Date of Award

Fall 12-7-2023

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

School

Ocean Science and Engineering

Committee Chair

Wei Wu

Committee Chair School

Ocean Science and Engineering

Committee Member 2

Eric Powell

Committee Member 2 School

Ocean Science and Engineering

Committee Member 3

Patrick Biber

Committee Member 3 School

Ocean Science and Engineering

Committee Member 4

Eric Saillant

Committee Member 4 School

Ocean Science and Engineering

Abstract

Oyster reefs provide a variety of important ecosystem services. However, the mortality rate of eastern oyster, Crassostrea virginica, the dominant species that produces oyster reefs in the northern Gulf of Mexico, is increasing at an alarming rate due to a variety of abiotic and biological factors. I examined how biophysical factors, including the less-studied fatty acid profiles of the suspended particulate matter on which oysters feed, influenced morphometric condition of C. virginica.

I sampled suspended particulate matter (SPM) and oysters in-situ in the western Mississippi Sound, which historically supported the majority of oyster production in Mississippi waters. Sampling was conducted from April to November 2018 and covered the main growing and spawning season of the eastern oyster. I studied the seasonal pattern of biophysical variables and morphometric condition of eastern oysters. I then developed a probabilistic model to predict morphometric condition of oysters based on in situ biophysical variables. Particularly, I evaluated if the inclusion of a food quality variable improves model performance in predicting the morphometric condition of oysters. The results showed that SPM was best predicted by wind stress, bottom temperature, and bottom salinity. However, the biophysical factors could not predict fatty acids of the SPM, except the ratio of phytoplankton polyunsaturated fatty acids (PUFA) to terrestrial PUFA. The ratio was best predicted using SPM, photosynthetic active radiation, and bottom temperature. The biophysical factors that were included in the best model to predict oyster condition included length, salinity, total PUFA, SPM, and monthly river discharge. Results of this research support the hypothesis that oyster condition is positively affected by both food quality and food quantity.

ORCID ID

0000-0002-8512-543X

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