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Jean-Olivier Irisson, Sakina-Dorothée Ayata, Dhugal Lindsay, Lee Karp-Boss, Lars Stemmann.
Annual Review of Marine Science (2022).
ART
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. Expected final online publication date for the Annual Review of Marine Science, Volume 14 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Carolyn Scheurle, E Copejans, M Mokos, C Dromgool-Regan, E Bastos, E Paredes-Coral, Lars Stemmann.
EMSEA conference (2022).
COMM
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Laetitia Drago, Thelma Panaïotis, Jean-Olivier Irisson, Marcel Babin, Tristan Biard, François Carlotti, Laurent Coppola, Lionel Guidi, Helena Hauss, Lee Karp-Boss, Fabien Lombard, Andrew M P Mcdonnell, Marc Picheral, Andreas Rogge, Anya M Waite, Lars Stemmann, Rainer Kiko.
Frontiers in Marine Science (2022).
ART
Abstract
Zooplankton plays a major role in ocean food webs and biogeochemical cycles, and provides major ecosystem services as a main driver of the biological carbon pump and in sustaining fish communities. Zooplankton is also sensitive to its environment and reacts to its changes. To better understand the importance of zooplankton, and to inform prognostic models that try to represent them, spatially-resolved biomass estimates of key plankton taxa are desirable. In this study we predict, for the first time, the global biomass distribution of 19 zooplankton taxa (1-50 mm Equivalent Spherical Diameter) using observations with the Underwater Vision Profiler 5, a quantitative in situ imaging instrument. After classification of 466,872 organisms from more than 3,549 profiles (0-500 m) obtained between 2008 and 2019 throughout the globe, we estimated their individual biovolumes and converted them to biomass using taxa-specific conversion factors. We then associated these biomass estimates with climatologies of environmental variables (temperature, salinity, oxygen, etc.), to build habitat models using boosted regression trees. The results reveal maximal zooplankton biomass values around 60°N and 55°S as well as minimal values around the oceanic gyres. An increased zooplankton biomass is also predicted for the equator. Global integrated biomass (0-500 m) was estimated at 0.403 PgC. It was largely dominated by Copepoda (35.7%, mostly in polar regions), followed by Eumalacostraca (26.6%) Rhizaria (16.4%, mostly in the intertropical convergence zone). The machine learning approach used here is sensitive to the size of the training set and generates reliable predictions for abundant groups such as Copepoda (R2 ≈ 20-66%) but not for rare ones (Ctenophora, Cnidaria, R2 < 5%). Still, this study offers a first protocol to estimate global, spatially resolved zooplankton biomass and community composition from in situ imaging observations of individual organisms. The underlying dataset covers a period of 10 years while approaches that rely on net samples utilized datasets gathered since the 1960s. Increased use of digital imaging approaches should enable us to obtain zooplankton biomass distribution estimates at basin to global scales in shorter time frames in the future.
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Patricia Martin-Cabrera, Ruben Perez Perez, Jean-Olivier Irrison, Fabien Lombard, Klas Ove Möller, Saskia Rühl, Veronique Creach, Markus Lindh, Lars Stemmann, Lennert Schepers.
Biodiversity Information Science and Standards (2022).
