture_nodate-1, - title = {Lecture 12: {Similarity}-based {Representation} {Learning}}, - language = {en}, - author = {Beery, Sara}, - file = {12_similarity_based_rep_learning-SB.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/12_similarity_based_rep_learning-SB.pdf:application/pdf}, -} - -@inproceedings{van_horn_benchmarking_2021, - address = {Nashville, TN, USA}, - title = {Benchmarking {Representation} {Learning} for {Natural} {World} {Image} {Collections}}, - isbn = {978-1-66544-509-2}, - url = {https://ieeexplore.ieee.org/document/9577285/}, - doi = {10.1109/CVPR46437.2021.01269}, - abstract = {Recent progress in self-supervised learning has resulted in models that are capable of extracting rich representations from image collections without requiring any explicit label supervision. However, to date the vast majority of these approaches have restricted themselves to training on standard benchmark datasets such as ImageNet. We argue that fine-grained visual categorization problems, such as plant and animal species classification, provide an informative testbed for self-supervised learning. In order to facilitate progress in this area we present two new natural world visual classification datasets, iNat2021 and NeWT. The former consists of 2.7M images from 10k different species uploaded by users of the citizen science application iNaturalist. We designed the latter, NeWT, in collaboration with domain experts with the aim of benchmarking the performance of representation learning algorithms on a suite of challenging natural world binary classification tasks that go beyond standard species classification. These two new datasets allow us to explore questions related to large-scale representation and transfer learning in the context of finegrained categories. We provide a comprehensive analysis of feature extractors trained with and without supervision on ImageNet and iNat2021, shedding light on the strengths and weaknesses of different learned features across a diverse set of tasks. We find that features produced by standard supervised methods still outperform those produced by self-supervised approaches such as SimCLR. However, improved self-supervised learning methods are constantly being released and the iNat2021 and NeWT datasets are a valuable resource for tracking their progress.}, - language = {en}, - urldate = {2023-10-24}, - booktitle = {2021 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})}, - publisher = {IEEE}, - author = {Van Horn, Grant and Cole, Elijah and Beery, Sara and Wilber, Kimberly and Belongie, Serge and MacAodha, Oisin}, - month = jun, - year = {2021}, - pages = {12879--12888}, - file = {Van Horn et al. - 2021 - Benchmarking Representation Learning for Natural W.pdf:/Users/birdy/Zotero/storage/PYLIPA9T/Van Horn et al. - 2021 - Benchmarking Representation Learning for Natural W.pdf:application/pdf}, -} - -@article{orenstein_machine_2022, - title = {Machine learning techniques to characterize functional traits of plankton from image data}, - volume = {67}, - issn = {0024-3590, 1939-5590}, - url = {https://aslopubs.onlinelibrary.wiley.com/doi/10.1002/lno.12101}, - doi = {10.1002/lno.12101}, - abstract = {Plankton imaging systems supported by automated classification and analysis have improved ecologists’ ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.}, - language = {en}, - number = {8}, - urldate = {2023-10-25}, - journal = {Limnology and Oceanography}, - author = {Orenstein, Eric C. and Ayata, Sakina‐Dorothée and Maps, Frédéric and Becker, Érica C. and Benedetti, Fabio and Biard, Tristan and De Garidel‐Thoron, Thibault and Ellen, Jeffrey S. and Ferrario, Filippo and Giering, Sarah L. C. and Guy‐Haim, Tamar and Hoebeke, Laura and Iversen, Morten Hvitfeldt and Kiørboe, Thomas and Lalonde, Jean‐François and Lana, Arancha and Laviale, Martin and Lombard, Fabien and Lorimer, Tom and Martini, Séverine and Meyer, Albin and Möller, Klas Ove and Niehoff, Barbara and Ohman, Mark D. and Pradalier, Cédric and Romagnan, Jean‐Baptiste and Schröder, Simon‐Martin and Sonnet, Virginie and Sosik, Heidi M. and Stemmann, Lars S. and Stock, Michiel and Terbiyik‐Kurt, Tuba and Valcárcel‐Pérez, Nerea and Vilgrain, Laure and Wacquet, Guillaume and Waite, Anya M. and Irisson, Jean‐Olivier}, - month = aug, - year = {2022}, - pages = {1647--1669}, - file = {Orensteinal._2022.pdf:/Users/birdy/Documents/mit-meng-thesis/papers/Orensteinal._2022.pdf:application/pdf}, -} - -@article{schanz_robust_2023, - title = {Robust detection of marine life with label-free image feature learning and probability calibration}, - volume = {4}, - issn = {2632-2153}, - url = {https://iopscience.iop.org/article/10.1088/2632-2153/ace417}, - doi = {10.1088/2632-2153/ace417}, - abstract = {Abstract - - Advances in - in situ - marine life imaging have significantly increased the size and quality of available datasets, but automatic image analysis has not kept pace. Machine learning has shown promise for image processing, but its effectiveness is limited by several open challenges: the requirement for large expert-labeled training datasets, disagreement among experts, under-representation of various species and unreliable or overconfident predictions. To overcome these obstacles for automated underwater imaging, we combine and test recent developments in deep classifier networks and self-supervised feature learning. We use unlabeled images for pretraining deep neural networks to extract task-relevant image features, allowing learning algorithms to cope with scarcity in expert labels, and carefully evaluate performance in subsequent label-based tasks. Performance on rare classes is improved by applying data rebalancing together with a Bayesian correction to avoid biasing inferred - in situ - class frequencies. A divergence-based loss allows training on multiple, conflicting labels for the same image, leading to better estimates of uncertainty which we quantify with a novel accuracy measure. Together, these techniques can reduce the required label counts ∼100-fold while maintaining the accuracy of standard supervised training, shorten training time, cope with expert disagreement and reduce overconfidence.}, - language = {en}, - number = {3}, - urldate = {2023-10-25}, - journal = {Machine Learning: Science and Technology}, - author = {Schanz, Tobias and Ove Möller, Klas and Rühl, Saskia and Greenberg, David S}, - month = sep, - year = {2023}, - pages = {035007}, - file = {Schanz et al. - 2023 - Robust detection of marine life with label-free im.pdf:/Users/birdy/Zotero/storage/KBQL5Q2G/Schanz et al. - 2023 - Robust detection of marine life with label-free im.pdf:application/pdf}, -} - -@article{catlett_temperature_2023, - title = {Temperature dependence of parasitoid infection and abundance of a diatom revealed by automated imaging and classification}, - volume = {120}, - issn = {0027-8424, 1091-6490}, - url = {https://pnas.org/doi/10.1073/pnas.2303356120}, - doi = {10.1073/pnas.2303356120}, - abstract = {Diatoms are a group of phytoplankton that contribute disproportionately to global primary production. Traditional paradigms that suggest diatoms are consumed primarily by larger zooplankton are challenged by sporadic parasitic “epidemics” within diatom populations. However, our understanding of diatom parasitism is limited by difficulties in quantifying these interactions. Here, we observe the dynamics of - Cryothecomonas aestivalis - (a protist) infection of an important diatom on the Northeast U.S. Shelf (NES), - Guinardia delicatula - , with a combination of automated imaging-in-flow cytometry and a convolutional neural network image classifier. Application of the classifier to {\textgreater}1 billion images from a nearshore time series and {\textgreater}20 survey cruises across the broader NES reveals the spatiotemporal gradients and temperature dependence of - G. delicatula - abundance and infection dynamics. Suppression of parasitoid infection at temperatures {\textless}4 °C drives annual cycles in both - G. delicatula - infection and abundance, with an annual maximum in infection observed in the fall-winter preceding an annual maximum in host abundance in the winter-spring. This annual cycle likely varies spatially across the NES in response to variable annual cycles in water temperature. We show that infection remains suppressed for {\textasciitilde}2 mo following cold periods, possibly due to temperature-induced local extinctions of the - C. aestivalis - strain(s) that infect - G. delicatula - . These findings have implications for predicting impacts of a warming NES surface ocean on - G. delicatula - abundance and infection dynamics and demonstrate the potential of automated plankton imaging and classification to quantify phytoplankton parasitism in nature across unprecedented spatiotemporal scales.}, - language = {en}, - number = {28}, - urldate = {2023-10-25}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Catlett, Dylan and Peacock, Emily E. and Crockford, E. Taylor and Futrelle, Joe and Batchelder, Sidney and Stevens, Bethany L. F. and Gast, Rebecca J. and Zhang, Weifeng G. and Sosik, Heidi M.