White-nose syndrome, winter duration, and pre-hibernation climate impact abundance of reproductive female bats

Sarah K. Krueger; Sarah C. Williams; Joy M. O’Keefe; Gene A. Zirkle; Catherine G. Haase

doi:10.1371/journal.pone.0298515

Abstract

White-nose syndrome (WNS) is an infectious disease that disrupts hibernation in bats, leading to premature exhaustion of fat stores. Though we know WNS does impact reproduction in hibernating female bats, we are unsure how these impacts are exacerbated by local climate factors. We compiled data from four southeastern U.S. states and used generalized linear mixed effects models to compare effects of WNS, pre-hibernation climate variables, and winter duration on the number of reproductive females in species across the range of WNS susceptibility. We predicted we would see a decline in the number of reproductive females in WNS-susceptible species, with the effect exaggerated by longer winter durations and pre-hibernation climate variables that lead to reductions in foraging. We found that the number of reproductive females in WNS-susceptible species was positively correlated with pre-hibernation local climate conditions conducive to foraging; however, WNS-susceptible species experienced an overall decline with the presence of WNS and as winter duration increased. Our long-term dataset provides evidence that pre-hibernation climate, specifically favorable summer weather conditions for foraging, greatly influences the reproduction, regardless of WNS status.

Citation: Krueger SK, Williams SC, O’Keefe JM, Zirkle GA, Haase CG (2024) White-nose syndrome, winter duration, and pre-hibernation climate impact abundance of reproductive female bats. PLoS ONE 19(4): e0298515. https://doi.org/10.1371/journal.pone.0298515

Editor: Camille Lebarbenchon, University of Reunion Island, RÉUNION

Received: August 22, 2023; Accepted: January 26, 2024; Published: April 26, 2024

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Data Availability: We provide some of the published data in the Supplementary Materials, but other data not published cannot be shared with public access. We noted the owners of the dataset in our manuscript so readers may request data from these contacts. Restriction on sharing the data publicly is due to data being owned by a third-party organization. Please contact Fort Campbell Fish & Wildlife (5290 8th St, Fort Campbell, TN 42223; 931-472-5820) for data requests.

Funding: Funding for this research and support for S. Krueger was provided by an Intergovernmental Support Agreement with the U.S. Army (Order # CAMP-IGSA-20-02). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In sexually reproducing species, timing parturition to coincide with peak food resource availability ensures adequate energetic resources for raising offspring [1, 2]. Many mammalian species have evolved strategies to delay reproduction to wait out unfavorable conditions for sustaining newborns, such as lack of food, to ensure the success of their young [3]. For example, the African buffalo (Synerus caffer) synchronizes birthing with periods when protein content is abundant in forage. Researchers believe this strategy is triggered by environmental cues such as rainfall and sprouting of seedlings and saplings [4]. In another example, little brown bats (Myotis lucifugus) mate before hibernation, during peak fitness, and females store sperm for parturition post-hibernation emergence [5]. These reproductive delays are driven by the physiology of the mother and include delayed fertilization through sperm storage [6], delayed implantation of the zygote [7], and delayed development of young [5].

Delayed reproduction also permits trade-offs between reproduction and other elements of a species’ life cycle, for instance, hibernation [3]. Reproductive delays throughout hibernation is a common strategy of many temperate insectivorous bat species in the family Vespertilionidae. Hibernation is a vital period in the life cycle of many mammals that require energy-saving strategies to maximize fitness over periods of food scarcity, such as during winter [8]. Hibernation is characterized by periods of torpor and arousal, where torpor is the short-term reduction in body temperature and metabolism that is interrupted by periodic arousals to normothermic body temperatures and metabolic rates [8]. Female bats delay fertilization or implantation during hibernation to minimize the energetic costs during winter while maximizing food availability for parturition [9]. Pups are born when prey availability is optimal and there is enough time to reach maximum adult mass before hibernation the following winter [6].

The phenology of emergence from hibernation by females is influenced by a balance between two contrasting factors: the benefit of giving birth early in the season and the potential risk associated with facing unfavorable spring weather or a shortage of food resources [10–12]. Likewise, juvenile survival is linked to the birth date of pups, with those born earlier in the summer having a greater likelihood of surviving their first year than those born later in the summer [10]. Thus the severity of winter and the duration of hibernation can drastically impact juvenile survival and potential population growth. For example, Burles et al. [13] found that Myotis lucifugus experienced delays in reproductive timing when their study area was experiencing unusually cool, wet weather from El Niño (in 1999). Low ambient temperatures increase the energetic costs of maintaining a normal body temperature and may result in prolonged gestation and lower reproductive success [13].

Though much research has determined the impacts of food availability and climate on delayed reproduction [10, 13–16], little is known about how the energetic costs of disease may alter this response. For example, white-nose syndrome (WNS), a disease of North American hibernating bats, increases energetic costs during hibernation through disruption of the torpor-arousal cycle, leading to loss of fat stores and high rates of mortality [17–19]. Physiological changes such as decreased fat mass, elevated CO₂ levels in the blood, dehydration, and increased fat consumption have been documented in WNS-affected bats [20]. These physiological changes can result in respiratory acidosis, hyperkalemia, and evaporative water loss, possibly stimulating increased arousal frequency that results in premature fat consumption [20, 21]. Due to the increased energetic requirements during hibernation in WNS-affected bats, fertilization is often postponed or completely forgone by mated females [21, 22]. Postponing fertilization to a later date can be problematic to WNS-susceptible species if juveniles lack adequate time to gain fat stores before hibernation the following winter [23].

Since its detection in New York in 2006 [24], WNS has been confirmed in at least twelve bat species in over half of the U.S. states and Canadian provinces (whitenosesyndrome.org); additionally, the causative fungus, Pseudogymnoascus destructans, has been found on six more species. Observed differences in fungal loads among species and resulting morbidity and mortality suggest variation in susceptibility to WNS across North American bats. Though the true mechanisms that drive this variation is unknown, various factors have been linked with interspecific variation in fungal loads, including sociality and grooming during hibernation, hibernaculum microclimate, skin microbiome, and the amount of pre-hibernation body fat [18, 25–29]. For example, the small-bodied tricolored bat (Perimyotis subflavus) hibernates in caves that sustain microclimates for high fungal growth, resulting in extremely high fungal loads and prevalence rates (2.5–15.8° C; [18, 25, 28, 30, 31]). Other species, such as Eptesicus fuscus (big brown bat), persist with low fungal loads and are resistant to mass mortality; the mechanism behind these low loads is still unclear, but the skin microbiome has been hypothesized to support resistance [32, 33]. Across hibernating bat species in North America, differing hibernation behaviors and immune responses can result in variation in susceptibility to this disease.

Given the variation in vulnerability to WNS, it is crucial to evaluate how hibernation behavior, local climate conditions, and the duration of winter collectively influence the likelihood of survival, and in turn, reproduction. In this study, we use data collected from multiple researchers across four states across the southeastern U.S. to test competing hypotheses that describe disease impacts on the number of reproductive female bats with respect to WNS. Female bats enter hibernation with a set amount of stored fat and may have opportunities to resupply throughout winter if winters are mild [34]. During short and/or mild winters, females may leave hibernation with excess fat, allowing immediate fertilization. Alternatively, during long or severe winters, females may end hibernation with no fat stores and need to resupply, thus delaying, or even foregoing, fertilization and pregnancy. As WNS increases energetic stress during hibernation, we may see interactive effects of winter duration and severity with disease on the abundance of reproductive females. Thus we hypothesize that the number of reproductive females will be a function of duration and severity of winter and time since WNS introduction, wherein we will see an increase in reproduction (i.e., pregnant, and lactating females) following short, mild winters in years post detection of WNS. On the other hand, we propose the hypothesis that reproductive females rely on foraging during the periods of spring, summer, and fall to build up their fat reserves before hibernation. As a result, we anticipate that favorable climate conditions leading up to hibernation will correlate positively with the abundance of reproductive females in the subsequent year.

