Predicting global distributions of eukaryotic plankton communities from satellite data
| Type | : | ACL |
|---|---|---|
| Nature | : | Production scientifique |
| Au bénéfice du Laboratoire | : | Oui |
| Statut de publication | : | Publié |
| Année de publication | : | 2023 |
| Auteurs (39) | : | KANEKO Hiroto ENDO Hisashi HENRY Nelly 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 TOMII Kentaro OGATA Hiroyuki COORDINATORS Tara-oceans |
| Revue scientifique | : | ISME Communications |
| Volume | : | 3 |
| Fascicule | : | 1 |
| Pages | : | |
| DOI | : | 10.1038/s43705-023-00308-7 |
| URL | : | https://doi.org/10.1038/s43705-023-00308-7 |
| 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. |
| Mots-clés | : | - |
| Commentaire | : | - |
| Tags | : | - |
| Fichier attaché | : | - |
| Citation | : |
Kaneko H, Endo H, Henry N, Berney C, Mahé F, Poulain J, Labadie K, Beluche O, El Hourany R, Acinas SG, Babin M, Bork P, Bowler C, Cochrane G, De Vargas C, Gorsky G, Guidi L, Grimsley N, Hingamp P, Iudicone D, Jaillon O, Kandels S, Karsenti E, Not F, Poulton N, Pesant S, Sardet C, Speich S, Stemmann L, Sullivan MB, Sunagawa S, Chaffron S, Wincker P, Nakamura R, Karp-Boss L, Boss E, Tomii K, Ogata H, Coordinators T-O (2023) Predicting global distributions of eukaryotic plankton communities from satellite data. ISME Commun 3 | doi: 10.1038/s43705-023-00308-7
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