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2. Machine learning techniques to characterize functional traits of plankton from image data

Author

Orenstein, Eric C, Ayata, Sakina‐Dorothée, Maps, Frédéric, Becker, Érica C, Benedetti, Fabio, Biard, Tristan, Garidel‐Thoron, Thibault, Ellen, Jeffrey S, Ferrario, Filippo, Giering, Sarah LC, Guy‐Haim, Tamar, Hoebeke, Laura, Iversen, Morten Hvitfeldt, Kiørboe, Thomas, Lalonde, Jean‐François, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, Mark D, Pradalier, Cédric, Romagnan, Jean‐Baptiste, Schröder, Simon‐Martin, Sonnet, Virginie, Sosik, Heidi M, Stemmann, Lars S, Stock, Michiel, Terbiyik‐Kurt, Tuba, Valcárcel‐Pérez, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya M, and Irisson, Jean‐Olivier

Subjects
Biological Sciences, Ecology, Earth Sciences, Environmental Sciences, Marine Biology & Hydrobiology, Biological sciences, Earth sciences, Environmental sciences
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.

Published
2022

3. Copepod diapause and the biogeography of the marine lipidscape

Author

Record, Nicholas R, Ji, Rubao, Maps, Frédéric, Varpe, Øystein, Runge, Jeffrey A, Petrik, Colleen M, and Johns, David

Subjects
Life Below Water, copepod, diapause, global biogeography, lipidscape, model, trait, Earth Sciences, Environmental Sciences, Biological Sciences, Ecology
Published
2018

8. Le concept d’approche écosystémique appliqué à l’estuaire maritime du Saint-Laurent (Canada)

Author

Savenkoff, Claude, Gagné, Jacques A., Gilbert, Michel, Castonguay, Martin, Chabot, Denis, Chassé, Joël, Comtois, Sophie, Dutil, Jean-Denis, Galbraith, Peter S., Gosselin, Jean-François, Grégoire, François, Larocque, Richard, Larouche, Pierre, Lavoie, Diane, Lebeuf, Michel, Lesage, Véronique, Maps, Frédéric, McQuinn, Ian H., Mosnier, Arnaud, Nozères, Claude, Ouellet, Patrick, Plourde, Stéphane, Sainte-Marie, Bernard, Savard, Louise, Scarratt, Michael, and Starr, Michel

Published
2017

9. Automatic estimation of lipid content from in situ images of Arctic copepods using machine learning.

Author

Maps, Frédéric, Storożenko, Piotr Pasza, Świeżewski, Jędrzej, and Ayata, Sakina-Dorothée

Subjects
*CONVOLUTIONAL neural networks, *MACHINE learning, *COPEPODA, *BIOLOGICAL networks, *MARINE ecology
Abstract

In Arctic marine ecosystems, large planktonic copepods form a crucial hub of matter and energy. Their energy-rich lipid stores play a central role in marine trophic networks and the biological carbon pump. Since the past ~15 years, in situ imaging devices provide images whose resolution allows us to estimate an individual copepod's lipid sac volume, and this reveals many ecological information inaccessible otherwise. One such device is the Lightframe On-sight Keyspecies Investigation. However, when done manually, weeks of work are needed by trained personnel to obtain such information for only a handful of sampled images. We removed this hurdle by training a machine learning algorithm (a convolutional neural network) to estimate the lipid content of individual Arctic copepods from the in situ images. This algorithm obtains such information at a speed (a few minutes) and a resolution (individuals, over half a meter on the vertical), allowing us to revisit historical datasets of in situ images to better understand the dynamics of lipid production and distribution and to develop efficient monitoring protocols at a moment when marine ecosystems are facing rapid upheavals and increasing threats. [ABSTRACT FROM AUTHOR]

Published
2024
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11. A food web model for the Baffin Bay coastal and shelf ecosystem. Part 1 : Ecopath Technical Report

