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RESEARCH ARTICLE
Hareport hazard: Identifying hare activity patterns and
increased mammal–aircraft strike risk at an International
Airport
Samantha Ball
1,2
, Anthony Caravaggi
3
& Fidelma Butler
1
1
School of Biological, Earth and Environmental Science, Distillery Fields, University College Cork, Cork T23 TK30, Ireland
2
daa, Airport Fire & Rescue Service, Dublin K67 CX65, Ireland
3
School of Applied Sciences, University of South Wales, 9 Graig Fach, Glyntaff, Pontypridd CF37 4BB, UK
Keywords
Airfield management, circadian activity,
wildlife hazard, wildlife management, wildlife
strikes
Correspondence
Samantha Ball, School of Biological, Earth
and Environmental Science, Distillery Fields,
University College Cork, Cork T23 TK30,
Ireland. Tel: +353 21 4904676; Email:
samantha.ball@ucc.ie
Editor: Marcus Rowcliffe
Associate Editor: Oliver Wearn
Funding Information
daa: EBPPG/2018/43 Irish Research Council
University College Cork Consortia
Received: 1 March 2022; Revised: 10 June
2022; Accepted: 27 June 2022
doi: 10.1002/rse2.293
Remote Sensing in Ecology and
Conservation 2023;9(1):33–45
Abstract
Reported strike events between wildlife and aircraft are hazardous to aircraft
and airfield operations and are increasing globally. To develop effective mitiga-
tion strategies, the relative hazard a species poses to aircraft, as well as informa-
tion relating to its life history, are key to the development of effective
mitigation strategies in Wildlife Hazard Management Plans. However, given the
complex nature of airfield environments with access restrictions and the pres-
ence of sensitive equipment, the collection of high-quality ecological data can
be difficult. Here we use motion-activated camera traps to collect activity data
on a population of Irish hares (Lepus timidus hibernicus) inhabiting the airfield
at Dublin International Airport, to investigate the link between hare activity
and aircraft activity in relation to hare strikes. Camera traps revealed that the
hare population at the airfield largely displayed a bimodal crepuscular activity
pattern, with activity peaking at sunrise and at sunset. Recorded hare strike
times at the airfield were closely associated with hare activity times with a high
temporal overlap between these datasets. In comparison, hare activity and air-
craft movement activity had a moderate overlap across all seasons, with strikes
peaking at times with low aircraft movements. We demonstrate the importance
of understanding the circadian and seasonal activity patterns of hazardous spe-
cies at airfields for targeted strike mitigation.
Introduction
Wildlife collisions or ‘strikes’ with transport vehicles can
have serious consequences for passenger safety, industry
economics (e.g. Dolbeer & Begier, 2021), the local econ-
omy (e.g. Jaren et al., 1991) and wildlife conservation
efforts (e.g. Clair et al., 2019). While the majority of
strike-related research has focused on road traffic net-
works (e.g. Popp & Boyle, 2017; Wright et al., 2020), sim-
ilar consequences are also reported for other modes of
transport including rail (Dorsey et al., 2017), shipping
networks (Laist et al., 2001) and air transportation
(Altringer et al., 2021). Mammals are well represented
within the literature regarding strikes for most modes of
transport (e.g. Pokorny et al., 2022), yet relatively little
research has focussed on mammals in the context of the
air transportation sector, despite mammalian strikes com-
posing 3–10% of reported strikes in the aviation industry
(Ball, Caravaggi, & Butler, 2021b). Terrestrial mammals
are hazardous to aircraft only when they move on to the
active runway, therefore, understanding the circadian
(over 24 hours) and seasonal activities of animals inhabit-
ing or using the airfield could help to identify periods of
increased risk when animals are likely to come into con-
tact with aircraft. Identifying these periods of risk can
then allow for the targeted development and application
of strike mitigation measures.
Animal behaviours and activity patterns are greatly
influenced by a variety of pressures within their environ-
ment, including- but not limited to- food availability
ª2022 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.
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33
(Pereira, 2010), predation risk (Ross et al., 2013),
disturbance and anthropogenic activities (Lendrum
et al., 2017). Circadian and seasonal activity patterns are
photoresponsive cycles regulated by the suprachiasmatic
nucleus of the hypothalamus in mammals (Meijer
et al., 2010), allowing for species to fulfil daily require-
ments (e.g. feeding) while adapting to seasonal day length.