}, - month = jul, - year = {2023}, - pages = {e2303356120}, - file = {catlett-et-al-2023-temperature-dependence-of-parasitoid-infection-and-abundance-of-a-diatom-revealed-by-automated.pdf:/Users/birdy/Documents/mit-meng-thesis/papers/catlett-et-al-2023-temperature-dependence-of-parasitoid-infection-and-abundance-of-a-diatom-revealed-by-automated.pdf:application/pdf}, -} - -@article{culverhouse_empirical_2014, - title = {An empirical assessment of the consistency of taxonomic identifications}, - volume = {10}, - issn = {1745-1000}, - url = {https://doi.org/10.1080/17451000.2013.810762}, - doi = {10.1080/17451000.2013.810762}, - abstract = {Plankton counting and analysis is essential in ecological study, yet scant literature exists as to the reliability of those counts and the consistency of the experts who make the counts. To assess how variable expert taxonomic identifications are, a set of six archived mesozooplankton samples from a series of Longhurst Hardy Plankton Recorder net hauls were counted by expert zooplankton analysts located at six marine laboratories. Sample identifications were repeated on two separate days with over 700 target specimens counted and identified on each day across the samples. Twenty percent of the analysts returned counts that varied by more than 10\%. Thirty-three percent of analysts exhibited low identification consistencies, returning Intraclass Correlation Coefficient scores of less than 0.80. Statistical analyses of these data suggest that over 83\% of the observed categorical count variance can be attributed to inconsistencies within analysts. We suggest this is the root cause of variation in expert specimen labelling consistency.}, - number = {1}, - urldate = {2023-10-25}, - journal = {Marine Biology Research}, - author = {Culverhouse, Philip F. and Macleod, Norman and Williams, Robert and Benfield, Mark C. and Lopes, Rubens M. and Picheral, Marc}, - month = jan, - year = {2014}, - note = {Publisher: Taylor \& Francis -\_eprint: https://doi.org/10.1080/17451000.2013.810762}, - keywords = {expert performance, Human factors, mesozooplankton identification, root cause of variation}, - pages = {73--84}, -} - -@article{schroder_morphocluster_2020, - title = {{MorphoCluster}: {Efficient} {Annotation} of {Plankton} {Images} by {Clustering}}, - volume = {20}, - copyright = {http://creativecommons.org/licenses/by/3.0/}, - issn = {1424-8220}, - shorttitle = {{MorphoCluster}}, - url = {https://www.mdpi.com/1424-8220/20/11/3060}, - doi = {10.3390/s20113060}, - abstract = {In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue to increase in the coming years. Still, this data requires interpretation. MorphoCluster augments the human ability to discover patterns and perform object classification in large amounts of data by embedding unsupervised clustering in an interactive process. By aggregating similar images into clusters, our novel approach to image annotation increases consistency, multiplies the throughput of an annotator, and allows experts to adapt the granularity of their sorting scheme to the structure in the data. By sorting a set of 1.2 M objects into 280 data-driven classes in 71 h (16 k objects per hour), with 90\% of these classes having a precision of 0.889 or higher. This shows that MorphoCluster is at the same time fast, accurate, and consistent; provides a fine-grained and data-driven classification; and enables novelty detection.}, - language = {en}, - number = {11}, - urldate = {2023-10-26}, - journal = {Sensors}, - author = {Schröder, Simon-Martin and Kiko, Rainer and Koch, Reinhard}, - month = jan, - year = {2020}, - note = {Number: 11 -Publisher: Multidisciplinary Digital Publishing Institute}, - keywords = {deep learning, clustering, machine learning, marine image annotation, marine image recognition, plankton image classification}, - pages = {3060}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/ZHNATHDJ/Schröder et al. - 2020 - MorphoCluster Efficient Annotation of Plankton Im.pdf:application/pdf}, -} - -@article{henrichs_application_2021, - title = {Application of a convolutional neural network to improve automated early warning of harmful algal blooms}, - volume = {28}, - issn = {1614-7499}, - url = {https://doi.org/10.1007/s11356-021-12471-2}, - doi = {10.1007/s11356-021-12471-2}, - abstract = {Continuous monitoring and early warning together represent an important mitigation strategy for harmful algal blooms (HAB). The coast of Texas experiences periodic blooms of three HAB dinoflagellates: Karenia brevis, Dinophysis ovum, and Prorocentrum texanum. A plankton image data set acquired by an Imaging FlowCytobot over a decade of operation was used to train and evaluate two new automated image classifiers. A 112 class, random forest classifier (RF\_112) and a 112 class, convolutional neural network classifier (CNN\_112) were developed and compared with an existing, 54 class, random forest classifier (RF\_54) already in use as an early warning notification system. Both 112 class classifiers exhibited improved performance over the RF\_54 classifier when tested on three different HAB species with the CNN\_112 classifier producing fewer false positives and false negatives in most of the cases tested. For K. brevis and P. texanum, the current threshold of 2 cells.mL−1 was identified as the best threshold to minimize the number of false positives and false negatives. For D. ovum, a threshold of 1 cell.mL−1 was found to produce the best results with regard to the number of false positives/negatives. A lower threshold will result in earlier notification of an increase in cell concentration and will provide state health managers with increased lead time to prepare for an impending HAB.}, - language = {en}, - number = {22}, - urldate = {2023-10-26}, - journal = {Environmental Science and Pollution Research}, - author = {Henrichs, Darren W. and Anglès, Sílvia and Gaonkar, Chetan C. and Campbell, Lisa}, - month = jun, - year = {2021}, - keywords = {CNN, Dinophysis ovum, Gulf of Mexico, HAB, Imaging FlowCytobot, Karenia brevis, Prorocentrum texanum}, - pages = {28544--28555}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/HPYZHY65/Henrichs et al. - 2021 - Application of a convolutional neural network to i.pdf:application/pdf}, -} - -@inproceedings{vaswani_attention_2017, - title = {Attention is {All} you {Need}}, - volume = {30}, - url = {https://proceedings.neurips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html}, - abstract = {The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration. The best performing such models also connect the encoder and decoder through an attentionm echanisms. We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely.Experiments on two machine translation tasks show these models to be superiorin quality while being more parallelizable and requiring significantly less timeto train. Our single model with 165 million parameters, achieves 27.5 BLEU onEnglish-to-German translation, improving over the existing best ensemble result by over 1 BLEU. On English-to-French translation, we outperform the previoussingle state-of-the-art with model by 0.7 BLEU, achieving a BLEU score of 41.1.}, - urldate = {2023-10-28}, - booktitle = {Advances in {Neural} {Information} {Processing} {Systems}}, - publisher = {Curran Associates, Inc.}, - author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, Łukasz and Polosukhin, Illia}, - year = {2017}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/G3A56AFM/Vaswani et al. - 2017 - Attention is All you Need.pdf:application/pdf}, -} - -@article{isola_lecture_nodate-4, - title = {Lecture 15: {Deep} {Generative} {Models} {I}}, - language = {en}, - author = {Isola, Phillip}, - file = {15_gen_models_1.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/15_gen_models_1.pdf:application/pdf}, -} - -@article{isola_lecture_nodate-5, - title = {Lecture 16: {Deep} {Generative} {Models} {II}}, - language = {en}, - author = {Isola, Phillip}, - file = {16_gen_models_2.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/16_gen_models_2.pdf:application/pdf}, -} - -@article{isola_lecture_nodate-6, - title = {Lecture 17: {Deep} {Generative} {Models} {III}}, - language = {en}, - author = {Isola, Phillip}, - file = {17_gen_models_3.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/17_gen_models_3.pdf:application/pdf}, -} - -@misc{1, - title = {How {Neural} {Networks} {Extrapolate}: {From} {Feedforward} to {Graph} {Neural} {Networks}}, - shorttitle = {How {Neural} {Networks} {Extrapolate}}, - url = {http://arxiv.org/abs/2009.11848}, - doi = {10.48550/arXiv.2009.11848}, - abstract = {We study how neural networks trained by gradient descent extrapolate, i.e., what they learn outside the support of the training distribution. Previous works report mixed empirical results when extrapolating with neural networks: while feedforward neural networks, a.k.a. multilayer perceptrons (MLPs), do not extrapolate well in certain simple tasks, Graph Neural Networks (GNNs) -- structured networks with MLP modules -- have shown some success in more complex tasks. Working towards a theoretical explanation, we identify conditions under which MLPs and GNNs extrapolate well. First, we quantify the observation that ReLU MLPs quickly converge to linear functions along any direction from the origin, which implies that ReLU MLPs do not extrapolate most nonlinear functions. But, they can provably learn a linear target function when the training distribution is sufficiently "diverse". Second, in connection to analyzing the successes and limitations of GNNs, these results suggest a hypothesis for which we provide theoretical and empirical evidence: the success of GNNs in extrapolating algorithmic tasks to new data (e.g., larger graphs or edge weights) relies on encoding task-specific non-linearities in the architecture or features. Our theoretical analysis builds on a connection of over-parameterized networks to the neural tangent kernel. Empirically, our theory holds across different training settings.