Materials and methods

Data collection

We compiled capture data over the years 1989–2020 from four southeastern states (GA, KY, NC, TN; Fig 1) from published surveys [28, 35, 36] and state and federal datasets (S1 Table). A total of 9,561 reproductive female bats from 9 species in 261 counties were collected from 1989–2020. Only those individuals that had clearly defined reproductive conditions were included in the analyses.

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Fig 1. Capture data.

Capture data from hibernating bat species were collected in 267 counties (black) in Tennessee, Kentucky, North Carolina, and Georgia, USA from 1989–2020.

https://doi.org/10.1371/journal.pone.0298515.g001

We defined WNS status in two ways in our analyses: first, before or after WNS occurrence in each county and second, the year since WNS was first observed in each county. We obtained the year that WNS was first documented in each county in each state from the US Fish and Wildlife Service (whitenosesyndrome.org). We then calculated the years since WNS for each county by counting the number of years since WNS was first documented. Since we lacked individual WNS status, the binning of bats to county-wide WNS status was unavoidable. Though it is likely that bats inhabiting WNS positive roosts may not be positive for the fungus, we must make the assumption that all bats captured in this analyses were exposed to the disease.

Prior to the onset of WNS, survey efforts across North America in the form of both mist-netting and acoustics remained relatively low [37–39]. Due to changes in research objectives, post-WNS efforts increased substantially [40, 41]. We calculated survey effort as the total number of net nights per county per year to include as a covariate; we used this metric to match our county-wide WNS designation.

Finally, we obtained winter duration, winter severity, and pre-hibernation climatic variables. We determined the mean, maximum, and minimum elevation for each county with capture data using a digital elevation model (DEM) provided by Wang et al [42]. We calculated a suite of summer, winter, spring, and autumn metrics (see S2 Table) for each county [43] with the ClimateNA v5.10 software package (http://tinyurl.com/ClimateNA), based on methodology described by Wang et al. [42]. Using methods described by Hranac et al. [44], we predicted the mean winter duration for each county based on county center latitude, elevation, and the number of days in frost gathered from ClimateNA.

We used a principal component analysis to find the key combinations of climate variables from pre-hibernation months per county per year that described most of the variation in the climate data (>90% [45]; S2 Table). The first two principal components had eigenvalues > 1 [46] and were used as covariates in our statistical models (PC1 = 1.75 and PC2 = 1.55; see S3 Table, S1 Fig). We interpreted the components by selecting the predictor variables with the highest eigenvectors (> |0.40|) associated with each component ([47] see S4 Table, S2 Fig). Independent variables for PC1 (noted as “humidity component”) with the highest eigenvectors included mean summer relative humidity (|0.44|) and mean spring relative humidity (|0.46|; see S4 Table). Independent variables for PC2 (noted as “temperature component”) with the highest eigenvectors included number of summer days above 18°C (|0.43|) and mean annual temperature (|0.49|; see S2 Fig). The humidity component and temperature component retained 61% of the variances contained in the data (S4 Table).

Statistical models

We developed a suite of generalized linear mixed models to test the competing hypotheses of the impacts of WNS, winter duration, and local climate on the abundance of reproductive female bats. In all models, the response variable was the total number of reproductive females (pregnant, lactating, or post-lactating) per WNS susceptibility group per county per year; we used the abundance of all reproductive females as our response rather than the abundance of each reproductive class because of low sample sizes post-WNS. Species susceptibility to WNS was grouped based on criteria discussed in Jackson et al. (2022): low susceptibility (E. fuscus, Myotis leibii, Myotis grisescens), mild susceptibility (Myotis sodalis), and high susceptibility (P. subflavus, M. lucifugus, Myotis septentrionalis). We included year and county as random effects and sampling effort (log[capture nights per year]) as a fixed effect in all models. To deal with the non-normality of count data, we included a Poisson distribution with a log-link to transform the error distribution of the residuals.

Our explanatory variables varied depending on the hypothesis of question (Table 1). We included fixed effects of winter duration (predicted days in winter per county), the two principal components for pre-hibernation climate variables per county (humidity component and temperature component), and a combination of all three variables as they are not mutually exclusive (Table 1). We also incorporated the effects of WNS as a fixed effect in each model in two ways: first as presence/absence, with presence dictated by the year first observed in the county in which that bat was surveyed, and second, year(s) since WNS was reported, to allow for variation in the effect of disease over time. All continuous variables were centered on the mean and scaled by standard deviation.

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Table 1. Models from statistical analyses.

Candidate models predicting number of reproductive female bats (pregnant, lactating, post-lactating) of southeastern bat species with Akaike information criterion for over-dispersed data (ΔAIC_c), log-likelihood values (LL), number of parameters (K), and AIC_c weights (w_t). All models had a fixed effect of survey effort (log[capture nights per year]), WNS susceptibility grouping (low susceptibility: Eptesicus fuscus, Myotis leibii, Myotis grisescens; mild susceptibility: Myotis sodalis; and high susceptibility: Perimyotis subflavus, Myotis lucifugus, Myotis septentrionalis), and random effects of year and county.

https://doi.org/10.1371/journal.pone.0298515.t001

We conducted model comparisons using the second-order Akaike’s Information Criterion (ΔAICc; Akaike 1973 [48]), giving preference to models with the lowest ΔAICc values as the most plausible options. To address overdispersion, which is common in Poisson regression, we employed the X² approximation of the residual variance (Zuur et al. 2009 [49]). Models with a c_t ratio < 1 were considered overdispersed. We compared models with a ΔAICc within 2 units, utilizing model weights, and provided adjusted standard errors accordingly. The explanatory power of the fixed effects and the overall selected model was assessed through calculated R² values. The significance of individual variables in the top-ranked model was determined using p-values (α = 0.05). All statistical analyses were performed using R v3.3.3 [50], with the R package lme4 [51].

Results

The dataset contained 31 years of capture data from 234 counties in the four states (Figs 1 and 2). There were a total of 11,896 reproductive females (pregnant = 2,069, lactating = 2,713, post-lactating = 2,601) across all species groups (low susceptibility = 3,164, mild susceptibility = 1,127, high susceptibility = 3,062). Of the low susceptible species, there were many more E. fuscus (n = 2,648) then M. leibii (n = 212) and M. grisescens (n = 334). Of the highly susceptible species, there were more M. septentrionalis (n = 1,490) then P. subflavus (n = 943) or M. lucifugus (n = 629). Finally, there were 1,127 M. sodalis in the mildly susceptible group.

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Fig 2. Mean nightly capture rates.

Mean nightly capture rates of reproductive (pregnant, lactating, post-lactating) females for three white-nose syndrome susceptibility groups (low susceptibility: Eptesicus fuscus, Myotis leibii, Myotis grisescens; mild susceptibility: Myotis sodalis; and high susceptibility: Perimyotis subflavus, Myotis lucifugus, Myotis septentrionalis) summarized by county and state for A) each year from 1989–2020 and B) since the onset of WNS in the southeastern United States.

https://doi.org/10.1371/journal.pone.0298515.g002

The strongest support for the abundance of reproductive females was observed in the interaction among species WNS susceptibility, winter duration, pre-hibernation climate (PC 2 = temperature), and the time elapsed since the initial documentation of the disease (AIC_C = 22078.13, LL = -11012.97, K = 26, w_t = 0.99); this model exhibited a difference > 20 AICc compared to the subsequent top-performing model (Table 1). Species exhibiting mild susceptibility and those highly susceptible to WNS experienced a decline in the abundance of reproductive females with the emergence of WNS, as expected (mild susceptibility: (β = -0.03 ± 0.02 SE, p = 0.04; high susceptibility: (β = -0.04 ± 0.01 SE, p < 0.0001; Table 2, Fig 2B).

