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which was set to 2.2 h (CLL, unpublished data ).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).