Behavioral patterns of bats at a wind turbine confirm seasonality of fatality risk

Abstract

Bat fatalities at wind energy facilities in North America are predominantly comprised of migratory, tree‐dependent species, but it is unclear why these bats are at higher risk. Factors influencing bat susceptibility to wind turbines might be revealed by temporal patterns in their behaviors around these dynamic landscape structures. In northern temperate zones, fatalities occur mostly from July through October, but whether this reflects seasonally variable behaviors, passage of migrants, or some combination of factors remains unknown. In this study, we examined video imagery spanning one year in the state of Colorado in the United States, to characterize patterns of seasonal and nightly variability in bat behavior at a wind turbine. We detected bats on 177 of 306 nights representing approximately 3,800 hr of video and > 2,000 discrete bat events. We observed bats approaching the turbine throughout the night across all months during which bats were observed. Two distinct seasonal peaks of bat activity occurred in July and September, representing 30% and 42% increases in discrete bat events from the preceding months June and August, respectively. Bats exhibited behaviors around the turbine that increased in both diversity and duration in July and September. The peaks in bat events were reflected in chasing and turbine approach behaviors. Many of the bat events involved multiple approaches to the turbine, including when bats were displaced through the air by moving blades. The seasonal and nightly patterns we observed were consistent with the possibility that wind turbines invoke investigative behaviors in bats in late summer and autumn coincident with migration and that bats may return and fly close to wind turbines even after experiencing potentially disruptive stimuli like moving blades. Our results point to the need for a deeper understanding of the seasonality, drivers, and characteristics of bat movement across spatial scales.

Full text

Ecology and Evolution. 2021;11:4843–4853.
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4843www.ecolevol.org
Received: 2 Septem ber 2020
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Revised: 3 Feb ruar y 2021
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Accepted: 5 Februar y 2021
DOI: 10.1002/ece 3.73 88
ORIGINAL RESEARCH
Behavioral patterns of bats at a wind turbine confirm
seasonality of fatality risk
Shifra Z. Goldenberg1,2 | Paul M. Cryan3 | Paulo Marcos Gorresen4,5 |
Lee Jay Fingersh6
This is an op en access arti cle under the ter ms of the Creative Commons Attribution L icense, which pe rmits use, dis tribu tion and reprod uction in any med ium,
provide d the original wor k is properly cited.
Publish ed 2021. This art icle is a U. S. Gover nment wo rk and is in the public domain in the USA. Ecology and Evolution published by John Wiley & Sons Ltd.
1Conser vation Ecology Center, Smithsonian
Conser vation Biology Institute, Front Royal,
VA, USA
2Instit ute for Conservation Re search, San
Diego Zoo Global, Escond ido, CA , USA
3U.S. Ge ologic al Sur vey (USG S), Fort C ollins ,
CO, USA
4University of Hawaii at Hilo, Hilo, HI , USA
5U.S. Ge ologic al Sur vey Paci fic Island
Ecosystems Scie nce Center, Hawaii
Volcanoes National Park, HI, USA
6U.S. Depart ment of Ene rgy, National
Renewab le Energy Laboratory, National
Wind Technology Center, Boulder, CO, USA
Correspondence
Paul M. Cryan, USGS For t Colli ns Scien ce
Center, 2150 Centre Avenu e, Building C,
Fort Collins, CO 8 0525, USA .
Email: cryanp@usgs.gov
Funding information
Nationa l Science Foundation Graduate
Research Fellowship Program, Grant/Award
Number : DGE- 1321845; USGS Fort Collins
Science C enter; U.S. De part ment of Ene rgy's
National Renewable Energy Laboratory
Abstract
Bat fatalities at wind energy facilities in North America are predominantly comprised
of migratory, tree- dependent species, but it is unclear why these bats are at higher
risk. Factors influencing bat susceptibility to wind turbines might be revealed by
temporal patterns in their behaviors around these dynamic landscape structures. In
northern temperate zones, fatalities occur mostly from July through October, but
whether this reflects seasonally variable behaviors, passage of migrants, or some
combination of factors remains unknown. In this study, we examined video imagery
spanning one year in the state of Colorado in the United States, to characterize pat-
terns of seasonal and nightly variability in bat behavior at a wind turbine. We detected
bats on 177 of 306 nights representing approximately 3,800 hr of video and > 2,000
discrete bat events. We observed bats approaching the turbine throughout the night
across all months during which bats were observed. Two distinct seasonal peaks of
bat activity occurred in July and September, representing 30% and 42% increases in
discrete bat events from the preceding months June and August, respectively. Bats
exhibited behaviors around the turbine that increased in both diversity and duration
in July and September. The peaks in bat events were reflected in chasing and turbine
approach behaviors. Many of the bat events involved multiple approaches to the
turbine, including when bats were displaced through the air by moving blades. The
seasonal and nightly patterns we observed were consistent with the possibility that
wind turbines invoke investigative behaviors in bats in late summer and autumn co-
incident with migration and that bats may return and fly close to wind turbines even
after experiencing potentially disruptive stimuli like moving blades. Our results point
to the need for a deeper understanding of the seasonality, drivers, and characteris-
tics of bat movement across spatial scales.
