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ENDANGERED SPECIES RESEARCH
Endang Species Res
Vol. 48: 99–106, 2022
https://doi.org/10.3354/esr01189 Published June 23
1. INTRODUCTION
North American bat species face several conserva-
tion challenges throughout their range, including
habitat loss and modification, pesticides, and mortality
associated with wind-energy development (Mickle -
burgh et al. 2002, Voigt & Kingston 2016, Frick et al.
2020). Since first documented in New York in 2006
(Blehert et al. 2009), white-nose syndrome (WNS), an
epizootic, infectious fungal disease caused by Pseudo -
gymnoascus destructans (Pd), has emerged as the
most serious threat to cave-dwelling North American
bats, with mortality estimated at more than 6 million
in eastern North America (US Fish and Wildlife Serv-
ice 2019). Myotis septentrionalis (northern long-eared
bat), M. lucifugus (little brown bat), and Perimyotis
sub flavus (tri-colored bat) are among the most sus-
ceptible species, with winter counts in WNS-positive
regions declining by more than 90 % for each species
since WNS detection (Cheng et al. 2021).
Monitoring populations affected by WNS is a
critical conservation action for bats in eastern North
© The authors 2022. Open Access under Creative Commons by
Attribution Licence. Use, distribution and reproduction are un -
restricted. Authors and original publication must be credited.
Publisher: Inter-Research · www.int-res.com
*Corresponding author: scastle@uga.edu
Seven-year impact of white-nose syndrome on
tri-colored bat (Perimyotis subflavus) populations
in Georgia, USA
Santiago Perea1, Julia A. Yearout1,2, Emily A. Ferrall1, 2, Katrina M. Morris2,
J. T. Pynne2, Steven B. Castleberry1,*
1Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA
2Wildlife Conservation Section, Georgia Department of Natural Resources, Social Circle, GA 30025, USA
ABSTRACT: White-nose syndrome (WNS) has emerged as the most serious threat to North Amer-
ican cave-dwelling bat species, with an estimated mortality of over 6 million since it was first doc-
umented in the USA in 2006. Tri-colored bat Peri myotis subflavus is one of the species most
affected, with hibernaculum counts at caves in WNS-positive regions reduced by >90% from pre-
vious counts. While declines have been documented in hibernaculum surveys, long-term monitor-
ing programs during active seasons provide a unique opportunity to examine population trends
and impact of population declines post-WNS. We developed generalized linear mixed models
using data from a state-wide, long-term (2011−2020) mobile bat acoustic monitoring program in
Georgia, USA, to better understand P. subflavus population trends before and after disease detec-
tion and between WNS-negative and WNS-positive regions. We recorded 5046 P. subflavus
passes across all acoustic routes during the 10 yr time period. We detected a significant decrease
in activity 2 yr after disease detection in the WNS-positive region, whereas activity in the WNS-
negative region remained stable over time. Understanding changes in bat populations as WNS
spreads and measuring the magnitude of population declines to assess disease impacts is crucial
for providing appropriate guidance for management. Our results provide evidence of the critical
status of P. subflavus in the southernmost WNS-positive region, but also emphasize the impor-
tance of monitoring WNS spread to new regions, as those that remain WNS-free could provide
refugia for the species and a potential source of recolonization to WNS-affected areas.
KEY WORDS: Perimyotis subflavus · Tri-colored bat · White-nose syndrome · Acoustic monitoring ·
Bat activity · GLMM · Mobile routes
O
PEN
PEN
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Endang Species Res 48: 99– 106, 2022
Ame ri ca. Many studies on the effects of WNS have fo-
cused on hibernating bat surveys or detecting Pd on
captured bats or in environmental samples (e.g. Lang-
wig et al. 2012, Powers et al. 2015, Verant et al. 2018).
However, long-term acoustic monitoring programs
can be used to characterize how WNS-associated
mortality and colony declines observed within hiber-
nacula translate into activity declines outside the
hiber nation period (e.g. Moosman et al. 2013, Pettit &
O’Keefe 2017, Nocera et al. 2019, Hicks et al. 2020,
Johnson et al. 2021). Acoustic monitoring is an effi-
cient method for collecting data at large spatial scales
and can be conducted in areas where bat capture is
not possible (Duy et al. 2000, Flaquer et al. 2007,
Kunz et al. 2009). In addition, the creation of stan-
dardized protocols by national programs, such as the
North American Bat Monitoring Program (Loeb et al.
