Elucidating the Relationship Between Phytoplankton and Primary Production in the Sargasso Sea Using New Observations of Nanoplankton and Picoplankton.

Julia Matheson1, Rodney J Johnson1, Nicholas Robert Bates1 and Rachel Jane Parsons2, (1)Bermuda Institute of Ocean Sciences, BATS, St. George's, Bermuda, (2)Bermuda Institute for Ocean Sciences, BIOS, St. George's, Bermuda
Attempts to model primary production in the subsurface of the Sargasso Sea frequently use HPLC marker pigments to infer phytoplankton community structure, which relies upon assumptions about the phytoplankton community typically determined with limited site-specific data. Recent estimates suggest that nano- and picoplankton account for 90% of the phytoplankton community at BATS and factors such as elevated growth rates and high abundances likely allow these two size classes to exert a strong influence on primary production. To help assess the contribution of nano- and picoplankton on primary production at the BATS site we determine abundances and biovolumes through direct measurements with epifluorescence microscopy in conjunction with flow cytometer picoplankton counts. Using this approach we are able to quantify prymnesiophytes, heterotrophic nano- and dinoflagellates, mixotrophic dinoflagellates, ciliates, diatoms, pico- and nano eukaryotes, and Prochlorococcus. Preliminary analysis of summertime distributions show prymnesiophytes are the dominant nanoplankton group (average upper 140 m concentration of ~500 cells ml-1) although heterotrophic nano- and dinoflagellates makeup a greater fraction of nanoplankton biovolume. During the summer period, pico-eukaryotes and Prochlorococcus were found to be the dominant picoplankton groups, which both increased with depth down to the deep chlorophyll maximum where they appear to drive variability. Using these direct observations we investigate the seasonal relationship between phytoplankton community and primary production, specifically by contrasting the stratified summer phase with a well-mixed winter system. Finally, we use these community structure observations with HPLC data to develop algorithms for taxonomy models (i.e. CHEMTAX) to assess modes of variability in phytoplankton community and consequential influences on primary production for the past 25 years at the BATS site.