Oral Session 4

Linking satellites to genes with machine learning to estimate phytoplankton community structure from space

Remote sensing techniques have been employed to elucidate phytoplankton community structure by analyzing spectral data from space, especially when coupled with in situ measurements of photosynthetic pigments. In this study, we introduce a novel ocean color algorithm designed to estimate the relative cell abundance of seven phytoplankton groups and their respective contributions to total chlorophyll a (Chl a) on a global scale. Leveraging machine learning, our algorithm utilizes remotely sensed parameters (including reflectance, backscattering, and attenuation coefficients at various wavelengths, as well as temperature and Chl a) in conjunction with an omics-based biomarker derived from Tara Oceans data. This biomarker targets a single-copy gene called psbO, encoding a component of the photosynthetic machinery present across all phytoplankton, spanning both prokaryotes and eukaryotes. This research delivers a comprehensive global dataset detailing the relative cell abundances of the seven phytoplankton groups and their impacts on total Chl a. These data types offer distinct insights: Chl a serves as a biomass proxy crucial for understanding energy and matter fluxes in ecological and biogeochemical processes, while cell abundance provides crucial information on community assembly processes. Moreover, our methodology allows comparisons with existing approaches, such as pigment-based methods. This integration underscores the potential of remote sensing observations as powerful tools for gathering Essential Biodiversity Variables (EBVs). By expanding our understanding of phytoplankton dynamics on a global scale, this study contributes to advancing ecological and biogeochemical research in marine environments.

Roy El Hourany, Université du Littoral-Côte-d’Opale, Université Lille, Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD), Laboratoire d’Océanologie et de Géosciences (LOG), France, https://orcid.org/0000-0002-6454-1645

Juan Pierella Karlusich, FAS Division of Science, Harvard University, USA, https://orcid.org/0000-0003-1739-4424

Lucy Zinger, Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, Centre National de la Recherche Scientifique (CNRS), L‘Institut National de la Santé et de la Recherche Médicale (INSERM), France, https://orcid.org/0000-0002-3400-5825

Hubert Loisel, Université du Littoral-Côte-d’Opale, Université Lille, Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD), Laboratoire d’Océanologie et de Géosciences (LOG), France

Marina Levy, Sorbonne Université, Laboratoire d’Océanographie et du Climat: Expérimentations et Approches Numériques (LOCEAN), Institut Pierre-Simon Laplace (IPSL), Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD), France, https://orcid.org/0000-0003-2961-608X

Chris Bowler, Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, Centre National de la Recherche Scientifique (CNRS), L‘Institut National de la Santé et de la Recherche Médicale (INSERM), France

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