This plasticity (Phillips et al., 2013) allows for mammals
to exploit the ecological environment they inhabit and
alter activities relative to changing sunrise and sunset
times. Hence, in the context of wildlife management,
wildlife hazards are likely to exhibit temporal variability,
particularly with increasing distances from the equator
where seasonality becomes increasingly pronounced. This
highlights the importance of understanding species com-
position and associated life histories of potentially haz-
ardous fauna on an airfield-specific basis.
Given the sensitive nature of airfield environments, the
collection of high-quality ecological data can often be com-
plicated, requiring the use of remote field methods due to
limited accessibility, such as camera traps (Carswell
et al., 2021; Scheideman et al., 2017), radio telemetry (York
et al., 2000), GPS tracking (Askren et al., 2019) and predic-
tive modelling based on pre-existing movement data
(Arrondo et al., 2021). Lagomorphs (particularly rabbits
and hares) are frequently reported in airfield environments
and are reportedly involved in strike events near globally
(Ball, Caravaggi, & Butler, 2021b; Dolbeer & Begier, 2021;
Kitowski, 2016). A population of the Irish hare (Lepus timi-
dus hibernicus, Bell 1837), an endemic subspecies of the
Mountain hare (L. timidus, Linnaeus 1758), resides at
Dublin Airport in the Republic of Ireland where strike
events between hares and aircraft have been increasing by an
average of 14% annually since 1997 (Ball, Butler,
et al., 2021a). The damage potential of a hare strike
(10,576 J; Ball, Butler, et al., 2021a), in tandem with the
conservation status (Caravaggi et al., 2017; Reid
et al., 2010) of this endemic subspecies, require that effec-
tive management strategies be developed to mitigate against
strike events. Here, we investigate whether motion-activated
camera traps –an easily accessible and relatively inexpensive
method of monitoring –can be successfully used to identify
periods of increased strike risk between aircraft and hares.
Understanding the relationship between animal activity
patterns and temporal distributions of air traffic can help to
comprehend and mitigate strike risk (Arrondo et al., 2021;
Carswell et al., 2021; Schwarz et al., 2014). Here, we apply
an approach more frequently used in inter-specific compe-
tition and predator–prey modelling (e.g. Caravaggi
et al., 2018; Ross et al., 2013) to an industry setting. We
hypothesize that aircraft-hare activity overlap will fluctuate
seasonally, and that hare activity represents a better indica-
tor of strike patterns than aircraft activity. We focus on the
Irish hare as a model species and propose that this
approach could be used to identify periods of risk associ-
ated with other ground dwelling species at airfields world-
wide and for use on public road networks.
Materials and Methods
Study area
Dublin International Airport (53.4264°N, 6.2499°W) is
Ireland’s largest civil airport and one of the busiest in Eur-
ope, with almost 250,000 aircraft movements recorded in
2019 alone. The airfield was composed of approximately
275 hectares (680 acres) of grassland throughout the study
period, which increased to approximately 370 hectares
(914 acres) in August 2021 due to the expansion of the air-
field to incorporate an additional runway. The grasslands
are maintained according to a long grass management pol-
icy (UKCAA Safety Regulation Group CAP, 2008) com-
prising of a blend of Italian ryegrass (Lolium multiflorum)
and tall fescue (Festuca arundinacea). The airfield is located
on the east coast of Ireland and experiences a temperate
climate. A mean temperature of 9.4°C and mean rainfall of
62.8 mm was recorded throughout the study period (July
2019–May 2021; MET Eireann, 2021).
Strike data
A database of all strike events at the airfield has been
maintained since 1990, encompassing all confirmed strike
events with avian and mammal species. The first strike
event with a hare was reported at the airfield in 1997.
These data were provided by the daa (Dublin Airport’s
managing body). Carcasses were recovered from active
areas (i.e. runways, taxiways) following a reported strike
event or during mandatory routine inspections with the
location and environmental conditions surrounding an
event recorded (e.g. weather; see Ball, Butler,
et al., 2021a). From 2012 onwards, temporal data detail-
ing the date and time of a strike were also recorded,
resulting in n =238 hare strike events with an associated
strike time from 2012 until December 2021. Despite the
presence of other mammal species occasionally reported
at the airfield including foxes (Vulpes vulpes), hedgehogs
(Erinaceus europaeus), rabbits (Oryctolagus cuniculus), rats
(Rattus norvegicus), domestic cats (Felis catus) and bats
(Kelly et al., 2017), strike events with these species are
rare (Bolger & Kelly, 2008). With an increasing number
of hare strike events reported annually and a sufficient
kinetic energy to cause damage to an aircraft (Ball, Butler,
et al., 2021a), the Irish hare is the most hazardous mam-
mal species at the airfield. Therefore, here we focus on
the Irish hare due to a high incidence of strike events.