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Table 2. Summary statistics for top model.

Summary statistics for top model predicting number of reproductive female bats (pregnant, lactating, post-lactating) per county per year of southeastern bat species (low WNS susceptibility: Eptesicus fuscus, Myotis leibii, Myotis grisescens; mild WNS susceptibility: Myotis sodalis; and high WNS susceptibility: Perimyotis subflavus, Myotis lucifugus, Myotis septentrionalis) with coefficient estimate (β), standard error (SE), and p-value. WNS susceptibility variables are against the reference of low susceptible species. The temperature variable is the composite principal component 2 of pre-hibernation climate variables; PC 2 included the number of days exceeding 18°C during summer and the mean annual temperature. Years since WNS is the number of years since WNS was first observed in the county.

https://doi.org/10.1371/journal.pone.0298515.t002

The relationship between the abundance of reproductive females and winter duration displayed a negative correlation (β = -0.10 ± 0.02 SE, p < 0.0001; Fig 3A), irrespective of the species’ susceptibility to WNS (p > 0.05 for all WNS susceptibility species groups) or the time elapsed since the onset of WNS (p = 0.06). Conversely, the impact of pre-hibernation climate, specifically temperature (PC 2), demonstrated a positive association with female abundance (β = 0.05 ± 0.02 SE, p = 0.001; Fig 3B). When considering the interaction between WNS susceptibility groups and climate, only the mildly susceptible species (M. sodalis) exhibited a distinct relationship compared to the other groups (β = -0.11 ± 0.03 SE, p < 0.001). The influence of pre-hibernation climate did not change based on the onset of WNS (p = 0.33) and did not mitigate the effect of winter duration on female abundance (p = 0.91). Among species highly susceptible to WNS (P. subflavus, M. lucifugus, M. septentrionalis), their population decreased with longer winters, even when accounting for the interaction with pre-hibernation temperature (β = -0.04 ± 0.02 SE, p = 0.02). In contrast, mildly susceptible species exhibited an increase in population with warmer pre-hibernation climates, even in the presence of WNS (β = 0.04 ± 0.01 SE, p = 0.004).

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Fig 3. Predicted number of females from analyses.

Predicted number of reproductive females of seven southeastern bat species against A) mean winter duration (in days) and B) the principal component (PC 2) that represents pre-hibernation temperature. Both predictive models set all other variables (survey effort (log[capture nights per year], time of WNS exposure) to the mean. Error bands represent 95% confidence intervals.

https://doi.org/10.1371/journal.pone.0298515.g003

Discussion

Our analysis of a long-term historical dataset provides evidence highlighting the substantial impact of pre-hibernation climate and winter duration on the population of reproductive females in species susceptible to white-nose syndrome (WNS) upon its onset. In particular, our findings demonstrate that warmer pre-hibernation climate conditions are associated with an increase in the number of reproductive females, while longer winters correlate with a decrease. These results coincide with previous work that has noted the importance of forage resources for survival [52–54]. Following the appearance of WNS, there was a notable reduction in reproductive females in those species susceptible to the disease, with the decline being more prominent among those classified as highly susceptible (P. subflavus, M. lucifugus, M. septentrionalis). These outcomes are in line with our expectations, given the predictable influence of pre-hibernation climate, winter duration, and WNS on bat energetics.

Environmental factors highly correlated with the number of reproductive females included the number of days exceeding 18°C during summer and the mean annual temperature (PC 2; refer to S4 Table). Not surprisingly, this aligns with existing evidence indicating that bats in temperate zones heavily rely on favorable foraging conditions before entering hibernation. Elevated temperatures in summer and spring, along with increased relative humidity, directly impact the availability of prey [52, 55]. When prey is abundant prior to hibernation and weather conditions permit successful foraging endeavors, the likelihood of bats accumulating fat reserves rises. This heightened fat storage enhances their potential to endure challenging winters and the pressures of WNS [19, 44]. Furthermore, complementary research proposes a link between the accumulation of fat before hibernation and the probability of successful reproduction [21], a notion supported by our data. Nonetheless, the connection between pre-hibernation body condition and successful reproduction has not been exclusively tested.

In addition to pre-hibernation climate conditions conducive to ample fat storage, our study also demonstrates a correlation between the number of reproductive females and the duration of winter in species susceptible to WNS. Notably, the number of reproductive females in WNS-susceptible species was lower following lengthier winters. This relationship is to be expected, given that extended winters demand greater fat reserves for prolonged hibernation. Consequently, bats affected by WNS appear to fare better in regions characterized by shorter winter durations [44]. Similarly, surviving hibernation with the presence of WNS does not guarantee that a bat will possess the necessary energy for successful offspring rearing. Johnson et al. [56] presented evidence indicating that the energetic demands on WNS-affected bats intensify as they endeavor to recover and engage in foraging activities to replenish the fat stores lost upon emerging from hibernation. This insight suggests that females might opt to forego reproduction when confronted with the dual stressors of prolonged winter durations and the impacts of WNS, aligning with the observations in our findings.

Our results add to the growing literature that there are there are energetic costs for reproductive-age females contending with the effects of WNS [38, 57]. We observed a post-WNS decline in captures of reproductive females of WNS-susceptible species (M. sodalis, M. lucifugus, M. septentrionalis, and P. subflavus). Pettit and O’Keefe [58] also observed negative effects of WNS on reproduction and hypothesized that WNS-induced fat depletion could force female bats to miscarry pups or prolong the development of young. Lengthening the gestation period after hibernation could negatively impact pup development prior to hibernation. If pups are born later in the summer, they might not have enough time to build up sufficient fat stores before going into their first hibernation, thus decreasing their probability of survival over winter, thereby further exacerbating population declines from WNS. Further evidence that WNS may be delaying reproduction is needed to determine the relevance of this phenomenon. We suggest future studies on the mechanisms of WNS that can lead to pregnancy loss.

Our results suggest that ecological release may be occurring with species less susceptible to WNS (E. fuscus, Myotis leibii, M. grisescens), allowing them to use resources that may have otherwise been depleted by other species [59]. For example, Jachowski et al. [60] found that following dramatic declines of the once common M. lucifugus, species not susceptible to WNS increased their spatial and temporal overlap with M. lucifugus in northwestern New York. Their results showed that due to reduced interspecific competition, some species were able to occupy aerial foraging space once used by these WNS-impacted species. Future investigations should focus on how these potential changes in niche partitioning and decreased competition between susceptible and non-susceptible species result in long-term changes to bat community structure.

Finally, the results of the methods and findings of this work would lend themselves to an increased, perhaps country wide study using archival data and data generated by ongoing surveillance efforts [such as NABat; 61]. It would also be telling to incorporate these results into demographic models of WNS susceptible bat populations to study how demographic shifts may contribute to population crashes. Most research on how bats survive hibernation to successfully reproduce has either focused on the impacts of winter duration or local climate or disease, but not the combined effects of all three. We conclude that WNS, pre-hibernation climate, and winter duration all impact reproductive success for southeastern bat species. Using data spanning from 1989–2020, we were able to assess the effects of disease and how reproductive success may vary with local climate. Predicting the interplay among these three variables must consider the variation of disease impacts across both susceptible and non-susceptible species across different parts of their range, and how local variation can modulate reproduction. Our results highly the key components important to reproductive success in southeastern bat species and provide a necessary stepping stone to further our understanding of the costs associated with surviving different life history stages in the context of disease.

Supporting information

S1 Fig. Eigenvalues from principal component analysis.

