Date of Award

Spring 2018

Document Type

Thesis

Department

Life & Environmental Sciences

First Advisor

Grant Hokit

Second Advisor

Daniel Gretch

Third Advisor

Gerald Schafer

Abstract

As the leading cause of arbovirus encephalitis in the United States, West Nile Virus (WNV) poses a public health risk in the state of Montana where infection rates in mosquitoes can be as high as 15%. Spatial modeling can serve as a tool for predicting outbreaks and directing prevention measures. Models for the entire state of Montana currently exist that predict WNV risk and habitat suitability of the predominant vector species Culex tarsalis. After collecting additional mosquito vector samples in the summer of 2017, I used niche modeling techniques with historic and new presence-only data to build a Cx. tarsalis habitat suitability model covering only the Great Plains region of Eastern Montana. An average area under the curve (AUC) value of 0.815 after replicate cross-validation testing indicated that my model performed better than a random model (AUC = 0.5) in predicting habitat suitability of Cx. tarsalis. Jackknife analyses indicated that land cover type, presence of virulent competent birds, early spring mean temperature, and early spring precipitation were the four most influential environmental variables in predicting optimal habitat for Cx. tarsalis. Additionally, my new data was used to perform validations on the previous model. A two-way analysis of variance (ANOVA) was calculated on the statewide model and Great Plains model’s predicted levels of Cx. tarsalis habitat suitability, and was found to be not significant (p = 0.373), suggesting that both models are equally good predictors of Cx. tarsalis distribution patterns across the state.

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