}, - urldate = {2023-11-12}, - publisher = {arXiv}, - author = {Xu, Keyulu and Zhang, Mozhi and Li, Jingling and Du, Simon S. and Kawarabayashi, Ken-ichi and Jegelka, Stefanie}, - month = mar, - year = {2021}, - note = {arXiv:2009.11848 [cs, stat]}, - keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/MAMEYIFL/2009.html:text/html;Xu et al. - 2021 - How Neural Networks Extrapolate From Feedforward .pdf:/Users/birdy/Zotero/storage/XUU8Z7E2/Xu et al. - 2021 - How Neural Networks Extrapolate From Feedforward .pdf:application/pdf}, -} - -@misc{xiao_noise_2020, - title = {Noise or {Signal}: {The} {Role} of {Image} {Backgrounds} in {Object} {Recognition}}, - shorttitle = {Noise or {Signal}}, - url = {http://arxiv.org/abs/2006.09994}, - abstract = {We assess the tendency of state-of-the-art object recognition models to depend on signals from image backgrounds. We create a toolkit for disentangling foreground and background signal on ImageNet images, and find that (a) models can achieve non-trivial accuracy by relying on the background alone, (b) models often misclassify images even in the presence of correctly classified foregrounds--up to 87.5\% of the time with adversarially chosen backgrounds, and (c) more accurate models tend to depend on backgrounds less. Our analysis of backgrounds brings us closer to understanding which correlations machine learning models use, and how they determine models' out of distribution performance.}, - urldate = {2023-11-12}, - publisher = {arXiv}, - author = {Xiao, Kai and Engstrom, Logan and Ilyas, Andrew and Madry, Aleksander}, - month = jun, - year = {2020}, - note = {arXiv:2006.09994 [cs]}, - keywords = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/W2NV28Q2/2006.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/MZIZ22HW/Xiao et al. - 2020 - Noise or Signal The Role of Image Backgrounds in .pdf:application/pdf}, -} - -@misc{yang_generalized_2022, - title = {Generalized {Out}-of-{Distribution} {Detection}: {A} {Survey}}, - shorttitle = {Generalized {Out}-of-{Distribution} {Detection}}, - url = {http://arxiv.org/abs/2110.11334}, - doi = {10.48550/arXiv.2110.11334}, - abstract = {Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of machine learning systems. For instance, in autonomous driving, we would like the driving system to issue an alert and hand over the control to humans when it detects unusual scenes or objects that it has never seen during training time and cannot make a safe decision. The term, OOD detection, first emerged in 2017 and since then has received increasing attention from the research community, leading to a plethora of methods developed, ranging from classification-based to density-based to distance-based ones. Meanwhile, several other problems, including anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD), are closely related to OOD detection in terms of motivation and methodology. Despite common goals, these topics develop in isolation, and their subtle differences in definition and problem setting often confuse readers and practitioners. In this survey, we first present a unified framework called generalized OOD detection, which encompasses the five aforementioned problems, i.e., AD, ND, OSR, OOD detection, and OD. Under our framework, these five problems can be seen as special cases or sub-tasks, and are easier to distinguish. We then review each of these five areas by summarizing their recent technical developments, with a special focus on OOD detection methodologies. We conclude this survey with open challenges and potential research directions.}, - urldate = {2023-11-12}, - publisher = {arXiv}, - author = {Yang, Jingkang and Zhou, Kaiyang and Li, Yixuan and Liu, Ziwei}, - month = aug, - year = {2022}, - note = {arXiv:2110.11334 [cs]}, - keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition}, - file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/MA6JB7TD/Yang et al. - 2022 - Generalized Out-of-Distribution Detection A Surve.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/BH3RP2BX/2110.html:text/html}, -} - -@article{cui_out--distribution_2022, - title = {Out-of-{Distribution} ({OOD}) {Detection} {Based} on {Deep} {Learning}: {A} {Review}}, - volume = {11}, - copyright = {http://creativecommons.org/licenses/by/3.0/}, - issn = {2079-9292}, - shorttitle = {Out-of-{Distribution} ({OOD}) {Detection} {Based} on {Deep} {Learning}}, - url = {https://www.mdpi.com/2079-9292/11/21/3500}, - doi = {10.3390/electronics11213500}, - abstract = {Out-of-Distribution (OOD) detection separates ID (In-Distribution) data and OOD data from input data through a model. This problem has attracted increasing attention in the area of machine learning. OOD detection has achieved good intrusion detection, fraud detection, system health monitoring, sensor network event detection, and ecosystem interference detection. The method based on deep learning is the most studied in OOD detection. In this paper , related basic information on OOD detection based on deep learning is described, and we categorize methods according to the training data. OOD detection is divided into supervised, semisupervised, and unsupervised. Where supervised data are used, the methods are categorized according to technical means: model-based, distance-based, and density-based. Each classification is introduced with background, examples, and applications. In addition, we present the latest applications of OOD detection based on deep learning and the problems and expectations in this field.}, - language = {en}, - number = {21}, - urldate = {2023-11-12}, - journal = {Electronics}, - author = {Cui, Peng and Wang, Jinjia}, - month = jan, - year = {2022}, - note = {Number: 21 -Publisher: Multidisciplinary Digital Publishing Institute}, - keywords = {deep learning, anomaly detection, neural networks, novelty detection, Out-of-Distribution detection, outlier detection}, - pages = {3500}, - file = {Cui and Wang - 2022 - Out-of-Distribution (OOD) Detection Based on Deep .pdf:/Users/birdy/Zotero/storage/MK3M7WBW/Cui and Wang - 2022 - Out-of-Distribution (OOD) Detection Based on Deep .pdf:application/pdf}, -} - -@misc{ghassemi_comprehensive_2022, - title = {A {Comprehensive} {Review} of {Trends}, {Applications} and {Challenges} {In} {Out}-of-{Distribution} {Detection}}, - url = {http://arxiv.org/abs/2209.12935}, - abstract = {With recent advancements in artificial intelligence, its applications can be seen in every aspect of humans' daily life. From voice assistants to mobile healthcare and autonomous driving, we rely on the performance of AI methods for many critical tasks; therefore, it is essential to assert the performance of models in proper means to prevent damage. One of the shortfalls of AI models in general, and deep machine learning in particular, is a drop in performance when faced with shifts in the distribution of data. Nonetheless, these shifts are always expected in real-world applications; thus, a field of study has emerged, focusing on detecting out-of-distribution data subsets and enabling a more comprehensive generalization. Furthermore, as many deep learning based models have achieved near-perfect results on benchmark datasets, the need to evaluate these models' reliability and trustworthiness for pushing towards real-world applications is felt more strongly than ever. This has given rise to a growing number of studies in the field of out-of-distribution detection and domain generalization, which begs the need for surveys that compare these studies from various perspectives and highlight their straightens and weaknesses. This paper presents a survey that, in addition to reviewing more than 70 papers in this field, presents challenges and directions for future works and offers a unifying look into various types of data shifts and solutions for better generalization.}, - urldate = {2023-11-12}, - publisher = {arXiv}, - author = {Ghassemi, Navid and Fazl-Ersi, Ehsan}, - month = sep, - year = {2022}, - note = {arXiv:2209.12935 [cs]}, - keywords = {Computer Science - Machine Learning}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/GJU4ZCYR/2209.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/TWZAVEUQ/Ghassemi and Fazl-Ersi - 2022 - A Comprehensive Review of Trends, Applications and.pdf:application/pdf}, -} - -@book{quinonero-candela_dataset_2009, - address = {Cambridge, Mass}, - series = {Neural information processing series}, - title = {Dataset shift in machine learning}, - isbn = {978-0-262-17005-5 978-0-262-54587-7}, - language = {en}, - publisher = {MIT Press}, - editor = {Quiñonero-Candela, Joaquin}, - year = {2009}, - note = {OCLC: ocn227205909}, - keywords = {Machine learning}, - file = {Quiñonero-Candela - 2009 - Dataset shift in machine learning.pdf:/Users/birdy/Zotero/storage/JJT973SR/Quiñonero-Candela - 2009 - Dataset shift in machine learning.pdf:application/pdf}, -} - -@article{may_will_1972, - title = {Will a {Large} {Complex} {System} be {Stable}?}, - volume = {238}, - copyright = {1972 Springer Nature Limited}, - issn = {1476-4687}, - url = {https://www.nature.com/articles/238413a0}, - doi = {10.1038/238413a0}, - abstract = {Gardner and Ashby1 have suggested that large complex systems which are assembled (connected) at random may be expected to be stable up to a certain critical level of connectance, and then, as this increases, to suddenly become unstable. Their conclusions were based on the trend of computer studies of systems with 4, 7 and 10 variables.}, - language = {en}, - number = {5364}, - urldate = {2023-11-13}, - journal = {Nature}, - author = {May, Robert M.