Abstract
Physical, chemical, geological, and biological factors interact in marine environments to shape complex but recurrent patterns of organization of life on multiple spatial and temporal scales. These factors define biogeographic regions in surface waters that we refer to as seascapes. We characterize seascapes for the Florida Keys National Marine Sanctuary (FKNMS) and southwest Florida shelf nearshore environment using multivariate satellite and in situ measurements of Essential Ocean Variables (EOVs) and Essential Biodiversity Variables (EBVs). The study focuses on three periods that cover separate oceanographic expeditions (March 11-18, May 9-13, and September 12-19, 2016). We collected observations on bio-optical parameters (particulate and dissolved spectral absorption coefficients), phytoplankton community composition, and hydrography from a ship. Phytoplankton community composition was evaluated using 1) chemotaxonomic analysis (CHEMTAX) based on high-performance liquid chromatography (HPLC) pigment measurements, and 2) analysis of spectral phytoplankton absorption coefficients (aphy). Dynamic seascapes were derived by combining satellite time series of sea surface temperature, chlorophyll-a concentration, and normalized fluorescent line height (nFLH) using a supervised thematic classification. The seascapes identified areas of different salinity and nutrient concentrations where different phytoplankton communities were present as determined by hierarchical cluster analyses of HPLC pigments and aphy spectra. Oligotrophic, Mesotrophic and Transition seascape classes of deeper offshore waters were dominated by small phytoplankton ( 60 %). Spectral analysis of aphy indicated higher absorption levels at 492 and 550 nm wavelengths in seascapes carrying predominantly small phytoplankton than in classes dominated by larger taxa. Seascapes carrying large phytoplankton showed absorption peaks at the 673 nm wavelength. The seascape framework promises to be a tool to detect different biogeographic domains quickly, providing information about the changing environmental conditions experienced by coral reef organisms including coral, sponges, fish and higher trophic levels. The effort illustrates best practices developed under the Marine Biodiversity Observation Network (MBON) demonstration project, in collaboration with the South Florida Ecosystem Restoration Research (SFER) project managed by the Atlantic Oceanographic and Meteorological Laboratory of NOAA (AOML-NOAA).
Highlights
Phytoplankton, a diverse group of prokaryotic and eukaryotic micro-organisms, are primary producers that support extensive food webs and are involved in various biogeochemical cycles in aquatic environments
We used as inputs concurrent observations of sea surface temperature (SST), chlorophyll-a concentration, and normalized fluorescence line height. nFLH is used in the seascape classification as a proxy for phytoplankton bloom conditions in Case II waters, which are typically found in our region; conventional bluegreen ratio algorithms tend to significantly overestimate chl-a concentration in shallow coastal areas due to bottom reflectance contamination or sensitivity of the blue spectral region to colored dissolved organic matter (CDOM) absorption (Hu et al, 2005)
The lone exception was the pixel at Station 12 in March, 2016, which was obscured; the seascape value was assumed to be that of neighboring stations with similar oceanographic properties [i.e., Looe Key (LK) and Station 18]
Summary
Phytoplankton, a diverse group of prokaryotic and eukaryotic micro-organisms, are primary producers that support extensive food webs and are involved in various biogeochemical cycles in aquatic environments. Empirical orthogonal functions and machine learning methods have been applied to in situ pigment data and satellite retrievals to examine the biogeography and succession of taxonomic groups (e.g., diatoms, cyanobacteria, and nanoeucaryotes) within regional domains and globally (Alvain et al, 2008; Taylor et al, 2011; Rêve-Lamarche et al, 2017; Catlett and Siegel, 2018; El Hourany et al, 2019a; Xi et al, 2020) These efforts have improved our understanding of the affinity of phytoplankton groups to static biogeographic provinces and phytoplankton responses to climate forcings (Alvain et al, 2008; Catlett and Siegel, 2018). This study complements this toolbox by characterizing phytoplankton communities within dynamic seascapes (Kavanaugh et al, 2014, 2016) derived from a machine learning classification of satellite ocean color and thermal data in south Florida waters
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