Predicting global distributions of eukaryotic plankton communities from satellite data
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.
Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Subjects: | U30 - Méthodes de recherche, M40 - Écologie aquatique, L60 - Taxonomie et géographie animales, plankton surveys [EN], distribution des populations, plancton, Observation satellitaire, modélisation, zooplancton, technique de prévision, télédétection, http://aims.fao.org/aos/agrovoc/c_231f4aef, http://aims.fao.org/aos/agrovoc/c_6113, http://aims.fao.org/aos/agrovoc/c_5950, http://aims.fao.org/aos/agrovoc/c_9000182, http://aims.fao.org/aos/agrovoc/c_230ab86c, http://aims.fao.org/aos/agrovoc/c_15490, http://aims.fao.org/aos/agrovoc/c_3041, http://aims.fao.org/aos/agrovoc/c_6498, |
Online Access: | http://agritrop.cirad.fr/609606/ http://agritrop.cirad.fr/609606/1/43705_2023_article_308.pdf |
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U30 - Méthodes de recherche M40 - Écologie aquatique L60 - Taxonomie et géographie animales plankton surveys [EN] distribution des populations plancton Observation satellitaire modélisation zooplancton technique de prévision télédétection http://aims.fao.org/aos/agrovoc/c_231f4aef http://aims.fao.org/aos/agrovoc/c_6113 http://aims.fao.org/aos/agrovoc/c_5950 http://aims.fao.org/aos/agrovoc/c_9000182 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_15490 http://aims.fao.org/aos/agrovoc/c_3041 http://aims.fao.org/aos/agrovoc/c_6498 U30 - Méthodes de recherche M40 - Écologie aquatique L60 - Taxonomie et géographie animales plankton surveys [EN] distribution des populations plancton Observation satellitaire modélisation zooplancton technique de prévision télédétection http://aims.fao.org/aos/agrovoc/c_231f4aef http://aims.fao.org/aos/agrovoc/c_6113 http://aims.fao.org/aos/agrovoc/c_5950 http://aims.fao.org/aos/agrovoc/c_9000182 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_15490 http://aims.fao.org/aos/agrovoc/c_3041 http://aims.fao.org/aos/agrovoc/c_6498 |
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U30 - Méthodes de recherche M40 - Écologie aquatique L60 - Taxonomie et géographie animales plankton surveys [EN] distribution des populations plancton Observation satellitaire modélisation zooplancton technique de prévision télédétection http://aims.fao.org/aos/agrovoc/c_231f4aef http://aims.fao.org/aos/agrovoc/c_6113 http://aims.fao.org/aos/agrovoc/c_5950 http://aims.fao.org/aos/agrovoc/c_9000182 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_15490 http://aims.fao.org/aos/agrovoc/c_3041 http://aims.fao.org/aos/agrovoc/c_6498 U30 - Méthodes de recherche M40 - Écologie aquatique L60 - Taxonomie et géographie animales plankton surveys [EN] distribution des populations plancton Observation satellitaire modélisation zooplancton technique de prévision télédétection http://aims.fao.org/aos/agrovoc/c_231f4aef http://aims.fao.org/aos/agrovoc/c_6113 http://aims.fao.org/aos/agrovoc/c_5950 http://aims.fao.org/aos/agrovoc/c_9000182 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_15490 http://aims.fao.org/aos/agrovoc/c_3041 http://aims.fao.org/aos/agrovoc/c_6498 Kaneko, Hiroto Endo, Hisashi Henry, Nicolas Berney, Cédric Mahe, Frédéric Poulain, Julie Labadie, Karine Beluche, Odette El Hourany, Roy Acinas, Silvia G. 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 B. Sunagawa, Shinichi Chaffron, Samuel Wincker, Patrick Nakamura, Ryosuke Karp-Boss, Lee Boss, Emmanuel Bowler, Chris de Vargas, Colomban Tomii, Kentaro Ogata, Hiroshi Y. Predicting global distributions of eukaryotic plankton communities from satellite data |
description |
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. |
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topic_facet |