Author

Takuvik, Québec Océan, Littoral, NeoLab, Pedro, Sara, Kenny, Tiff-Annie, Maps, Frédéric, Lemire, Mélanie, Saint-Béat, Blanche, Babin, Marcel, Tremblay, Jean-Éric, Yunda-Guarin, Gustavo, Moisan, Marie-Ange, Boissinot, Justin, Hoover, Carie, Janjua, Muhammad Yamin, Herbig, Jennifer, Geoffroy, Maxime, Little, Matthew, Chan, Laurie, Takuvik, Québec Océan, Littoral, NeoLab, Pedro, Sara, Kenny, Tiff-Annie, Maps, Frédéric, Lemire, Mélanie, Saint-Béat, Blanche, Babin, Marcel, Tremblay, Jean-Éric, Yunda-Guarin, Gustavo, Moisan, Marie-Ange, Boissinot, Justin, Hoover, Carie, Janjua, Muhammad Yamin, Herbig, Jennifer, Geoffroy, Maxime, Little, Matthew, and Chan, Laurie

Abstract

This work was undertaken as part of a multidisciplinary research project funded by the Marine Observation Prediction and Assessment Network - MEOPAR (at ULaval), Canadian Institute of Health Research – CIHR (at University of Ottawa), and Sentinel North (at ULaval), and hosted at Université Laval, in Canada. The objective of the overall project is to support the food security (i.e., the availability and access to sufficient, safe, nutritious food that meets dietary preference) of Inuit communities of the Eastern Canadian Arctic, as well as to explore ways to adapt to effects of climate change. Inuit fish and hunt local marine species, from invertebrates to fish and marine mammals, which make a large part of their diet and are central to their food security. With temperatures increasing twice as fast as the global average and sea ice becoming thinner and forming later, climate change effects on the distribution and abundance of Arctic marine species are already taking place. To better understand the effects of climate change in important subsistence species, a multi-species model (Ecopath with Ecosim) will be used to inform the development of an integrated ecosystem assessment. The model will be used as a tool to co-create scenarios of ecosystem change with the community of Qikiqtarjuaq, Nunavut, to inform adaptation strategies regarding food security (e.g., potential of new fisheries in the region). This report describes the development of an Ecopath model of the Baffin Bay coastal and shelf ecosystem. The methodology, data used to construct the model, data gaps and limitations are described.

Published
2023

12. Structure and function of the western Baffin Bay coastal and shelf ecosystem

Author

Pedro, Sara, Lemire, Mélanie, Hoover, Carie, Saint Beat, Blanche, Janjua, Muhammad Y., Herbig, Jennifer, Geoffroy, Maxime, Yunda-guarin, Gustavo, Moisan, Marie-ange, Boissinot, Justin, Tremblay, Jean-éric, Little, Matthew, Chan, Laurie, Babin, Marcel, Kenny, Tiff-annie, Maps, Frédéric, Pedro, Sara, Lemire, Mélanie, Hoover, Carie, Saint Beat, Blanche, Janjua, Muhammad Y., Herbig, Jennifer, Geoffroy, Maxime, Yunda-guarin, Gustavo, Moisan, Marie-ange, Boissinot, Justin, Tremblay, Jean-éric, Little, Matthew, Chan, Laurie, Babin, Marcel, Kenny, Tiff-annie, and Maps, Frédéric

Abstract

Arctic marine species, from benthos to fish and mammals, are essential for food security and sovereignty of Inuit people. Inuit food security is dependent on the availability, accessibility, quality, and sustainability of country food resources. However, climate change effects are threatening Inuit food systems through changes in abundance and nutritional quality of locally harvested species, while foundational knowledge of Arctic food webs remains elusive. Here, we summarized scientific knowledge available for the western Baffin Bay coastal and shelf ecosystem by building a food web model using the Ecopath with Ecosim modeling framework. Based on this model, we calculated ecological network analysis indices to describe structure and function of the system. We used Linear Inverse Modeling and Monte Carlo analysis to assess parameter uncertainty, generating plausible parameterizations of this ecosystem from which a probability density distribution for each index was generated. Our findings suggest that the system is controlled by intermediate trophic levels, highlighting the key role of Arctic cod (Boreogadus saida) as prey fish, as well as the importance of other less studied groups like cephalopods in controlling energy flows. Most of the ecosystem biomass is retained in the system, with very little lost to subsistence harvest and commercial fisheries, indicating that these activities were within a sustainable range during the modeling period. Our model also highlights the scientific knowledge gaps that still exist (e.g., species abundances), including valued harvest species like Arctic char (Salvelinus alpinus), walrus (Odobenus rosmarus), and seals, and importantly our poor understanding of the system in winter. Moving forward, we will collaborate with Inuit partners in Qikiqtarjuaq, NU, Canada, to improve this modeling tool by including Inuit knowledge. This tool thus serves as a starting point for collaborative discussions with Inuit partners and how its use ca

