Date Awarded

Winter 2016

Document Type

Thesis

Degree Name

Master of Science (M.Sc.)

Department

Virginia Institute of Marine Science

Advisor

Mark J Brush

Committee Member

Iris C Anderson

Committee Member

Donglai Gong

Committee Member

Hans W Paerl

Abstract

Phytoplankton account for at least half of all primary production in estuarine waters and are at the center of biogeochemical cycles and material budgets. Environmental managers use water column chlorophyll-a (chl-a) concentrations as a basic water quality indictor, as the problems of eutrophication and hypoxia are intrinsically linked to excessive phytoplankton growth. Evidence suggests that the distribution and frequency of harmful algal blooms may be increasing worldwide. For the most part, phytoplankton communities follow a standard seasonal pattern, with specific groups dominating the assemblage during the time of year when environmental conditions correspond to their requisites for growth. However, climate change will result in incremental but consistent shifts in some environmental factors known to affect phytoplankton production and biomass accumulation. Mean surface temperatures in North American mid-Atlantic coastal and estuarine regions are steadily rising, and the frequency and severity of drought and storm events are projected to fluctuate, potentially increasing the severity of extreme weather events. Anthropogenically-induced nutrient loading, especially from non-point sources, is one of the largest consistent contributors to coastal marine eutrophication. The consequences of changes in these environmental factors to estuarine ecosystems and phytoplankton community dynamics are unclear. Because different phytoplankton groups respond to environmental changes in distinctive ways, some classes thrive during periods of environmental stability and others at times of temporary or sustained disturbance. To predict how phytoplankton and therefore water quality might respond to changes in climate and land use, we built mathematical phytoplankton kinetics sub-models that differentiate phytoplankton groups using taxonomic classes with well-defined functional characteristics. Then we integrated them into a reduced-complexity estuarine ecosystem model. The sub-models were designed to simulate daily biomass of diatoms, dinoflagellates, cyanobacteria, and raphidophytes in the New River Estuary, NC. We calibrated and validated the model using data collected from 2007 – 2012 through the Aquatic Estuarine monitoring module of the Defense Coastal/Estuarine Research Program. The model was a relatively good predictor of total chl-a and primary production, and a fair predictor of group dynamics. The model was employed in heuristic simulations of changes in temperature, nutrient loading, and freshwater delivery to predict their effects on overall phytoplankton biomass, productivity, and community composition. Increases in temperature had a modest effect on mean daily simulated phytoplankton production and chl-a, but considerably decreased the relative abundance of diatoms and simultaneously increased the relative abundance of cyanobacteria. The seasonal phenology of phytoplankton abundance also shifted in response to increased temperatures: chl-a concentrations were larger in the winter and spring and smaller in the summer and fall. The model was most sensitive to changes in the watershed nutrient load. Nutrient influx had a dramatic effect on the temporal and spatial extent of phytoplankton blooms. The relative abundance of dinoflagellates and raphidophytes increased in response to elevated nutrient loading, regardless of whether load was increased directly as in nutrient simulations or indirectly as in freshwater simulations. Initially, greater freshwater discharge increased total chl-a, productivity, and the frequency of phytoplankton blooms. However, these relationships leveled off or were reversed as flow continued to increase due to greater rates of flushing and light attenuation. Results demonstrated how models like this can be important tools for both heuristic understanding and environmental management. A benefit of this model is how easy it is to update to other estuarine systems through the re-parameterization of the phytoplankton groups.

DOI

http://dx.doi.org/10.21220/M20D1C

Rights

© The Author