34 ª2022 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.
Camera Traps Inform Airport Wildlife-Hazard S. Ball et al.
Camera trapping
Seven Bushnell Trophy Cam HD (model 119476) camera
traps were deployed into the grasslands surrounding the
main runway at Dublin Airport (RW28-10 L), at seven
fixed locations, identified a priori by airfield safety
authorities and dependant on the location of appropriate
permanent structures (e.g., gate posts). Camera positions
were not changed due to ongoing construction work
throughout the study period. Cameras were positioned
50 cm above ground level with a ~15
o
downwards tilt
with cameras pointing away from areas of high aircraft
and vehicle traffic, to prevent false triggers and operated
24/7 with the use of infrared for nocturnal image capture
(Appendix 1and Appendix 2). The shortest distance
between the two closest camera traps was 520 m (0.5 km,
range 520 m–920 m), to minimize spatial replication.
This was well beyond the typical hare home range
(0.14km
2
0.02 m Caravaggi et al., 2016) and slightly
over the median home range size of male hares reported
by Wolfe and Hayden (1996) of 0.5 km
2
. Cameras were
left in situ for 32–69 days (dependant on logistics, camera
performance and accessibility), for each season between
July 2019–May 2021 (Appendix 3), for a total of 360 cal-
endar days. Seasons were defined as spring (March–May),
summer (June–August), autumn (September–November)
and winter (December–February). Interannual survey data
were pooled for each season. Cameras were programmed
to record two-time stamped images when triggered by
movement at medium sensitivity, with a 60-second inter-
val between images.
Circadian activity- data analysis
Camera trap images were assumed independent of each
other if they were separated by a minimum of 30 min-
utes, or by a clearly different animal (e.g. distinguish-
able markings; Viviano et al., 2021) and therefore
defined as an independent mammal detection. As cam-
era traps were only triggered by movement (i.e., activ-
ity), we also assumed that detections of hares were a
true reflection of the circadian activity of the species.
For trigger events where the image contained more than
one individual (n =39 events), only a single event was
recorded (Caravaggi et al., 2018). Aircraft movement
data for the runway (RW28-10 L) were obtained from
the daa, whereby a movement is defined as a take-off
or landing manoeuvre. All aircraft manoeuvres were
considered to be independent events. These data were
truncated to include only those dates where camera
traps were deployed on the airfield. All statistical analy-
sis was carried out in programme R v 4.0.4. (R Core
Team, 2021).
A cross-correlation function (CCF) was used to deter-
mine negative (h) or positive (h+) lags in time series
data between aircraft and hares. CCFs show correlations
between events in time series data (X
a
,Y
a
;a=time),
where a positive lag (h+) shows a correlation between
X
a+i
and Y
a
(i.e., X succeeds Y). Likewise, a negative lag
(h) shows a correlation between X
a-i
and Y
a
(i.e., X pre-
cedes Y). The significance of the correlation coefficient
was established by calculating the tvalue, where the criti-
cal tvalue (P=0.05, 22 degrees of freedom, one-
tailed) =1.72. The ‘Overlap’ package (Meredith & Rid-
out, 2021) was used to determine the temporal overlap
between hares and aircraft, by estimating the overlap
coefficient (Δ), where Δ= 0 indicated no overlap and
Δ= 1 indicated complete temporal overlap. Data were
bootstrapped 1,000 times to generate 95% confidence
intervals (CI) of the overlap coefficient (Zanni
et al., 2021). The Δ
4
estimator was used for all pairwise
comparisons between aircraft movements and hare activ-
ity and was also used to compare aircraft movements and
hare strike events across the whole year. The Δ
1
estimator
was used for seasonal aircraft strike pairwise combinations
due to seasonal strike records ranging from 54–69 events
(Meredith & Ridout, 2021) and for seasonal hare activity
vs. seasonal hare strike comparisons. Temporal overlaps
between hares and aircraft were ranked as either high
(Δ>0.75), moderate (0.50 <Δ<0.75) or low
(Δ<0.50), for each season (Monterroso et al., 2014).
