Date of Award

8-2018

Degree Type

Thesis

Degree Name

Master of Science - Biology

Department

Biology

First Advisor

Stephen J. Mullin, Ph.D.

Second Advisor

Matthew A. Kwiatkowski, Ph.D.

Third Advisor

D. Brent Burt, Ph.D.

Fourth Advisor

Daniel Saenz, Ph.D.

Fifth Advisor

Josephine Taylor, Ph.D.

Abstract

Snakes are among the least understood vertebrate groups despite their considerable diversity. A diverse community of snakes in an ecosystem can indicate a complex habitat structure that is capable of supporting a robust assemblage of other biota. I used remote photography arrays (RPA) to quantify metrics of diversity for the snake community occurring in a ~7,000–ha tract of contiguous Longleaf Pine (Pinus palustris) Savanna within the Angelina National Forest (ANF; Angelina and Jasper Cos., TX), over the course of two consecutive snake activity seasons. I quantified the snake species richness, Shannon diversity, and Shannon equitability for the snake community in ANF. I performed several statistical analyses to identify the factors (both habitat and temporal variables) that most influenced the assemblage of snake species detected in ANF. In total, RPA detected 1,094 snakes representing 19 species and four families. An additional five species were documented during field survey efforts. Habitat parameters that typically correlate well with metrics of snake diversity were weak predictors at my study site, and temporal variables that might be reliable for predicting snake activity patterns were similarly poor. The ecological roles of individual species of snakes and the structuring of the snake community in ANF remain unclear. Nevertheless, the 24 species of snakes that I detected at ANF, several of which were poorly documented during previous sampling efforts at the same site, represent a diverse community within a relatively homogeneous habitat.

Keywords: snakes, longleaf pine savanna, community ecology, conservation, biodiversity, wildlife

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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