Integrated observation of phytoplankton dynamics at sub-mesoscale in coastal and frontal systems: insights from deploying automated optical approaches

Addressing hydrological, biogeochemical and biological variability in marine systems at different scales is essential for understanding their functioning and fate in the context of global change and increasing anthropogenic pressure. Sub-mesoscale features and processes are particularly relevant in coastal, marginal and shelf seas, and frontal systems (such as Regions Of Freshwater Influence, tidal fronts, upwelling and eddies), being particularly sensitive to extreme events and influencing human health, economy, and well-being. Phytoplankton integrated observation combines reference methods, remote sensing, and innovative automated approaches to decipher intricate dynamics. For more than a decade, in vivo/in situ techniques for monitoring phytoplankton have been deployed in different coastal and offshore systems, on a variety of platforms and frames, from dedicated research cruises, fixed stations and moorings to measurements of opportunity, supported by different collaborative national or international projects. These measurements provide information on the variability of phytoplankton functional groups (automated flow cytometry, multispectral fluorometry) and main taxa (imaging in flow) as well as physiological changes (variable fluorometry) at high spatial and temporal resolution. They help assigning community changes to different environmental conditions, complementing physical, biogeochemical and biological observations. In the present study, we present and discuss some applications of automated in vivo approaches addressing phytoplankton dynamics and functional diversity in a variety of marine systems, at sub mesoscale, spanning from coastal and frontal temperate and tropical systems, to open ocean. We emphasise the need for better interoperability between devices and techniques and better harmonisation in data pipelines conforming to the FAIR principles, as well as for applying automated data classification and synthesis tools based on conventional statistics as well as on Artificial Intelligence/Machine Learning. These approaches allow addressing scientific, societal and economic challenges through a new perspective, facing local and global changes.