2015), advance the collection of large-scale data on
bat foraging activity (Simonis et al. 2020). These data
are important for documenting changes in activity for
species threatened by wind energy or WNS (Whitby
et al. 2014, Loeb et al. 2015).
P. subflavus is a solitary bat species that roosts in
trees or buildings in summer and hibernates in trees,
caves, rock crevices, mines, bridges, and culverts
(Fujita & Kunz 1984, Leivers et al. 2019). It was for-
merly considered one of the most common and
widely distributed bats in eastern North America
(see Fig. 1), but is currently listed as Vulnerable by
the IUCN (Solari 2018) and is being considered for
listing under the US Endangered Species Act (ESA)
(US Fish and Wildlife Service 2017) due to WNS-
related de clines. Presence of WNS in P. subflavus
hiber nacula has been confirmed across an estimated
59% of the total distribution (Cheng et al. 2021), and
population declines have been documented through-
out most of the range (Hoyt et al. 2021).
WNS was first documented in P. subflavus in north-
western Georgia in 2013 during hibernaculum counts,
and the population subsequently experienced a sig-
nificant decline (Georgia Department of Natural Re-
sources 2020). P. subflavus predominately hibernates
in caves in northwestern Georgia (Georgia Depart-
ment of Natural Resources 2020), which is character-
ized by karst topography and high cave density. How-
ever, populations in the remainder of the state, which
has low cave density, have not been assessed to docu-
ment the full impact of WNS. Throughout the range,
some P. subflavus individuals exhibit short- (Bisson et
al. 2009, Samoray et al. 2019) or long-distance (Fraser
et al. 2012) latitudinal migrations. Anecdotal informa-
tion suggests that some individuals in Georgia un-
dergo latitudinal migrations between summer areas
and hibernation sites (Lutsch 2019, Samoray et al.
2019). However, the role of bat migration in spreading
Pd between summer areas and hibernacula is not
fully understood (Bernard et al. 2020). Our objective
was to determine the magnitude of WNS-related pop-
ulation declines before and after the arrival of WNS
and in areas with and without significant numbers of
caves. Although Pd was detected in road culverts at 2
locations outside of the known WNS-affected area in
the state during the study, no WNS-affected bats were
observed (Georgia Department of Natural Resources
2020). Thus, we hypothesized a decline in P. subflavus
activity in the northern region of the state after WNS
was detected, but that activity would be stable across
years in the southern region.
2. MATERIALS AND METHODS
2.1. Mobile acoustic route protocols
We used data from mobile acoustic surveys con-
ducted by the Georgia Department of Natural Re -
sources (GADNR) from 2011 to 2020. Surveys were
conducted by GADNR biologists, federal agency
biologists, and private citizens as part of a volunteer-
based citizen science program. Volunteers were re -
quired to register with GADNR, have a vehicle capa-
ble of driving on secondary roads, and commit to
conducting the surveys over multiple seasons. Vol-
unteers were also required to watch an instructional
training video prior to conducting surveys. Based on
volunteer availability and weather conditions, some
routes were not surveyed every year.
GADNR initially established mobile acoustic routes
in 2011 following established protocols (Britzke & Her -
zog 2009, Loeb et al. 2015), adding routes through out
the study up to a total of 45 (Fig. 1). Route selection
was based on long-term route accessibility, safety of
surveyors and other motorists, and consideration for
representing available habitat types. Roads selected
for routes were primarily 2-laned secondary or ter -
tiary roads with minimal stops. Route lengths ranged
from 10 to 67 km. Surveys started 30 to 45 min after
sunset, with surveyors driving 24 to 32 km h−1 to in-
crease the likelihood that each bat de tection was an
individual bat without repeats (Roche et al. 2011).
2.2. Bat acoustic sampling
We recorded bat echolocation calls in zero-crossing
format using Anabat SD1 and SD2 acoustic recording
100
Perea et al.: Impact of WNS on Perimyotis subflavus
units (Titley Electronics) with omnidirectional micro-
phones pointed straight up from the roof of a vehicle.
Anabat units were calibrated each year to minimize
variability in sensitivity among detectors (Larson &
Hayes 2000). We set recording sensitivity to 7 (but ad-
justed when needed based on noise and environment
throughout the route), audio division ratio to 16, and
data division ratio to 8 to reduce data storage by re-
ducing the resolution of each call (Britzke & Herzog
2009, Loeb et al. 2015). A Global Positioning System
(GPS) accessory was connected to the acoustic re -
corder to geo-reference routes and call locations.
