Predicting global distributions of eukaryotic plankton communities from satellite data

• Published in: ISME Communications
• Main Authors: Kaneko, Hiroto, Endo, Hisashi, Henry, Nicolas, Berney, Cédric, Mahé, Frédéric, Poulain, Julie, Labadie, Karine, Beluche, Odette, El Hourany, Roy, Tara Oceans Coordinators, Acinas, Silvia G., Chaffron, Samuel, Wincker, Patrick, Nakamura, Ryosuke, Karp-Boss, Lee, Boss, Emmanuel, Bowler, Chris, Vargas, Colomban de, Tomii, Kentaro, Ogata, Hiroyuki
• Other Authors: Kyoto University, France Génomique, European Research Council, European Commission, Agencia Estatal de Investigación (España)
• Format: Article in Journal/Newspaper
• Language: English
• Published: International Society for Microbial Ecology 2023
• Subjects: Conserve and sustainably use the oceans; seas and marine resources for sustainable development; Subarctic
• Online Access: http://hdl.handle.net/10261/355404; https://doi.org/10.1038/s43705-023-00308-7

This is contribution number 146 of the Tara Oceans Expedition 2009–2013.-- 9 pages, 6 figures, 1 table, supplementary data https://doi.org/10.1038/s43705-023-00308-7.-- Data Availability: Figures S1–S19, Tables S1 and S2, Data S1–S3, and Video S1 are provided as supplementary materials. Video S1 shows the 19-year time series of community-type distributions predicted from satellite-derived parameters, related to Fig. 6. Newly sequenced Tara Oceans 18 S V4 data have been deposited to EMBL/EBI-ENA: PRJEB6610 (Tara Oceans), PRJEB9737 (TARA Oceans Polar Circle). Data and codes used in the analysis are available at the GenomeNet FTP: https://www.genome.jp/ftp/db/community/tara/Satellite/. Essential codes are also available at the GitHub repository: https://github.com/hirotokaneko/plankton-from-satellite 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 ...