Eigenvalues and the percent variance explained by each principal component from a principal component analysis summarizing climate variables (mean annual temperature, number of summer days above 18°C, number of spring days above 18°C, mean annual precipitation, spring mean relative humidity, summer mean relative humidity, autumn mean relative humidity, number of frost-free days, and number of spring days below 0°C) from Tennessee, North Carolina, Georgia, and Kentucky from 1989–2020.

https://doi.org/10.1371/journal.pone.0298515.s001

(DOCX)

S2 Fig. Factor loadings from principal component analysis.

Factor loadings for the first two principal components of a principal component analysis summarizing climate variables for Tennessee, North Carolina, Georgia, and Kentucky from 1989–2020. Identifiers of the variables: mean annual temperature, number of summer days above 18°C, number of spring days above 18°C, mean annual precipitation, spring mean relative humidity, summer mean relative humidity, autumn mean relative humidity, number of frost-free days, and number of spring days below 0°C.

https://doi.org/10.1371/journal.pone.0298515.s002

(DOCX)

S1 Table. Capture data used in the analyses.

Capture data from hibernating bat species were collected in 267 counties (black) in Tennessee, Kentucky, North Carolina, and Georgia, USA from 1989–2020. We report only the capture data available in published articles; other data available from listed state and federal agencies (see Data Availability statement).

https://doi.org/10.1371/journal.pone.0298515.s003

(DOCX)

S2 Table. Climate variables used in principal component analysis.

Winter (December [previous year for an individual year], January, and February), spring (March, April, and May), summer (June, July, and August), autumn (September, October, and November), and annual severity metrics calculated per county for Tennessee, North Carolina, Georgia, and Kentucky for each year from 1989–2020. These metrics were used in a principal component analysis to describe pre-hibernation climate variables.

https://doi.org/10.1371/journal.pone.0298515.s004

(DOCX)

S3 Table. Eigenvalues from principal component analysis.

Eigenvalues and proportion of total variance explained by each axis derived from a principal component analysis of pre-hibernation climate data for Tennessee, North Carolina, Georgia, and Kentucky from 1989–2020.

https://doi.org/10.1371/journal.pone.0298515.s005

(DOCX)

S4 Table. Eigenvectors from principal component analysis.

Eigenvectors associated with pre-hibernation climate variables used in a principal component analysis summarizing climate metrics for Tennessee, North Carolina, Georgia, and Kentucky from 1989–2020.

https://doi.org/10.1371/journal.pone.0298515.s006

(DOCX)

Acknowledgments

We would like to thank T. Walker, B. Williams, and N. Deans for help with data collection, C.R. Hranac for winter duration model development and E. Rehm and C. Gienger for comments on the manuscript. We would like to acknowledge R. Bernard, Fort Campbell Fish & Wildlife, Kentucky Fish & Wildlife, Georgia Department of Natural Resources, North Carolina Wildlife Resources Commission, and Tennessee Wildlife Resources Agency for data.