Surveys were scheduled when the weather was fore-
casted to be optimal (i.e. no rain and light or minimal
wind), and most were conducted twice from late May
through early September. Start and end times, tem-
perature, wind speed, and cloud cover were recorded
before and after each survey. We recorded deviations
to the route or survey protocol (inclement weather,
pro longed periods of stopping, road closures, con-
struction, etc.).
2.3. Bat call analysis
We used auto ID software and subsequent visual
vetting to identify calls to species, as recommended
by the North American Bat Monitoring Program
(NABat; Reichert et al. 2018). We first filtered out
noise files using Kaleidoscope Pro 5.4.1 software
(Wildlife Acoustics). We selected default filter setting
parameters for bat analysis specifying a signal of
interest between 8 and 120 kHz, 2 to 500 ms, and at
least 2 pulses per sequence. We used the Batch func-
tion in Kaleidoscope Pro to split each sequence to a
maximum duration of 10 s for standardization, and
the auto classifier of Kaleidoscope Pro with a bal-
anced sensitivity level for classification to assist the
visual vetting. Subsequently, we manually analyzed
all non-noise files using call structure, frequency of
minimum and maximum energy, duration, and inter-
pulse interval (O’Farrell & Gannon 1999, Russo &
Jones 2002).
2.4. Data analysis
We examined trends in relative Perimyotis subflavus
activity over 10 yr (2011−2020) between northern
(WNS-positive) and southern (WNS-negative) regions
of Georgia using generalized linear mixed-effects
models (GLMM) in the R (R Core Team 2020) package
‘glmmTMB’ (Brooks et al. 2017). We quantified rela-
tive bat activity as the mean number of bat passes
during the nights sampled each year divided by the
length (km) of each route (passes km−1). Be cause bats
101
Fig. 1. Left: map of Georgia, USA, showing mobile acoustic
routes surveyed for Perimyotis subflavus activity in white-
nose syndrome (WNS-)-positive (yellow lines) and WNS-
negative (green lines) regions in 2011−2020. The gray shad-
ing indicates the WNS-positive region defined in our study.
Cross-hatching indicates the Pseudogymnoascus destructans
(Pd) positive counties in the WNS-negative region. The map
on the right shows the distribution of P. subflavus (dark gray;
source: IUCN Red List Data, Solari 2018) and Georgia (black)
Endang Species Res 48: 99– 106, 2022
are assumed to be encountered only once along a
route, our measure of relative activity can be consid-
ered an index of relative abundance (Roche et al.
2011, Braun de Torrez et al. 2017). We performed a
Shapiro-Wilks test for normality and found that the
response variable (relative bat activity) was not nor-
mally distributed (p < 0.001). Therefore, we used a
negative binomial distribution, which also accounts
for overdispersion (Brooks et al. 2017). We included
route as a random effect to account for inconsistencies
due to surveyors. We created 1-county buffers adja-
cent to known WNS-positive counties (those in which
bats were observed with lesions indicative of WNS)
to define the WNS-positive and WNS-negative re-
gions (Fig. 1). With relative activity as the response
variable, we built 11 candidate models (including
global and null) that included individual variables
and plausible additive and interactive combinations
of region (WNS-positive and WNS-negative), year, el-
evation, and climate vari ables (wind and cloud cover
at the beginning of the survey, and mean tempera-
ture). We specified the WNS-negative region as the
reference group. We de termined mean elevation on
each route using the summarize elevation tool in Ar-
cGIS Pro 2.8.0 (Esri). We tested for correlation among
continuous predictor variables using Pear-
son’s correlation coefficient to ensure that
highly correlated (r ≥ |0.7|) variables were
not included in the same model. We used
Akaike’s Information Criterion corrected
for small sample sizes (AICc) to calculate
Akaike model weights (ωi) and determine
the most parsimonious model(s) (Burnham
& Anderson 2002). We considered models
<2 AICc units from the top model to be po-
tentially informative. We evaluated the
best-supported models for goodness-of-fit
and over- and under-dispersion in the form of a QQ
plot, residual plot, and a 1-sample Kolmogorov-
Smirnov test using the DHARMa package (Hartig
2020) in R.
3. RESULTS
We recorded a total of 5046 Perimyotis subflavus
echolocation passes on routes from 2011 (2 yr prior
to the detection of WNS in northwestern Georgia)
through 2020 (Table 1). The top model explaining
P. subflavus relative activity included the variables
region (WNS-positive and WNS-negative), year, and
their interaction (Table 2). No other model was
within ΔAICc < 2 of the top model.