}, - month = aug, - year = {1972}, - note = {Number: 5364 -Publisher: Nature Publishing Group}, - keywords = {Humanities and Social Sciences, multidisciplinary, Science}, - pages = {413--414}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/K3ZKQCSD/May - 1972 - Will a Large Complex System be Stable.pdf:application/pdf}, -} - -@article{armstrong_stable_1999, - title = {Stable model structures for representing biogeochemical diversity and size spectra in plankton communities}, - volume = {21}, - issn = {0142-7873}, - url = {https://doi.org/10.1093/plankt/21.3.445}, - doi = {10.1093/plankt/21.3.445}, - abstract = {There is a fundamental duality in the way we view planktonic communities. On one hand, we see these communities as composed of a diverse mixture of taxonomically and biogeochemically distinct species; pelagic ecosystem models capable of predicting the ocean's role in the global carbon cycle will need to resolve these multiple plankton taxa. However, we have also come to appreciate that superimposed on this diversity are regular patterns of size structure, where plots of abundance within size classes typically show a power-law dependence on size. A truly complete model description of planktonic community structure must reflect both aspects of this duality. Here I examine two classes of ecosystem models that are capable of representing this duality. The first structure is based on multiple (\<it\>n\</it\>) couplets of phytoplankton -(\<it\>P\</it\>) and zooplankton (\<it\>Z\</it\>) [an '\<it\>n\</it\>(\<it\>PZ\</it\>)' model]. Within certain regions of parameter space, this model structure can produce anomalous oscillatory behaviors, the mathematical origin of which is explored in detail. I then examine an alternative food web structure in which total -herbivore abundance is represented by a single state variable,\<it\>G\</it\>, and where grazing pressure is distributed among -taxa in proportion to their abundances. This '(\<it\>nP)G\</it\>' -system is stabilized by the redistribution of grazing intensity among taxa -in response to changes in their densities. This 'distributed grazing' model -also naturally produces size spectra of plankton abundance; this last -observation argues that the model with distributed grazing can help in -uncovering and representing the mechanistic basis for the genesis and -maintenance of planktonic size spectra of phytoplankton and bacteria.}, - number = {3}, - urldate = {2023-11-13}, - journal = {Journal of Plankton Research}, - author = {Armstrong, Robert A.}, - month = mar, - year = {1999}, - pages = {445--464}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/LP2EYBJD/1999 - Stable model structures for representing biogeoche.pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/MN45F6HS/1395820.html:text/html}, -} - -@article{ashkezari_simons_2021, - title = {Simons {Collaborative} {Marine} {Atlas} {Project} ({Simons} {CMAP}): {An} open-source portal to share, visualize, and analyze ocean data}, - volume = {19}, - copyright = {© 2021 The Authors. Limnology and Oceanography: Methods published by Wiley Periodicals LLC on behalf of Association for the Sciences of Limnology and Oceanography.}, - issn = {1541-5856}, - shorttitle = {Simons {Collaborative} {Marine} {Atlas} {Project} ({Simons} {CMAP})}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lom3.10439}, - doi = {10.1002/lom3.10439}, - abstract = {Simons Collaborative Marine Atlas Project (Simons CMAP) is an open-source data portal that interconnects large, complex, and diverse public data sets currently dispersed in different formats across different Oceanography discipline-specific databases. Simons CMAP is designed to streamline the retrieval of custom subsets of data, the generation of data visualizations, and the analyses of diverse data, thus expanding the power of these potentially underutilized data sets for cross-disciplinary studies of ocean processes. We describe a unified architecture that allows numerical model outputs, satellite products, and field observations to be readily shared, mined, and integrated regardless of data set size or resolution. A current focus of Simons CMAP is integration of physical, chemical, and biological data sets essential for characterizing the biogeography of key marine microbes across ocean basins and seasonal cycles. Using a practical example, we demonstrate how our unifying data harmonization plans significantly simplifies and allows for systematic data integration across all Simons CMAP data sets.}, - language = {en}, - number = {7}, - urldate = {2023-11-13}, - journal = {Limnology and Oceanography: Methods}, - author = {Ashkezari, Mohammad D. and Hagen, Norland R. and Denholtz, Michael and Neang, Andrew and Burns, Tansy C. and Morales, Rhonda L. and Lee, Charlotte P. and Hill, Christopher N. and Armbrust, E. Virginia}, - year = {2021}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10439}, - pages = {488--496}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/DUNQVLE5/Ashkezari et al. - 2021 - Simons Collaborative Marine Atlas Project (Simons .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/CPQTRWWV/lom3.html:text/html}, -} - -@article{aumont_pisces-v2_2015, - title = {{PISCES}-v2: an ocean biogeochemical model for carbon and ecosystem studies}, - volume = {8}, - url = {https://gmd.copernicus.org/articles/8/2465/2015/}, - doi = {10.5194/gmd-8-2465-2015}, - number = {8}, - journal = {Geoscientific Model Development}, - author = {Aumont, O. and Ethé, C. and Tagliabue, A. and Bopp, L. and Gehlen, M.}, - year = {2015}, - pages = {2465--2513}, -} - -@article{bardon_testing_2021-1, - title = {Testing the {Skill} of a {Species} {Distribution} {Model} {Using} a 21st {Century} {Virtual} {Ecosystem}}, - volume = {48}, - copyright = {© 2021. The Authors.}, - issn = {1944-8007}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2021GL093455}, - doi = {10.1029/2021GL093455}, - abstract = {Plankton communities play an important role in marine food webs, in biogeochemical cycling, and in Earth's climate; yet observations are sparse, and predictions of how they might respond to climate change vary. Correlative species distribution models (SDM's) have been applied to predicting biogeography based on relationships to observed environmental variables. To investigate sources of uncertainty, we use a correlative SDM to predict the plankton biogeography of a 21st century marine ecosystem model (Darwin). Darwin output is sampled to mimic historical ocean observations, and the SDM is trained using generalized additive models. We find that predictive skill varies across test cases, and between functional groups, with errors that are more attributable to spatiotemporal sampling bias than sample size. End-of-century predictions are poor, limited by changes in target-predictor relationships over time. Our findings illustrate the fundamental challenges faced by empirical models in using limited observational data to predict complex, dynamic systems.}, - language = {en}, - number = {22}, - urldate = {2023-11-13}, - journal = {Geophysical Research Letters}, - author = {Bardon, L. R. and Ward, B. A. and Dutkiewicz, S. and Cael, B. B.}, - year = {2021}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021GL093455}, - keywords = {climate change, complex systems, numerical models, oceanography, plankton ecology, statistical learning}, - pages = {e2021GL093455}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/CD5IBV65/Bardon et al. - 2021 - Testing the Skill of a Species Distribution Model .pdf:application/pdf;Snapshot:/Users/birdy/Zotero/storage/DK86CFJT/2021GL093455.html:text/html}, -} - -@article{bian_understanding_2023-1, - title = {Understanding opposing predictions of {Prochlorococcus} in a changing climate}, - volume = {14}, - copyright = {2023 The Author(s)}, - issn = {2041-1723}, - url = {https://www.nature.com/articles/s41467-023-36928-9}, - doi = {10.1038/s41467-023-36928-9}, - abstract = {Statistically derived species distribution models (SDMs) are increasingly used to predict ecological changes on a warming planet. For Prochlorococcus, the most abundant phytoplankton, an established statistical prediction conflicts with dynamical models as they predict large, opposite, changes in abundance. We probe the SDM at various spatial-temporal scales, showing that light and temperature fail to explain both temporal fluctuations and sharp spatial transitions. Strong correlations between changes in temperature and population emerge only at very large spatial scales, as transects pass through transitions between regions of high and low abundance. Furthermore, a two-state model based on a temperature threshold matches the original SDM in the surface ocean. We conclude that the original SDM has little power to predict changes when Prochlorococcus is already abundant, which resolves the conflict with dynamical models. Our conclusion suggests that SDMs should prove efficacy across multiple spatial-temporal scales before being trusted in a changing ocean.}, - language = {en}, - number = {1}, - urldate = {2023-11-13}, - journal = {Nature Communications}, - author = {Bian, Vincent and Cai, Merrick and Follett, Christopher L.