ART
Abstract
Over the last decade, plankton research has experienced extensive developments in automatic image acquisition for identifying and quantifying plankton species. This information is useful for the reporting of plankton occurrences and ecological data. Imagery instruments can vary in the way they sample (benchtop or in situ imagers) and the particle’s size range they target (see Lombard et al. (2019) for an extensive comparison of instruments and specifications). However, due to the wide variety of instruments and their (automatic) output data and formats, it is challenging to integrate datasets that originate from different sources. For this reason, we developed recommendations for plankton imagery data management, which can promote the ability to make these datasets as FAIR (Findable, Accessible, Interoperable and Reusable principles), as possible. The workflow presented here could inspire other Biodiversity Information Standards TDWG communities working with (automated) imagery data (e.g., camera traps) such as the Audubon Core and Machine Observations Interest Group. The recommended data format follows the OBIS-ENV-DATA format (De Pooter et al. 2017), a Darwin Core-based approach to standardise biodiversity data (Wieczorek et al. 2012) used in EurOBIS, the European node of the Ocean Biodiversity Information System (OBIS) and EMODnet Biology, the European Marine Biodiversity Data Network. However, this format does not include sufficient information for imagery data, therefore we propose the use of additional Darwin Core terms. For example, by including the terms identifiedBy, identificationVerificationStatus and identificationReferences in the Occurrence table, more clarity is reported regarding the uncertainty of the classification made by an algorithm. Thus, data providers can publish manually validated datasets or datasets produced by fully automated plankton identification workflows; and users can choose to use validated or not validated data. See in Suppl. material 1 a practical example on how to report an imagery dataset following the best practices. Moreover, the OBIS-ENV-DATA format allows the ingestion of additional information thanks to the use of the Darwin Core (DwC) Extended Measurement Or Facts or eMoF extension in the DwC Event core. The eMoF stores biotic, abiotic and sampling measurements and facts that are related to the Event and Occurrence table. An important aspect of this extension is that it includes standardised terms and controlled vocabularies, such as the British Oceanographic Data Centre (BODC) vocabularies, to standardise parameters that are not covered by DwC. The advantages of these is to unambiguously report information and to include those measurements that cannot be reported in the Event and Occurrence table (e.g., reporting abundance or biomass of plankton), and that are crucial to investigate ecosystem functioning questions. As a consequence, biodiversity data aggregators can extend their scope beyond species occurrence data. Fig. 1 summarises a typical dataflow that goes from imagery data acquisition to publication in several steps: Images are cropped and classified with software. This can be done in EcoTaxa, a web application that allows users to taxonomically classify images of individual organisms. Data is formatted in OBIS-ENV-DATA format. This format can be exported from EcoTaxa through its API. Data is submitted to EurOBIS via the IPT (Integrated Publishing Toolkit). Data is quality controlled by the BioCheck tool. Data in EurOBIS can flow to EMODnet Biology, OBIS and GBIF (Global Biodiversity Information Facility). Images are cropped and classified with software. This can be done in EcoTaxa, a web application that allows users to taxonomically classify images of individual organisms. Data is formatted in OBIS-ENV-DATA format. This format can be exported from EcoTaxa through its API. Data is submitted to EurOBIS via the IPT (Integrated Publishing Toolkit). Data is quality controlled by the BioCheck tool. Data in EurOBIS can flow to EMODnet Biology, OBIS and GBIF (Global Biodiversity Information Facility). Plankton imagery instrument operators now have the possibility to format their data following the best practices and recommendations for plankton imagery data management (Martin-Cabrera et al. 2022). After a dataset is formatted following these guidelines, it can be submitted to the international biodiversity data aggregators, EurOBIS, EMODnet Biology and GBIF. Additionally a (semi) automated dataflow is presented where data providers can classify images in EcoTaxa and export the data in the required formats using an API before submission to EurOBIS. The next steps are to disseminate these best practices, encouraging plankton imagery data generators to implement these workflows to share their data easily, enriching these data portals and encouraging cross collaborations to create data products covering broader geographic scales and plankton species.
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Yawouvi Dodji Soviadan, Fabio Benedetti, Manoela Brandão, Sakina-Dorothée Ayata, Jean-Olivier Irisson, Jean-Louis Jamet, Rainer Kiko, Fabien Lombard, Kissao Gnandi, Lars Stemmann.
Progress in Oceanography (2022).
