Predicting global distributions of eukaryotic ...
Document type :
Article dans une revue scientifique: Article original
Title :
Predicting global distributions of eukaryotic plankton communities from satellite data
Author(s) :
Kaneko, Hiroto [Auteur]
Endo, Hisashi [Auteur]
Henry, Nicolas [Auteur]
Station biologique de Roscoff = Roscoff Marine Station [SBR]
Fédération de recherche de Roscoff [FR2424]
Berney, Cédric [Auteur]
Fédération de recherche de Roscoff [FR2424]
ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science [ABIMS]
Station biologique de Roscoff = Roscoff Marine Station [SBR]
Mahé, Frédéric [Auteur]
ARIA Technologies
Poulain, Julie [Auteur]
Génomique métabolique [UMR 8030]
Global Oceans Systems Ecology & Evolution - Tara Oceans [GOSEE]
Institut de Biologie François JACOB [JACOB]
Labadie, Karine [Auteur]
Global Oceans Systems Ecology & Evolution - Tara Oceans [GOSEE]
Génomique métabolique [UMR 8030]
Beluche, Odette [Auteur]
Genoscope - Centre national de séquençage [Evry] [GENOSCOPE]
El Hourany, Roy [Auteur]
Université du Littoral Côte d'Opale [ULCO]
Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 [LOG]
Acinas, Silvia [Auteur]
Institute of Marine Sciences / Institut de Ciències del Mar [Barcelona] [ICM]
Babin, Marcel [Auteur]
Takuvik Joint International Laboratory ULAVAL-CNRS
Bork, Peer [Auteur]
Institut de biologie de l'ENS Paris [IBENS]
Bowler, Chris [Auteur]
Institut de biologie de l'ENS Paris [IBENS]
Global Oceans Systems Ecology & Evolution - Tara Oceans [GOSEE]
Cochrane, Guy [Auteur]
de Vargas, Colomban [Auteur]
Adaptation et diversité en milieu marin [ADMM]
Gorsky, Gabriel [Auteur]
Laboratoire d'océanographie de Villefranche [LOV]
Guidi, Lionel [Auteur]
Laboratoire d'océanographie de Villefranche [LOV]
Grimsley, Nigel [Auteur]
Biologie intégrative des organismes marins [BIOM]
Hingamp, Pascal [Auteur]
Institut méditerranéen d'océanologie [MIO]
Iudicone, Daniele [Auteur]
Stazione Zoologica Anton Dohrn [SZN]
Jaillon, Olivier [Auteur]
Global Oceans Systems Ecology & Evolution - Tara Oceans [GOSEE]
Génomique métabolique [UMR 8030]
Laboratoire d'Analyses Génomiques des Eucaryotes [LAGE]
Genoscope - Centre national de séquençage [Evry] [GENOSCOPE]
Kandels, Stefanie [Auteur]
Karsenti, Eric [Auteur]
Not, Fabrice [Auteur]
Poulton, Nicole [Auteur]
Pesant, Stéphane [Auteur]
Sardet, Christian [Auteur]
Speich, Sabrina [Auteur]
Stemmann, Lars [Auteur]
Sullivan, Matthew [Auteur]
Sunagawa, Shinichi [Auteur]
Wincker, Patrick [Auteur]
Laboratoire d'Analyses Génomiques des Eucaryotes [LAGE]
Nakamura, Ryosuke [Auteur]
Karp-Boss, Lee [Auteur]
Boss, Emmanuel [Auteur]
Tomii, Kentaro [Auteur]
Ogata, Hiroyuki [Auteur]
Chaffron, Samuel [Auteur]
Global Oceans Systems Ecology & Evolution - Tara Oceans [GOSEE]
Combinatoire et Bioinformatique [LS2N - équipe COMBI]
Laboratoire des Sciences du Numérique de Nantes [LS2N]
Endo, Hisashi [Auteur]
Henry, Nicolas [Auteur]
Station biologique de Roscoff = Roscoff Marine Station [SBR]
Fédération de recherche de Roscoff [FR2424]
Berney, Cédric [Auteur]
Fédération de recherche de Roscoff [FR2424]
ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science [ABIMS]
Station biologique de Roscoff = Roscoff Marine Station [SBR]
Mahé, Frédéric [Auteur]
ARIA Technologies
Poulain, Julie [Auteur]
Génomique métabolique [UMR 8030]
Global Oceans Systems Ecology & Evolution - Tara Oceans [GOSEE]
Institut de Biologie François JACOB [JACOB]
Labadie, Karine [Auteur]
Global Oceans Systems Ecology & Evolution - Tara Oceans [GOSEE]
Génomique métabolique [UMR 8030]
Beluche, Odette [Auteur]
Genoscope - Centre national de séquençage [Evry] [GENOSCOPE]
El Hourany, Roy [Auteur]
Université du Littoral Côte d'Opale [ULCO]
Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 [LOG]
Acinas, Silvia [Auteur]
Institute of Marine Sciences / Institut de Ciències del Mar [Barcelona] [ICM]
Babin, Marcel [Auteur]
Takuvik Joint International Laboratory ULAVAL-CNRS
Bork, Peer [Auteur]
Institut de biologie de l'ENS Paris [IBENS]
Bowler, Chris [Auteur]
Institut de biologie de l'ENS Paris [IBENS]
Global Oceans Systems Ecology & Evolution - Tara Oceans [GOSEE]
Cochrane, Guy [Auteur]
de Vargas, Colomban [Auteur]
Adaptation et diversité en milieu marin [ADMM]
Gorsky, Gabriel [Auteur]
Laboratoire d'océanographie de Villefranche [LOV]
Guidi, Lionel [Auteur]
Laboratoire d'océanographie de Villefranche [LOV]
Grimsley, Nigel [Auteur]
Biologie intégrative des organismes marins [BIOM]
Hingamp, Pascal [Auteur]
Institut méditerranéen d'océanologie [MIO]
Iudicone, Daniele [Auteur]
Stazione Zoologica Anton Dohrn [SZN]
Jaillon, Olivier [Auteur]
Global Oceans Systems Ecology & Evolution - Tara Oceans [GOSEE]
Génomique métabolique [UMR 8030]
Laboratoire d'Analyses Génomiques des Eucaryotes [LAGE]
Genoscope - Centre national de séquençage [Evry] [GENOSCOPE]
Kandels, Stefanie [Auteur]
Karsenti, Eric [Auteur]
Not, Fabrice [Auteur]
Poulton, Nicole [Auteur]
Pesant, Stéphane [Auteur]
Sardet, Christian [Auteur]
Speich, Sabrina [Auteur]
Stemmann, Lars [Auteur]
Sullivan, Matthew [Auteur]
Sunagawa, Shinichi [Auteur]
Wincker, Patrick [Auteur]
Laboratoire d'Analyses Génomiques des Eucaryotes [LAGE]
Nakamura, Ryosuke [Auteur]
Karp-Boss, Lee [Auteur]
Boss, Emmanuel [Auteur]
Tomii, Kentaro [Auteur]
Ogata, Hiroyuki [Auteur]
Chaffron, Samuel [Auteur]
Global Oceans Systems Ecology & Evolution - Tara Oceans [GOSEE]
Combinatoire et Bioinformatique [LS2N - équipe COMBI]
Laboratoire des Sciences du Numérique de Nantes [LS2N]
Journal title :
ISME Communications
Pages :
101
Publisher :
Springer Nature
Publication date :
2023
HAL domain(s) :
Sciences du Vivant [q-bio]
English abstract : [en]
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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Source :
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