KEY WORDS
conservation behavior, ecological trap, migration, renewable energy, thermal infrared, video
surveillance

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GOLDENBERG Et aL .
1 | INTRODUCTION
Wind energy is advancing as an environmentally clean alternative
to fossil fuels that diversifies energy portfolios and creates new
jobs (Gibson et al., 2017; Lindenberg et al., 2008). It has repre-
sented one of the fastest growing energy sectors in recent years,
with over 90 countries incorporating wind energy by the end of
2016 (REN21, 2017). In the United States, wind represented 4.5%
of the country's annual electricit y production at the end of 2013
and may feasibly reach 20% by the year 2030 and 35% by 2050
(Lindenberg et al., 2008; USDOE, 2015). However, wind energy
has been associated with wildlife fat ality as birds and bats collide
with turbine blades, the tips of which can spin faster than 50 m/s.
These impacts are likely to intensify as wind development continues
(Gibson et al., 2017; Kunz et al., 2007; Northrup & Wittemyer, 2013;
O’Shea et al., 2016). Avoiding turbine placement along fly ways and
within identifiable preferred habitat has emerged as a viable miti-
gation strategy for birds, yet site selection of turbines may not
have a similar benefit for bats as bats may be attracted to turbines
(Arnett & May, 2016; Mojica et al., 2016). At present, curtailment
of turbine blades during specified weather conditions is one prom-
ising mitigation strategy (Arnett et al., 2011; Arnet t & May, 2016),
but is a coarse approach that may be further refined with a better
understanding of bat behavior at turbines. Insectivorous bats play
important ecological roles and provide critical ecosystem services
(Kunz et al., 2011). The possibility that wind turbines may act as
population sinks for bats is therefore of considerable conservation
concern (Cryan & Barclay, 2009), especially given their slow life
history (e.g., long- lived with few, slow- maturing offspring; Barclay
& Harder, 2003) and evidence that wind turbines are among the
most prominent threats to the well- being of certain bat populations
(Frick et al., 2017; O’Shea et al., 2016). Although fatalities of many
bat species have been found at wind turbines in temperate parts
of the United States (USA) and Canada (Grodsky et al., 2012; Jain
et al., 2011), the vast majority (>75%) involve three species that are
ecologically similar: the hoary bat (Lasiurus cinereus), eastern red bat
(Lasiurus borealis), and silver- haired bat (Lasionycteris noctivagans)
(Arnett et al., 2008; Frick et al., 2017) (Figure 1). These species are
unique among North American bat species in being almost exclu-
sively dependent on trees for roosting, continental in their distribu-
tion, and migrator y. Therefore, it is possible that aspects of their life
history make them more susceptible to turbine collisions than other
species.
Another clear pattern in temperate parts of North America is that
most turbine bat fatalities occur from July through October, usually
peaking in August or September (Arnett & Baerwald, 2013; Cryan
& Brown, 2007). Patterns of turbine- related bat mortality in Europe
show a similar temporal pattern but often involve a greater diversity
FIGURE 1 Migratory tree bats,
like this silver- haired bat (Lasionycteris
noctivagans) seen roosting on a tree trunk
during autumn, are among the most
frequently found dead at wind turbines
in North America during late summer and
autumn

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GOLDENB ERG Et aL.
of species in terms of both migratory behavior and roosting ecology
(Rydell et al., 2010a; Voigt et al., 2012, 2015). Video imagery has re-
vealed bats closely approaching and exhibiting unexplained behav-
iors at turbine blades, nacelles (housing that holds turbine machinery
at top of structure), and monopoles (cylindrical steel tower suppor t-
ing nacelle and blades), as well as repeatedly approaching turbines
after near cont act with moving blades (Cr yan, Gorresen, et al., 2014;
Horn et al., 2008). Despite the pattern of most North American bat
fatalities occurring at wind turbines in autumn and involving migra-
tory, tree- dependent species, it is not known whether mortality pat-
terns are attributable to seasonal bat prevalence at wind turbines,
temporally variable investigative behaviors, or some combination
thereof. Determining whether behaviors at wind turbines are sea-
sonal and discovering any underlying causes of bat investigation are
promising paths toward enhancing concrete, evidence- based recom-
mendations for effectively mitigating the impacts of wind energy on
bat populations (Cryan & Barclay, 2009; Jameson & Willis, 2014).