ART
Abstract
Vertical variations in physical and chemical conditions drive changes in marine zooplankton community composition. In turn, zooplankton communities play a critical role in regulating the transfer of organic matter produced in the surface ocean to deeper layers. Yet, the links between zooplankton community composition and the strength of vertical fluxes of particles remain elusive, especially on a global scale. Here, we provide a comprehensive analysis of variations in zooplankton community composition and vertical particle flux in the upper kilometer of the global ocean. Zooplankton samples were collected across five depth layers and vertical particle fluxes were assessed using continuous profiles of the Underwater Vision Profiler (UVP5) at 57 stations covering seven ocean basins. Zooplankton samples were analysed using a Zooscan and individual organisms were classified into 19 groups for the quantitative analyses. Zooplankton abundance, biomass and vertical particle flux decreased from the surface to 1000 m depth at all latitudes. The zooplankton abundance decrease rate was stronger at sites characterised by oxygen minima (<5µmol O<sub>2</sub>.kg<sup>−1</sup>) where most zooplankton groups showed a marked decline in abundance, except the jellyfishes, molluscs, annelids, large protists and a few copepod families. The attenuation rate of vertical particle fluxes was weaker at such oxygen-depleted sites. Canonical redundancy analyses showed that the epipelagic zooplankton community composition depended on the temperature, on the phytoplankton size distribution and the surface large particulate organic matter while oxygen was an additional important factor for structuring zooplankton in the mesopelagic. Our results further suggest that future changes in surface phytoplankton size and taxa composition and mesopelagic oxygen loss might lead to profound shift in zooplankton abundance and community structure in both the euphotic and mesopelagic ocean. These changes may affect the vertical export and hereby the strength of the biological carbon pump.
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Tom Delmont, Morgan Gaia, Damien Hinsinger, Paul Frémont, Chiara Vanni, Antonio Fernandez-Guerra, A. Murat Eren, Artem Kourlaiev, Leo d'Agata, Quentin Clayssen, Emilie Villar, Karine Labadie, Corinne Cruaud, Julie Poulain, Corinne da Silva, Marc Wessner, Benjamin Noel, Jean-Marc Aury, Colomban de Vargas, Chris Bowler, Eric Karsenti, Shinichi Sunagawa, Silvia Acinas, Peer Bork, Eric Karsenti, Chris Bowler, Christian Sardet, Lars Stemmann, Colomban de Vargas, Magali Lescot, Marcel Babin, Gabriel Gorsky, Nigel Grimsley, Lionel Guidi, Pascal Hingamp, Stefanie Kandels, Daniele Iudicone, Hiroyuki Ogata, Stéphane Pesant, Matthew Sullivan, Fabrice Not, Karp-Boss Lee, Emmanuel Boss, Guy Cochrane, Michael Follows, Olivier Jaillon, Nicole Poulton, Jeroen Raes, Mike Sieracki, Sabrina Speich, Eric Pelletier, Patrick Wincker.
Cell Genomics (2022).
ART
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M. Beck, Sakina-Dorothée Ayata, C. Cailleton, L. Stemmann, L Guidi, Jean-Olivier Irisson.
4th Marine Imaging Workshop (2022).
COMM
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Miriam Beck, Sakina-Dorothée Ayata, Marc Picheral, Fabien Lombard, Rainer Kiko, Lars Stemmann, Lionel Guidi, Jean-Olivier Irisson.
SFEcologie 2022 (2022).
COMM
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Guillaume Feuilloley, Jean-Marc Fromentin, Claire Saraux, Jean-Olivier Irisson, Laetitia Jalabert, Lars Stemmann.
ICES Journal of Marine Science (2022).
ART
Abstract
Abstract In the Gulf of Lions, small pelagic fish have shown reduced body size and body condition after 2007 that would result from changes in zooplankton community. We therefore examined zooplankton density, body size, and taxonomic composition at the closest long-term monitoring station (1995–2019): the coastal Point-B. To cover a broader spectrum of zooplankton community, samples obtained from two nets, the WP2 (200 µm mesh size) and the Regent (690 µm), were analysed with the imaging Zooscan method. One important result was the high stability through time of the zooplankton community. No long-term monotonous trends in density, size, and taxonomic composition were detected. Interannual variations in zooplankton size and density were not significantly correlated to any environmental variable, suggesting the possible importance of biotic interactions. Still, an increase in temperature was followed by a sharp decrease of zooplankton density in 2015, after which only gelatinous groups recovered. No change in the zooplankton community was detected around 2007 to support bottom-up control on small pelagic fish. Whether this derives from different local processes between the Gulf of Lions and the Ligurian Sea cannot be excluded, highlighting the need for simultaneous monitoring of different ecosystem compartments to fully understand the impact of climate change.
