Phytoplankton Bloom Dynamics and Development of Methods for Estimating Primary Productivity in the California Current System

The California Current System (CCS) is a coastal upwelling region that supports diverse and abundant mesopredators including fishes, seabirds, and marine mammals. Many fisheries, including Pacific salmon, anchovy, and sardine are present along the coast, making effective management of coastal marine resources in the CCS both ecologically and economically important. Oceanographic observation networks can provide useful information for resource management, but because of limited spatial coverage, fail to provide a complete picture of water mass dynamics. Next-generation ocean color radiometers with improved spectral resolution will provide opportunities for development of new algorithms to resolve important ecological questions in complex coastal environments. This dissertation focused first on phytoplankton bloom dynamics in the upwelling shadow of Monterey Bay, California. Using partial least squares regression, environmental factors were related to microbial and phytoplankton abundances in order to evaluate how environmental factors influence the local phytoplankton community. Microbial and phytoplankton abundances in the upwelling shadow were positively associated with warmer, nutrient-depleted water. “Major” blooms of all biological groups primarily occurred during the oceanic season when these environmental conditions persisted. Chapters 2 and 3 built on knowledge from Chapter 1 and included remote sensing techniques. Chapter 2 used hyperspectral ocean color and sea surface temperature (SST) data collected from low-flying aircraft and a systematic cross-shelf survey design to characterize water types in the northern CCS. Using three derived ocean color parameters (chlorophyll a, colored dissolved organic matter, and particle concentration) and SST, k-means clustering produced tenable water mass classifications; comparative k-means clustering using 20 spectral shape coefficients derived using functional data analysis failed to resolve meaningful water mass classifications. Data for this chapter were collected as part of the U.S. Geological Survey’s Pacific Continental Shelf Ecosystem Assessment (PaCSEA) program. Finally in Chapter 3, I used hyperspectral data to understand bloom dynamics in Monterey Bay, California, Chapter 3 modified working primary productivity (PP) models to include a particle size distribution component. Based on phytoplankton physiology, I expected modified PP algorithms would improve PP estimates compared with traditional methods. Although modified PP models improved estimates by a factor of two to three when compared to 14C-derived ground truth values, no significant improvement was made over other regionally-tuned algorithms. This indicates that one or more other variables, such as depth resolved chlorophyll a, are needed to significantly improve future models. Data for this chapter were collected as part of the Hyperspectral Infrared Imager (HyspIRI) flight campaign using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. Due to issues with atmospheric corrections, not enough data were available to make statistically significant conclusions on the performance of this sensor in Monterey Bay