Analyses of the behavior of bats at wind turbines offer a unique
opportunity to better understand bat susceptibility to this emerg-
ing technology. In this study, we used thermal video imagery from
a wind turbine continuously monitored over a year- long period in
Colorado, USA, to describe temporal trends in the behaviors of bats.
We discuss our result s in the context of potential bat at trac tion to
turbines and knowledge gaps in bat ecology.
2 | MATERIALS AND METHODS
2.1 | Data collection
We recorded video imager y on a near- nightly basis over one year
from a wind turbine at the National Wind Technology Center,
National Renewable Energy Laboratory in Boulder, Colorado.
We selected this turbine for the study because it was conveni-
ently located and made available to us for year- round observation
and maintenance access. The 1.5- MW wind turbine (39.9121°N,
105.2200°W; Model GE 1.5sle, General Electric Renewable
Energy, Schenectady, New York, USA) had a tapered monopole
that was 80 m tall and 4.2 m in diameter at the base. The nacelle
at the top of the monopole housed the generator and turbine
blades with a 77- m rotor diameter. The turbine was surrounded
by urbanized and arid rangeland transected by several drainages
to th e no rth, east, and south, and transi tioni ng into foothills of the
Rocky Mountains approximately 5 km to the east (Figure 2a). Five
FIGURE 2 Bats were observed over
a one- year period (17 March 2016 to 16
March 2017) at a wind turbine in Colorado
with a thermal- spectrum video camera (a).
The camera was magnetically mounted on
the turbine monopole approximately 2 m
above the ground (b) and pointing upward
(c). By pointing toward the turbine nacelle
and rotor- swept area (d, inset), the camera
recorded bats as they flew at rotor- swept
heights (approx. 42– 119 m; d, e), as well as
bats flying closer to the ground (f, g)
(a) (b) (c)
(d) (e)
(f) (g)

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GOLDENBERG Et aL .
additional wind turbines of various makes and sizes also operated
in an approximate line running 1 km to the southwest. Historical
and current weather conditions at the site, measured at approxi-
mately 1.25 km WSW of the turbine, are available at https://midcd
mz.nrel.gov/apps/go2url.pl?site=NWTC.
We monitored airspace swept by the rotors of the turbine using
a surveillance camera equipped with a 19- mm lens (Axis Q1932- E,
Axis Communications, Lund, Sweden) that imaged in the thermal in-
frared spectrum (~9,000– 14,000 μm) of electromagnetic radiation.
The c amera was operated at a sampling rate of 30 frames per sec-
ond (fps), a resolution of 640 by 480 pixels, and in the built- in false-
color scheme “Ice- and- Fire” with no supplemental illumination. We
magnetically mounted the camera approximately 2 m above ground
level on the east side of the turbine monopole using an industrial-
strength camera mounting base (RigMount X6 Magnet Camera
Mounting Platform, Rigwheels, Minneapolis, Minnesota, USA;
Fi gur e 2b,c ) . Th e camera wa s aim e d st r a igh t up th e mon opo le so th at
the lower thir d of the vide o im age inclu ded the monopole, while the
upper two- thirds of the image included the turbine blades, nacelle,
and surrounding airspace, encompassing approximately 0.30 km3 of
airspace from a field of view 86.6 m wide by 64.2 m high at a range
of 160 m (Figure 2d, ins et). Th is conf iguration allowed us to observe
the behavior of bats as they approached the leeward side of the tur-
bine (prevailing wind direction was from the west) at various heights
above the ground (Figure 2d– g). This monopole- mounted configu-
ration also provided an ideal view of the bat's horizontal proxim-
ity to the turbine relative to prior video configurations (e.g., Cryan,
Gorresen, et al., 2014; Horn et al., 2008). The camera view became
obscured after precipitation events, but usually cleared within a few
hours after precipit ation ceased due to the heated window on the
weather proof housing. Video monitori ng fo r this study began on 03
March 2016 and continued through 17 March 2017. The vide o ca m-
era was programmed to record each night from 18:30 to 07:00 the
following morning (Denver local time) between 03 March and 20
July of 2016, from 19:00 to 07:00 between 21 July and 26 October
of 2016, and then 17:0 0 to 07:00 from 27 Octob er 2016 thro ugh 17
March 2017. This shifting schedule ensured that the camera con-
sistently recorded during twilight hours throughout the year. The
camera was powered through a single cable using a power- over-
ethernet (POE) network switch (Model GS105E, NetGear, San Jose,
CA, USA) and communicated through the same cable with a laptop
computer (Model Latitude E5430, Dell Inc., Round Rock, TX, USA)
situated just inside the door in the base of the monopole. Video
recording software included with the camera (Axis Camera Station
5.x, Axis Communications, Lund, Sweden) was used to export imag-
ery buffered on the computer's hard drive to a 1- to 2- TB external
hard drive (Backup Plus Slim, Seagate Technolog y LLC, Cupertino,
CA, USA) after recording ended each morning. These nightly video
files were containerized into Advanced Systems Format (.asf) using
the H.264 compression codec, then converted into audio- video in-
terleave (.avi) container format using a GNU General Public License
video editing program (VirtualDub, http://www.virtu aldub.org/)
prior to analysis.