}, - month = mar, - year = {2023}, - note = {Number: 1 -Publisher: Nature Publishing Group}, - keywords = {Biogeography, Projection and prediction}, - pages = {1445}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/257L3PIB/Bian et al. - 2023 - Understanding opposing predictions of Prochlorococ.pdf:application/pdf}, -} - -@misc{winkens_contrastive_2020, - title = {Contrastive {Training} for {Improved} {Out}-of-{Distribution} {Detection}}, - url = {http://arxiv.org/abs/2007.05566}, - abstract = {Reliable detection of out-of-distribution (OOD) inputs is increasingly understood to be a precondition for deployment of machine learning systems. This paper proposes and investigates the use of contrastive training to boost OOD detection performance. Unlike leading methods for OOD detection, our approach does not require access to examples labeled explicitly as OOD, which can be difficult to collect in practice. We show in extensive experiments that contrastive training significantly helps OOD detection performance on a number of common benchmarks. By introducing and employing the Confusion Log Probability (CLP) score, which quantifies the difficulty of the OOD detection task by capturing the similarity of inlier and outlier datasets, we show that our method especially improves performance in the `near OOD' classes -- a particularly challenging setting for previous methods.}, - urldate = {2023-11-13}, - publisher = {arXiv}, - author = {Winkens, Jim and Bunel, Rudy and Roy, Abhijit Guha and Stanforth, Robert and Natarajan, Vivek and Ledsam, Joseph R. and MacWilliams, Patricia and Kohli, Pushmeet and Karthikesalingam, Alan and Kohl, Simon and Cemgil, Taylan and Eslami, S. M. Ali and Ronneberger, Olaf}, - month = jul, - year = {2020}, - note = {arXiv:2007.05566 [cs, stat]}, - keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/ZW3DG7B6/2007.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/5M6W3QNV/Winkens et al. - 2020 - Contrastive Training for Improved Out-of-Distribut.pdf:application/pdf}, -} - -@inproceedings{zhou_contrastive_2021, - title = {Contrastive {Out}-of-{Distribution} {Detection} for {Pretrained} {Transformers}}, - url = {http://arxiv.org/abs/2104.08812}, - doi = {10.18653/v1/2021.emnlp-main.84}, - abstract = {Pretrained Transformers achieve remarkable performance when training and test data are from the same distribution. However, in real-world scenarios, the model often faces out-of-distribution (OOD) instances that can cause severe semantic shift problems at inference time. Therefore, in practice, a reliable model should identify such instances, and then either reject them during inference or pass them over to models that handle another distribution. In this paper, we develop an unsupervised OOD detection method, in which only the in-distribution (ID) data are used in training. We propose to fine-tune the Transformers with a contrastive loss, which improves the compactness of representations, such that OOD instances can be better differentiated from ID ones. These OOD instances can then be accurately detected using the Mahalanobis distance in the model's penultimate layer. We experiment with comprehensive settings and achieve near-perfect OOD detection performance, outperforming baselines drastically. We further investigate the rationales behind the improvement, finding that more compact representations through margin-based contrastive learning bring the improvement. We release our code to the community for future research.}, - urldate = {2023-11-13}, - booktitle = {Proceedings of the 2021 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}}, - author = {Zhou, Wenxuan and Liu, Fangyu and Chen, Muhao}, - year = {2021}, - note = {arXiv:2104.08812 [cs]}, - keywords = {Computer Science - Computation and Language}, - pages = {1100--1111}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/IB8D8VD8/2104.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/XFIETIDR/Zhou et al. - 2021 - Contrastive Out-of-Distribution Detection for Pret.pdf:application/pdf}, -} - -@article{beery_lecture_nodate-2, - title = {Lecture 20: {Transfer} {Learning} {II}}, - language = {en}, - author = {Beery, Sara}, - file = {20_transfer_learning_2_SB.pdf:/Users/birdy/Documents/Meng Classes/6.S898 Deep Learning/20_transfer_learning_2_SB.pdf:application/pdf}, -} - -@misc{kingma_auto-encoding_2022, - title = {Auto-{Encoding} {Variational} {Bayes}}, - url = {http://arxiv.org/abs/1312.6114}, - abstract = {How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions are two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.}, - urldate = {2023-11-27}, - publisher = {arXiv}, - author = {Kingma, Diederik P. and Welling, Max}, - month = dec, - year = {2022}, - note = {arXiv:1312.6114 [cs, stat]}, - keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/H7BQXHMR/1312.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/QR4A6NNA/Kingma and Welling - 2022 - Auto-Encoding Variational Bayes.pdf:application/pdf}, -} - -@article{sonnewald_elucidating_2020, - title = {Elucidating ecological complexity: {Unsupervised} learning determines global marine eco-provinces}, - volume = {6}, - shorttitle = {Elucidating ecological complexity}, - url = {https://www.science.org/doi/full/10.1126/sciadv.aay4740}, - doi = {10.1126/sciadv.aay4740}, - abstract = {An unsupervised learning method is presented for determining global marine ecological provinces (eco-provinces) from plankton community structure and nutrient flux data. The systematic aggregated eco-province (SAGE) method identifies eco-provinces within a highly nonlinear ecosystem model. To accommodate the non-Gaussian covariance of the data, SAGE uses t-stochastic neighbor embedding (t-SNE) to reduce dimensionality. Over a hundred eco-provinces are identified with the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Using a connectivity graph with ecological dissimilarity as the distance metric, robust aggregated eco-provinces (AEPs) are objectively defined by nesting the eco-provinces. Using the AEPs, the control of nutrient supply rates on community structure is explored. Eco-provinces and AEPs are unique and aid model interpretation. They could facilitate model intercomparison and potentially improve understanding and monitoring of marine ecosystems.}, - number = {22}, - urldate = {2023-11-28}, - journal = {Science Advances}, - author = {Sonnewald, Maike and Dutkiewicz, Stephanie and Hill, Christopher and Forget, Gael}, - month = may, - year = {2020}, - note = {Publisher: American Association for the Advancement of Science}, - pages = {eaay4740}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/FSUVU9LS/Sonnewald et al. - 2020 - Elucidating ecological complexity Unsupervised le.pdf:application/pdf}, -} - -@misc{ho_denoising_2020, - title = {Denoising {Diffusion} {Probabilistic} {Models}}, - url = {http://arxiv.org/abs/2006.11239}, - doi = {10.48550/arXiv.2006.11239}, - abstract = {We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at https://github.com/hojonathanho/diffusion}, - urldate = {2023-11-30}, - publisher = {arXiv}, - author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter}, - month = dec, - year = {2020}, - note = {arXiv:2006.11239 [cs, stat]}, - keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, - file = {arXiv Fulltext PDF:/Users/birdy/Zotero/storage/A854EPRR/Ho et al. - 2020 - Denoising Diffusion Probabilistic Models.pdf:application/pdf;arXiv.org Snapshot:/Users/birdy/Zotero/storage/R4HY8MGJ/2006.html:text/html}, -} - -@inproceedings{wang_partial_2022, - title = {Partial and {Asymmetric} {Contrastive} {Learning} for {Out}-of-{Distribution} {Detection} in {Long}-{Tailed} {Recognition}}, - url = {https://proceedings.mlr.press/v162/wang22aq.html}, - abstract = {Existing out-of-distribution (OOD) detection methods are typically benchmarked on training sets with balanced class distributions. However, in real-world applications, it is common for the training sets to have long-tailed distributions. In this work, we first demonstrate that existing OOD detection methods commonly suffer from significant performance degradation when the training set is long-tail distributed. Through analysis, we posit that this is because the models struggle to distinguish the minority tail-class in-distribution samples, from the true OOD samples, making the tail classes more prone to be falsely detected as OOD. To solve this problem, we propose Partial and Asymmetric Supervised Contrastive Learning (PASCL), which explicitly encourages the model to distinguish between tail-class in-distribution samples and OOD samples. To further boost in-distribution classification accuracy, we propose Auxiliary Branch Finetuning, which uses two separate branches of BN and classification layers for anomaly detection and in-distribution classification, respectively. The intuition is that in-distribution and OOD anomaly data have different underlying distributions. Our method outperforms previous state-of-the-art method by \$1.29\%\$, \$1.45\%\$, \$0.69\%\$ anomaly detection false positive rate (FPR) and \$3.24\%\$, \$4.06\%\$, \$7.89\%\$ in-distribution classification accuracy on CIFAR10-LT, CIFAR100-LT, and ImageNet-LT, respectively. Code and pre-trained models are available at https://github.com/amazon-research/long-tailed-ood-detection.