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Andre Abreu, Etienne Bourgois, Adam Gristwood, Romain Troublé, Silvia Acinas, Peer Bork, Emmanuel Boss, Chris Bowler, Marko Budinich, Samuel Chaffron, Colomban de Vargas, Tom Delmont, Damien Eveillard, Lionel Guidi, Daniele Iudicone, Stephanie Kandels, Hélène Morlon, Fabien Lombard, Rainer Pepperkok, Juan José Pierella Karlusich, Gwenael Piganeau, Antoine Régimbeau, Guilhem Sommeria-Klein, Lars Stemmann, Matthew Sullivan, Shinichi Sunagawa, Patrick Wincker, Olivier Zablocki, Detlev Arendt, Josipa Bilic, Robert Finn, Edith Heard, Brendan Rouse, Jessica Vamathevan, Raffaella Casotti, Ibon Cancio, Michael Cunliffe, Anne Emmanuelle Kervella, Wiebe Kooistra, Matthias Obst, Nicolas Pade, Deborah Power, Ioulia Santi, Tatiana Margo Tsagaraki, Jan Vanaverbeke.
Nature Microbiology (2022).
ART
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Rainer Kiko, Marc Picheral, David Antoine, Marcel Babin, Léo Berline, Tristan Biard, Emmanuel Boss, Peter Brandt, François Carlotti, Svenja Christiansen, Laurent Coppola, Leandro de la Cruz, Emilie Diamond-Riquier, Xavier Durrieu de Madron, Amanda Elineau, Gabriel Gorsky, Lionel Guidi, Helena Hauss, Jean-Olivier Irisson, Lee Karp-Boss, Johannes Karstensen, Dong-Gyun Kim, Rachel Lekanoff, Fabien Lombard, Rubens Lopes, Claudie Marec, Andrew Mcdonnell, Daniela Niemeyer, Margaux Noyon, Stephanie O'Daly, Mark Ohman, Jessica Pretty, Andreas Rogge, Sarah Searson, Masashi Shibata, Yuji Tanaka, Toste Tanhua, Jan Taucher, Emilia Trudnowska, Jessica Turner, Anya Waite, Lars Stemmann.
Earth System Science Data (2022).
ART
Abstract
Marine particles of different nature are found throughout the global ocean. The term “marine particles” describes detritus aggregates and fecal pellets as well as bacterioplankton, phytoplankton, zooplankton and nekton. Here, we present a global particle size distribution dataset obtained with several Underwater Vision Profiler 5 (UVP5) camera systems. Overall, within the 64 µm to about 50 mm size range covered by the UVP5, detrital particles are the most abundant component of all marine particles; thus, measurements of the particle size distribution with the UVP5 can yield important information on detrital particle dynamics. During deployment, which is possible down to 6000 m depth, the UVP5 images a volume of about 1 L at a frequency of 6 to 20 Hz. Each image is segmented in real time, and size measurements of particles are automatically stored. All UVP5 units used to generate the dataset presented here were inter-calibrated using a UVP5 high-definition unit as reference. Our consistent particle size distribution dataset contains 8805 vertical profiles collected between 19 June 2008 and 23 November 2020. All major ocean basins, as well as the Mediterranean Sea and the Baltic Sea, were sampled. A total of 19 % of all profiles had a maximum sampling depth shallower than 200 dbar, 38 % sampled at least the upper 1000 dbar depth range and 11 % went down to at least 3000 dbar depth. First analysis of the particle size distribution dataset shows that particle abundance is found to be high at high latitudes and in coastal areas where surface productivity or continental inputs are elevated. The lowest values are found in the deep ocean and in the oceanic gyres. Our dataset should be valuable for more in-depth studies that focus on the analysis of regional, temporal and global patterns of particle size distribution and flux as well as for the development and adjustment of regional and global biogeochemical models. The marine particle size distribution dataset (Kiko et al., 2021) is available at https://doi.org/10.1594/PANGAEA.924375.
