Gehman, Carson
Gienger, C. M.
Department of Biology
Center of Excellence in Field Biology
Austin Peay State University
Clarksville, Tennessee, USA
Species distribution modeling has become a valuable tool for predicting quality habitat for a species as it relates to their realized niche. Habitat conditions are dependent on environmental factors such as temperature, precipitation, etc., all of which determine the vegetation, prey, and cover availability in that area. Using this framework, predictive modeling can extrapolate where a species may be found and determine what environmental variables are most important to their habitat. This is an especially insightful technique when applied to rare or endangered species in need of greater conservation efforts. We collected presence-only data for Gila Monsters across their known range in the North American
Southwest and selected environmental variables known to be biologically relevant such as temperature, precipitation, vegetative cover, and elevation. Next, we compiled an extensive protected natural area (PNA) dataset for the study area. We developed our species distribution model using the program MaxEnt (maximum entropy) and overlayed that model with our PNA dataset. Preliminary results show that only a small percentage of predicted quality habitat is located within PNAs, suggesting that conservation efforts concerning Gila Monster habitat may need to be reevaluated. Results also showed that temperature was most impactful in determining high quality habitat.