Predicting global distributions of eukaryotic plankton communities from satellite data

• Published in: ISME Communications
• Main Authors: Kaneko, Hiroto, Endo, Hisashi, Henry, Nicolas, Berney, Cédric, Mahé, Frédéric, Poulain, Julie, Labadie, Karine, Beluche, Odette, El Hourany, Roy, Acinas, Silvia, Babin, Marcel, Bork, Peer, Bowler, Chris, Cochrane, Guy, de Vargas, Colomban, Gorsky, Gabriel, Guidi, Lionel, Grimsley, Nigel, Hingamp, Pascal, Iudicone, Daniele, Jaillon, Olivier, Kandels, Stefanie, Karsenti, Eric, Not, Fabrice, Poulton, Nicole, Pesant, Stéphane, Sardet, Christian, Speich, Sabrina, Stemmann, Lars, Sullivan, Matthew, Sunagawa, Shinichi, Chaffron, Samuel, Wincker, Patrick, Nakamura, Ryosuke, Karp-Boss, Lee, Boss, Emmanuel, Tomii, Kentaro, Ogata, Hiroyuki
• Other Authors: 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, Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2023
• Subjects: [SDE]Environmental Sciences; Subarctic
• Online Access: https://hal.science/hal-04252462; https://doi.org/10.1038/s43705-023-00308-7

International audience Abstract Satellite remote sensing is a powerful tool to monitor the global dynamics of marine plankton. Previous research has focused on developing models to predict the size or taxonomic groups of phytoplankton. Here, we present an approach to identify community types from a global plankton network that includes phytoplankton and heterotrophic protists and to predict their biogeography using global satellite observations. Six plankton community types were identified from a co-occurrence network inferred using a novel rDNA 18 S V4 planetary-scale eukaryotic metabarcoding dataset. Machine learning techniques were then applied to construct a model that predicted these community types from satellite data. The model showed an overall 67% accuracy in the prediction of the community types. The prediction using 17 satellite-derived parameters showed better performance than that using only temperature and/or the concentration of chlorophyll a . The constructed model predicted the global spatiotemporal distribution of community types over 19 years. The predicted distributions exhibited strong seasonal changes in community types in the subarctic–subtropical boundary regions, which were consistent with previous field observations. The model also identified the long-term trends in the distribution of community types, which suggested responses to ocean warming.