Abstract
Phytoplankton community composition impacts food webs, climate, and fisheries on regional and global scales, and can be assessed at coarse taxonomic resolution from biomarker pigments measured using high-performance liquid chromatography (HPLC). Presently, satellite ocean color provides unprecedented coverage of the global surface ocean and offers reliable estimates of bulk biological properties; however, existing multispectral sensors have limited ability to provide information about phytoplankton community composition. Satellite ocean color at hyperspectral resolution (e.g., NASA's upcoming Plankton, Aerosol, Cloud, and ocean Ecosystem sensor, PACE) is expected to improve estimates of phytoplankton community composition from space. Phytoplankton impact ocean color via contributions to absorption and fluorescence (through phytoplankton pigments) and scattering, especially on narrow spectral scales (5–100 nm). Here, a global open ocean dataset of concurrent HPLC pigments and hyperspectral remote sensing reflectance (Rrs(λ)) observations is used to model phytoplankton pigment composition from optical data. Phytoplankton pigments are reconstructed from Rrs(λ) using optimized principal components regression modeling. This work demonstrates that thirteen phytoplankton pigments, representing five phytoplankton pigment groups (e.g., diatoms, dinoflagellates, haptophytes, green algae, and cyanobacteria), can be modeled from hyperspectral Rrs(λ). Spectral information needed to model each phytoplankton pigment concentration is found throughout the entire visible spectrum and the model results are best at high spectral resolution (≤5 nm). The resulting model recreates observed relationships among pigment concentrations, providing support for the designation of five pigment-based phytoplankton groups for the global open ocean. This work represents a step toward developing robust, global spectral models for phytoplankton pigment composition. However, more high-quality data from a wide range of ecosystems and environments are still needed to achieve this goal.
Highlights
Phytoplankton community composition has a strong influence on the structure of planktic ecosystems, global biogeochemical cycles, and the ecosystem services that the oceans provide (Legendre, 1990; Vanni and Findlay, 1990; Le Quere et al, 2005; Falkowski and Oliver, 2007)
The relationships between and among phytoplankton pigment ratios to total chlorophyll-a (Tchla) in the measured high-performance liquid chromatography (HPLC) pigment dataset constrain the number of distinct groups that can be identified from any subsequent modeling using the Rrs(λ) data (Kramer and Siegel, 2019; Kramer et al, 2020)
In this HPLC dataset, hierarchical cluster analysis separates five distinct phytoplankton pigment groups (Fig. 3A), each of which can be distin guished by one biomarker pigment: Fuco, Perid, hex anoyloxyfucoxanthin (HexFuco), monovinyl chloro phyll b (MVchlb), and Zea
Summary
Phytoplankton community composition has a strong influence on the structure of planktic ecosystems, global biogeochemical cycles, and the ecosystem services that the oceans provide (Legendre, 1990; Vanni and Findlay, 1990; Le Quere et al, 2005; Falkowski and Oliver, 2007). Characterizing the diversity of phytoplankton is crucial to develop ma rine food web and ocean carbon cycle models with improved accuracy (e.g., Legendre, 1990; Siegel et al, 2014). Many methods have been developed to characterize phytoplankton community composition from ocean color measurements, including both phytoplankton abundance-based (e.g., Brewin et al, 2010; Hirata et al, 2011) and radiance-based (e.g., Alvain et al, 2008; Bracher et al, 2009; Uitz et al, 2015; Chase et al, 2017) approaches (see Mouw et al, 2017 and Bracher et al, 2017 for reviews of these approaches). Improving the spectral resolution of ocean color measurements from multispectral to hyperspectral is expected to provide improved esti mates of phytoplankton community composition from satellites (Wola nin et al, 2016; Xi et al, 2017; Werdell et al, 2018; Cael et al, 2020), highlighting the need for new phytoplankton community composition algorithms that take advantage of this higher spectral resolution
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