Relative activity of P. subflavus declined over time
following WNS detection in Georgia (p = 0.001,
Fig. 2, Table 3). Our analyses also indicated differ-
ences in activity between regions (p < 0.001, Table 3),
beginning in 2015 and stabilizing in the WNS-posi-
tive region at values below half of pre-WNS detec-
tion activity levels between 2016 and 2020 (Fig. 2). In
contrast, relative activity remained constant through-
out the study in the WNS-negative region.
102
Year Nights Total route WNS− WNS+ WNS− WNS+ WNS− WNS+
distance routes routes total passes total passes mean activity (SD) mean activity (SD)
2011 15 525.7 11 4 230 107 0.76 (0.62) 0.63 (0.27)
2012 23 812.0 10 7 253 189 0.59 (0.41) 0.57 (0.37)
2013 24 1040.0 9 7 274 205 0.44 (0.34) 0.56 (0.38)
2014 31 1125.5 15 5 395 228 0.70 (0.66) 0.62 (0.42)
2015 34 1249.5 15 5 518 149 0.81 (0.99) 0.41 (0.22)
2016 45 1671.0 21 8 488 130 0.59 (0.95) 0.22 (0.35)
2017 43 1517.2 19 10 560 59 0.94 (1.42) 0.10 (0.07)
2018 50 1796.75 20 10 351 54 0.45 (0.58) 0.09 (0.08)
2019 41 1435.5 16 8 402 44 0.51 (0.46) 0.11 (0.09)
2020 48 1731.3 17 9 363 47 0.41 (0.49) 0.09 (0.13)
Table 1. Summary of mobile acoustic routes conducted to examine Perimyotis subflavus activity in Georgia, USA, 2011−2020,
including number of nights sampled, total route distance (km) surveyed, number of white-nose syndrome (WNS)-negative (−)
and WNS-positive (+) routes, total number of passes recorded on WNS− and WNS+ routes, and mean activity (passes km−1 of
route) in WNS− and WNS+ areas each year
Model K AICc ΔAICc ωi
Region + Year + Region × Year + (1|Route) 6 1589.76 0.00 1
Global 15 1621.35 31.59 0
Region + Year + (1|Route) 5 1622.29 32.53 0
Year + (1|Route) 4 1631.42 41.66 0
Elevation + (1|Route) 4 1663.79 74.03 0
Table 2. Top 5 models, number of parameters (K), corrected Akaike’s In-
formation Criterion (AICc), difference between a model and the model
with the lowest AICc value (ΔAICc), and model weight (ωi) used to predict
Perimyotis subflavus relative activity in Georgia, USA, 2011−2020
Perea et al.: Impact of WNS on Perimyotis subflavus
4. DISCUSSION
The changes in Perimyotis subflavus acoustic
activity we observed were consistent with our pre-
dictions and similar to patterns in northern WNS-
positive regions of eastern North America. Previous
studies found a decrease in acoustic activity follow-
ing WNS detection (Ingersoll et al. 2013, Nocera et
al. 2019, Deeley et al. 2021). A similar trend in pop-
ulation decline was reported for the federally threat-
ened Myotis septentrionalis following WNS detec-
tion in the same WNS-positive region in Georgia
where our study occurred (Grider 2020). Both spe-
cies were abundant and widely distributed in north-
ern Georgia prior to WNS. As observed in multiple
bat species affected by WNS across eastern North
America, (Ingersoll et al. 2013, Powers et al. 2015,
Rey nolds et al. 2016, Nocera et al. 2019, Cheng et
al. 2021), our temporal and
spatial ana lysis of relative
activity suggests no signs of
re covery in the WNS-positive
re gion since the disease was
de tected. Furthermore, our
data indicate that P. subflavus
populations are no longer de -
clin ing, but stabilized at low
densities within 3 yr of the
arrival of WNS. Stabilization
at low population densities
following ra pid initial decline
is likely explained by density-
dependent transmission due
to the so litary hibernating be -
havior of P. sub flavus (Lang-
wig et al. 2012).
Although relative activity de -
creased in the WNS-positive
region following WNS detec-
tion, as we hypothesized, activ-
ity re mained relatively stable
during the study in the WNS-
negative region. Pd was de-
tected in 2 counties in the
WNS-negative region in 2020,
but with no signs of the disease
affecting individual bats or
population abundance (Geor-
gia Department of Natural Re-
sources 2020). Whether Pd will
continue to spread southward
or whether bats outside the
current WNS-positive area will
acquire clinical disease is unknown. If populations in
the southern extent of the range are not affected, that
area could provide refugia and potentially function as
a source to ultimately recolonize northern WNS-
affected populations of P. subflavus and other WNS-
susceptible bat species with distributions extending
outside high cave density areas. Conversely, latitudi-
nal movements from southern summer areas to north-
ern hiber nacula in WNS-positive areas (Samoray et
al. 2019) could result in northern hibernacula func -
tion ing as a population sink in the long term.
