Complementary Approaches to Assess Phytoplankton Groups and Size Classes on a Long Transect in the Atlantic Ocean

Phytoplankton biomass, through its proxy, Chlorophyll a , has been assessed at synoptic temporal and spatial scales with satellite remote sensing (RS) for over two decades. Also, RS algorithms to monitor relative size classes abundance are widely used; however, differentiating functional types from RS, as well the assessment of phytoplankton structure, in terms carbon remains challenge. Hence, main motivation this work it discuss links between groups, order foster capability assessing community structure fractionated budgets. To accomplish our goal, we used data (on nutrients, photosynthetic pigments concentration cell numbers per taxa) collected surface samples along transect on Atlantic Ocean, during 25th Meridional Transect cruise (AMT25) 50° N S, nutrient-rich high latitudes oligotrophic gyres. We compared methodological approaches: (i) using diagnostic pigments, three classes, micro-, nano-, picoplankton, (ii) identifying enumerating taxa by microscopy or flow cytometry, converting into carbon, dividing five according their content. The distribution different oceanographic regions is presented taxonomic groups types, discussed relation environmental conditions. seven showed dominance picoautotrophs gyres biomass diatoms autotrophic dinoflagellates (ADinos) higher northern southern latitudes, where larger cells constituted major component biomass. Total ranged 65 4 mg m –3 45° S 27° N, respectively. pigment approaches gave good consistency picoplankton microplankton but nanoplankton class was overestimated pigment-based approach. limitation methods accurately resolve 5 10 μm might be cause mismatch, highlighted knowledge gap. Finally, three-component model Brewin et al. fitted (Chl ) and, first time, data, extract phytoplankton. general pattern accordance fits Chl data. ratio parameter representing asymptotic maximum reasonable values Carbon:Chl ratios, an overall median 112, (170) than combined pico-nanoplankton (36). approach may useful inferring size-fractionated Earth Observation.