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How Can Phytoplankton Pigments Be Best Used to Characterize Surface Ocean Phytoplankton Groups for Ocean Color Remote Sensing Algorithms?

Sasha J KramerDavid A Siegel
Published in: Journal of geophysical research. Oceans (2019)
High-performance liquid chromatography (HPLC) remains one of the most widely applied methods for estimation of phytoplankton community structure from ocean samples, which are used to create and validate satellite retrievals of phytoplankton community structure. HPLC measures the concentrations of phytoplankton pigments, some of which are useful chemotaxonomic markers for phytoplankton groups. Here, consistent suites of HPLC phytoplankton pigments measured on global surface water samples are compiled across spatial scales. The global dataset includes >4,000 samples from every major ocean basin and representing a wide range of ecological regimes. The local dataset is composed of six time series from long-term observatory sites. These samples are used to quantify the potential and limitations of HPLC for understanding surface ocean phytoplankton groups. Hierarchical cluster and empirical orthogonal function analyses are used to examine the associations between and among groups of phytoplankton pigments and to diagnose the main controls on these associations. These methods identify four major groups of phytoplankton on global scales (cyanobacteria, diatoms/dinoflagellates, haptophytes, and green algae) that can be identified by diagnostic biomarker pigments. On local scales, the same methods identify more and different taxonomic groups of phytoplankton than are detectable in the global dataset. Notably, diatom and dinoflagellate pigments group together on global scales, but dinoflagellate marker pigments always separate from diatoms on local scales. Together, these results confirm that HPLC pigments can be used for satellite algorithm quantification of no more than four phytoplankton groups on global scales, but can provide higher resolution for local-scale algorithm development and validation.
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