Climate change is anticipated to reduce global biodiversity in the 21st century through habitat loss, extinctions, changes in community dynamics, and shifts in species distributions. In the North Cascades Ecosystem in western North America, large, high-severity fires are closely linked to climate warming and predictive models suggest that novel disturbance regimes will develop in the next century. Improving landscape connectivity is a frequently cited climate mitigation strategy, but current applications portray connectivity as a static process. Understanding the mechanisms that contribute to connectivity change over time (connectivity dynamics) is vital for predicting climate response for organisms. Camera traps were deployed at 46 sites in 2016 using a probabilistic sampling design, stratified across land cover and fire history. The probability of site occupancy for each species was estimated based on environmental conditions at the patch level. Circuit theory was used to predict the change in species landscape connectivity in a dynamic heterogeneous system by calculating the change in network connectivity between a static connectivity scenario and multiple dynamic connectivity scenarios representing potential future wildfire conditions.
This poster presents preliminary findings from the first year of study in a transboundary protected area along the border between the United States and Canada. 20 species were detected with mean site richness of 3.86. AIC model averaging shows fire year and fire severity as important for predictors of occupancy for mule deer (Odocoileus hemionus; AICw = 0.44) and topography and fire as important for coyote (Canis latrans; AICw =0.49) and bobcat (Felis rufus; AICw =0.46). Our research evaluates connectivity dynamics for multiple mammal species in a protected area network with an active wildfire regime using simulations parameterized by empirical data. We are currently exploring opportunities to use dynamic connectivity metrics in population persistence models to evaluate long-term viability for impacted populations.