}, - language = {en}, - urldate = {2023-12-04}, - booktitle = {Proceedings of the 39th {International} {Conference} on {Machine} {Learning}}, - publisher = {PMLR}, - author = {Wang, Haotao and Zhang, Aston and Zhu, Yi and Zheng, Shuai and Li, Mu and Smola, Alex J. and Wang, Zhangyang}, - month = jun, - year = {2022}, - note = {ISSN: 2640-3498}, - pages = {23446--23458}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/Y2THTBUA/Wang et al. - 2022 - Partial and Asymmetric Contrastive Learning for Ou.pdf:application/pdf}, -} - -@article{higgins_-vae_2017, - title = {β-{VAE}: {LEARNING} {BASIC} {VISUAL} {CONCEPTS} {WITH} {A} {CONSTRAINED} {VARIATIONAL} {FRAMEWORK}}, - abstract = {Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to learn and reason in the same way that humans do. We introduce β-VAE, a new state-of-the-art framework for automated discovery of interpretable factorised latent representations from raw image data in a completely unsupervised manner. Our approach is a modification of the variational autoencoder (VAE) framework. We introduce an adjustable hyperparameter β that balances latent channel capacity and independence constraints with reconstruction accuracy. We demonstrate that β-VAE with appropriately tuned β {\textgreater} 1 qualitatively outperforms VAE (β = 1), as well as state of the art unsupervised (InfoGAN) and semi-supervised (DC-IGN) approaches to disentangled factor learning on a variety of datasets (celebA, faces and chairs). Furthermore, we devise a protocol to quantitatively compare the degree of disentanglement learnt by different models, and show that our approach also significantly outperforms all baselines quantitatively. Unlike InfoGAN, β-VAE is stable to train, makes few assumptions about the data and relies on tuning a single hyperparameter β, which can be directly optimised through a hyperparameter search using weakly labelled data or through heuristic visual inspection for purely unsupervised data.}, - language = {en}, - author = {Higgins, Irina and Matthey, Loic and Pal, Arka and Burgess, Christopher and Glorot, Xavier and Botvinick, Matthew and Mohamed, Shakir and Lerchner, Alexander}, - year = {2017}, - file = {Higgins et al. - 2017 - β-VAE LEARNING BASIC VISUAL CONCEPTS WITH A CONST.pdf:/Users/birdy/Zotero/storage/9VGRX2XM/Higgins et al. - 2017 - β-VAE LEARNING BASIC VISUAL CONCEPTS WITH A CONST.pdf:application/pdf}, -} - -@article{schmarje_fuzzy_2021, - title = {Fuzzy {Overclustering}: {Semi}-{Supervised} {Classification} of {Fuzzy} {Labels} with {Overclustering} and {Inverse} {Cross}-{Entropy}}, - volume = {21}, - copyright = {http://creativecommons.org/licenses/by/3.0/}, - issn = {1424-8220}, - shorttitle = {Fuzzy {Overclustering}}, - url = {https://www.mdpi.com/1424-8220/21/19/6661}, - doi = {10.3390/s21196661}, - abstract = {Deep learning has been successfully applied to many classification problems including underwater challenges. However, a long-standing issue with deep learning is the need for large and consistently labeled datasets. Although current approaches in semi-supervised learning can decrease the required amount of annotated data by a factor of 10 or even more, this line of research still uses distinct classes. For underwater classification, and uncurated real-world datasets in general, clean class boundaries can often not be given due to a limited information content in the images and transitional stages of the depicted objects. This leads to different experts having different opinions and thus producing fuzzy labels which could also be considered ambiguous or divergent. We propose a novel framework for handling semi-supervised classifications of such fuzzy labels. It is based on the idea of overclustering to detect substructures in these fuzzy labels. We propose a novel loss to improve the overclustering capability of our framework and show the benefit of overclustering for fuzzy labels. We show that our framework is superior to previous state-of-the-art semi-supervised methods when applied to real-world plankton data with fuzzy labels. Moreover, we acquire 5 to 10\% more consistent predictions of substructures.}, - language = {en}, - number = {19}, - urldate = {2023-12-04}, - journal = {Sensors}, - author = {Schmarje, Lars and Brünger, Johannes and Santarossa, Monty and Schröder, Simon-Martin and Kiko, Rainer and Koch, Reinhard}, - month = jan, - year = {2021}, - note = {Number: 19 -Publisher: Multidisciplinary Digital Publishing Institute}, - keywords = {deep learning, fuzzy, marine, noisy, plankton, real-world, semi-supervised}, - pages = {6661}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/WILN3PSB/Schmarje et al. - 2021 - Fuzzy Overclustering Semi-Supervised Classificati.pdf:application/pdf}, -} - -@inproceedings{liu_good-d_2023, - address = {New York, NY, USA}, - series = {{WSDM} '23}, - title = {{GOOD}-{D}: {On} {Unsupervised} {Graph} {Out}-{Of}-{Distribution} {Detection}}, - isbn = {978-1-4503-9407-9}, - shorttitle = {{GOOD}-{D}}, - url = {https://dl.acm.org/doi/10.1145/3539597.3570446}, - doi = {10.1145/3539597.3570446}, - abstract = {Most existing deep learning models are trained based on the closed-world assumption, where the test data is assumed to be drawn i.i.d. from the same distribution as the training data, known as in-distribution (ID). However, when models are deployed in an open-world scenario, test samples can be out-of-distribution (OOD) and therefore should be handled with caution. To detect such OOD samples drawn from unknown distribution, OOD detection has received increasing attention lately. However, current endeavors mostly focus on grid-structured data and its application for graph-structured data remains under-explored. Considering the fact that data labeling on graphs is commonly time-expensive and labor-intensive, in this work we study the problem of unsupervised graph OOD detection, aiming at detecting OOD graphs solely based on unlabeled ID data. To achieve this goal, we develop a new graph contrastive learning framework GOOD-D for detecting OOD graphs without using any ground-truth labels. By performing hierarchical contrastive learning on the augmented graphs generated by our perturbation-free graph data augmentation method, GOOD-D is able to capture the latent ID patterns and accurately detect OOD graphs based on the semantic inconsistency in different granularities (i.e., node-level, graph-level, and group-level). As a pioneering work in unsupervised graph-level OOD detection, we build a comprehensive benchmark to compare our proposed approach with different state-of-the-art methods. The experiment results demonstrate the superiority of our approach over different methods on various datasets.}, - urldate = {2023-12-04}, - booktitle = {Proceedings of the {Sixteenth} {ACM} {International} {Conference} on {Web} {Search} and {Data} {Mining}}, - publisher = {Association for Computing Machinery}, - author = {Liu, Yixin and Ding, Kaize and Liu, Huan and Pan, Shirui}, - month = feb, - year = {2023}, - keywords = {contrastive learning, graph neural networks, out-of-distribution detection}, - pages = {339--347}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/I8T5QYSC/Liu et al. - 2023 - GOOD-D On Unsupervised Graph Out-Of-Distribution .pdf:application/pdf}, -} - -@misc{wang_understanding_2022, - title = {Understanding {Contrastive} {Representation} {Learning} through {Alignment} and {Uniformity} on the {Hypersphere}}, - url = {http://arxiv.org/abs/2005.10242}, - abstract = {Contrastive representation learning has been outstandingly successful in practice. In this work, we identify two key properties related to the contrastive loss: (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere. We prove that, asymptotically, the contrastive loss optimizes these properties, and analyze their positive effects on downstream tasks. Empirically, we introduce an optimizable metric to quantify each property. Extensive experiments on standard vision and language datasets confirm the strong agreement between both metrics and downstream task performance. Remarkably, directly optimizing for these two metrics leads to representations with comparable or better performance at downstream tasks than contrastive learning. Project Page: https://tongzhouwang.info/hypersphere Code: https://github.com/SsnL/align\_uniform , https://github.com/SsnL/moco\_align\_uniform}, - urldate = {2023-12-04}, - publisher = {arXiv}, - author = {Wang, Tongzhou and Isola, Phillip}, - month = aug, - year = {2022}, - note = {arXiv:2005.10242 [cs, stat]}, - keywords = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/WFW3P94A/2005.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/KJZGVSLC/Wang and Isola - 2022 - Understanding Contrastive Representation Learning .pdf:application/pdf}, -} - -@inproceedings{chen_atom_2021, - address = {Cham}, - series = {Lecture {Notes} in {Computer} {Science}}, - title = {{ATOM}: {Robustifying} {Out}-of-{Distribution} {Detection} {Using} {Outlier} {Mining}}, - isbn = {978-3-030-86523-8}, - shorttitle = {{ATOM}}, - doi = {10.1007/978-3-030-86523-8_26}, - abstract = {Detecting out-of-distribution (OOD) inputs is critical for safely deploying deep learning models in an open-world setting. However, existing OOD detection solutions can be brittle in the open world, facing various types of adversarial OOD inputs. While methods leveraging auxiliary OOD data have emerged, our analysis on illuminative examples reveals a key insight that the majority of auxiliary OOD examples may not meaningfully improve or even hurt the decision boundary of the OOD detector, which is also observed in empirical results on real data. In this paper, we provide a theoretically motivated method, Adversarial Training with informative Outlier Mining (ATOM), which improves the robustness of OOD detection. We show that, by mining informative auxiliary OOD data, one can significantly improve OOD detection performance, and somewhat surprisingly, generalize to unseen adversarial attacks. ATOM achieves state-of-the-art performance under a broad family of classic and adversarial OOD evaluation tasks. For example, on the CIFAR-10 in-distribution dataset, ATOM reduces the FPR (at TPR 95\%) by up to 57.99\% under adversarial OOD inputs, surpassing the previous best baseline by a large margin.}, - language = {en}, - booktitle = {Machine {Learning} and {Knowledge} {Discovery} in {Databases}. {Research} {Track}}, - publisher = {Springer International Publishing}, - author = {Chen, Jiefeng and Li, Yixuan and Wu, Xi and Liang, Yingyu and Jha, Somesh}, - editor = {Oliver, Nuria and Pérez-Cruz, Fernando and Kramer, Stefan and Read, Jesse and Lozano, Jose A.