Published
2023
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13. Copepods’true colors : astaxanthin pigmentationas an indicator of fitness

Author

Vilgrain, Laure, Maps, Frédéric, Basedow, Sünnje, Trudnowska, Emilia, Madoui, Mohammed-Amin, Niehoff, Barbara, Ayata, Sakina-Dorothée, Vilgrain, Laure, Maps, Frédéric, Basedow, Sünnje, Trudnowska, Emilia, Madoui, Mohammed-Amin, Niehoff, Barbara, and Ayata, Sakina-Dorothée

Abstract

Pigmentation is often overlooked in zooplankton, since these organisms are mostly colorless to fit the translucid water medium. However, one of the dominant zooplankton taxa in aquatic ecosystems - copepods - often show a bright red-orange or blue coloration owing to the accumulation of carotenoid pigments in some parts of their bodies. Even though there are many functional traits describing copepod’s performance (e.g. size, feeding and reproductive modes), it is surprising that the role of such a simple and visible trait as coloration has not been studied in a coherent manner yet. Here, by reviewing 95 studies, we demonstrate that carotenoid-based pigmentation (mainly caused by astaxanthin molecules) is a widespread functional trait in freshwater and marine copepods. We propose a way to disentangle the complex and thus intriguing patterns of pigment expression along latitudinal and altitudinal gradients, addressing its relationship to diet quality and quantity, temperature, ultraviolet radiation stress, predation pressure, lipid metabolism, and reproduction. We also show that large scale variations in pigmentation are difficult to tackle because of the fundamental plasticity of this trait at short time scales (i.e. hours, days), and the most recent information about carotenoid bioconversion are addressed (genes and enzyme identification, influence of microbiota). From this literature review, we hypothesize that pigments play a “swiss-army knife” role for copepod’s fitness, useful in various ecosystem conditions, owing to the strong antioxidant power and the finely-tuned metabolism of astaxanthin. With larger antioxidant capacities (survival), higher metabolisms (growth) and more offspring in better condition (reproduction), red morphs appear more successful than their uncolored siblings. Also, the potential camouflage strategies allowed by red and blue pigmentation are discussed. We finally formulate new directions and future research fields from molecular to ecosy

Published
2023

14. Functional trait-based approaches as a common framework for aquatic ecologists: Synthesis and recent results

Author

Martini, Séverine, Larras, Floriane, Boyé, Aurélien, Faure, Emile, Aberle, Nicole, Archambault, Philippe, Bacouillard, Lise, Beisner, Beatrix, Bittner, Lucie, Castella, Emmanuel, Danger, Michael, Gauthier, Olivier, Karp-Boss, Lee, Lombard, Fabien, Maps, Frédéric, Stemmann, Lars, Thiébaut, Éric, Usseglio-Polatera, Philippe, Vogt, Meike, Laviale, Martin, Ayata, Sakina‐dorothée, Institut méditerranéen d'océanologie (MIO), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Helmholtz Zentrum für Umweltforschung = Helmholtz Centre for Environmental Research (UFZ), Laboratoire des Sciences de l'Environnement Marin (LEMAR) (LEMAR), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), Norwegian University of Science and Technology [Trondheim] (NTNU), Norwegian University of Science and Technology (NTNU), Institut des Sciences de la MER de Rimouski (ISMER), Université du Québec à Rimouski (UQAR), Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université du Québec à Montréal = University of Québec in Montréal (UQAM), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), Laboratoire d'Ecologie et de Biologie Aquatique - LEBA (Genève, Suisse), Université de Genève = University of Geneva (UNIGE), Laboratoire Interdisciplinaire des Environnements Continentaux (LIEC), Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Terre et Environnement de Lorraine (OTELo), Institut national des sciences de l'Univers (INSU - CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), University of Maine, Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Global Oceans Systems Ecology & Evolution - Tara Oceans (GOSEE), Université de Perpignan Via Domitia (UPVD)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Aix Marseille Université (AMU)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Université de Toulon (UTLN)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche pour le Développement (IRD [France-Nord])-Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay)-European Molecular Biology Laboratory (EMBL)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Université australe du Chili, Takuvik International Research Laboratory, Université Laval [Québec] (ULaval)-Centre National de la Recherche Scientifique (CNRS), Institute of Biogeochemistry and Pollutant Dynamics [ETH Zürich] (IBP), Department of Environmental Systems Science [ETH Zürich] (D-USYS), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich)- Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Zone Atelier du Bassin de la Moselle [LTSER France] (ZAM), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), and Association for the Sciences of Limnology and Oceanography (ASLO)