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Janaina Rigonato, Marko Budinich, Alejandro Murillo, Manoela Brandão, Juan Karlusich, Yawouvi Dodji Soviadan, Ann Gregory, Hisashi Endo, Florian Kokoszka, Dean Vik, Nicolas Henry, Paul Frémont, Karine Labadie, Ahmed Zayed, Céline Dimier, Marc Picheral, Sarah Searson, Julie Poulain, Stefanie Kandels, Stéphane Pesant, Eric Karsenti, Peer Bork, Chris Bowler, Samuel Chaffron, Colomban de Vargas, Damien Eveillard, Marion Gehlen, Daniele Iudicone, Fabien Lombard, Hiroyuki Ogata, Lars Stemmann, Matthew Sullivan, Shinichi Sunagawa, Patrick Wincker, Olivier Jaillon.
UNDEFINED
Abstract
Marine plankton mitigate anthropogenic greenhouse gases, modulate biogeochemical cycles, and provide fishery resources. Plankton is distributed across a stratified ecosystem of sunlit surface waters and a vast, though understudied, mesopelagic ‘dark ocean’. In this study, we mapped viruses, prokaryotes, and pico-eukaryotes across 32 globally-distributed cross-depth samples collected during the Tara Oceans Expedition, and assessed their ecologies. Based on depth and O 2 measurements, we divided the marine habitat into epipelagic, oxic mesopelagic, and oxygen minimum zone (OMZ) eco-regions. We identified specific communities associated with each marine habitat, and pinpoint environmental drivers of dark ocean communities. Our results indicate that water masses primarily control mesopelagic community composition. Through co-occurrence network inference and analysis, we identified signature communities strongly associated with OMZ eco-regions. Mesopelagic communities appear to be constrained by a combination of factors compared to epipelagic communities. Thus, variations in a given abiotic factor may cause different responses in sunlit and dark ocean communities. This study expands our knowledge about the ecology of planktonic organisms inhabiting the mesopelagic zone.
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Caroline Vernette, Julien Lecubin, Pablo Sánchez, Silvia G Acinas, Marcel Babin, Peer Bork, Emmanuel Boss, Chris Bowler, Guy Cochrane, Colomban de Vargas, Gabriel Gorsky, Lionel Guidi, Nigel Grimsley, Daniele Iudicone, Olivier Jaillon, Stefanie Kandels-Lewis, Lee Karp-Boss, Eric Karsenti, Fabrice Not, Hiroyuki Ogata, Nicole Poulton, Stéphane Pesant, Christian Sardet, Sabrina Speich, Lars Stemmann, Matthew Sullivan, Shinichi Sunagawa, Patrick Wincker, Shinichi Sunagawa, Tom O. Delmont, Silvia G Acinas, Eric Pelletier, Pascal Hingamp, Magali Lescot.
Nucleic Acids Research (2022).
ART
Abstract
Abstract Testing hypothesis about the biogeography of genes using large data resources such as Tara Oceans marine metagenomes and metatranscriptomes requires significant hardware resources and programming skills. The new release of the ‘Ocean Gene Atlas’ (OGA2) is a freely available intuitive online service to mine large and complex marine environmental genomic databases. OGA2 datasets available have been extended and now include, from the Tara Oceans portfolio: (i) eukaryotic Metagenome-Assembled-Genomes (MAGs) and Single-cell Assembled Genomes (SAGs) (10.2E+6 coding genes), (ii) version 2 of Ocean Microbial Reference Gene Catalogue (46.8E+6 non-redundant genes), (iii) 924 MetaGenomic Transcriptomes (7E+6 unigenes), (iv) 530 MAGs from an Arctic MAG catalogue (1E+6 genes) and (v) 1888 Bacterial and Archaeal Genomes (4.5E+6 genes), and an additional dataset from the Malaspina 2010 global circumnavigation: (vi) 317 Malaspina Deep Metagenome Assembled Genomes (0.9E+6 genes). Novel analyses enabled by OGA2 include phylogenetic tree inference to visualize user queries within their context of sequence homologues from both the marine environmental dataset and the RefSeq database. An Application Programming Interface (API) now allows users to query OGA2 using command-line tools, hence providing local workflow integration. Finally, gene abundance can be interactively filtered directly on map displays using any of the available environmental variables. Ocean Gene Atlas v2.0 is freely-available at: https://tara-oceans.mio.osupytheas.fr/ocean-gene-atlas/.