References

1. Sadler RM. The Ecology of Reproduction in Wild and Domestic Mammals. Springer Science & Business Media; 2012.
- View Article
- Google Scholar
2. Bronson FH. Mammalian Reproductive Biology. University of Chicago Press; 1989.
3. Orr TJ, Zuk M. Does delayed fertilization facilitate sperm competition in bats? Behav Ecol Sociobiol. 2013;67: 1903–1913.
- View Article
- Google Scholar
4. Ryan S, Knechtel C, Getz W. Ecological cues, gestation length, and birth timing in African buffalo (Syncerus caffer). Behavioral Ecology. 2007;18: 635–644.
- View Article
- Google Scholar
5. Racey PA. Ecology of bat reproduction. In: Kunz TH, editor. Ecology of Bats. New York: Plenum Press; 1982. pp. 57–104.
6. Racey PA. Environmental factors affecting the length of gestation in heterothermic bats. J Reprod Fertil Suppl. 1973;19: 175–189. pmid:4522371
- View Article
- PubMed/NCBI
- Google Scholar
7. Birkhead TR, Moller AP. Sexual selection and the temporal separation of reproductive events: sperm storage data from reptiles, birds and mammals. Biological Journal of the Linnean Society. 1993;50: 295–311.
- View Article
- Google Scholar
8. Ruf T, Geiser F. Daily torpor and hibernation in birds and mammals. Biological Reviews. 2015;90: 891–926. pmid:25123049
- View Article
- PubMed/NCBI
- Google Scholar
9. Humphries MM, Kramer DL, Thomas DW. The role of energy availability in mammalian hibernation: An experimental test in free‐ranging eastern chipmunks. Physiological and Biochemical Zoology. 2003;76: 180–186. pmid:12794671
- View Article
- PubMed/NCBI
- Google Scholar
10. Frick WF, Reynolds DS, Kunz TH. Influence of climate and reproductive timing on demography of little brown myotis Myotis lucifugus. Journal of Animal Ecology. 2010;79: 128–136. https://doi.org/10.1111/j.1365-2656.2009.01615.x
- View Article
- Google Scholar
11. Norquay KJO, Willis CKR. Hibernation phenology of Myotis lucifugus. Journal of Zoology. 2018;294: 85–92.
- View Article
- Google Scholar
12. Lane JE, Kruuk LEB, Charmantier A, Murie JO, Dobson FS. Delayed phenology and reduced fitness associated with climate change in a wild hibernator. Nature. 2012;489: 554–557. pmid:22878721
- View Article
- PubMed/NCBI
- Google Scholar
13. Burles DW, Fenton MB, Barclay RM, Brigham RM, Volkers D. Aspects of the Winter Ecology of Bats on Haida Gwaii, British Columbia. Northwestern Naturalist. 2014;95: 289–299.
- View Article
- Google Scholar
14. Bronson FH. Climate change and seasonal reproduction in mammals. Philosophical Transactions of the Royal Society B: Biological Sciences. 2009;364: 3331–3340. pmid:19833645
- View Article
- PubMed/NCBI
- Google Scholar
15. Adams RA. Bat reproduction declines when conditions mimic climate change projections for western North America. Ecology. 2010;91: 2437–2445. pmid:20836465
- View Article
- PubMed/NCBI
- Google Scholar
16. Pfeiffer B, Mayer F. Spermatogenesis, sperm storage and reproductive timing in bats. Journal of Zoology. 2013;289: 77–85.
- View Article
- Google Scholar
17. Lorch JM, Meteyer CU, Behr MJ, Boyles JG, Cryan PM, Hicks AC, et al. Experimental infection of bats with Geomyces destructans causes white-nose syndrome. Nature. 2011;480: 376–378. pmid:22031324
- View Article
- PubMed/NCBI
- Google Scholar
18. Bernard RF, McCracken GF. Winter behavior of bats and the progression of white-nose syndrome in the southeastern United States. Ecology and Evolution. 2017;7: 1487–1496. pmid:28261459
- View Article
- PubMed/NCBI
- Google Scholar
19. Ehlman SM, Cox JJ, Crowley PH. Evaporative water loss, spatial distributions, and survival in white-nose-syndrome-affected little brown myotis: a model. J Mammal. 2013;94: 572–583.
- View Article
- Google Scholar
20. Verant ML, Meteyer CU, Speakman JR, Cryan PM, Lorch JM, Blehert DS. White-nose syndrome initiates a cascade of physiologic disturbances in the hibernating bat host. BMC Physiology. 2014;14: 10. pmid:25487871
- View Article
- PubMed/NCBI
- Google Scholar
21. Jonasson KA, Willis CKR. Changes in Body Condition of Hibernating Bats Support the Thrifty Female Hypothesis and Predict Consequences for Populations with White-Nose Syndrome. PLOS ONE. 2011;6: e21061. pmid:21731647
- View Article
- PubMed/NCBI
- Google Scholar
22. Frick WF, Pollock JF, Hicks AC, Langwig KE, Reynolds DS, Turner GG, et al. An emerging disease causes regional population collapse of a common North American bat species. Science. 2010;329: 679–682. pmid:20689016
- View Article
- PubMed/NCBI
- Google Scholar
23. Bernard RF, Reichard JD, Coleman JTH, Blackwood JC, Verant ML, Segers JL, et al. Identifying research needs to inform white-nose syndrome management decisions. Conservation Science and Practice. 2020;2: e220.
- View Article
- Google Scholar
24. Blehert DS, Hicks AC, Behr M, Meteyer CU, Berlowski-Zier BM, Buckles EL, et al. Bat white-nose syndrome: An emerging fungal pathogen? Science. 2009;323: 227–227. pmid:18974316
- View Article
- PubMed/NCBI
- Google Scholar
25. Langwig KE, Frick WF, Bried JT, Hicks AC, Kunz TH, Marm Kilpatrick A. Sociality, density-dependence and microclimates determine the persistence of populations suffering from a novel fungal disease, white-nose syndrome. Ecol Lett. 2012;15: 1050–1057. pmid:22747672
- View Article
- PubMed/NCBI
- Google Scholar
26. Grieneisen L. Hibernacula microclimate and white-nose syndrome susceptibility in the little brown Myotis (Myotis lucifugus). Master’s Theses. 2011. Available: https://digitalcommons.bucknell.edu/masters_theses/12
- View Article
- Google Scholar
27. Haase CG, Fuller NW, Dzal YA, Hranac CR, Hayman DTS, Lausen CL, et al. Body mass and hibernation microclimate may predict bat susceptibility to white-nose syndrome. Ecology and Evolution. 2021;11: 506–515. pmid:33437446
- View Article
- PubMed/NCBI
- Google Scholar
28. Bernard RF, Willcox EV, Parise KL, Foster JT, McCracken GF. White-nose syndrome fungus, Pseudogymnoascus destructans, on bats captured emerging from caves during winter in the southeastern United States. BMC Zoology. 2017;2: 12.
- View Article
- Google Scholar
29. Davy CM, Donaldson ME, Willis CKR, Saville BJ, McGuire LP, Mayberry H, et al. The other white-nose syndrome transcriptome: Tolerant and susceptible hosts respond differently to the pathogen Pseudogymnoascus destructans. Ecology and Evolution. 2017;7: 7161–7170. pmid:28944007
- View Article
- PubMed/NCBI
- Google Scholar
30. Vanderwolf KJ, Campbell LJ, Goldberg TL, Blehert DS, Lorch JM. Skin fungal assemblages of bats vary based on susceptibility to white-nose syndrome. ISME J. 2021;15: 909–920. pmid:33149209
- View Article
- PubMed/NCBI
- Google Scholar
31. Frick WF, Cheng TL, Langwig KE, Hoyt JR, Janicki AF, Parise KL, et al. Pathogen dynamics during invasion and establishment of white-nose syndrome explain mechanisms of host persistence. Ecology. 2017;98: 624–631. pmid:27992970
- View Article
- PubMed/NCBI
- Google Scholar
32. Lemieux-Labonté V, Dorville NAS-Y, Willis CKR, Lapointe F-J. Antifungal Potential of the Skin Microbiota of Hibernating Big Brown Bats (Eptesicus fuscus) Infected With the Causal Agent of White-Nose Syndrome. Front Microbiol. 2020;11: 1776. pmid:32793178
- View Article
- PubMed/NCBI
- Google Scholar
33. Frank CL, Michalski A, McDonough AA, Rahimian M, Rudd RJ, Herzog C. The resistance of a North American bat species (Eptesicus fuscus) to white-nose syndrome (WNS). PLOS ONE. 2014;9: e113958. pmid:25437448
- View Article
- PubMed/NCBI
- Google Scholar
34. Czenze ZJ, Jonasson KA, Willis CKR. Thrifty females, frisky males: winter energetics of hibernating bats from a cold climate. Physiological and Biochemical Zoology. 2017;90: 502–511. pmid:28641050
- View Article
- PubMed/NCBI
- Google Scholar
35. Rojas VGO’Keefe JM, Loeb SC. Baseline capture rates and roosting habits of Myotis septentrionalis (Northern Long-Eared Bat) prior to white-nose syndrome detection in the Southern Appalachians. sena. 2017;16: 140–148.
- View Article
- Google Scholar
36. O’Keefe JM, Pettit JL, Loeb SC, Stiver WH. White-nose syndrome dramatically altered the summer bat assemblage in a temperate southern Appalachian forest. Mammalian Biology. 2019;98: 146–153.
- View Article
- Google Scholar
37. Ford WM, Britzke ER, Dobony CA, Rodrigue JL, Johnson JB. Patterns of acoustical activity of bats prior to and following white-nose syndrome occurrence. Journal of Fish and Wildlife Management. 2011;2: 125–134.
- View Article
- Google Scholar
38. Francl KE, Ford WM, Sparks DW, Brack V. Capture and reproductive trends in summer bat communities in West Virginia: Assessing the impact of white-nose syndrome. Journal of Fish and Wildlife Management. 2012;3: 33–42.
- View Article
- Google Scholar
39. Nocera T, Ford WM, Silvis A, Dobony CA. Patterns of acoustical activity of bats prior to and 10 years after WNS on Fort Drum Army Installation, New York. Global Ecology and Conservation. 2019;18: e00633.
- View Article
- Google Scholar
40. Balzer EW, Grottoli AD, Phinney LJ, Burns LE, Vanderwolf KJ, Broders HG. Capture rate declines of Northern Myotis in the Canadian Maritimes. Wildlife Society Bulletin. 2021;45: 719–724.
- View Article
- Google Scholar
41. Deeley SM, Kalen NJ, Freeze SR, Barr EL, Ford WM. Post-white-nose syndrome passive acoustic sampling effort for determining bat species occupancy within the mid-Atlantic region. Ecological Indicators. 2021;125: 107489.
- View Article
- Google Scholar
42. Wang T, Hamann A, Spittlehouse D, Carrol C. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLOS ONE. 2016;11: e0156720. pmid:27275583
- View Article
- PubMed/NCBI
- Google Scholar
43. Bilotta R, Bell JE, Shepherd E, Arguez A. Calculation and Evaluation of an Air-Freezing Index for the 1981–2010 Climate Normals Period in the Coterminous United States. Journal of Applied Meteorology and Climatology. 2015;54: 69–76.
- View Article
- Google Scholar
44. Hranac CR, Haase CG, Fuller NW, McClure ML, Marshall JC, Lausen CL, et al. What is winter? Modeling spatial variation in bat host traits and hibernation and their implications for overwintering energetics. Ecology and Evolution. 2021;11: 11604–11614. pmid:34522327
- View Article
- PubMed/NCBI
- Google Scholar
45. Júnior PDM, Nóbrega CC. Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation. PLOS ONE. 2018;13: e0202403. pmid:30204749
- View Article
- PubMed/NCBI
- Google Scholar
46. Jackson DA. Stopping rules in Principal Components Analysis: A comparison of heuristical and statistical approaches. Ecology. 1993;74: 2204–2214.
- View Article
- Google Scholar
47. Peres-Neto PR, Jackson DA, Somers KM. How many principal components? stopping rules for determining the number of non-trivial axes revisited. Computational Statistics & Data Analysis. 2005;49: 974–997.
- View Article
- Google Scholar
48. Akaike H. (1973). Information theory and an extension of the maximum likelihood principle. In Petrov B. N. & Csaki F.(Eds.),2nd International Symposium on Information Theory (pp. 267–281). Budapest: Akademiai Kiado.
- View Article
- Google Scholar
49. Zuur A.F., Ieno E.N., Walker N.J., Saveliev A.A., Smith G.M. (2009). Meet the Exponential Family. In: Mixed effects models and extensions in ecology with R. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-0-387-87458-6_8
50. R Development Core Team. R: a language and environment for statistical computing R Foundation for Statistical Computing. Vienna, Austria; 2009. Available: http://www.R-project.org
51. Bates D, Maechler M, Bolker B, Walter S. Fitting linear mixed-effects models using lme4. Journal of Statistical Software. 2010;6: 1–48.
- View Article
- Google Scholar
52. Bernard RF, Willcox EV, Jackson RT, Brown VA, McCracken GF. Feasting, not fasting: winter diets of cave hibernating bats in the United States. Frontiers in Zoology. 2021;18: 48. pmid:34556122
- View Article
- PubMed/NCBI
- Google Scholar
53. Frick WF, Dzal YA, Jonasson KA, Whitby MD, Adams AM, Long C, et al. Bats increased foraging activity at experimental prey patches near hibernacula. Ecological Solutions and Evidence. 2023;4: e12217.
- View Article
- Google Scholar
54. Cheng TL, Gerson A, Moore MS, Reichard JD, DeSimone J, Willis CKR, et al. Higher fat stores contribute to persistence of little brown bat populations with white-nose syndrome. Journal of Animal Ecology. 2019;88: 591–600. pmid:30779125
- View Article
- PubMed/NCBI
- Google Scholar
55. Jackson RT, Willcox EV, Bernard RF. Winter torpor expression varies in four bat species with differential susceptibility to white-nose syndrome. Sci Rep. 2022;12: 5688. pmid:35383238
- View Article
- PubMed/NCBI
- Google Scholar
56. Johnson JS. Hibernating bat species in Pennsylvania use colder winter habitats following the arrival of white-nose syndrome. 2016 [cited 27 May 2020]. Available: /paper/Chapter-12-Hibernating-Bat-Species-in-Pennsylvania-Johnson/244bbf63ce34dfcd876b625fcdcea096ab2541e7
- View Article
- Google Scholar
57. Johnson C, Brown DJ, Sanders C, Stihler CW. Long-term changes in occurrence, relative abundance, and reproductive fitness of bat species in relation to arrival of White-nose Syndrome in West Virginia, USA. Ecology and Evolution. 2021;11: 12453–12467. pmid:34594512
- View Article
- PubMed/NCBI
- Google Scholar
58. Pettit JL O’Keefe JM. Impacts of white-nose syndrome observed during long-term monitoring of a midwestern bat community. Journal of Fish and Wildlife Management. 2017;8: 69–78.
- View Article
- Google Scholar
59. Herrmann NC, Stroud JT, Losos JB. The evolution of ‘ecological release’ into the 21st century. Trends in Ecology & Evolution. 2021;36: 206–215. pmid:33223276
- View Article
- PubMed/NCBI
- Google Scholar
60. Jachowski DS, Dobony CA, Coleman LS, Ford WM, Britzke ER, Rodrigue JL. Disease and community structure: white-nose syndrome alters spatial and temporal niche partitioning in sympatric bat species. Diversity Distrib. 2014;20: 1002–1015.
- View Article
- Google Scholar
61. Loeb SC, Rodhouse TJ, Ellison LE, Lausen CL, Reichard JD, Irvine KM, et al. A plan for the North American Bat Monitoring Program (NABat). Gen Tech Rep SRS-208 Asheville, NC: US Department of Agriculture Forest Service, Southern Research Station. 2015;208: 1–100.
- View Article
- Google Scholar

