Defense Date

8-12-2022

Document Type

Thesis

Degree Type

Master of Science

Degree Name

Marine Science

First Advisor

Rosanna Milligan, Ph.D.

Second Advisor

Tracey T. Sutton, Ph.D.

Third Advisor

Tamara Frank, Ph.D.

Abstract

Knowledge of community assemblages and biodiversity is important for monitoring health and resilience in an ecosystem. Taxonomic and functional biodiversity of mesopelagic (200- 1000 m) fishes is extremely rich in the Gulf of Mexico. The aim of this study was to compare calculations of biodiversity and community structures at varying taxonomic resolutions for deep pelagic fishes to inform future decisions about deep-sea ecosystem monitoring. This study analyzed biodiversity and assemblage structure patterns from a biological inventory of deep-sea fishes collected with a large mesh, commercial-sized, high-speed rope trawl in the Gulf of Mexico between June 21st and July 14th, 2011. Twelve stations were sampled 2-4 times each, collecting 37,431 pelagic fishes that were identified to species resolution (with respective genus, family, and order taxonomic groupings) from the epipelagic, mesopelagic, and upper bathypelagic zones (0-1800 m, collectively). Alpha and beta biodiversity results were compared at each resolution for each net deployment. Species resolution presented patterns in biodiversity that were different than genus, family, and order resolutions utilizing the Shannon Diversity (1D) and Simpson’s Diversity (2D) indices. Community structures were analyzed by creating Bray Curtis similarity matrices and conducting ANOSIM and SIMPER analyses, taxon associations, a 2Stage resemblance coefficient analysis, and a 2nd Stage nMDS plot on the varying taxonomic resolution data. The results show that genus resolution data provided similar assemblage characterization utility as species data when species resolution data was not available for measures of beta diversity and could be effectively used to characterize an ecosystem after suspected environmental damage.

Creative Commons License

Creative Commons Attribution-Share Alike 4.0 International License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.

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