Statistical analysis
We conducted all analyses using package “lme4”123 in
R v.3.6.0 (R Core Team 2019). We constructed separate models for each
disease severity metric, each of which included an interactive term of
site*origin site (“origin site” was the site from which bats were
collected at the beginning of the experiment, whereas “site” was that
within which bats were ultimately caged) as the predictor variable and a
unique cage ID as a random effect. Pathogen growth rate was the only
disease severity metric with a significant effect of origin site, so we
report the effect for this metric but drop the origin site term in the
other models. In all analyses, we only used data from alive bats except
for analyses on tissue invasion. We did not use pathogen load data from
dead bats because Pseudogymnoascus destructans is a poor
competitor on bat carcasses and, therefore, swab data from dead bats
does not accurately convey the pathogen load at the time of mortality.
To measure pathogen growth rate for each individual bat, we subtracted
its early hibernation fungal load value from its late hibernation value
to quantify the change in fungal loads. Bats that had no detectable
fungus at the time of swabbing were assigned a value of 4.35e -03
pg (equivalent to a Ct value of 40) for that swab
sample24, or a single P. destructans conidia.
We then added a constant of 10 and log10-transformed the growth rate
values. We used a linear regression to assess the differences in change
in fungal loads on bats at each site. Incorporating cage ID as a random
effect did not add explanatory power to this model and was dropped. In
addition, to understand the relationship between roosting temperature
and the change in fungal loads, we constructed a separate linear mixed
model with the average roosting temperature (data collected by iButtons
within each cage), origin site, and their interaction as fixed effects
and cage ID as a random effect.
To test for differences in the severity of tissue invasion across the
sites, we used a logistic regression with orange pixels indicating
infection as successes and non-orange pixels as failures (generalized
linear mixed model with binomial error distribution and logit link
function), site as a fixed effect, and cage ID as a random effect.
Additionally, to test for differences in tissue invasion between caged
bats and free-flying bats opportunistically sampled at the end of
hibernation in each of the persisting sites, we used a logistic
regression with the same response variable (generalized linear mixed
model with binomial error distribution and logit link function) and
site, caging status (caged vs. free-flying), and their interaction as
fixed effects. To explore the differences in weight loss across the
three sites, we used a generalized linear mixed model (gamma error
distribution, log link function) with weight loss as the response
variable, translocation site as a fixed effect, and cage ID as a random
effect. To test for differences in late hibernation body mass between
caged and free-flying bats in each persisting site, we used a multiple
linear regression with body mass as the response variable and an
interaction term of site and caging status as the explanatory variable.
We used a generalized linear mixed model (binomial error distribution,
logit link function) to explore the relationship between early
hibernation body mass and survival. Finally, we used a generalized
linear mixed model (binomial error distribution, logit link function) to
investigate how survival varied across the sites.