Given Irelands northern latitude, day length varies sub-
stantially with seasons with the longest days in the sum-
mer experiencing ~17 hours of daylight, and the shortest
days in the winter experiencing ~7 hours of daylight.
Therefore, to investigate how activity patterns changed in
relation to the rising of a setting sun seasonally, trigger
events were offset to either sunrise or sunset (i.e. 00:00–
11:59 offset relative to sunrise, 12:00–23:59 offset relative
to sunset; Caravaggi et al., 2018). All daytime offsets (i.e.,
events which occurred between sunrise and sunset) were
converted to positive integers and night-time offsets (i.e.,
events which occurred between sunset and sunrise) to
negative integers. As an example, a detection at 10:15 on
a day where sunrise was at 08:00 would have an offset
value of +2 hrs 15mins, which would indicate diurnal
activity. Similarly, a detection at 20:30 on a day where
sunset was at 19:00 would have an offset value of -1 hr.
30 mins, indicating nocturnal activity. Finally, a detection
which occurred at 03:55 (after midnight) would be offset
to sunrise and on a day where this was at 07:15, an offset
value of -3 hrs 20mins would be allocated, indicating
nocturnal activity closer to sunrise than sunset. These off-
set values were used as the dependant variable in a one-
way analysis of variance with post hoc Tukey tests, to test
differences in activity patterns across seasons. Likewise, as
ª2022 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. 35
S. Ball et al. Camera Traps Inform Airport Wildlife-Hazard
the Covid-19 pandemic substantially impacted on the
number of aircraft movements at Dublin Airport, the
same method was used to compare hare activity between
seasons (i.e., Summer 2019 vs. Summer 2020) to test
whether hares exhibited altered activity patterns with the
relief in aircraft movements.
Camera trap data and aircraft movement data were
from the same sampling periods with aircraft movement
data having been truncated to match camera trap deploy-
ment dates (between July 2019–May 2021). As hare strike
data were available from 2012–2021 (n=238), the entire
data set was used to investigate the overlap patterns with
aircraft movements and hare activity to ensure a sufficient
sample size for robust estimates (Lashley et al., 2018;
Meredith & Ridout, 2021). Truncating hare strike data to
the sampling period dates (July 2019–May 2021) would
result in an insufficient sample size (n=17) to reliably
estimate activity overlap.
Results
Camera trap detections
Of the potential 2,520 recording days (i.e., 7 cameras
recording for 360 calendar days), 2,144 were successful
(85%). Camera failings were a result of removal by per-
sonnel, hares chewing through camera straps as well as
battery and mechanical failure. A total of 684 indepen-
dent mammalian detections were recorded on the airfield
at Dublin Airport, from 5 species, across 360 calendar
days (i.e., full 24-hour periods; Table 1). The Irish hare
was the most frequently detected species, making up
84.9% (n=574) of detections, followed by the red fox
(Vulpes vulpes) with 14.6% (n=100) of detections. The
European hedgehog (Erinaceus europaeus,n=5), Euro-
pean rabbit (Oryctolagus cuniculus,n=3) and domestic
cat (Felis catus, n =2) were rarely recorded. Over the
course of the study period (360 days), a total of 114,559
aircraft movements were recorded at the airfield
(Table 2).
Activity patterns
Hares demonstrated a largely crepuscular activity pattern,
with peaks in activity recorded at, or close to, sunrise and
sunset (Fig. 1). This pattern was less defined during the
winter months, when hares were active over a longer per-
iod, likely due to the prolonged hours of darkness typical
of the season in Ireland (e.g., day length on winter solstice
in Dublin is approximately 7 hours and 30 minutes, com-
pared with approximately 17 hours on the summer sol-
stice). In contrast, while aircraft movements were
recorded continuously throughout the year, the majority
of movements occurred according to a diurnal pattern
(Fig. 1). Movements rapidly increased around 06:00
across all seasons and remained high for the duration of
the day, decreasing in volume approaching midnight
(00:00). Overlap between hares and aircraft was moderate
across the year (58%; CI 53–60%) and across all seasons
(Table 3), with spring having the highest activity overlap
(63%; CI 55–65%). Activity overlap estimates for the
sampling periods prior to the Covid-19 pandemic were
slightly lower than those when both years were considered
together (41–51%; Appendix 4).