Subjects
aquatic ecology, [SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, functional traits
Abstract

International audience; Aquatic ecologists face challenges in identifying the general rules of the functioning of ecosystems. A common framework, including freshwater, marine, benthic, and pelagic ecologists, is needed to bridge communication gaps and foster knowledge sharing. This framework should transcend local specificities and taxonomy in order to provide a common ground and shareable tools to address common scientific challenges. In a recent review paper published in Limnology and Oceanography (Martini et al., 2021), we advocated the use of functional trait-based approaches (FTBAs) for aquatic ecologists and proposed concrete paths to go forward. In this presentation, we will first present an overview of this synthesis work. Then, using a few examples of recent results, including from quantitative imaging, genomics, and machine learning, we will then concretely illustrate how FTBAs can allow aquatic ecologists to tackle some of the scientific challenges identified in the paper.

Published
2023
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15. Structure and function of the western Baffin Bay coastal and shelf ecosystem

Author

Pedro, Sara, primary, Lemire, Mélanie, additional, Hoover, Carie, additional, Saint-Béat, Blanche, additional, Janjua, Muhammad Y., additional, Herbig, Jennifer, additional, Geoffroy, Maxime, additional, Yunda-Guarin, Gustavo, additional, Moisan, Marie-Ange, additional, Boissinot, Justin, additional, Tremblay, Jean-Éric, additional, Little, Matthew, additional, Chan, Laurie, additional, Babin, Marcel, additional, Kenny, Tiff-Annie, additional, and Maps, Frédéric, additional

Published
2023
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17. Machine learning techniques to characterize functional traits of plankton from image data

Author

Orenstein, Eric C., Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Erica C., Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey S., Ferrario, Filippo, Giering, Sarah L.C., Guy-Haim, Tamar, Hoebeke, Laura, Iversen, Morten Hvitfeldt, Kiørboe, Thomas, Lalonde, Jean-Francois, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, and Martini, Séverine

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., Limnology and Oceanography, 67 (8), ISSN:0024-3590, ISSN:1939-5590

Published
2022

19. Machine learning techniques to characterise functional traits of plankton image data

Author

Orenstein, Eric, Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica, Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey, Ferrario, Filippo, Giering, Sarah, Guy-Haim, Tamar, Hoebeke, Laura, Iversen, Morten, Kiorboe, Thomas, Lalonde, Jean-Francois, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, Mark, Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon-Martin, Sonnet, Virginie, Sosik, Heidi, Stemmann, Lars, Stock, Michiel, Terbiyik-Kurt, Tuba, Valcárcel-Pérez, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya, Irisson, Jean-Olivier, Orenstein, Eric, Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica, Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey, Ferrario, Filippo, Giering, Sarah, Guy-Haim, Tamar, Hoebeke, Laura, Iversen, Morten, Kiorboe, Thomas, Lalonde, Jean-Francois, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, Mark, Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon-Martin, Sonnet, Virginie, Sosik, Heidi, Stemmann, Lars, Stock, Michiel, Terbiyik-Kurt, Tuba, Valcárcel-Pérez, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya, and Irisson, Jean-Olivier

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.