[ref1] 1. Sadler RM. The Ecology of Reproduction in Wild and Domestic Mammals. Springer Science & Business Media; 2012.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Bronson FH. Mammalian Reproductive Biology. University of Chicago Press; 1989.

[ref3] 3. Orr TJ, Zuk M. Does delayed fertilization facilitate sperm competition in bats? Behav Ecol Sociobiol. 2013;67: 1903–1913.
View Article
Google Scholar

[6] View Article

[7] Google Scholar

[ref4] 4. Ryan S, Knechtel C, Getz W. Ecological cues, gestation length, and birth timing in African buffalo (Syncerus caffer). Behavioral Ecology. 2007;18: 635–644.
View Article
Google Scholar

[9] View Article

[10] Google Scholar

[ref5] 5. Racey PA. Ecology of bat reproduction. In: Kunz TH, editor. Ecology of Bats. New York: Plenum Press; 1982. pp. 57–104.

[ref6] 6. Racey PA. Environmental factors affecting the length of gestation in heterothermic bats. J Reprod Fertil Suppl. 1973;19: 175–189. pmid:4522371
View Article
PubMed/NCBI
Google Scholar

[13] View Article

[14] PubMed/NCBI

[15] Google Scholar

[ref7] 7. Birkhead TR, Moller AP. Sexual selection and the temporal separation of reproductive events: sperm storage data from reptiles, birds and mammals. Biological Journal of the Linnean Society. 1993;50: 295–311.
View Article
Google Scholar

[17] View Article

[18] Google Scholar

[ref8] 8. Ruf T, Geiser F. Daily torpor and hibernation in birds and mammals. Biological Reviews. 2015;90: 891–926. pmid:25123049
View Article
PubMed/NCBI
Google Scholar

[20] View Article

[21] PubMed/NCBI

[22] Google Scholar

[ref9] 9. Humphries MM, Kramer DL, Thomas DW. The role of energy availability in mammalian hibernation: An experimental test in free‐ranging eastern chipmunks. Physiological and Biochemical Zoology. 2003;76: 180–186. pmid:12794671
View Article
PubMed/NCBI
Google Scholar

[24] View Article

[25] PubMed/NCBI

[26] Google Scholar

[ref10] 10. Frick WF, Reynolds DS, Kunz TH. Influence of climate and reproductive timing on demography of little brown myotis Myotis lucifugus. Journal of Animal Ecology. 2010;79: 128–136. https://doi.org/10.1111/j.1365-2656.2009.01615.x
View Article
Google Scholar

[28] View Article

[29] Google Scholar

[ref11] 11. Norquay KJO, Willis CKR. Hibernation phenology of Myotis lucifugus. Journal of Zoology. 2018;294: 85–92.
View Article
Google Scholar

[31] View Article

[32] Google Scholar

[ref12] 12. Lane JE, Kruuk LEB, Charmantier A, Murie JO, Dobson FS. Delayed phenology and reduced fitness associated with climate change in a wild hibernator. Nature. 2012;489: 554–557. pmid:22878721
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref13] 13. Burles DW, Fenton MB, Barclay RM, Brigham RM, Volkers D. Aspects of the Winter Ecology of Bats on Haida Gwaii, British Columbia. Northwestern Naturalist. 2014;95: 289–299.
View Article
Google Scholar

[38] View Article

[39] Google Scholar

[ref14] 14. Bronson FH. Climate change and seasonal reproduction in mammals. Philosophical Transactions of the Royal Society B: Biological Sciences. 2009;364: 3331–3340. pmid:19833645
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref15] 15. Adams RA. Bat reproduction declines when conditions mimic climate change projections for western North America. Ecology. 2010;91: 2437–2445. pmid:20836465
View Article
PubMed/NCBI
Google Scholar

[45] View Article

[46] PubMed/NCBI

[47] Google Scholar

[ref16] 16. Pfeiffer B, Mayer F. Spermatogenesis, sperm storage and reproductive timing in bats. Journal of Zoology. 2013;289: 77–85.
View Article
Google Scholar

[49] View Article

[50] Google Scholar

[ref17] 17. Lorch JM, Meteyer CU, Behr MJ, Boyles JG, Cryan PM, Hicks AC, et al. Experimental infection of bats with Geomyces destructans causes white-nose syndrome. Nature. 2011;480: 376–378. pmid:22031324
View Article
PubMed/NCBI
Google Scholar

