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, Laboratoire d’Océanologie et de Géosciences, Université du Littoral-Côte-d’Opale, Université Lille, France, https://orcid.org/ https://orcid.org/0000-0002-6454-1645

Lino Sander de Carvalho, Universidade Federal do Rio de Janeiro (UFRJ), Brazil

Leonardo Contreira Pereira, Universidade Federal de Pelotas (UFPel), Brazil

José Fonseca, Universidade Federal de Pelotas (UFPel), Brazil

Fernanda Giannini, Universidade Federal do Rio Grande (FURG), Brazil

Lorenzo Valente, Universidade Federal de Pelotas (UFPel), Brazil

Janice Silveira, Universidade Federal de Pelotas (UFPel), Brazil

Code of Conduct