Published
2022

20. Supplemental Information: Machine learning techniques to characterize functional traits of plankton from image data

Author

Orenstein, Eric C., Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica C., Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey S., Ferrario, Filippo, Giering, Sarah L. C., Guy-Haim, Tamar, Hoebeke, Laura, Hvitfeldt, Morten, Kiørboe, Thomas, Lalonde, Jean François, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, M. D., Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon Martin, Sonnet, Virginie, Sosik, Heidi M., Stemmann, Lars, Stock, Michiel, Terbiyik Kurt, Tuba, Valcárcel, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya M., Irisson, Jean-Olivier, Orenstein, Eric C., Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica C., Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey S., Ferrario, Filippo, Giering, Sarah L. C., Guy-Haim, Tamar, Hoebeke, Laura, Hvitfeldt, Morten, Kiørboe, Thomas, Lalonde, Jean François, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, M. D., Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon Martin, Sonnet, Virginie, Sosik, Heidi M., Stemmann, Lars, Stock, Michiel, Terbiyik Kurt, Tuba, Valcárcel, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya M., and Irisson, Jean-Olivier

Published
2022

21. Machine learning techniques to characterize functional traits of plankton from image data

Author

Centre National de la Recherche Scientifique (France), Belmont Forum, Université Laval, Natural Sciences and Engineering Research Council of Canada, Research Foundation - Flanders, ETH Zurich, Gordon and Betty Moore Foundation, National Science Foundation (US), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brasil), Agence Nationale de la Recherche (France), Ministerio de Economía y Competitividad (España), Institut Universitaire de France, Simons Foundation, Sorbonne Université, Orenstein, Eric C., Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica C., Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey S., Ferrario, Filippo, Giering, Sarah L. C., Guy-Haim, Tamar, Hoebeke, Laura, Hvitfeldt, Morten, Kiørboe, Thomas, Lalonde, Jean François, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, M. D., Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon Martin, Sonnet, Virginie, Sosik, Heidi M., Stemmann, Lars, Stock, Michiel, Terbiyik Kurt, Tuba, Valcárcel, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya M., Irisson, Jean-Olivier, Centre National de la Recherche Scientifique (France), Belmont Forum, Université Laval, Natural Sciences and Engineering Research Council of Canada, Research Foundation - Flanders, ETH Zurich, Gordon and Betty Moore Foundation, National Science Foundation (US), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brasil), Agence Nationale de la Recherche (France), Ministerio de Economía y Competitividad (España), Institut Universitaire de France, Simons Foundation, Sorbonne Université, Orenstein, Eric C., Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica C., Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey S., Ferrario, Filippo, Giering, Sarah L. C., Guy-Haim, Tamar, Hoebeke, Laura, Hvitfeldt, Morten, Kiørboe, Thomas, Lalonde, Jean François, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, M. D., Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon Martin, Sonnet, Virginie, Sosik, Heidi M., Stemmann, Lars, Stock, Michiel, Terbiyik Kurt, Tuba, Valcárcel, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya M., and Irisson, Jean-Olivier

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.

Published
2022

28. Functional trait‐based approaches as a common framework for aquatic ecologists

Author

Martini, Séverine, Larras, Floriane, Boye, Aurelien, Faure, Emile, Aberle, Nicole, Archambault, Philippe, Bacouillard, Lise, Beisner, Beatrix E, Bittner, Lucie, Castella, Emmanuel, Danger, Michael, Gauthier, Olivier, Karp‐boss, Lee, Lombard, Fabien, Maps, Frédéric, Stemmann, Lars, Thiébaut, Eric, Usseglio‐polatera, Philippe, Vogt, Meike, Laviale, Martin, Ayata, Sakina‐dorothée, Martini, Séverine, Larras, Floriane, Boye, Aurelien, Faure, Emile, Aberle, Nicole, Archambault, Philippe, Bacouillard, Lise, Beisner, Beatrix E, Bittner, Lucie, Castella, Emmanuel, Danger, Michael, Gauthier, Olivier, Karp‐boss, Lee, Lombard, Fabien, Maps, Frédéric, Stemmann, Lars, Thiébaut, Eric, Usseglio‐polatera, Philippe, Vogt, Meike, Laviale, Martin, and Ayata, Sakina‐dorothée