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D. J. Clements, S. Yang, T. Weber, A. M. P. Mcdonnell, R. Kiko, L. Stemmann, D. Bianchi.
Global Biogeochemical Cycles (2022).
ART
Abstract
The abundance and size distribution of marine particles control a range of biogeochemical and ecological processes in the ocean, including carbon sequestration. These quantities are the result of complex physical-biological interactions that are difficult to observe, and their spatial and temporal patterns remain uncertain. Here, we present a novel analysis of particle size distributions (PSDs) from a global compilation of in situ Underwater Vision Profiler 5 (UVP5) optical measurements. Using a machine learning algorithm, we extrapolate sparse UVP5 observations to the global ocean from well-sampled oceanographic variables. We reconstruct global maps of PSD parameters (biovolume [BV] and slope) for particles at the base of the euphotic zone. These reconstructions reveal consistent global patterns, with high chlorophyll regions generally characterized by high particle BV and flatter PSD slope, that is, a high relative abundance of large versus small particles. The resulting negative correlations between particle BV and slope further suggests synergistic effects on size-dependent processes such as sinking particle fluxes. Our approach and estimates provide a baseline for an improved understanding of particle cycles in the ocean, and pave the way to global, three-dimensional reconstructions of PSD and sinking particle fluxes from the growing body of UVP5 observations.
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Marc Picheral, Camille Catalano, Denis Brousseau, Hervé Claustre, Laurent Coppola, Edouard Leymarie, Jérôme Coindat, Fabio Dias, Sylvain Fevre, Lionel Guidi, Jean-Olivier Irisson, Louis Legendre, Fabien Lombard, Laurent Mortier, Christophe Penkerch, Andreas Rogge, Catherine Schmechtig, Simon Thibault, Thierry Tixier, Anya Waite, Lars Stemmann.
Limnology and Oceanography: Methods (2022).
ART
Abstract
Autonomous and cabled platforms are revolutionizing our understanding of ocean systems by providing 4D monitoring of the water column, thus going beyond the reach of ship-based surveys and increasing the depth of remotely sensed observations. However, very few commercially available sensors for such platforms are capable of monitoring large particulate matter (100-2000 μm) and plankton despite their important roles in the biological carbon pump and as trophic links from phytoplankton to fish. Here, we provide details of a new, commercially available scientific camera-based particle counter, specifically designed to be deployed on autonomous and cabled platforms: the Underwater Vision Profiler 6 (UVP6). Indeed, the UVP6 camera-and-lighting and processing system, while small in size and requiring low power, provides data of quality comparable to that of previous much larger UVPs deployed from ships. We detail the UVP6 camera settings, its performance when acquiring data on aquatic particles and plankton, their quality control, analysis of its recordings, and streaming from in situ acquisition to users. In addition, we explain how the UVP6 has already been integrated into platforms such as BGC-Argo floats, gliders and long-term mooring systems (autonomous platforms). Finally, we use results from actual deployments to illustrate how UVP6 data can contribute to addressing longstanding questions in marine science, and also suggest new avenues that can be explored using UVP6-equipped autonomous platforms.
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Eric C. Orenstein, Sakina-Dorothée Ayata, Frédéric Maps, Érica C. Becker, Fabio Benedetti, Tristan Biard, Thibault de Garidel-Thoron, Jeffrey S. Ellen, Filippo Ferrario, Sarah L. C. Giering, Tamar Guy-Haim, Laura Hoebeke, Morten Hvitfeldt Iversen, Thomas Kiørboe, Jean-François Lalonde, Arancha Lana, Martin Laviale, Fabien Lombard, Tom Lorimer, Severine Martini, Albin Meyer, Klas Ove Möller, Barbara Niehoff, Mark D. Ohman, Cedric Pradalier, Jean-Baptiste Romagnan, Simon-Martin Schröder, Virginie Sonnet, Heidi M. Sosik, Lars S Stemmann, Michiel Stock, Tuba Terbiyik-Kurt, Nerea Valcárcel-Pérez, Laure Vilgrain, Guillaume Wacquet, Anya M. Waite, Jean-Olivier Irisson.
Limnology and Oceanography (2022).
ART
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 streams 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 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 other aquatic or terrestrial organisms.