White-nose syndrome (WNS), caused by the fungal pathogen Pseudogymnoascus destructans, is the most devastating disease currently affecting North American wild mammals. WNS infection alters the physiology and bioenergetics of bat hibernation leading to increased arousal that depletes fat stores. Since the pathogen emerged in the winter of 2006, it has caused widespread mortality and threatened several species with extinction. The pathogen has spread throughout eastern and central North America, and is advancing westward. As the pathogen progresses west, it will infect new populations and species and their hibernacula, increasing pathogen exposure pathways and disrupting the important ecosystem contributions of bats. 

Our project is developing the science to help identify species that are susceptible to WNS and thereby species of management concern. To do so, we are using a mechanistic WNS survivorship model based on host bioenergetics, the pathogen and environment. This research will help predict the impact of WNS in western North America where bat diversity is highest on the continent. We are then combining this model with species distribution models to explore the ecology and management of WNS disease dynamics under non-stationary conditions. Technical information about the project can be found here.

Our core objectives are:

1. Collect robust morphometrics, bioenergetics and hibernacula environmental data on western North American bat species representing different hibernating behaviors and geographic settings.
2. Examine the transferability of the mechanistic WNS bioenergetics survivorship model (based on host, pathogen and environmental characteristics) developed for bat species affected by WNS in the East to a set of representative bat species found in the West.
3. Develop approaches that integrate the mechanistic WNS survivorship model with species distribution models to evaluate the presence of WNS with plausible scenarios of non-stationary conditions (e.g. climate change) and to explore the sensitivity of the integrated model to different parameters and data availability.

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