Abstract

Aquatic ecologists face challenges in identifying the general rules of the functioning of ecosystems. A common framework, including freshwater, marine, benthic, and pelagic ecologists, is needed to bridge communication gaps and foster knowledge sharing. This framework should transcend local specificities and taxonomy in order to provide a common ground and shareable tools to address common scientific challenges. Here, we advocate the use of functional trait‐based approaches (FTBAs) for aquatic ecologists and propose concrete paths to go forward. Firstly, we propose to unify existing definitions in FTBAs to adopt a common language. Secondly, we list the numerous databases referencing functional traits for aquatic organisms. Thirdly, we present a synthesis on traditional as well as recent promising methods for the study of aquatic functional traits, including imaging and genomics. Finally, we conclude with a highlight on scientific challenges and promising venues for which FTBAs should foster opportunities for future research. By offering practical tools, our framework provides a clear path forward to the adoption of trait‐based approaches in aquatic ecology.

Published
2021
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29. Timing of Calanus finmarchicus diapause in stochastic environments

Author

Bandara, Kanchana, Varpe, Øystein, Maps, Frédéric, Ji, Rubao, Eiane, Ketil, Tverberg, Vigdis, Bandara, Kanchana, Varpe, Øystein, Maps, Frédéric, Ji, Rubao, Eiane, Ketil, and Tverberg, Vigdis

Abstract

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Bandara, K., Varpe, O., Maps, F., Ji, R., Eiane, K., & Tverberg, V. Timing of Calanus finmarchicus diapause in stochastic environments. Ecological Modelling, 460, (2021): 109739, https://doi.org/10.1016/j.ecolmodel.2021.109739., In environments with strong seasonality, many herbivorous zooplankton remain active only during the productive season and undergo a period of inactivity and suppressed development termed ‘diapause’ during the unproductive season. The ability to time the diapause entry and exit in response to the seasonality of the environment is thus essential for their survival. However, timing of diapause may become challenging when environmental conditions vary stochastically across shorter and longer timescales, and particularly when zooplankton lack external cues to predict these variations. In this study, we used a novel individual-based model to study the emerging patterns of diapause timing of the high-latitude marine herbivorous copepod Calanus finmarchicus under shorter- (6-h) and longer-term (interannual) environmental stochasticity. The model simulated growth, development, survival and reproduction (income breeding) of a C. finmarchicus population over multiple calendar years and traced the emergence of behavioral responses and life history strategies. The emergent timing of diapause entry and exit were robust to shorter-term environmental stochasticity, which was manifested through morphological (i.e., body and energy reserve sizes) and behavioral plasticity (i.e., diel vertical migration). Longer-term stochastic variations of temperature and food environments altered the timing of diapause entry, which occurred earlier in warmer years with higher growth potential and vice versa. Irrespective of the modelled environmental variability, diapause exit occurred asynchronously throughout the year. This appeared to be a consequence of a diversified bet hedging strategy, where parents spread the starvation mortality risk of ascending to the upper pelagial at food-deprived times of the year among their offspring. This was a potent strategy, particularly in simulations where the timing of the algal bloom varied stochastically between years, since a fraction of the population was, This work was funded by the project GLIDER, financed by The Research Council of Norway Demo2000 and ConocoPhillips Norge (Grant no. 269188/E30).

