Machine learning techniques to characterize functional traits of plankton from image data
Type | Article | ||||||||||||
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Date | 2022-08 | ||||||||||||
Language | English | ||||||||||||
Author(s) | Orenstein Eric C.1, Ayata Sakina‐dorothée1, 2, Maps Frédéric3, 4, Becker Érica C.5, Benedetti Fabio6, Biard Tristan7, de Garidel‐thoron Thibault8, Ellen Jeffrey S.9, Ferrario Filippo3, 4, 10, Giering Sarah L. C.11, Guy‐haim Tamar12, Hoebeke Laura13, Iversen Morten Hvitfeldt14, Kiørboe Thomas15, Lalonde Jean‐françois16, Lana Arancha17, Laviale Martin18, Lombard Fabien1, Lorimer Tom19, Martini Séverine20, Meyer Albin18, Möller Klas Ove21, Niehoff Barbara14, Ohman Mark D.9, Pradalier Cédric22, Romagnan Jean-Baptiste23, Schröder Simon‐martin24, Sonnet Virginie25, Sosik Heidi M.26, Stemmann Lars S.1, Stock Michiel13, Terbiyik‐kurt Tuba27, Valcárcel‐pérez Nerea28, Vilgrain Laure1, Wacquet Guillaume![]() |
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Affiliation(s) | 1 : Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche Villefranche‐sur‐Mer ,France 2 : Sorbonne Université, Laboratoire d'Océanographie et du Climat, Institut Pierre Simon Laplace (LOCEAN‐IPSL, SU/CNRS/IRD/MNHN) Paris ,France 3 : Département de Biologie Université Laval Québec, Canada 4 : Takuvik Joint International Laboratory Université Laval‐CNRS (UMI 3376), Québec‐Océan, Université Laval Québec ,Canada 5 : Universidade Federal de Santa Catarina (UFSC) Florianópolis Santa Catarina ,Brazil 6 : ETH Zürich Institute of Biogeochemistry and Pollutant Dynamics Zürich ,Switzerland 7 : Laboratoire d'Océanologie et de Géosciences Université du Littoral Côte d'Opale, Université de Lille, CNRS, UMR 8187 Wimereux ,France 8 : Aix‐Marseille Université, CNRS, IRD, Coll. de France, INRAE, CEREGE Aix en Provence, France 9 : Scripps Institution of Oceanography, University of California San Diego La Jolla California, USA 10 : Department of Fisheries and Oceans Maurice Lamontagne Institute Mont‐Joli Québec, Canada 11 : Ocean Biogeosciences National Oceanography Centre Southampton, UK 12 : National Institute of Oceanography, Israel Oceanographic and Limnological Research Haifa ,Israel 13 : KERMIT, Department of Data Analysis and Mathematical Modelling Ghent University Ghent ,Belgium 14 : Alfred Wegener Institute for Polar and Marine Research Bremerhaven, Germany 15 : Centre for Ocean Life, DTU‐Aqua Technical University of Denmark Kongens Lyngby, Denmark 16 : Laboratoire de Vision et Systèmes Numériques Université Laval Québec City Québec ,Canada 17 : Institut Mediterrani d'Estudis Avançats (IMEDEA, UIB‐CSIC) Balearic Islands ,Spain 18 : Université de Lorraine, CNRS, LIEC Metz ,France 19 : Eawag Dübendorf, Switzerland 20 : Aix Marseille University, Université de Toulon, CNRS, IRD, MIO UM Marseille ,France 21 : Helmholtz‐Zentrum Hereon Institute of Carbon Cycle Geesthacht,Germany 22 : GeorgiaTech Lorraine CNRS IRL GT‐CNRS Metz ,France 23 : IFREMER, Centre Atlantique, Laboratoire Ecologie et Modèles pour l'Halieutique (EMH) Unité HALGO, UMR DECOD Nantes ,France 24 : Kiel University Kiel, Germany 25 : Graduate School of Oceanography University of Rhode Island Narragansett Rhode Island ,USA 26 : Woods Hole Oceanographic Institution Woods Hole Massachusetts ,USA 27 : Department of Basic Sciences Cukurova University, Faculty of Fisheries Adana ,Turkey 28 : Centro Oceanográfico de Málaga, IEO, CSIC Fuengirola, Spain 29 : IFREMER, Laboratoire Environnement Ressources Boulogne‐sur‐Mer ,France 30 : Ocean Frontier Institute, Dalhousie University Halifax Nova Scotia, Canada |
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Source | Limnology And Oceanography (0024-3590) (Wiley), 2022-08 , Vol. 67 , N. 8 , P. 1647-1669 | ||||||||||||
DOI | 10.1002/lno.12101 | ||||||||||||
WOS© Times Cited | 14 | ||||||||||||
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. |
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