2.2 | Data analysis
We analyzed imagery spanning one year, from 17 March 2016
through 16 March 2017. Of the 365 night s during which we at-
tempted to record imager y, recording failed on 49 nights (13%). Gaps
in recording were primarily spread over the off- season (December,
January, Februar y, and early March), with the exception of two con-
secutive nights in August and three consecutive nights in September.
Recordings on 13 nights (4%) were incomplete (<10 hr of imagery)
due to technical issues, and imagery recorded on 10 nights (3%)
was obscured by precipitation. Incomplete nights were distributed
across the study period, with a cluster of four incomplete nights over
the course of a week in April, two consecutive nights in June, two
consecutive nights in July, and the remaining five incomplete nights
scattered across months of low bat occurrence. Eight of the 10
nights obscured by precipitation were clustered as 3 and 5 consecu-
tive nights in April. We thus recorded approximately 3,800 hr of ana-
lyzable imagery over the course of 306 nights. Videos were loaded
into the program MATLAB with the Image Processing Toolbox (ver-
sions 2015a,b, MathWorks, Inc., Matick, MA) using previously de-
veloped custom code (Cr yan, Gorresen, et al., 2014). The algorithm
detected videos containing bat- sized objects not associated with the
visual footprint of the turbine moving through the field of view. We
manually reviewed these videos (hereafter “detections”), categoriz-
ing them as “bat,” “bird,” or “insect” and assessed confidence that the
object was a bat by categorizing them as “high,” “medium,” or “low”
based on bat appearance and movement characteristics (Huzzen
et al., 2020). Only high- confidence bat detections (hereafter “bats”)
were included in analyses. For consistency, this categorization was
done by one primary observer (SZG), and then, all high- confidence
bats were reviewed and cross- validated by a secondary observer
(PMC). An “event” was temporally defined as any string of detections
occurring one minute or less apart, such that if bats went out of view
they were not counted as independent events if they reappeared
within one minute or less; this is consistent with previous work by
Cryan, Gorresen, et al., (2014). Events were comprised of one or
more activities that characterized the approach location (monopole,
nacelle, blade), flight type (nonfocal pass (sensu Cryan, Gorresen,
et al., 2014; Huzzen et al., 2020), hovering, chase involving at least
one other bat), and outcome (displacement or possible strike by tur-
bine blade) of the detection (Table 1). We defined a displacement
as any event during which a bat was visibly moved through the air
after it passed within approximately 5 m of a moving turbine blade,
but during which there was no visible contact between the bat and
the turbine blade. Unambiguous contact between flying animals and
moving turbine blades is difficult to determine in thermal imagery,
and because we did not conduct concurrent ground searches for
bat fatalities around the wind turbine, the events in which a moving
blade appeared to make physical contact with a bat are hereafter
referred to as “possible strikes.”
To determine temporal trends in bat behavior, we used nega-
tive binomial regression models with nightly tallies of each activity
as response variables. We included the day of the bat season as a

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GOLDENB ERG Et aL.
predictor variable and defined bat season as beginning on the night
of the first bat detection and ending on the night of the last bat de-
tection. To control for irregular occurrence of bats throughout the
year, we included the log of the total number of bat events recorded
for that night as an offset in models. Regression models were run
using the package glmmTMB (Magnusson et al., 2017) and checked
for overdispersion using the package DHARMa (Hartig, 2020) in R (R
Development Core Team, 2010).
3 | RESULTS
We detected bats during 177 of the 306 nights analyzed. The earli-
est date on which bats were detected at the wind turbine was 04
April 2016 and the last bat detection occurred 19 November 2016
(Figure 2). Bat events gradually increased throughout the spring,
summ er, and ea rly aut umn wit h the hig hes t nu mbe r of bat eve nts oc-
curring in September (n = 748), July (n = 563), and August (n = 528).
The median number of bat events per night (excluding nights when
no bats were detected) was 11 (interquartile range: 4– 21). All high
activity nights (nights above the interquar tile range with > 21 event s
per night) occu rred betwee n 04 Jun e and 08 October, with the high-
est number of single- night events (92) occurring on 02 October.