[52] View Article

[53] PubMed/NCBI

[54] Google Scholar

[ref18] 18. Bernard RF, McCracken GF. Winter behavior of bats and the progression of white-nose syndrome in the southeastern United States. Ecology and Evolution. 2017;7: 1487–1496. pmid:28261459
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref19] 19. Ehlman SM, Cox JJ, Crowley PH. Evaporative water loss, spatial distributions, and survival in white-nose-syndrome-affected little brown myotis: a model. J Mammal. 2013;94: 572–583.
View Article
Google Scholar

[60] View Article

[61] Google Scholar

[ref20] 20. Verant ML, Meteyer CU, Speakman JR, Cryan PM, Lorch JM, Blehert DS. White-nose syndrome initiates a cascade of physiologic disturbances in the hibernating bat host. BMC Physiology. 2014;14: 10. pmid:25487871
View Article
PubMed/NCBI
Google Scholar

[63] View Article

[64] PubMed/NCBI

[65] Google Scholar

[ref21] 21. Jonasson KA, Willis CKR. Changes in Body Condition of Hibernating Bats Support the Thrifty Female Hypothesis and Predict Consequences for Populations with White-Nose Syndrome. PLOS ONE. 2011;6: e21061. pmid:21731647
View Article
PubMed/NCBI
Google Scholar

[67] View Article

[68] PubMed/NCBI

[69] Google Scholar

[ref22] 22. Frick WF, Pollock JF, Hicks AC, Langwig KE, Reynolds DS, Turner GG, et al. An emerging disease causes regional population collapse of a common North American bat species. Science. 2010;329: 679–682. pmid:20689016
View Article
PubMed/NCBI
Google Scholar

[71] View Article

[72] PubMed/NCBI

[73] Google Scholar

[ref23] 23. Bernard RF, Reichard JD, Coleman JTH, Blackwood JC, Verant ML, Segers JL, et al. Identifying research needs to inform white-nose syndrome management decisions. Conservation Science and Practice. 2020;2: e220.
View Article
Google Scholar

[75] View Article

[76] Google Scholar

[ref24] 24. Blehert DS, Hicks AC, Behr M, Meteyer CU, Berlowski-Zier BM, Buckles EL, et al. Bat white-nose syndrome: An emerging fungal pathogen? Science. 2009;323: 227–227. pmid:18974316
View Article
PubMed/NCBI
Google Scholar

[78] View Article

[79] PubMed/NCBI

[80] Google Scholar

[ref25] 25. Langwig KE, Frick WF, Bried JT, Hicks AC, Kunz TH, Marm Kilpatrick A. Sociality, density-dependence and microclimates determine the persistence of populations suffering from a novel fungal disease, white-nose syndrome. Ecol Lett. 2012;15: 1050–1057. pmid:22747672
View Article
PubMed/NCBI
Google Scholar

[82] View Article

[83] PubMed/NCBI

[84] Google Scholar

[ref26] 26. Grieneisen L. Hibernacula microclimate and white-nose syndrome susceptibility in the little brown Myotis (Myotis lucifugus). Master’s Theses. 2011. Available: https://digitalcommons.bucknell.edu/masters_theses/12
View Article
Google Scholar

[86] View Article

[87] Google Scholar

[ref27] 27. Haase CG, Fuller NW, Dzal YA, Hranac CR, Hayman DTS, Lausen CL, et al. Body mass and hibernation microclimate may predict bat susceptibility to white-nose syndrome. Ecology and Evolution. 2021;11: 506–515. pmid:33437446
View Article
PubMed/NCBI
Google Scholar

[89] View Article

[90] PubMed/NCBI

[91] Google Scholar

[ref28] 28. Bernard RF, Willcox EV, Parise KL, Foster JT, McCracken GF. White-nose syndrome fungus, Pseudogymnoascus destructans, on bats captured emerging from caves during winter in the southeastern United States. BMC Zoology. 2017;2: 12.
View Article
Google Scholar

[93] View Article

[94] Google Scholar

[ref29] 29. Davy CM, Donaldson ME, Willis CKR, Saville BJ, McGuire LP, Mayberry H, et al. The other white-nose syndrome transcriptome: Tolerant and susceptible hosts respond differently to the pathogen Pseudogymnoascus destructans. Ecology and Evolution. 2017;7: 7161–7170. pmid:28944007
View Article
PubMed/NCBI
Google Scholar

[96] View Article

[97] PubMed/NCBI

[98] Google Scholar

[ref30] 30. Vanderwolf KJ, Campbell LJ, Goldberg TL, Blehert DS, Lorch JM. Skin fungal assemblages of bats vary based on susceptibility to white-nose syndrome. ISME J. 2021;15: 909–920. pmid:33149209
View Article
PubMed/NCBI
Google Scholar

[100] View Article

[101] PubMed/NCBI

[102] Google Scholar

[ref31] 31. Frick WF, Cheng TL, Langwig KE, Hoyt JR, Janicki AF, Parise KL, et al. Pathogen dynamics during invasion and establishment of white-nose syndrome explain mechanisms of host persistence. Ecology. 2017;98: 624–631. pmid:27992970
View Article
PubMed/NCBI
Google Scholar

[104] View Article

[105] PubMed/NCBI

[106] Google Scholar

[ref32] 32. Lemieux-Labonté V, Dorville NAS-Y, Willis CKR, Lapointe F-J. Antifungal Potential of the Skin Microbiota of Hibernating Big Brown Bats (Eptesicus fuscus) Infected With the Causal Agent of White-Nose Syndrome. Front Microbiol. 2020;11: 1776. pmid:32793178
View Article
PubMed/NCBI
Google Scholar

[108] View Article

[109] PubMed/NCBI

[110] Google Scholar

[ref33] 33. Frank CL, Michalski A, McDonough AA, Rahimian M, Rudd RJ, Herzog C. The resistance of a North American bat species (Eptesicus fuscus) to white-nose syndrome (WNS). PLOS ONE. 2014;9: e113958. pmid:25437448
View Article
PubMed/NCBI
Google Scholar

[112] View Article

[113] PubMed/NCBI

[114] Google Scholar

[ref34] 34. Czenze ZJ, Jonasson KA, Willis CKR. Thrifty females, frisky males: winter energetics of hibernating bats from a cold climate. Physiological and Biochemical Zoology. 2017;90: 502–511. pmid:28641050
View Article
PubMed/NCBI
Google Scholar

[116] View Article

[117] PubMed/NCBI

[118] Google Scholar

[ref35] 35. Rojas VGO’Keefe JM, Loeb SC. Baseline capture rates and roosting habits of Myotis septentrionalis (Northern Long-Eared Bat) prior to white-nose syndrome detection in the Southern Appalachians. sena. 2017;16: 140–148.
View Article
Google Scholar

[120] View Article

[121] Google Scholar

[ref36] 36. O’Keefe JM, Pettit JL, Loeb SC, Stiver WH. White-nose syndrome dramatically altered the summer bat assemblage in a temperate southern Appalachian forest. Mammalian Biology. 2019;98: 146–153.
View Article
Google Scholar

[123] View Article

[124] Google Scholar

[ref37] 37. Ford WM, Britzke ER, Dobony CA, Rodrigue JL, Johnson JB. Patterns of acoustical activity of bats prior to and following white-nose syndrome occurrence. Journal of Fish and Wildlife Management. 2011;2: 125–134.
View Article
Google Scholar

[126] View Article

[127] Google Scholar

[ref38] 38. Francl KE, Ford WM, Sparks DW, Brack V. Capture and reproductive trends in summer bat communities in West Virginia: Assessing the impact of white-nose syndrome. Journal of Fish and Wildlife Management. 2012;3: 33–42.
View Article
Google Scholar

