Phytoplankton Pigment Communities Can be Modeled Using Unique Relationships With Spectral Absorption Signatures in a Dynamic Coastal Environment

Abstract

Understanding the roles of phytoplankton community composition in the functioning of marine ecosystems and ocean biogeochemical cycles is important for many ocean science problems of societal relevance. Remote sensing currently offers the only feasible method for continuously assessing phytoplankton community structure on regional to global scales. However, methods are presently hindered by the limited spectral resolution of most satellite sensors and by uncertainties associated with deriving quantitative indices of phytoplankton community structure from phytoplankton pigment concentrations. Here we analyze a data set of concurrent phytoplankton pigment concentrations and phytoplankton absorption coefficient spectra from the Santa Barbara Channel, California, to develop novel optical oceanographic models for retrieving metrics of phytoplankton community composition. Cluster and Empirical Orthogonal Function analyses of phytoplankton pigment concentrations are used to define up to five phytoplankton pigment communities as a representation of phytoplankton functional types. Unique statistical relationships are found between phytoplankton pigment communities and absorption features isolated using spectral derivative analysis and are the basis of predictive models. Model performance is substantially better for phytoplankton pigment community indices compared with determinations of the contributions of individual pigments or taxa to chlorophyll a. These results highlight the application of data‐driven chemotaxonomic approaches for developing and validating bio‐optical algorithms and illustrate the potential and limitations for retrieving phytoplankton community composition from hyperspectral satellite ocean color observations.

Phytoplankton Community Composition Determined From Co-variability Among Phytoplankton Pigments From the NAAMES Field Campaign

“…A hierarchical cluster analysis was performed on the NAAMES 1-4 HPLC pigment dataset, using all sixteen pigments described above after normalization to Tchla (e.g., Fuco:Tchla, etc.). This method uses Ward's linkage method (the inner squared distance), based on the correlation distance (1-R, where R is Pearson's correlation coefficient between phytoplankton pigment ratios), as in Latasa and Bidigare (1998) and Catlett and Siegel (2018). A linkage cutoff distance of 1 is used to divide the resulting dendrogram into distinct phytoplankton community clusters.…”

“…An Empirical Orthogonal Function (EOF) analysis was performed on the NAAMES 1-4 surface HPLC pigment dataset to evaluate the co-variability in groups of phytoplankton pigments (following Catlett and Siegel, 2018;Kramer and Siegel, 2019). This analysis decomposes the data into dominant orthogonal functions descriptive of the major modes of variability in the dataset.…”

“…This pigment plasticity along with the high degree of correlation between phytoplankton pigment concentrations preclude the routine use of methods that assume specific ratios of pigments in certain phytoplankton communities (Higgins et al, 2011;Kramer and Siegel, 2019). However, despite these limitations, a quality-controlled HPLC dataset can be used in conjunction with data-driven statistical methods to characterize the phytoplankton community with reasonable confidence (Anderson et al, 2008;Catlett and Siegel, 2018;Kramer and Siegel, 2019).…”

Data Needs for Hyperspectral Detection of Algal Diversity Across the Globe

“…With hyperspectral data, several researchers have independently demonstrated with field and laboratory data that mixtures of major PTs (e.g., diatoms, Prochlorococcus [cyanobacteria], coccolithophores) can be differentiated for members contributing largely total chlorophyll a (Bracher et al, 2009;Xi et al, 2015;Organelli et al, 2017;Catlett et al, 2018). Multispectral imagery can only capture the average trends in the open ocean related to dominant PGs (Figure 1).…”

Developing a Community of Practice for Applied Uses of Future PACE Data to Address Marine Food Security Challenges

“…While ocean color methods for identifying different phytoplankton groups exist (e.g., Bracher et al, 2017;Mouw et al, 2017), most remain confounded by the limited number of wavelengths available on heritage instruments. Increased spectral resolution in the visible region offers improved characterization of spectral phytoplankton pigment absorption and particulate backscattering, both of which provide highly useful metrics for discriminating between phytoplankton groups and plankton size composition (Lubac et al, 2008;Bracher et al, 2009;Torrecilla et al, 2011;Catlett and Siegel, 2018). Several studies identify needs for hyperspectral measurements of at least 5 nm resolution to support classification of phytoplankton community structure (Lee et al, 2007;Vandermeulen et al, 2017).…”