Only two possible strikes were detected; these activities were
pooled with displacements for subsequent analyses. The duration of
events and the number of discrete activities observed per event dif-
fered over the co ur se of the sea son (Fi gures 3– 5 ). Bats exh ibite d lo n-
ger events at turbines between July- September compared to other
months: the mean number of discrete activities per event for these
months (July: 3.03, August: 2.58, September: 2.87) was higher than
the 95% confidence intervals of all other months during which bats
were recorded (Figures 3, 4). The number of activities obser ved per
event se as onally pe aked in Jul y and Septe mb er, with a signif ic ant de-
cline in August (the mean in August was below the 95% confidence
intervals of July and September) (Figures 5, 6). Approaches to all com-
ponent s of th e tur bine wer e mos t frequent in July and September, as
was chasing flight involving two or more bats (Figures 6, 7a). Other
bat activity (nonfocal passes and hovering flight) and the outcome
of turbine interactions (displacements) peaked in mid- September
(Figure 7a). However, when the incidence of each activity was ana-
lyzed while controlling for the total number of bat events on a given
night in generalized linear models, nonfocal passes were negatively
Activity Definition
Outcome Displacement Bat appears to be moved by turbine blade
Possible strike Bat appears to be struck by blade and falls
Flight behavior Chase Close following flight involving at least two
bats
Nonfocal pass Pass by turbine air space without interaction
Hovering Persistent nondirectional flight in same
location
Turbine location Blade approach Flight path directed toward blade
Monopole approach Flight path directed toward monopole
Nacelle approach Flight path directed toward nacelle
TABLE 1 Definition of observed
bat activity grouped by outcome
(displacement, possible strike), flight
behavior (chase, nonfocal pass, hovering),
and turbine location (blade, monopole,
nacelle)
FIGURE 3 Duration (seconds) of
individual bat event s (red points) by date.
Loess cur ve and 95% CI (shading) highlight
change in duration over time, and y- axis
is truncated at 100 s to better depict
seasonal pattern. Black points and vertical
dash symbols depict sampled night s with
no detections and unsampled nights,
respectively

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GOLDENBERG Et aL .
related to day of the season (β (SE) = −0.002 (0.001), p = .021),
whereas monopole and nacelle approaches were positively related to
day of the season (monopole: β (SE ) = 0.002 (0.001), p = .017; nacel le:
β (SE) = 0.003 (0.001), p = .018). Blade approaches, displacements,
hovering flight, and chases were not significantly related to date,
likely due to the relative infrequency of these events.
FIGURE 4 Total duration (minutes)
of all bat events (i.e., detections that are
connected by 1- min or less) by survey
night
FIGURE 5 Number of nightly obser ved
activities per detection event per week
(mean with 95% confidence intervals)
FIGURE 6 Observed bat activity
by activity type and date. See Table 1
for definitions. Activities exhibited
two distinct seasonal peaks in July and
September, which were largely driven by
approaches to all parts of the turbine

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GOLDENB ERG Et aL.
Bats being displaced by turbine blades were observed 104 times
during 79 discrete events (Table 2). Many of these displacement
events (58%) involved a separate turbine approach behavior prior to
the displacement (i.e., within the same bat event). Bats returned to
approach turbines following displacement in about half (51%) of the
observed events that did not end in possible strikes (n = 77), 35 of
which involved bats that never left the field of view and were there-
fore certainly the same bat returning to the turbine after displace-
ment. Multiple displacements during a single event were recorded
during 22% of the events that did not end prematurely due to a
possible strike. Most of these multiple displacement events (65%)
occurred in September.
Most bat detections occurred during the first half of the night, and
activities and interaction outcomes also exhibited trends throughout
the night (Figure 7b). The pattern of proportionally more detections
early in the night was especially pronounced for blade approaches,
chase behavior, and nonfocal passes, which were concentrated near
dusk. Activities involving close approaches to stationary parts of the
upper wind turbine, such as the nacelle and monopole, showed bi-
modal propor tional increases after dusk and then again in the early
morning hours (approximately 01:00– 03:00). Hovering flight and
observations of displacements were more dispersed throughout the
night than other types of observations.
4 | DISCUSSION
Given the lack of understanding about behaviors of migratory bats
in general, assessing seasonality of their interactions with wind tur-
bines offers valuable information toward understanding fatalities— a
distinctly seasonal phenomenon. This is particularly true if wind tur-
bines are being perceived as trees by migrating bats and attracting
them to landscapes with turbines regardless of whether bats were
common in the area prior to wind facility siting. If such attraction is
occurring, wind turbines might be acting as ecological traps (poor-
quality habit at that animals visit after following environment al cues
FIGURE 7 Seasonal (a) and nightly
(b) trends in the timing of activities and
interaction outcomes of bats obser ved.
