Predicting global distributions of eukaryotic plankton communities from satellite data
International audience 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 pl...
Published in: | ISME Communications |
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Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Other Authors: | , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
HAL CCSD
2023
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Subjects: | |
Online Access: | https://hal.science/hal-04252462 https://hal.science/hal-04252462/document https://hal.science/hal-04252462/file/s43705-023-00308-7.pdf https://doi.org/10.1038/s43705-023-00308-7 |
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ftunivevry:oai:HAL:hal-04252462v1 |
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openpolar |
institution |
Open Polar |
collection |
Université d'Évry-Val-d'Essonne: HAL |
op_collection_id |
ftunivevry |
language |
English |
topic |
Biooceanography Microbial ecology [SDE]Environmental Sciences |
spellingShingle |
Biooceanography Microbial ecology [SDE]Environmental Sciences 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 Predicting global distributions of eukaryotic plankton communities from satellite data |
topic_facet |
Biooceanography Microbial ecology [SDE]Environmental Sciences |
description |
International audience 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. |
author2 |
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 |
author |
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 |
author_facet |
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 |
author_sort |
Kaneko, Hiroto |
title |
Predicting global distributions of eukaryotic plankton communities from satellite data |
title_short |
Predicting global distributions of eukaryotic plankton communities from satellite data |
title_full |
Predicting global distributions of eukaryotic plankton communities from satellite data |
title_fullStr |
Predicting global distributions of eukaryotic plankton communities from satellite data |
title_full_unstemmed |
Predicting global distributions of eukaryotic plankton communities from satellite data |
title_sort |
predicting global distributions of eukaryotic plankton communities from satellite data |
publisher |
HAL CCSD |
publishDate |
2023 |
url |
https://hal.science/hal-04252462 https://hal.science/hal-04252462/document https://hal.science/hal-04252462/file/s43705-023-00308-7.pdf https://doi.org/10.1038/s43705-023-00308-7 |
genre |
Subarctic |
genre_facet |
Subarctic |
op_source |
EISSN: 2730-6151 ISME Communications https://hal.science/hal-04252462 ISME Communications, 2023, 3 (1), pp.101. ⟨10.1038/s43705-023-00308-7⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1038/s43705-023-00308-7 hal-04252462 https://hal.science/hal-04252462 https://hal.science/hal-04252462/document https://hal.science/hal-04252462/file/s43705-023-00308-7.pdf doi:10.1038/s43705-023-00308-7 |
op_rights |
http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1038/s43705-023-00308-7 |
container_title |
ISME Communications |
container_volume |
3 |
container_issue |
1 |
_version_ |
1790608677806276608 |
spelling |
ftunivevry:oai:HAL:hal-04252462v1 2024-02-11T10:09:00+01:00 Predicting global distributions of eukaryotic plankton communities from satellite data 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 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) 2023-12 https://hal.science/hal-04252462 https://hal.science/hal-04252462/document https://hal.science/hal-04252462/file/s43705-023-00308-7.pdf https://doi.org/10.1038/s43705-023-00308-7 en eng HAL CCSD Springer Nature info:eu-repo/semantics/altIdentifier/doi/10.1038/s43705-023-00308-7 hal-04252462 https://hal.science/hal-04252462 https://hal.science/hal-04252462/document https://hal.science/hal-04252462/file/s43705-023-00308-7.pdf doi:10.1038/s43705-023-00308-7 http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess EISSN: 2730-6151 ISME Communications https://hal.science/hal-04252462 ISME Communications, 2023, 3 (1), pp.101. ⟨10.1038/s43705-023-00308-7⟩ Biooceanography Microbial ecology [SDE]Environmental Sciences info:eu-repo/semantics/article Journal articles 2023 ftunivevry https://doi.org/10.1038/s43705-023-00308-7 2024-01-17T17:26:07Z International audience 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. Article in Journal/Newspaper Subarctic Université d'Évry-Val-d'Essonne: HAL ISME Communications 3 1 |