Flory, Abigail R., authorBachand, Annette M., advisorDuncan, Colleen G., committee memberReif, John S., committee memberStohlgren, Thomas J., committee member2007-01-032007-01-032010http://hdl.handle.net/10217/39349Department Head: Jac A. Nickoloff.The emergence of mortality-causing bat White-Nose Syndrome (WNS) in the Northeastern United States during 2006 prompted an immediate need for research surrounding possible causation factors influencing its spread. Due to the mysterious nature of fungal pathogens, it has been very difficult to determine how the WNS-related Geomyces destructans fungus is causing bat mortality. Several different hypotheses have been formulated by bat and ecological experts in the field, but major influencing factors remain undetermined. To initiate WNS environmental research, this study utilizes a new machine-learning modeling technique, Maxent modeling, along with a case-control study to assess the hypothesis that certain environmental variables may be associated with the occurrence and distribution of bat WNS. Maxent data uses presence-only data and bases its algorithms on the principal of maximum entropy. Maxent results using 58 environmental predictor variables revealed Slope, Growing-Degree Days, Annual Temperature Range, and Land-Cover as the top four predicting variables for WNS infected bat hibernacula locations. Similarly, the case-control study showed that two of these top four predictor variables (Growing-Degree Days and Annual Temperature Range) were statistically significantly associated with a hibernacula's WNS infection status. Cases had a slightly higher mean Average Temperature Range than controls (Cases=38.0, Controls=36.0) and lower mean Growing-Degree Days than controls (Cases=3419.1, Controls=3838.1). Both of these variables, along with their correlated terms, are largely temperature-dependent, suggesting a need for further research on the role of temperature in predicting the occurrence and distribution of Geomyces destructans. As a starting point for future research, this study has identified the most likely environmental variables related to the potential devastating ecological consequences of WNS-related bat mortality.masters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.Text