Observations are grouped by outcome
(displacement), flight behavior (chase,
hovering, nonfocal pass), and turbine
structural location of close approaches
(blade, nacelle, monopole). Violin plots
depict density of observations by width
of plot bar. Points (red) indicate distinct
events. Each state's bar has an equal
area to make comparable the density of
distributions over time

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GOLDENBERG Et aL .
generally associated with natural favorable habitats (Schlaepfer
et al., 2002)).
Our f indings from this o bse rv ational study are consiste nt with
previously reported trends from other wind energy facilities in
temperate North America and Europe indicating that bat activ-
ity near wind turbines increases in the later months of summer
and in to autumn (Arn et t & Baer wald, 2013; Cr ya n & B ro wn , 2007;
Rydell et al., 2010b). The number of bat detection events we ob-
se r ved , as well as the nu m b er of tim es ba t s re p eat e d ly appr o a che d
turbines within events, increased as the season progressed.
Further, most events involving the highest risk behavior (multiple
displacements of bats by turbine blades) occurred in September,
which has consistently been a peak mor tality month for bats
at wind turbines in North America (Arnett & Baerwald, 2013).
Together these results show seasonal variability in the behavior
of bats in proximity to wind turbines and support the notion that
the risk of turbine- related bat mortality increases as summer pro-
gresses to autumn.
Bat activity during the recorded year was highest at the turbine
in July and September, in terms of both the number and length of
bat events (Figures 4- 5). The increased duration of bat events at the
turbine appeared to be primarily driven by close- approach behav-
iors. Indeed, these investigative behaviors were positively related
to day of the season whereas passing through the airspace with-
out investigation (“nonfocal pass”) was negatively related to the
day of the season when controlling for the number of events in a
night, indicating a seasonal shift in the activities bats exhibited. The
high prevalence of behaviors associated with collision risk, such as
repeated approaches close to turbine surfaces and the increased
duration of time spent at turbines during times bats were making
close approaches, lends further support to the idea that bats exhibit
seasonal investigative behavior at wind turbines. These behavioral
patterns are consistent with observations from earlier video- based
studies (Cryan, Gorresen, et al., 2014; Horn et al., 2008), yet our new
year- long study confirms the previously assumed seasonality of risky
behaviors of bats at turbines. Given the association between risky
behaviors and the migration season for bats, these results further
point to the need for a deeper understanding of migratory processes
in bats and how they may differ among years, sites, and species.
Visual obser vations of bats at wind turbines thus far indicate that
bats often seek some type of resource around these tall landscape
structures. Several potentially perceived resources are suspected of
attracting bats to wind turbines, including insect prey, roosts, and
mating opportunities (Cryan, 2008; Cryan & Barclay, 2009; Horn
et al., 2008; Kunz et al., 2007; Rydell et al., 2010b; Rydell, 2016).
The seasonally variable behaviors bats exhibit at wind turbines could
be influenced by multiple underlying factors, including the onset
of mating, hyperphagia driven by late- summer concentrations of
insect s, the need to accumulate fat for hibernation and migration,
or simply required rest stops and shelter during migration. If bats
obtain the resources they seek at turbines, the presence of such re-
sources (e.g., insect concentrations or other bats) or utilization of any
resources present (e.g., successfully roosting on turbines or regularly
foraging) should be obser vable.
There is indirect evidence that bat s feed in the minutes to hours
before fatally colliding with turbines (Bennett et al., 2017; Foo
et al., 2017; Reimer et al., 2010; Rydell, 2016; Valdez & Cryan, 2013),
and indications from feces in door slat s and transformer gills that
bats may roost on turbine features during nightly foraging bouts
(Bennet t et al., 2017; Foo et al., 2017). The behaviors we obser ved
around wind turbines could not be attributed solely to feeding, nor
did we see bats landing and roosting on turbine surfaces. Thus, while
bats may be roosting on or feeding near turbines in certain situations,
these activities were not obvious to us. Most bat s dying at wind tur-
bines tend to involve species thought to feed primarily “on the wing”
and that are morphologically adapted to an aerial- hawking feeding
strateg y (e.g., long, narrow wings; Norberg & Rayner, 1987). The
gastrointestinal contents of bat c arcasses found beneath wind tur-
bines sometimes contain potentially nonflying insect forms, suggest-
ing bat s might sometimes glean prey from turbine surfaces (Reimer
et al., 2010; Cryan and Valdez, 2013). It is possible that the close
approaches we frequently obser ve bats making to wind turbines
during late summer and autumn involve attempts to glean insects
from turbine surfaces (e.g., Rydell, 2016; Valdez & Cryan, 2013). We
observed no instances of bats actually gleaning insects, which were
often present on turbine surfaces within view of the camera, in the
hundreds of hours of video we analyzed for this study. We remain
skeptical that the close and risky approaches bats frequently make
to wind turbine surfaces in late summer and autumn are exclusively
driven by foraging attempt s (Reimer et al., 2018), although compari-
son with other turbine types and sites is warranted.
