Ecological niche modeling as a conservation tool to predict actual and potential habitat for the bog turtle, Glyptemys muhlenbergii.
- UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
- Elizabeth M. Walton (Creator)
- Institution
- The University of North Carolina at Greensboro (UNCG )
- Web Site: http://library.uncg.edu/
- Advisor
- Roy Stine
Abstract: The bog turtle (Glyptemys muhlenbergii) is faced with two principle threats: wetland habitat loss and, to a lesser degree, the illegal collection for pet trade demands. Current methodologies for bog turtle population discovery in the Southeast rely primarily on field surveys, which are labor intensive and fiscally exhaustive. The purpose of this research was to evaluate the role of geographic information science technologies, remote sensing and ecological niche modeling
to predict potential bog turtle habitats in the Southeast. Environmental data were organized in a geographic information system. The Genetic Algorithm for Ruleset Production was used to develop an ecological niche model to identify
additional habitat sites with the same signatures and potential capacity for support. The results showed the area under the curve as 97%; the model correctly predicted 98.889% of the data points; and the model predicted 1.67% of the total
research area as potential habitat. Areas of highest prediction will be investigated for bog turtle occupancy by trained professionals. This information will be beneficial to researchers in setting conservation priorities for the bog turtle.
Ecological niche modeling as a conservation tool to predict actual and potential habitat for the bog turtle, Glyptemys muhlenbergii.
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Created on 8/1/2009
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Additional Information
- Publication
- Dissertation
- Language: English
- Date: 2009
- Keywords
- Ecological niche models, Bog turtles, Genetic algorithm, GARP, Habitat prediction, Geographic information systems, Remote sensing, Wetlands
- Subjects
- Bog turtle $x Habitat $z Southern States $x Mathematical models.
- Genetic algorithms.
- Wetlands $x Remote sensing.
- Ecological mapping $x Remote sensing.
- Geographic information systems.