U30 - Méthodes de recherche M40 - Écologie aquatique L60 - Taxonomie et géographie animales plankton surveys [EN] distribution des populations plancton Observation satellitaire modélisation zooplancton technique de prévision télédétection http://aims.fao.org/aos/agrovoc/c_231f4aef http://aims.fao.org/aos/agrovoc/c_6113 http://aims.fao.org/aos/agrovoc/c_5950 http://aims.fao.org/aos/agrovoc/c_9000182 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_15490 http://aims.fao.org/aos/agrovoc/c_3041 http://aims.fao.org/aos/agrovoc/c_6498 |
author |
Kaneko, Hiroto Endo, Hisashi Henry, Nicolas Berney, Cédric Mahe, Frédéric Poulain, Julie Labadie, Karine Beluche, Odette El Hourany, Roy Acinas, Silvia G. 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 B. Sunagawa, Shinichi Chaffron, Samuel Wincker, Patrick Nakamura, Ryosuke Karp-Boss, Lee Boss, Emmanuel Bowler, Chris de Vargas, Colomban Tomii, Kentaro Ogata, Hiroshi Y. |
author_facet |
Kaneko, Hiroto Endo, Hisashi Henry, Nicolas Berney, Cédric Mahe, Frédéric Poulain, Julie Labadie, Karine Beluche, Odette El Hourany, Roy Acinas, Silvia G. 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 B. Sunagawa, Shinichi Chaffron, Samuel Wincker, Patrick Nakamura, Ryosuke Karp-Boss, Lee Boss, Emmanuel Bowler, Chris de Vargas, Colomban Tomii, Kentaro Ogata, Hiroshi Y. |
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 |
url |
http://agritrop.cirad.fr/609606/ http://agritrop.cirad.fr/609606/1/43705_2023_article_308.pdf |
work_keys_str_mv |
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dig-cirad-fr-6096062024-06-06T18:01:23Z http://agritrop.cirad.fr/609606/ http://agritrop.cirad.fr/609606/ Predicting global distributions of eukaryotic plankton communities from satellite data. Kaneko Hiroto, Endo Hisashi, Henry Nicolas, Berney Cédric, Mahe Frédéric, Poulain Julie, Labadie Karine, Beluche Odette, El Hourany Roy, Acinas Silvia G., 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 B., Sunagawa Shinichi, Chaffron Samuel, Wincker Patrick, Nakamura Ryosuke, Karp-Boss Lee, Boss Emmanuel, Bowler Chris, de Vargas Colomban, Tomii Kentaro, Ogata Hiroshi Y.. 2023. ISME Communications, 3 (1), 9 p.https://doi.org/10.1038/s43705-023-00308-7 <https://doi.org/10.1038/s43705-023-00308-7> Predicting global distributions of eukaryotic plankton communities from satellite data Kaneko, Hiroto Endo, Hisashi Henry, Nicolas Berney, Cédric Mahe, Frédéric Poulain, Julie Labadie, Karine Beluche, Odette El Hourany, Roy Acinas, Silvia G. 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 B. Sunagawa, Shinichi Chaffron, Samuel Wincker, Patrick Nakamura, Ryosuke Karp-Boss, Lee Boss, Emmanuel Bowler, Chris de Vargas, Colomban Tomii, Kentaro Ogata, Hiroshi Y. eng 2023 ISME Communications U30 - Méthodes de recherche M40 - Écologie aquatique L60 - Taxonomie et géographie animales plankton surveys [EN] distribution des populations plancton Observation satellitaire modélisation zooplancton technique de prévision télédétection http://aims.fao.org/aos/agrovoc/c_231f4aef http://aims.fao.org/aos/agrovoc/c_6113 http://aims.fao.org/aos/agrovoc/c_5950 http://aims.fao.org/aos/agrovoc/c_9000182 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_15490 http://aims.fao.org/aos/agrovoc/c_3041 http://aims.fao.org/aos/agrovoc/c_6498 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 info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/609606/1/43705_2023_article_308.pdf text cc_by info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1038/s43705-023-00308-7 10.1038/s43705-023-00308-7 info:eu-repo/semantics/altIdentifier/doi/10.1038/s43705-023-00308-7 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1038/s43705-023-00308-7 info:eu-repo/semantics/dataset/purl/https://www.genome.jp/ftp/db/community/tara/Satellite/ info:eu-repo/semantics/reference/purl/https://github.com/hirotokaneko/plankton-from-satellite info:eu-repo/grantAgreement/ERC/H2020/ANR-10-INBS-0009//(FRA) Organisation et montée en puissance d'une Infrastructure Nationale de Génomique/France-Génomique info:eu-repo/grantAgreement/ERC/H2020/835067//(EU) Untangling eco-evolutionary impacts on diatom genomes over timescales relevant to current climate change/DIATOMIC |