Predicting global distributions of eukaryotic plankton communities from satellite data
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...
Published in: | ISME Communications |
---|---|
Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Other Authors: | , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Oxford University Press (OUP)
2023
|
Subjects: | |
Online Access: | http://dx.doi.org/10.1038/s43705-023-00308-7 https://www.nature.com/articles/s43705-023-00308-7.pdf https://www.nature.com/articles/s43705-023-00308-7 https://academic.oup.com/ismecommun/article-pdf/3/1/101/56376091/43705_2023_article_308.pdf |
id |
croxfordunivpr:10.1038/s43705-023-00308-7 |
---|---|
record_format |
openpolar |
spelling |
croxfordunivpr:10.1038/s43705-023-00308-7 2024-03-03T08:49:02+00:00 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 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, Hiroyuki MEXT | Japan Society for the Promotion of Science MEXT | Japan Science and Technology Agency Centre National d'Etudes Spatiales Agence Nationale de la Recherche EC | Horizon 2020 Framework Programme Kyoto University MEXT | Japan Society for the Promotion of Science MEXT | Japan Science and Technology Agency Centre National d'Etudes Spatiales Agence Nationale de la Recherche EC | Horizon 2020 Framework Programme Kyoto University 2023 http://dx.doi.org/10.1038/s43705-023-00308-7 https://www.nature.com/articles/s43705-023-00308-7.pdf https://www.nature.com/articles/s43705-023-00308-7 https://academic.oup.com/ismecommun/article-pdf/3/1/101/56376091/43705_2023_article_308.pdf en eng Oxford University Press (OUP) https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 ISME Communications volume 3, issue 1 ISSN 2730-6151 General Medicine journal-article 2023 croxfordunivpr https://doi.org/10.1038/s43705-023-00308-7 2024-02-05T10:33:47Z 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. Article in Journal/Newspaper Subarctic Oxford University Press ISME Communications 3 1 |
institution |
Open Polar |
collection |
Oxford University Press |
op_collection_id |
croxfordunivpr |
language |
English |
topic |
General Medicine |
spellingShingle |
General Medicine Kaneko, Hiroto Endo, Hisashi Henry, Nicolas Berney, Cédric Mahé, 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, Hiroyuki Predicting global distributions of eukaryotic plankton communities from satellite data |
topic_facet |
General Medicine |
description |
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. |
author2 |
MEXT | Japan Society for the Promotion of Science MEXT | Japan Science and Technology Agency Centre National d'Etudes Spatiales Agence Nationale de la Recherche EC | Horizon 2020 Framework Programme Kyoto University MEXT | Japan Society for the Promotion of Science MEXT | Japan Science and Technology Agency Centre National d'Etudes Spatiales Agence Nationale de la Recherche EC | Horizon 2020 Framework Programme Kyoto University |
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 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, Hiroyuki |
author_facet |
Kaneko, Hiroto Endo, Hisashi Henry, Nicolas Berney, Cédric Mahé, 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, 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 |
Oxford University Press (OUP) |
publishDate |
2023 |
url |
http://dx.doi.org/10.1038/s43705-023-00308-7 https://www.nature.com/articles/s43705-023-00308-7.pdf https://www.nature.com/articles/s43705-023-00308-7 https://academic.oup.com/ismecommun/article-pdf/3/1/101/56376091/43705_2023_article_308.pdf |
genre |
Subarctic |
genre_facet |
Subarctic |
op_source |
ISME Communications volume 3, issue 1 ISSN 2730-6151 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 |
op_doi |
https://doi.org/10.1038/s43705-023-00308-7 |
container_title |
ISME Communications |
container_volume |
3 |
container_issue |
1 |
_version_ |
1792506102301065216 |