Published
2021

35. Contrasting pelagic ecosystem functioning in eastern and western Baffin Bay revealed by trophic network modeling

Author

Saint-Béat, Blanche, Fath, Brian, Aubry, Cyril, Colombet, Jonathan, Dinasquet, Julie, Fortier, Louis, Galindo, Virginie, Grondin, Pierre-Luc, Joux, Fabien, Lalande, Catherine, Leblanc, Mathieu, Raimbault, Patrick, Sime-Ngando, Télesphore, Tremblay, Jean-ERic, Vaulot, Daniel, Maps, Frédéric, Babin, Marcel, Deming, Jody, Bowman, Jeff, Laboratoire Microorganismes : Génome et Environnement (LMGE), Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Océanographie Microbienne (LOMIC), Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Banyuls (OOB), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Takuvik Joint International Laboratory ULAVAL-CNRS, Université Laval [Québec] (ULaval)-Centre National de la Recherche Scientifique (CNRS), Takuvik Joint International Laboratory, Université Laval (Canada) – CNRS (France) (CNRS), Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Centre National de la Recherche Scientifique (CNRS)-Université Laval [Québec] (ULaval), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Banyuls (OOB), and Université Laval [Québec] (ULaval)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0106 biological sciences, Atmospheric Science, Environmental Engineering, 010504 meteorology & atmospheric sciences, Food web modeling, Carbon sequestration, Oceanography, 01 natural sciences, Carbon cycle, Baffin bay, Ecosystem, 14. Life underwater, lcsh:Environmental sciences, ComputingMilieux_MISCELLANEOUS, [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography, 0105 earth and related environmental sciences, Trophic level, lcsh:GE1-350, Ecology, 010604 marine biology & hydrobiology, Geology, 15. Life on land, Plankton, Geotechnical Engineering and Engineering Geology, Food web, 13. Climate action, [SDE]Environmental Sciences, Ecosystem functioning, Environmental science, Network analysis, Arctic ocean, Microbial loop, Bay
Abstract

Baffin Bay, located at the Arctic Ocean’s ‘doorstep’, is a heterogeneous environment where a warm and salty eastern current flows northwards in the opposite direction of a cold and relatively fresh Arctic current flowing along the west coast of the bay. This circulation affects the physical and biogeochemical environment on both sides of the bay. The phytoplanktonic species composition is driven by its environment and, in turn, shapes carbon transfer through the planktonic food web. This study aims at determining the effects of such contrasting environments on ecosystem structure and functioning and the consequences for the carbon cycle. Ecological indices calculated from food web flow values provide ecosystem properties that are not accessible by direct in situ measurement. From new biological data gathered during the Green Edge project, we built a planktonic food web model for each side of Baffin Bay, considering several biological processes involved in the carbon cycle, notably in the gravitational, lipid, and microbial carbon pumps. Missing flow values were estimated by linear inverse modeling. Calculated ecological network analysis indices revealed significant differences in the functioning of each ecosystem. The eastern Baffin Bay food web presents a more specialized food web that constrains carbon through specific and efficient pathways, leading to segregation of the microbial loop from the classical grazing chain. In contrast, the western food web showed redundant and shorter pathways that caused a higher carbon export, especially via lipid and microbial pumps, and thus promoted carbon sequestration. Moreover, indirect effects resulting from bottom-up and top-down control impacted pairwise relations between species differently and led to the dominance of mutualism in the eastern food web. These differences in pairwise relations affect the dynamics and evolution of each food web and thus might lead to contrasting responses to ongoing climate change.

Published
2020
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40. Functional trait‐based approaches as a common framework for aquatic ecologists

Author

Martini, Séverine, primary, Larras, Floriane, additional, Boyé, Aurélien, additional, Faure, Emile, additional, Aberle, Nicole, additional, Archambault, Philippe, additional, Bacouillard, Lise, additional, Beisner, Beatrix E, additional, Bittner, Lucie, additional, Castella, Emmanuel, additional, Danger, Michael, additional, Gauthier, Olivier, additional, Karp‐Boss, Lee, additional, Lombard, Fabien, additional, Maps, Frédéric, additional, Stemmann, Lars, additional, Thiébaut, Eric, additional, Usseglio‐Polatera, Philippe, additional, Vogt, Meike, additional, Laviale, Martin, additional, and Ayata, Sakina‐Dorothée, additional

Published
2020
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44. Functional trait-based approaches as a common framework for freshwater and marine ecologists

