Degree

Doctor of Philosophy (PhD)

Department

Oceanography and Coastal Sciences

Document Type

Dissertation

Abstract

Several of the northwestern Gulf of Mexico (GOM) shelf-edge banks provide critical hard bottom habitat for coral and fish communities, supporting a wide diversity of ecologically and economically important species. These sites may be fish aggregation and spawning sites and provide important habitat for fish growth and reproduction. Already designated as habitat areas of particular concern, many of these banks are also under consideration for inclusion in the expansion of the Flower Garden Banks National Marine Sanctuary. This project aimed to gain a more comprehensive understanding of the communities and fish species on shelf-edge banks by way of gonad histology, baited remote underwater video, and hydroacoustics, as well as traditional statistical analyses, Bayesian estimation, and machine learning techniques. The study had several objectives: (1) estimate size at sexual transition for six GOM grouper species, (2) determine the optimal number of cameras on a baited remote underwater video system, (3) create a predictive model to provide presence of fish species based on habitat, and (4) grow a model to predict fish backscatter and density based on habitat parameters. Bayesian estimation allowed for size at sexual transition determinations for the six grouper species, outperforming the tradition frequentist models, especially for situations where tradition models failed to converge. Random forests based on video data had mixed results, but models for several species were able to predict fish presences with class and overall accuracies of greater than 80%. Boosted regression trees based on hydroacoustic data reinforced the importance of depth as a driving factor in fish distributions. The study provided greater understanding and predictive ability regarding fish on the bank habitats.

Date

11-6-2019

Committee Chair

Cowan, James

DOI

10.31390/gradschool_dissertations.5092

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