Automated optical approaches for in vivo multiscale monitoring of phytoplankton communities and HABs in the English Channel and North Sea
2 ULCO - Université du Littoral Côte d'Opale
3 NIOO-KNAW - Netherlands Institute of Ecology
4 CNRS - Centre National de la Recherche Scientifique
5 CEFAS - Centre for Environment, Fisheries and Aquaculture Science [Lowestoft]
6 LERBL - Laboratoire Environnement Ressources de Boulogne-sur-Mer
7 LISIC - Laboratoire d'Informatique Signal et Image de la Côte d'Opale
8 VLIZ - Flanders Marine Institute
9 GKSS - Helmholtz-Zentrum Geesthacht
10 RWS RIJKSWATERSTAAT CENTRE FOR WATER MANAGEMENT LELYSTAD NLD
11 BOREA - Biologie des Organismes et Ecosystèmes Aquatiques
12 UNICAEN - Université de Caen Normandie
13 MIO - Institut méditerranéen d'océanologie
14 RDT - Unité Recherches et Développements Technologiques
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Résumé
Automated optical observations of phytoplankton communities are currently implemented in eastern English Channel and southern North Sea on different platforms (fixed autonomous stations, moorings, dedicated cruises and measurements/ships of opportunity) as a complement of long term monitoring networks in the. These marginal seas are characterized by significant connectivity to adjacent ocean and seas, strong hydrodynamics and low to high riverine inputs, influencing biogeochemical and biological processes like high productivity and recurring phytoplankton blooms, including some Harmful Algal Blooms-HABs of potential impact on marine food webs, as well as on human health and economy. Integrated phytoplankton observations are carried out combining reference methods with innovative automated in vivo imaging inflow/in situ/benchtop devices, pulse shape-recording flow cytometers, as well as in vivo multispectral fluorometers. These measurements provide information on the distribution and dynamics of phytoplankton functional groups (flow cytometry) and main taxa (imaging) at high spatial and temporal resolution assigning community changes to different bloom situations and pelagic habitats state, complementing physical, biogeochemical and biological variables. The in vivo approaches allow tracking HABs at different spatial and temporal scales, through the harmonisation of observations (including near-real time in vivo automated approaches implemented for almost a decade), at almost real time, and become a helpful tool for managers of these events. Finally, harmonisation in data pipelines conform to the FAIR principle and synthesis based on conventional statistics as well as on IA/ML approaches makes it possible to address scientific, societal and economic challenges through a new perspective, facing global and local changes.