Master of Science / Department of Diagnostic Medicine and Pathobiology / Ram K. Raghavan / Dermacentor variabilis (Say) (Acari: Ixodidae), commonly known as the American dog tick is a medically important tick species in N. America, which has been implicated as a competent vector for several diseases, including tularemia, bovine anaplasmosis and canine tick paralysis. This tick is also the primary suspect for the transmission of Rickettsia rickettsii, the causative agent of Rocky Mountain spotted fever (RMSF). The spatial distribution and geographic extent of D. variabilis territory in N. America is suspected to have changed in the recent times due to natural and anthropogenic, non-stationary forces. A clear understanding of the spatial distribution and environmental factors contributing to the distribution has public health significance, allowing us to make informed management decisions and for setting robust future research goals aimed at understanding vector-biology and disease management. Additionally, ongoing climate-change is expected to alter species spatial distribution and abundance within distribution range. In this research, I studied the current and likely future spatial distribution of D. variabilis ticks in N. America based on two representative concentration pathways, RCP 4.5 and RCP 8.5, representing lower and higher emission scenarios, respectively, under several global circulation models (GCM). The spatial distribution models were constructed using MaxEnt program and BioClim data was used as environmental data for modeling. Best models were selected based on Partial ROC curves, AIC, and omission rates. Median prediction of these models indicate a wider spread of D. variabilis from its currently known extent, and much further spread as a result of climate change. Different environmental variables that significantly influenced current and future D. variabilis distribution included annual mean temperature, mean diurnal range, maximum temperature of the warmest month, annual precipitation, precipitation seasonality, and precipitation of the wettest quarter.