}, - year = {2021}, - keywords = {Out-of-distribution detection, Outlier mining, Robustness}, - pages = {430--445}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/5PNM3HAW/Chen et al. - 2021 - ATOM Robustifying Out-of-Distribution Detection U.pdf:application/pdf}, -} - -@misc{chen_simple_2020, - title = {A {Simple} {Framework} for {Contrastive} {Learning} of {Visual} {Representations}}, - url = {http://arxiv.org/abs/2002.05709}, - abstract = {This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5\% top-1 accuracy, which is a 7\% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1\% of the labels, we achieve 85.8\% top-5 accuracy, outperforming AlexNet with 100X fewer labels.}, - urldate = {2024-01-04}, - publisher = {arXiv}, - author = {Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey}, - month = jun, - year = {2020}, - note = {arXiv:2002.05709 [cs, stat]}, - keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/QSFZN5RI/2002.html:text/html;Chen et al. - 2020 - A Simple Framework for Contrastive Learning of Vis.pdf:/Users/birdy/Zotero/storage/UA8HJXR5/Chen et al. - 2020 - A Simple Framework for Contrastive Learning of Vis.pdf:application/pdf}, -} - -@misc{pang_rethinking_2020, - title = {Rethinking {Softmax} {Cross}-{Entropy} {Loss} for {Adversarial} {Robustness}}, - url = {http://arxiv.org/abs/1905.10626}, - abstract = {Previous work shows that adversarially robust generalization requires larger sample complexity, and the same dataset, e.g., CIFAR-10, which enables good standard accuracy may not suffice to train robust models. Since collecting new training data could be costly, we focus on better utilizing the given data by inducing the regions with high sample density in the feature space, which could lead to locally sufficient samples for robust learning. We first formally show that the softmax cross-entropy (SCE) loss and its variants convey inappropriate supervisory signals, which encourage the learned feature points to spread over the space sparsely in training. This inspires us to propose the Max-Mahalanobis center (MMC) loss to explicitly induce dense feature regions in order to benefit robustness. Namely, the MMC loss encourages the model to concentrate on learning ordered and compact representations, which gather around the preset optimal centers for different classes. We empirically demonstrate that applying the MMC loss can significantly improve robustness even under strong adaptive attacks, while keeping state-of-the-art accuracy on clean inputs with little extra computation compared to the SCE loss.}, - urldate = {2024-01-06}, - publisher = {arXiv}, - author = {Pang, Tianyu and Xu, Kun and Dong, Yinpeng and Du, Chao and Chen, Ning and Zhu, Jun}, - month = feb, - year = {2020}, - note = {arXiv:1905.10626 [cs, stat]}, - keywords = {Computer Science - Cryptography and Security, Computer Science - Machine Learning, Statistics - Machine Learning}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/ZQWMNTQ4/1905.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/L6RK8EVE/Pang et al. - 2020 - Rethinking Softmax Cross-Entropy Loss for Adversar.pdf:application/pdf}, -} - -@inproceedings{zhai_s4l_2019, - address = {Seoul, Korea (South)}, - title = {{S4L}: {Self}-{Supervised} {Semi}-{Supervised} {Learning}}, - isbn = {978-1-72814-803-8}, - shorttitle = {{S4L}}, - url = {https://ieeexplore.ieee.org/document/9010283/}, - doi = {10.1109/ICCV.2019.00156}, - abstract = {This work tackles the problem of semi-supervised learning of image classifiers. Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual representation learning. Unifying these two approaches, we propose the framework of self-supervised semi-supervised learning (S4L) and use it to derive two novel semi-supervised image classification methods. We demonstrate the effectiveness of these methods in comparison to both carefully tuned baselines, and existing semi-supervised learning methods. We then show that S4L and existing semi-supervised methods can be jointly trained, yielding a new state-of-the-art result on semi-supervised ILSVRC-2012 with 10\% of labels.}, - language = {en}, - urldate = {2024-01-06}, - booktitle = {2019 {IEEE}/{CVF} {International} {Conference} on {Computer} {Vision} ({ICCV})}, - publisher = {IEEE}, - author = {Zhai, Xiaohua and Oliver, Avital and Kolesnikov, Alexander and Beyer, Lucas}, - month = oct, - year = {2019}, - pages = {1476--1485}, - file = {Zhai et al. - 2019 - S4L Self-Supervised Semi-Supervised Learning.pdf:/Users/birdy/Zotero/storage/8NNHKTJM/Zhai et al. - 2019 - S4L Self-Supervised Semi-Supervised Learning.pdf:application/pdf}, -} - -@misc{duckworth_dar1_2023, - title = {{DAR1} - {An} {Easy} to {Use}, {Single}-{Box}, {Configuration} of a {Global} {Scale} {Biogeochemical} {Ecosystem} {Model}}, - copyright = {© 2023, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/}, - url = {https://www.biorxiv.org/content/10.1101/2023.11.23.568480v1}, - doi = {10.1101/2023.11.23.568480}, - abstract = {Global scale biogeochemical ocean models are key to understanding the mechanisms which drive elemental cycles and their links to marine ecosystems. However, these models are rarely straightforward to use and their complexity can make them difficult to interpret. Here, we aim to address this problem by developing DAR1 – a single box configuration of the MIT Darwin model, which is itself a global scale biogeochemical ecosystem model designed for understanding the emergent patterns of global plankton distributions. Our configuration is packaged inside Docker and implemented in the Julia programming language. This helps make it both computationally efficient and straightforward to implement on any local machine, independent of hardware and operating system. It allows for the replication of field and laboratory experiments in a model ocean and the rapid comparison of global runs with local predictions. We provide three case studies: a framework for nutrient amendment bottle experiments; a test of steady state assumptions on plankton community structure at the global scale; and an exploration of the niche space of nitrogen-fixing plankton. Our method has the potential to facilitate broader direct testing of global model assumptions in the context of small-scale measurements. DAR1 will also allow observational and laboratory oceanographers to directly test their results against model predictions in a straightforward, easy to use, manner.}, - language = {en}, - urldate = {2024-01-06}, - publisher = {bioRxiv}, - author = {Duckworth, Barbara and Dutkiewicz, Stephanie and Jahn, Oliver and Hill, Christopher N. and Follett, Christopher L.}, - month = nov, - year = {2023}, - note = {Pages: 2023.11.23.568480 -Section: New Results}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/3376RTZG/Duckworth et al. - 2023 - DAR1 - An Easy to Use, Single-Box, Configuration o.pdf:application/pdf}, -} - -@article{falkowski_role_1994, - title = {The role of phytoplankton photosynthesis in global biogeochemical cycles}, - volume = {39}, - issn = {1573-5079}, - url = {https://doi.org/10.1007/BF00014586}, - doi = {10.1007/BF00014586}, - abstract = {Phytoplankton biomass in the world's oceans amounts to only ∽1–2\% of the total global plant carbon, yet these organisms fix between 30 and 50 billion metric tons of carbon annually, which is about 40\% of the total. On geological time scales there is profound evidence of the importance of phytoplankton photosynthesis in biogeochemical cycles. It is generally assumed that present phytoplankton productivity is in a quasi steady-state (on the time scale of decades). However, in a global context, the stability of oceanic photosynthetic processes is dependent on the physical circulation of the upper ocean and is therefore strongly influenced by the atmosphere. The net flux of atmospheric radiation is critical to determining the depth of the upper mixed layer and the vertical fluxes of nutrients. These latter two parameters are keys to determining the intensity, and spatial and temporal distributions of phytoplankton blooms. Atmospheric radiation budgets are not in steady-state. Driven largely by anthropogenic activities in the 20th century, increased levels of IR- absorbing gases such as CO2, CH4 and CFC's and NOx will potentially increase atmospheric temperatures on a global scale. The atmospheric radiation budget can affect phytoplankton photosynthesis directly and indirectly. Increased temperature differences between the continents and oceans have been implicated in higher wind stresses at the ocean margins. Increased wind speeds can lead to higher nutrient fluxes. Throughout most of the central oceans, nitrate concentrations are sub-micromolar and there is strong evidence that the quantum efficiency of Photosystem II is impaired by nutrient stress. Higher nutrient fluxes would lead to both an increase in phytoplankton biomass and higher biomass-specific rates of carbon fixation. However, in the center of the ocean gyres, increased radiative heating could reduce the vertical flux of nutrients to the euphotic zone, and hence lead to a reduction in phytoplankton carbon fixation. Increased desertification in terrestrial ecosystems can lead to increased aeolean loadings of essential micronutrients, such as iron. An increased flux of aeolean micronutrients could fertilize nutrient-replete areas of the open ocean with limiting trace elements, thereby stimulating photosynthetic rates. The factors which limit phytoplankton biomass and photosynthesis are discussed and examined with regard to potential changes in the Earth climate system which can lead the oceans away from steady-state. While it is difficult to confidently deduce changes in either phytoplankton biomass or photosynthetic rates on decadal time scales, time-series analysis of ocean transparency data suggest long-term trends have occurred in the North Pacific Ocean in the 20th century. However, calculations of net carbon uptake by the oceans resulting from phytoplankton photosynthesis suggest that without a supply of nutrients external to the ocean, carbon fixation in the open ocean is not presently a significant sink for excess atmospheric CO2.