Morphological Diversity of Phytoplankton: Identification of Traits, Morphological Succession and Periodicities from Imagery in Narragansett Bay, U.S.

Virginie Sonnet, University of Rhode Island, Graduate School of Oceanography, Narragansett, RI, United States, Lionel Guidi, Laboratoire d'Océanographie de Villefranche (LOV), UMR 7093, Sorbonne Université, Villefranche-sur-Mer, France, Colleen B Mouw, University of Rhode Island, Narragansett, RI, United States and Sakina-Dorothée Ayata, LOV UPMC/CNRS, Villefranche sur mer Cedex, France
Phytoplankton diversity is essential to understand the dynamics of marine ecosystems, global biogeochemical cycles and impacts of climate change. However, changes in phytoplankton communities are complex and occur at different time scales, from daily to decadal, thus making our understanding of the environmental processes driving them more difficult. Here, we focus on the morphological diversity; the individual morphological characteristics influencing the evolutionary success of organisms and the functioning of ecosystems, regardless of their taxonomic classification. We use high-resolution phytoplankton imagery generated by an Imaging FlowCytobot deployed in Narragansett Bay, United States, to detect the main morphological features of the local phytoplankton community, along with their seasonality and periodicities. We show that morphological analysis can uncover successions of communities and demonstrate significant changes with a 27.5 day and 24-hour periodicity related to the moon apogee-perigee cycle and the diel cycle. Building on these results we aim at linking morphology and taxonomy with multispectral backscattering, hyperspectral absorption and attenuation signals also coincidently recorded, moving towards the development of algorithms to retrieve phytoplankton groups from hyperspectral satellite data.