We observed a time lag between initial WNS de -
tection and significant changes in P. subflavus rela-
tive activity becoming evident. Although the disease
was first documented in northwest Georgia in 2013
and spread across the northern part of the state dur-
ing 2013 to 2014 (US Fish and Wildlife Service 2019),
our results suggest that relative activity started de -
103
Parameter Estimate SE CI lower CI upper z p
Intercept 151.69 46.49 75.22 228.18 3.26 0.001
Year −0.07 0.02 −0.11 −0.04 −3.24 0.001
Region WNS-positive 583.22 102.95 413.89 752.55 5.67 < 0.001
Region WNS-positive × Year −0.29 0.05 −0.37 −0.21 −5.68 < 0.001
Table 3. Parameters with estimates, SE, 95% confidence intervals (CI), z-values, and p-
values for top model output of Perimyotis subflavus relative activity in Georgia, USA,
2011−2020
Fig. 2. Relative activity (bat passes per km of route) of Perimyotis subflavus determined
using mobile acoustic routes for each year and region (WNS-positive [+] and WNS-
negative [−]) in Georgia, USA, 2011−2020. WNS was first documented in northwestern
Georgia in 2013. Density plots show the data distribution; white vertical lines: inter -
quartile ranges; white squares: median; black circles: mean. Note: width of plots ad-
justed for visualization purposes
Endang Species Res 48: 99– 106, 2022
clining 2 yr after WNS detection and reached a stable
ob served low by 2016. Similar time lags were ob -
served in other bat species following initial detection
(Reynolds et al. 2015, Nocera et al. 2020). Indeed,
studies indicate that the transition from Pd introduc-
tion to populations showing signs of decline occurs
within 1 to 5 yr, with variation among species and
locations (Bernard & McCracken 2017, Frick et al.
2017, Barr et al. 2021). Based on disease progression,
monitoring time lags in WNS manifestation is impor-
tant for understanding how it will affect new popula-
tions and for implementing proactive management
actions prior to the mass mortality characteristic of
peak WNS (Bernard et al. 2019).
To date, few studies have used mobile acoustic
monitoring to examine bat activity trends following
disease outbreak and other mass mortality causes
(Simonis et al. 2020). Stationary acoustic surveys may
be more efficient than mobile acoustic surveys in
sampling bat community richness and in detecting
rare and/or road-avoiding species, such as bats of the
genus Myotis (Tonos et al. 2014, Braun de Torrez et
al. 2017). However, mobile acoustic surveys offer an
effective way to increase the geographic scope of
surveys, providing useful information on bat trends
and distribution by sampling diverse habitats over
large areas (Roche et al. 2011, Whitby et al. 2014,
Fisher-Phelps et al. 2017). In our study, the mobile
acoustic methodology used was effective in docu-
menting changes in activity of a WNS-threatened
species at a large scale across landscape conditions
(Whitby et al. 2014, Loeb et al. 2015). Although vol-
unteer-based surveys may introduce additional vari-
ability, the standardized approach, required training,
and accounting for route variability in models en -
sured that the data were valid for making relative
comparisons across temporal and spatial scales.
Our long-term study provides strong evidence of a
decline in P. subflavus activity during summer in the
WNS-positive region of Georgia since WNS detec-
tion and emphasizes the difference in activity within
and outside WNS-positive regions. As not all areas
within the species range are affected equally, moni-
toring and surveillance of unaffected areas is critical,
as they could provide a refugium for the species and
a potential source of recolonization to WNS-affected
areas. Our results will be particularly useful consid-
ering that P. subflavus is currently under review for
listing under the US ESA. In addition, our results
exemplify the benefits of using a mobile acoustic
monitoring program with volunteer participation to
assess large-scale bat mortality trends for a species
affected by WNS.
Acknowledgements. We thank the Georgia Department of
Natural Resources Wildlife Conservation Section for data
access, coordination, and collaboration. We thank the US
Forest Service staff and volunteers who contributed their
time and resources to conduct mobile acoustic surveys.
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106
Editorial responsibility: Anna Nekaris,
Oxford, UK
Reviewed by: 3 anonymous referees
Submitted: December 20, 2021
Accepted: April 13, 2022
Proofs received from author(s): June 6, 2022