[129] View Article

[130] Google Scholar

[ref39] 39. Nocera T, Ford WM, Silvis A, Dobony CA. Patterns of acoustical activity of bats prior to and 10 years after WNS on Fort Drum Army Installation, New York. Global Ecology and Conservation. 2019;18: e00633.
View Article
Google Scholar

[132] View Article

[133] Google Scholar

[ref40] 40. Balzer EW, Grottoli AD, Phinney LJ, Burns LE, Vanderwolf KJ, Broders HG. Capture rate declines of Northern Myotis in the Canadian Maritimes. Wildlife Society Bulletin. 2021;45: 719–724.
View Article
Google Scholar

[135] View Article

[136] Google Scholar

[ref41] 41. Deeley SM, Kalen NJ, Freeze SR, Barr EL, Ford WM. Post-white-nose syndrome passive acoustic sampling effort for determining bat species occupancy within the mid-Atlantic region. Ecological Indicators. 2021;125: 107489.
View Article
Google Scholar

[138] View Article

[139] Google Scholar

[ref42] 42. Wang T, Hamann A, Spittlehouse D, Carrol C. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLOS ONE. 2016;11: e0156720. pmid:27275583
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

[ref43] 43. Bilotta R, Bell JE, Shepherd E, Arguez A. Calculation and Evaluation of an Air-Freezing Index for the 1981–2010 Climate Normals Period in the Coterminous United States. Journal of Applied Meteorology and Climatology. 2015;54: 69–76.
View Article
Google Scholar

[145] View Article

[146] Google Scholar

[ref44] 44. Hranac CR, Haase CG, Fuller NW, McClure ML, Marshall JC, Lausen CL, et al. What is winter? Modeling spatial variation in bat host traits and hibernation and their implications for overwintering energetics. Ecology and Evolution. 2021;11: 11604–11614. pmid:34522327
View Article
PubMed/NCBI
Google Scholar

[148] View Article

[149] PubMed/NCBI

[150] Google Scholar

[ref45] 45. Júnior PDM, Nóbrega CC. Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation. PLOS ONE. 2018;13: e0202403. pmid:30204749
View Article
PubMed/NCBI
Google Scholar

[152] View Article

[153] PubMed/NCBI

[154] Google Scholar

[ref46] 46. Jackson DA. Stopping rules in Principal Components Analysis: A comparison of heuristical and statistical approaches. Ecology. 1993;74: 2204–2214.
View Article
Google Scholar

[156] View Article

[157] Google Scholar

[ref47] 47. Peres-Neto PR, Jackson DA, Somers KM. How many principal components? stopping rules for determining the number of non-trivial axes revisited. Computational Statistics & Data Analysis. 2005;49: 974–997.
View Article
Google Scholar

[159] View Article

[160] Google Scholar

[ref48] 48. Akaike H. (1973). Information theory and an extension of the maximum likelihood principle. In Petrov B. N. & Csaki F.(Eds.),2nd International Symposium on Information Theory (pp. 267–281). Budapest: Akademiai Kiado.
View Article
Google Scholar

[162] View Article

[163] Google Scholar

[ref49] 49. Zuur A.F., Ieno E.N., Walker N.J., Saveliev A.A., Smith G.M. (2009). Meet the Exponential Family. In: Mixed effects models and extensions in ecology with R. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-0-387-87458-6_8

[ref50] 50. R Development Core Team. R: a language and environment for statistical computing R Foundation for Statistical Computing. Vienna, Austria; 2009. Available: http://www.R-project.org

[ref51] 51. Bates D, Maechler M, Bolker B, Walter S. Fitting linear mixed-effects models using lme4. Journal of Statistical Software. 2010;6: 1–48.
View Article
Google Scholar

[167] View Article

[168] Google Scholar

[ref52] 52. Bernard RF, Willcox EV, Jackson RT, Brown VA, McCracken GF. Feasting, not fasting: winter diets of cave hibernating bats in the United States. Frontiers in Zoology. 2021;18: 48. pmid:34556122
View Article
PubMed/NCBI
Google Scholar

[170] View Article

[171] PubMed/NCBI

[172] Google Scholar

[ref53] 53. Frick WF, Dzal YA, Jonasson KA, Whitby MD, Adams AM, Long C, et al. Bats increased foraging activity at experimental prey patches near hibernacula. Ecological Solutions and Evidence. 2023;4: e12217.
View Article
Google Scholar

[174] View Article

[175] Google Scholar

[ref54] 54. Cheng TL, Gerson A, Moore MS, Reichard JD, DeSimone J, Willis CKR, et al. Higher fat stores contribute to persistence of little brown bat populations with white-nose syndrome. Journal of Animal Ecology. 2019;88: 591–600. pmid:30779125
View Article
PubMed/NCBI
Google Scholar

[177] View Article

[178] PubMed/NCBI

[179] Google Scholar

[ref55] 55. Jackson RT, Willcox EV, Bernard RF. Winter torpor expression varies in four bat species with differential susceptibility to white-nose syndrome. Sci Rep. 2022;12: 5688. pmid:35383238
View Article
PubMed/NCBI
Google Scholar

[181] View Article

[182] PubMed/NCBI

[183] Google Scholar

[ref56] 56. Johnson JS. Hibernating bat species in Pennsylvania use colder winter habitats following the arrival of white-nose syndrome. 2016 [cited 27 May 2020]. Available: /paper/Chapter-12-Hibernating-Bat-Species-in-Pennsylvania-Johnson/244bbf63ce34dfcd876b625fcdcea096ab2541e7
View Article
Google Scholar

[185] View Article

[186] Google Scholar

[ref57] 57. Johnson C, Brown DJ, Sanders C, Stihler CW. Long-term changes in occurrence, relative abundance, and reproductive fitness of bat species in relation to arrival of White-nose Syndrome in West Virginia, USA. Ecology and Evolution. 2021;11: 12453–12467. pmid:34594512
View Article
PubMed/NCBI
Google Scholar

[188] View Article

[189] PubMed/NCBI

[190] Google Scholar

[ref58] 58. Pettit JL O’Keefe JM. Impacts of white-nose syndrome observed during long-term monitoring of a midwestern bat community. Journal of Fish and Wildlife Management. 2017;8: 69–78.
View Article
Google Scholar

[192] View Article

[193] Google Scholar

[ref59] 59. Herrmann NC, Stroud JT, Losos JB. The evolution of ‘ecological release’ into the 21st century. Trends in Ecology & Evolution. 2021;36: 206–215. pmid:33223276
View Article
PubMed/NCBI
Google Scholar

[195] View Article

[196] PubMed/NCBI

[197] Google Scholar

[ref60] 60. Jachowski DS, Dobony CA, Coleman LS, Ford WM, Britzke ER, Rodrigue JL. Disease and community structure: white-nose syndrome alters spatial and temporal niche partitioning in sympatric bat species. Diversity Distrib. 2014;20: 1002–1015.
View Article
Google Scholar

[199] View Article

[200] Google Scholar

[ref61] 61. Loeb SC, Rodhouse TJ, Ellison LE, Lausen CL, Reichard JD, Irvine KM, et al. A plan for the North American Bat Monitoring Program (NABat). Gen Tech Rep SRS-208 Asheville, NC: US Department of Agriculture Forest Service, Southern Research Station. 2015;208: 1–100.
View Article
Google Scholar

[202] View Article

[203] Google Scholar

Figures

Abstract

Introduction

Materials and methods

Data collection

Statistical models

Results

Discussion

Supporting information

S1 Fig. Eigenvalues from principal component analysis.

S2 Fig. Factor loadings from principal component analysis.

S1 Table. Capture data used in the analyses.

S2 Table. Climate variables used in principal component analysis.

S3 Table. Eigenvalues from principal component analysis.

S4 Table. Eigenvectors from principal component analysis.

Acknowledgments

References