If bats approach wind turbines with an expectation of resources
that is not met (e.g., they are seeking suitable roosts and do not
find them), they may move on rather quickly once they learn that
the resource is not available (Cryan, Gorresen, et al., 2014). A plau-
sible explanation for why we observed bats closely approaching
the turbine monopole during July and September more frequently
than any other seasonal activity is that they simply mistook it for
the trunk of a tree. It is possible that the waves of bat activity and
close- approach behaviors we observed at the turbine in July and
September, and August to a lesser extent, were attributable to
passing migrants investigating unfamiliar structures, and therefore
TABLE 2 Number of nightly bat activities (n = 6,987) obser ved
for all bat detection events (n = 2,656) recorded from 17 March
2016 to 16 March 2017
Activity Tot a l Mean Max
Displacement 104 1.32 4
Possible strike 21.00 1
Chase 83 1.41 4
Nonfocal pass 1,198 1.28 10
Hovering 48 1.26 3
Blade approach 794 1.50 10
Monopole appr 3 ,679 2 .26 22
Nacelle appr 1,079 1.66 22

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GOLDENB ERG Et aL.
increased abundance of bats in the area investigating potential re-
sources during those seasonal peaks. The sudden decline in bat ac-
tivity following the early October peak is also consistent with the
possibility of migration waves of nonresidents driving turbine be-
haviors. Hoary bats and silver- haired bats migrate through Colorado,
perhaps at different times, and their presence in the area around the
turbine is expected to vary with season (Cryan, 2003). For example,
at a wind facility in Alberta, Canada, peak fatality numbers of hoary
and silver- haired bats found beneath wind turbines during the day
corresponded to the temporal pattern of echolocation calls detected
at night— these species- specific peaks differed by a few weeks, indi-
cating that silver- haired bats were migrating through the area later
than hoar y bats (Baerwald & Barclay, 2011). We did not differenti-
ate bats by species, so it is unclear whether observed patterns are
indicative of species- specific movement trends. Future research
that pairs information on bat species composition at turbines with
species- specific seasonal behavioral patterns may provide unique
insight, as the resources sought or found at turbines may differ
across species, seasons, years, and sites (Bennett et al., 2017; Foo
et al., 2017). The extent to which bats track migratory insects (Hu
et al., 2016; Rydell et al., 2010b; Satterfield et al., 2020) may also be
an important factor in untangling these seasonal patterns.
Temporal patterns in bat activity and behavior that we obser ved
during the nights at the wind turbine may also hint at the origins of
the bats involved. Although bats tended to visit turbines more often
before midnight, most behaviors were scattered throughout the
night. If bat s we obser ved resided near the turbine and were famil-
iar with the area, we would have expected activity to be associated
with foraging, which typically peaks in bats during the early part of
the night, followed by cessation of foraging activity near the middle
of the night (Erkert, 1982). Such a pattern was not apparent in our
observations. We cannot rule out the possibility that a proportion of
our observations involved local resident bats, some of which might
belong to the same species that also seasonally migrate through the
area in larger numbers. Future studies that integrate acoustic de-
tectors with tracking devices could help determine whether migra-
tory bat s are more abundant or engaging in some type of seasonally
variable behavior that places them at higher risk than nonmigratory,
resident bats. To this end, determining differences in behavioral pat-
terns across or within species could be illuminating. Studies in other
taxa have revealed considerable differences in movement strategies
among individuals within the same species. For example, while they
partially overlap in space, resident and transient orcas (Orcinus orca)
specialize in different prey species leading to resource tracking over
much broader ranges in transient as compared to resident orcas
(Andrews et al., 20 08). The determinants of residency versus migra-
tion in eastern red, hoary, and silver- haired bats, and whether these
bats are more vulnerable at wind turbines if they are not resident,
remain key questions.
Neither the extent to which bats involved in wind turbine fa-
talities exhibit range residency, nor the routes taken by individu-
als that actually make large- scale movements are well understood
(Fleming, 2019). GPS tracking data across numerous taxa, including
bats, has allowed for clearer categorization of movement patterns
(Abrahms et al., 2017; Roeleke et al., 2016; Weller et al., 2016). These
studies have implications for better understanding the proportion of
individuals comprising a population that make long- distance move-
ments, as well as the resources that animals track and revisit over
time. Whether eastern red, hoary, and silver- haired bats exhibit no-
madism, characterized by limited site fidelit y across years or migra-
tion, charac terized by high interannual site fidelity, has implications
for the risk that turbines pose to individuals that encounter them.
