Date of Award

Fall 2014

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Biological Sciences

Program/Concentration

Biology

Committee Director

Kent E. Carpenter

Committee Member

Holly Gaff

Committee Member

Ken Lindeman

Call Number for Print

Special Collections LD4331.B46 C66 2014

Abstract

A variety of human-induced changes are having profound impacts on the marine environment, and no area on the planet remains unaffected by the detrimental effects of our activities. These stressors can potentially lead to synergistic effects, causing accelerated biodiversity loss and diminished ecosystem functioning. Identification and understanding of the factors that drive species to heightened risk of extinction are important goals in conservation.

The Sparidae are commercially important and ecologically complex marine fishes; global extinction risk assessments using the IUCN Red List of Threatened Species methodology show that 9% (13 species) have increased vulnerability to population declines from intense fishing pressure and habitat destruction, These threatened species exhibit biological correlates such as: large body size, slow life history, complex reproductive strategy, limited distributional ranges, and specialized habitat requirements that contribute to its elevated risk of extinction. The majority of these threatened species are valuable components of commercial fisheries, achieve highest concentrations in southern Africa, and occur in regions with low coverage of marine Protected Areas. Additionally, 12 (7%) species are listed as Near Threatened and share ecological characteristics similar to species in higher extinction risk levels. The majority of the sparids (70%) listed as Least Concern have smaller body sizes, widespread distributions, and shorter life spans. The rest of the sparids, 21 species (14%) are listed as Data Deficient.

Predictive models of extinction risk are important for informing present and future science-based conservation actions. Despite this recognized role in conservation, relevant models have not yet been developed in earnest for marine fishes. Here we used a machine-learning tool to identify correlates of extinction risk in the Sparidae using 34 biological and threat variables, The Random Forest model indicates six major predictors of extinction risk: population trend, generation length, longevity, area of occupancy, maximum size, and rocky reef habitat. This model provides tor the first time, a robust and systematic method to identify the intrinsic and extrinsic traits that correlate to a higher risk of extinction in this important group of marine fishes. This type of predictive modeling can provide a systematic method to improve conservation prioritization, and has the potential to identify and mitigate threats impacting similar groups of high-valued and ecologically important marine fishes.

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DOI

10.25777/xht4-g544

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