Author

Martini, Severine, Larras, Floriane, Boyé, Aurélien, Faure, Emile, Aberle, Nicole, Archambault, Philippe, Bacouillard, Lise, Beisner, Beatrix, Bittner, Lucie, Castella, Emmanuel, Danger, Michael, Gauthier, Olivier, Karp-Boss, Lee, Lombard, Fabien, Maps, Frédéric, Stemmann, Lars, Thiébaut, Éric, Usseglio-Polatera, Philippe, Vogt, Meike, Laviale, Martin, Ayata, Sakina-Dorothée, Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Helmholtz Zentrum für Umweltforschung = Helmholtz Centre for Environmental Research (UFZ), Laboratoire des Sciences de l'Environnement Marin (LEMAR) (LEMAR), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), Norwegian University of Science and Technology [Trondheim] (NTNU), Norwegian University of Science and Technology (NTNU), Takuvik International Research Laboratory, Université Laval [Québec] (ULaval)-Centre National de la Recherche Scientifique (CNRS), Adaptation et diversité en milieu marin (AD2M), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université du Québec à Montréal = University of Québec in Montréal (UQAM), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), Institute for Environmental Sciences [Geneva] (ISE), University of Geneva [Switzerland], Laboratoire Interdisciplinaire des Environnements Continentaux (LIEC), Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Terre et Environnement de Lorraine (OTELo), Institut national des sciences de l'Univers (INSU - CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), University of Maine, Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Zone Atelier du Bassin de la Moselle [LTSER France] (ZAM), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Adaptation et diversité en milieu marin (ADMM), Institut national des sciences de l'Univers (INSU - CNRS)-Station biologique de Roscoff (SBR), Muséum national d'Histoire naturelle (MNHN)-École Pratique des Hautes Études (EPHE), Université de Genève = University of Geneva (UNIGE), Institut Universitaire Européen de la Mer (IUEM), and Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Centre National de la Recherche Scientifique (CNRS)-Université de Brest (UBO)

Subjects
[SDV.EE]Life Sciences [q-bio]/Ecology, environment, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

45. Book of abstracts of the ARTIFACTZ Workshop Artificial intelligence for characterizing plankton traits from images

Author

Maps, Frédéric, Irisson, Jean-Olivier, Ayata, Sakina-Dorothée, Université Laval [Québec] (ULaval), Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), Université Laval (Sentinelle Nord), Université Côte d'Azur (Modelife), Sorbonne Université, Université Laval, Université Côte d'Azur, and ARTIFACTZ

Subjects
machine learning, plankton, imaging, marine, ecology, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, freshwater, functional trait, artificial intelligence, [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Held the 24th-26th of April 2019, in Villefranche-sur-Mer, France, this workshop gathered researchers interested in applying machine learning to identify and quantify functional traits of aquatic organisms from individual images.

Published
2019

48. Marine ecosystems model development should be rooted in past experiences, not anchored in old habits.

Author

Maps, Frédéric and Record, Nicholas R

Subjects
*MARINE ecology, *SCIENTIFIC community, *CALANUS, *QUANTITATIVE research, *HABIT, *ONTOGENY
Abstract

Numerical models of marine ecosystems tend to increase in complexity, incorporating a growing number of functions and parameters. Here, we reflect on the issue of "anchoring" inherent to model development, i.e. the tendency for modellers to take processes, functional forms and parameters from previous studies as granted. We focused on the particular example of the parameterization of temperature-dependent ontogeny in Calanus spp. copepods. We could identify 68 studies that implemented and parameterized this functional relationship. Semantic analysis identified distinct clusters of research scopes and coauthor networks. We showed that biases in parameters origin have the potential to produce misleading results, while recent experimental studies were often not assimilated into contemporary modelling studies. Anchoring involves external constraints in numerical models' development such as conceptual gaps and data scarcity, as well as internal drivers such as academic ontogeny and cultural background of the modeller. Retrospective quantitative literature analyses help identify how biases have worked their way into the collective understanding and help to suggest ways forward for the research community. These involve implementation of revision management systems for parameters and functional forms as already exists for numerical codes, and, as always, a more efficient dialogue between modellers, experimentalists and field ecologists. [ABSTRACT FROM AUTHOR]

Published
2020
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