High-resolution analysis of a North Sea phytoplankton community structure based on in situ flow cytometry observations and potential implication for remote sensing

Abstract. Phytoplankton observation in the ocean can be a challenge in oceanography. Accurate estimations of its biomass and dynamics will help to understand ocean ecosystems and refine global climate models. Relevant data sets of phytoplankton defined at a functional level and on a sub-meso- and daily scale are thus required. In order to achieve this, an automated, high-frequency, dedicated scanning flow cytometer (SFC, Cytobuoy b.v., the Netherlands) has been developed to cover the entire size range of phytoplankton cells whilst simultaneously taking pictures of the largest of them. This cytometer was directly connected to the water inlet of a PocketFerryBox during a cruise in the North Sea, 08–12 May 2011 (DYMAPHY project, INTERREG IV A "2 Seas"), in order to identify the phytoplankton community structure of near surface waters (6 m) with a high spatial resolution basis (2.2 ± 1.8 km). Ten groups of cells, distinguished on the basis of their optical pulse shapes, were described (abundance, size estimate, red fluorescence per unit volume). Abundances varied depending on the hydrological status of the traversed waters, reflecting different stages of the North Sea blooming period. Comparisons between several techniques analysing chlorophyll a and the scanning flow cytometer, using the integrated red fluorescence emitted by each counted cell, showed significant correlations. For the first time, the community structure observed from the automated flow cytometry data set was compared with PHYSAT reflectance anomalies over a daily scale. The number of matchups observed between the SFC automated high-frequency in situ sampling and remote sensing was found to be more than 2 times better than when using traditional water sampling strategies. Significant differences in the phytoplankton community structure within the 2 days for which matchups were available suggest that it is possible to label PHYSAT anomalies using automated flow cytometry to resolve not only dominant groups but also community structure.