The findings presented here highlight the many knowledge gaps that
remain in bat migration ecology. Narrowing these gaps may be highly
beneficial to developing ef fective mitigation strategies at wind facil-
ities. We recommend future research address the drivers of migra-
tory bat movement at different spatial scales.
Our results should be interpreted with c aution given our focus
on a single turbine at a single site, yet the high temporal and behav-
ioral resolution of data presented here sheds new light on bat be-
havior at wind turbines while highlighting potential future research
directions. Reliance on video- based studies such as ours has only
revealed behaviors of bats at wind turbines (within approximately
50 m) (e.g., Cr yan, Gorresen, et al., 2014; Horn et al., 20 08; Huzzen
et al., 2020). Experiments to determine whether and how wind tur-
bines seasonally attract bats from farther distances and at relevant
landscape scales in North America have not been published but
are critical for elucidating the concept of resource selection along
migratory routes and determining whether turbine design or siting
criteria could help mitigate the risk to bats. Such studies could also
help inform the potential of deterrents (such as sound or light- based
deterrence devices) to reduce bat mortality. However, our documen-
tation of repeated displacement s, if they are also perceived by the
bats as aversive stimuli, raises concerns for the efficacy of these mit-
igation measures. Our observations of repeated interac tions of bats
after being physically displaced by the turbine blades emphasize the
importance of identifying the behavioral motivations of bats within
the rotor- swept zone. The thermal video surveillance and behavioral
analysis approach developed for this study represents a practical
and robust way to quantify these interactions and may help guide
the development of strategies that reduce bat fatalities.
ACKNOWLEDGEMENTS
SZG was supported by the National Science Foundation (NSF)
Graduate Research Fellowship Program (Grant #DGE- 1321845) and
an internship provided through the Graduate Research Internship
Program (GRIP). Any findings and conclusions or recommenda-
tions expressed in this material are those of the author(s) and do
not necessarily reflect the views of the National Science Foundation.
Coordination of GRIP at USGS is through the Youth and Education in
Science programs within the Office of Science Quality and Integrit y.
PMC was supported by the USGS For t Collins Science Center. This
work would not have been possible without the competent and
dedicated science and engineering st aff at the U.S. Department of
Energy's National Renewable Energy Laborator y, National Wind
Technology Center, particularly, Karin Sinclair, Robert Thresher, Elise

4852
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GOLDENBERG Et aL .
DeGeorge, Bethany Straw, Tom Ryon, and Jason Roadman. Special
thanks to Samuel Ross for competent assistance with the video pro-
cessing code. We are grateful to Scott Reynolds and two anonymous
reviewers for construc tive comments that improved the manuscript.
Any use of trade, firm, or product names is for descriptive purposes
only and does not imply endorsement by the U.S. Government.
CONFLICT OF INTEREST
We declare no competing interests.
AUTHOR CONTRIBUTIONS
Shifra Goldenberg: Conceptualization (equal); Data curation (equal);
Formal analysis (equal); Funding acquisition (equal); Investigation
(equal); Methodology (equal); Resources (equal); Validation (equal);
Visualization (supporting); Writing- original draft (lead); Writing-
review & editing (equal). Paul Cryan: Conceptualization (equal); Data
curation (equal); Funding acquisition (equal); Investigation (equal);
Methodology (equal); Project administration (equal); Resources
(equal); Software (equal); Supervision (equal); Validation (equal);
Visualization (equal); Writing- review & editing (equal). Marcos
Gorresen: Conceptualization (equal); Data curation (equal); Formal
analysis (equal); Methodology (equal); Software (equal); Validation
(equal); Visualization (lead); Writing- review & editing (equal). Lee Jay
Fingersh: Conceptualization (equal); Methodology (equal); Project
administration (equal); Resources (equal); Writing- review & editing
(equal).
ETHICAL APPROVAL
This study was entirely observational of natural bat behavior, and
therefore, no ethics approval was required.
DATA AVA ILAB ILITY STATE MEN T
The dataset and accompanying metadata are available on Dryad:
https://doi.org/10.5061/dryad.q83bk 3jh3.
ORCID
Shifra Z. Goldenberg https://orcid.org/0000-0002-9468-8920
Paul M. Cryan https://orcid.org/0000-0002-2915-8894
Paulo Marcos Gorresen https://orcid.org/0000-0002-0707-9212
Lee Jay Fingersh https://orcid.org/0000-0003-4816-8331
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How to cite this article: Goldenberg SZ, Cryan PM, Gorresen
PM, Fingersh LJ. Behavioral patterns of bats at a wind
turbine confirm seasonality of fatality risk. Ecol Evol.
2021;11:4843– 4853. https://doi.org/10.1002/ece3.7388