Prediction of spatially-varying hibernation survival
We used a mechanistic hibernation energetics model to estimate the total cost of hibernation (in grams of fat) for a hibernating M. lucifugus across its distribution (see Haase et al. (2019) for complete model documentation). The model is dynamic and species-specific, using metabolic and morphometric parameters to estimate the amount of fat used during hibernation. We used published parameter values for M. lucifugus (Haase et al. 2019) with the exception of arousal duration, which was set to 2.2 h (CLL, unpublished data ).
These energetic functions are dependent on temperature and relative humidity of the roost to provide estimates of energy expenditure across a wide range of potential conditions. The temperature and humidity dependent growth of P. destructans can be included in the model to estimate the metabolic impact of infection on energy consumption as the fungal load increases. We modelled the total energetic costs of hibernation with and without the impacts of P. destructans across two hibernaculum microclimate scenarios. First, we used fixed microclimate conditions that assumed a bat could access the preferred optimal hibernaculum microclimate conditions across the species’ distribution. We used conditions of 4°C and 98% relative humidity based on observations reported in the literature (Table S3). These microclimatic conditions are thought to provide for the longest possible hibernation duration, a hypothesis supported by recent findings (Haase et al. 2019). Second, we used a spatially-explicit model of subterranean temperature conditions to predict the best available (i.e., closest to optimal) temperature at a given location. This approach assumes that bats will select roosts within hibernacula that offer their preferred temperature when possible but will likely tolerate warmer or cooler temperatures when necessary, especially at the range margins. Unfortunately, the not much information exists for relative humidity; therefore, we used the optimal 98% relative humidity for both the optimal and best available temperature scenarios.
To estimate the closest available temperature to the optimal temperature at any given location, we used a spatially explicit model of subterranean winter temperatures (McClure et al. 2020). This model estimates subterranean winter temperature should a potential hibernaculum exist based on mean annual surface temperature, site type (cave or mine), distance from the site entrance, and several less influential predictors representing topography, land cover, and presence of water. The model predicts an increase in subterranean temperature with increasing mean annual surface temperature and distance from the site entrance and higher temperatures in mines than in caves. For each 1 km2 cell we estimated minimum and maximum roosting temperatures likely to be present within a hibernaculum using methods described in McClure et al. 2020. We then assigned each raster cell the best available temperature; either the preferred temperature if that temperature was predicted to be available given the minimum and maximum temperature estimations, or the closest temperature available to the preferred roost temperature.
Finally, we estimated the amount of fat required to survive hibernation for each of our 1 km2 cells for both healthy and WNS-impacted bats by using the dynamic hibernation energetics model and our spatially-varying predictions of hibernation duration and fat. We determined if an average bat could survive hibernation for each cell by subtracting the predicted fat required to survive the hibernation duration from the fat available prior to hibernation. Positive values indicate a bat’s ability to survive hibernation with excess fat, while negative values indicate the depletion of fat stores prior to the end of the hibernation period. We compared the predicted amount of fat required by healthy bats against the fat required by infected bats to estimate the relative increase of energetic costs of P. destructans onM. lucifugus as a percentage (the difference between resources required to survive hibernation when infected and when healthy in grams of fat, divided by grams of fat required to survive hibernation as healthy bat, multiplied by 100). We also translated the amount of fat “leftover” after the conclusion of hibernation into days post hibernation, i.e., how long the bat could continue hibernating, for both healthy and infected bats.
All analyses were performed in R (R Development Core Team 2009), with spatial handling tools from raster (Hijmans 2016) and sppackages (Pebesma and Bivand 2005).