Predicting global distributions of eukaryotic plankton communities from satellite data

Hiroto Kaneko (1) , Hisashi Endo (2) , Nicolas Henry (3, 4) , Cédric Berney (4, 5, 3) , Frédéric Mahé (6) , Julie Poulain (7, 8, 9) , Karine Labadie (9, 8) , Odette Beluche (10) , Roy El Hourany (11, 12) , Silvia Acinas (13) , Marcel Babin (14) , Peer Bork (15, 16) , Chris Bowler (8, 15) , Guy Cochrane , Colomban de Vargas (17) , Gabriel Gorsky (18) , Lionel Guidi (18) , Nigel Grimsley (19) , Pascal Hingamp (20) , Daniele Iudicone (21) , Olivier Jaillon (10, 22, 9, 8) , Stefanie Kandels , Eric Karsenti , Fabrice Not , Nicole Poulton , Stéphane Pesant , Christian Sardet , Sabrina Speich , Lars Stemmann , Matthew Sullivan , Shinichi Sunagawa , Samuel Chaffron , Patrick Wincker (22) , Ryosuke Nakamura , Lee Karp-Boss , Emmanuel Boss , Chris Bowler (8, 15) , Colomban de Vargas , Kentaro Tomii , Hiroyuki Ogata

1 Bioinformatics Center, Institute for Chemical Research, Kyoto University
2 KUICR - Institute for Chemical Research, Kyoto University
3 FR2424 - Fédération de recherche de Roscoff
4 SBR - Station biologique de Roscoff = Roscoff Marine Station
5 ABIMS - ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science
6 ARIA Technologies
7 JACOB - Institut de Biologie François JACOB
8 GOSEE - Global Oceans Systems Ecology & Evolution - Tara Oceans
9 UMR 8030 - Génomique métabolique
10 GENOSCOPE - Genoscope - Centre national de séquençage [Evry]
11 LOG - Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187
12 ULCO - Université du Littoral Côte d'Opale
13 ICM - Institute of Marine Sciences / Institut de Ciències del Mar [Barcelona]
14 Takuvik International Research Laboratory
15 IBENS - Institut de biologie de l'ENS Paris
16 Department of Bioinformatics
17 ADMM - Adaptation et diversité en milieu marin
18 LOV - Laboratoire d'océanographie de Villefranche
19 BIOM - Biologie intégrative des organismes marins
20 MIO - Institut méditerranéen d'océanologie
21 SZN - Stazione Zoologica Anton Dohrn
22 LAGE - Laboratoire d'Analyses Génomiques des Eucaryotes

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.

Domains: Life Sciences [q-bio]

Hiroto Kaneko, Hisashi Endo, Nicolas Henry, Cédric Berney, Frédéric Mahé, et al.. Predicting global distributions of eukaryotic plankton communities from satellite data. ISME Communications, 2023, 3 (1), pp.101. ⟨10.1038/s43705-023-00308-7⟩. ⟨hal-04394784⟩