JGOFS-Synthesis: The Role of Phytoplankton Community Structure in Determining Particulate Elemental Composition, Carbon Fixation, and Bio-Optical Properties

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Lizotte Phytoplankton community structure and biogeography have been identified as key gaps in the knowledge needed to progress from current ocean biogeochemistry models (with generic phytoplankton parameterizations) to more explicit models. In particular, deviations from generic phytoplankton descriptions may be greatest during episodic blooms of a few species, and in some regions these episodic events are associated with rapid flux of particulate carbon to the seafloor, distinct biogeochemical processes (e.g., N-fixation, carbonate precipitation, dimethylsulfide production), or shifts in elemental composition of particulate matter. Thus, future biogeochemical models may need to shift parameterizations to reflect biogeographic regions and/or seasonal blooms. During the JGOFS era, High-Performance-Liquid-Chromatography (HPLC) methods for characterizing phytoplankton pigments were used widely, and these data sets can be used to make a first-order approximation of phytoplankton community structure. This project will analyze HPLC pigment data sets collected worldwide to develop algorithms (on event, regional, and global scales) for estimating phytoplankton community structure. The estimates of phytoplankton community structure from HPLC pigments will be compared to microscopy and flow-cytometry data, when available, to test the validity of these new algorithms. Drs. Lizotte and DiTullio will compile and synthesize data sets to study biogeographical distributions of phytoplankton. They will also extract subsets of the data for samples dominated by single taxonomic groups (e.g., diatoms, coccolithophorids, picocyanobacteria, Phaeocystis spp.) to determine whether other phytoplankton-related parameters are correlated with community structure. The ancillary data sets compiled for phytoplankton-related parameters will be those most useful for developing SMP biogeochemical models and for remote sensing of phytoplankton biomass (e.g. particulate carbon, nitrogen, and absorbance; carbon:chlorophyll a ratios; photosynthetic rates; photosynthesis-irradiance parameters and quantum yields; nutrient utilization indices and ratios; light attenuation; reflectance ratios; and water column stability).