}, - language = {en}, - number = {3}, - urldate = {2024-01-06}, - journal = {Photosynthesis Research}, - author = {Falkowski, Paul G.}, - month = mar, - year = {1994}, - keywords = {biogeochemical cycles, oceans, photoacclimation, Photosystem II nutrient limitation, phytoplankton quantum efficiency of photosynthesis}, - pages = {235--258}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/6ECKTMBR/Falkowski - 1994 - The role of phytoplankton photosynthesis in global.pdf:application/pdf}, -} - -@article{treguer_influence_2018, - title = {Influence of diatom diversity on the ocean biological carbon pump}, - volume = {11}, - copyright = {2017 Springer Nature Limited}, - issn = {1752-0908}, - url = {https://www.nature.com/articles/s41561-017-0028-x}, - doi = {10.1038/s41561-017-0028-x}, - abstract = {Diatoms sustain the marine food web and contribute to the export of carbon from the surface ocean to depth. They account for about 40\% of marine primary productivity and particulate carbon exported to depth as part of the biological pump. Diatoms have long been known to be abundant in turbulent, nutrient-rich waters, but observations and simulations indicate that they are dominant also in meso- and submesoscale structures such as fronts and filaments, and in the deep chlorophyll maximum. Diatoms vary widely in size, morphology and elemental composition, all of which control the quality, quantity and sinking speed of biogenic matter to depth. In particular, their silica shells provide ballast to marine snow and faecal pellets, and can help transport carbon to both the mesopelagic layer and deep ocean. Herein we show that the extent to which diatoms contribute to the export of carbon varies by diatom type, with carbon transfer modulated by the Si/C ratio of diatom cells, the thickness of the shells and their life strategies; for instance, the tendency to form aggregates or resting spores. Model simulations project a decline in the contribution of diatoms to primary production everywhere outside of the Southern Ocean. We argue that we need to understand changes in diatom diversity, life cycle and plankton interactions in a warmer and more acidic ocean in much more detail to fully assess any changes in their contribution to the biological pump.}, - language = {en}, - number = {1}, - urldate = {2024-01-06}, - journal = {Nature Geoscience}, - author = {Tréguer, Paul and Bowler, Chris and Moriceau, Brivaela and Dutkiewicz, Stephanie and Gehlen, Marion and Aumont, Olivier and Bittner, Lucie and Dugdale, Richard and Finkel, Zoe and Iudicone, Daniele and Jahn, Oliver and Guidi, Lionel and Lasbleiz, Marine and Leblanc, Karine and Levy, Marina and Pondaven, Philippe}, - month = jan, - year = {2018}, - note = {Number: 1 -Publisher: Nature Publishing Group}, - keywords = {Biogeochemistry, Carbon cycle, Marine biology, Ocean sciences}, - pages = {27--37}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/VLLSAC7H/Tréguer et al. - 2018 - Influence of diatom diversity on the ocean biologi.pdf:application/pdf}, -} - -@misc{bengio_representation_2014, - title = {Representation {Learning}: {A} {Review} and {New} {Perspectives}}, - shorttitle = {Representation {Learning}}, - url = {http://arxiv.org/abs/1206.5538}, - abstract = {The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning.}, - urldate = {2024-01-06}, - publisher = {arXiv}, - author = {Bengio, Yoshua and Courville, Aaron and Vincent, Pascal}, - month = apr, - year = {2014}, - note = {arXiv:1206.5538 [cs]}, - keywords = {Computer Science - Machine Learning}, - file = {arXiv.org Snapshot:/Users/birdy/Zotero/storage/EYA2B52H/1206.html:text/html;Full Text PDF:/Users/birdy/Zotero/storage/2CFSH6MT/Bengio et al. - 2014 - Representation Learning A Review and New Perspect.pdf:application/pdf}, -} - -@misc{white_gradients2-mgl1704-ifcb-abundance_2020-04-01_v10_2020, - title = {Gradients2-{MGL1704}-{IFCB}-{Abundance}\_2020-04-01\_v1.0}, - url = {https://zenodo.org/records/4267140}, - doi = {10.5281/zenodo.4267140}, - abstract = {Cruise: Gradients 2, MGL1704 Project Name: Simons Foundation, Gradient NPSG Dataset Description: The Imaging FlowCytoBot (IFCB) is an in situ automated imaging flow cytometer that generates images of particles suspended in seawater, in this case from the underway uncontaminated seawater system aboard the R/V Langseth (intake 5m).   The IFCB uses a recycled sheath fluid (0.2 µm filtered seawater) to align and drive particles individually towards a light source (red laser, 4.5 mW) in order to detect and identify single or colonial cells using a combination of optical properties (red fluorescence and light scattering intensities) and high resolution images (3.2 pixels per micron) by a mounted camera. Both optical properties are used to trigger targeted image acquisition of suspended particles in the size range {\textless}4 to 100 μm. The instrument continuously samples (few seconds) from  {\textasciitilde}5 ml aliquots from the intake, and processes all particles contained in that volume for the next 20 mins.  Images corresponding to the "Sample" variable are available on the cruise's dashboard (http://ifcb-data.soest.hawaii.edu/IFCB\_NPTZ).  This dataset is for the abundance of imaged cells by genus.  For each sample, the total number of cells classified to the genus-level by a random forest algorithm (Sosik and Olson, 2007 doi:10.4319/lom.2007.5.204) is counted and divided by the corresponding volume analyzed ({\textasciitilde}5 mL). Note that we used all the images collected during Gradients 2.0 to train the random forest algorithm and that classification is therefore highly accurate for this dataset. Using 7 µm calibration beads, we estimated that the error of cell concentration due to cell detection during sample acquisition averages 11 ± 10 \%, independently of concentrations in the range 1-10000 cell/mL.}, - urldate = {2024-01-06}, - publisher = {Zenodo}, - author = {White, Angelicque}, - month = apr, - year = {2020}, - file = {Zenodo Snapshot:/Users/birdy/Zotero/storage/456EVCSQ/4267140.html:text/html}, -} - -@article{kanungo_efficient_2002, - title = {An efficient k-means clustering algorithm: analysis and implementation}, - volume = {24}, - issn = {1939-3539}, - shorttitle = {An efficient k-means clustering algorithm}, - url = {https://ieeexplore.ieee.org/document/1017616}, - doi = {10.1109/TPAMI.2002.1017616}, - abstract = {In k-means clustering, we are given a set of n data points in d-dimensional space R/sup d/ and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd's (1982) algorithm. We present a simple and efficient implementation of Lloyd's k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm's running time, which shows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in color quantization, data compression, and image segmentation.}, - number = {7}, - urldate = {2024-01-06}, - journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, - author = {Kanungo, T. and Mount, D.M. and Netanyahu, N.S. and Piatko, C.D. and Silverman, R. and Wu, A.Y.}, - month = jul, - year = {2002}, - note = {Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence}, - pages = {881--892}, - file = {IEEE Xplore Abstract Record:/Users/birdy/Zotero/storage/RC5LGQ6V/1017616.html:text/html;IEEE Xplore Full Text PDF:/Users/birdy/Zotero/storage/NUTPDLG4/Kanungo et al. - 2002 - An efficient k-means clustering algorithm analysi.pdf:application/pdf}, -} - -@inproceedings{he_deep_2016, - title = {Deep {Residual} {Learning} for {Image} {Recognition}}, - url = {https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html}, - urldate = {2024-01-06}, - author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, - year = {2016}, - pages = {770--778}, - file = {Full Text PDF:/Users/birdy/Zotero/storage/RYXX2LD8/He et al. - 2016 - Deep Residual Learning for Image Recognition.pdf:application/pdf}, -} From 566a844b8926416b8b304175696b9cb871251056 Mon Sep 17 00:00:00 2001 From: Barbie Date: Sat, 6 Jan 2024 17:42:14 -0500 Subject: [PATCH 4/4] add chatgpt acknowledgement --- _posts/2023-11-11-plankton-representation.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/_posts/2023-11-11-plankton-representation.md b/_posts/2023-11-11-plankton-representation.md index 8780972c..b6cff043 100644 --- a/_posts/2023-11-11-plankton-representation.md +++ b/_posts/2023-11-11-plankton-representation.md @@ -123,4 +123,8 @@ Latent space representations will be extracted from each of the models at the la I will use t-SNE (t-Distributed Stochastic Neighbor Embedding) to visualize the feature vectors. -I will then use K-means clustering and compare the emergent groups to the labels in the dataset. \ No newline at end of file +I will then use K-means clustering and compare the emergent groups to the labels in the dataset. + +# Aknowledgments + +ChatGPT was used to create visualizations, write some paragraphs which were then edited, and generate latex equations. \ No newline at end of file

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