Hare activity generally preceded aircraft activity, with a
significant peak of activity at zero indicating contempora-
neous activity patterns recorded only for the winter sam-
pling period (peak =0, r =0.576, t =3.30; Table 4),
when hare activity was less confined to a strictly crepus-
cular pattern. Significant correlations between aircraft and
hares were observed across all seasons, with activity pat-
terns crossing zero throughout the year (peak =3,
r=0.628, t =3.79) as well as for the summer
(peak =1, r =0.592, t =3.45), autumn (peak =2,
r=0.608, t =3.59) and winter sampling periods
(Table 4).
Table 1. Total number of seasonal detections of Irish hare and red fox using camera traps at Dublin Airport 2019–2021. Sampling commenced
in the Summer of 2019 until Spring 2021.
Species Year Summer Autumn Winter Spring
No. of independent
triggers
No. days
in-situ
No. of animal
detections
No. of triggers per
50 days
Irish hare
Lepus timidus
hibernicus
140 30 45 59*
,†
174 152 175 57.2
282*46*114*158*400 208 444 94.7
Red fox
Vulpes vulpes
118 9 10 12*49 152 49 16.1
27*13*10*21*51 208 51 12.1
Year 1 =Summer 2019- Spring 2020.
Year 2 =Summer 2020- Spring 2021.
†
Denotes the onset of the Covid-19 pandemic in Ireland.
*Denotes sampling periods during the Covid-19 pandemic in Ireland.
36 ª2022 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.
Camera Traps Inform Airport Wildlife-Hazard S. Ball et al.
Significant differences (P<0.001) in hare activity rela-
tive to sunrise/sunset were recorded across all seasons at
Dublin Airport (F
3,570
=81.89, P<0.0001), with the
exception of autumn–winter and spring–summer (Fig. 2).
Due to disruptions caused by the Covid-19 pandemic to
standard airfield activity patterns, interannual hare activ-
ity for each season was compared (F
3,566
=37.46,
P<0.0001) to ensure that activity patterns were a true
reflection of circadian activity with no significant differ-
ences observed between the same season for each year.
Using Tukey’s HSD test, small differences (diff) in mean
activity offsets were observed for spring (diff: -0.87, 95%
CI =2.13, 0.39, P=0.42), summer (diff:1.32, 95%
CI =0.28, 2.92, P=0.19), autumn (diff: -0.33, 95%
CI =2.27, 1.61, P=0.99) and winter (diff:-0.62, 95%
CI =2.07, 0.84, P=0.90).
Table 2. Total number of Aircraft Movements (ACM) 2019–2021 recorded by the (daa) for the time frame during which the cameras were
deployed on the airfield to record wildlife activity. Sampling commenced in the Summer of 2019 until Spring 2021.
Year Summer Autumn Winter Spring Total No. days No. of ACM per 50 days No. of ACM per 50 days prior to Covid-19
1 24,219 36,363*17,287 2,363*
,†
80,232 152 26,392 32,445
2 16,035*5,697*8,022*4,613*34,367 211 8,144 NA
Year 1 =Summer 2019- Spring 2020.
Year 2 =Summer 2020- Spring 2021.
†
Denotes the onset of the Covid-19 pandemic in Ireland.
*Denotes sampling periods during the Covid-19 pandemic in Ireland.
0.00 0.06
Time
Density of Activity
0:00 6:00 12:00 18:00 24:00
Aircraft (n=6,976)
Hare (n=271)
Spring
Δ =0.63
0.00 0.04 0.08
Time
Density of Activity
0:00 6:00 12:00 18:00 24:00
Aircraft (n=40,254)
Hare (n=122)
Summer
Δ =0.55
0.00 0.04 0.08
Time
Density of Activity
0:00 6:00 12:00 18:00 24:00
Aircraft (n=42,060)
Hare (n=76)
Autumn
Δ =0.52
0.00 0.04
Time
Density of Activity
0:00 6:00 12:00 18:00 24:00
Aircraft (n=25,309)
Hare (n=159)
Winter
Δ =0.59
0.00 0.04 0.08
Time
Density of Activity
0:00 6:00 12:00 18:00 24:00
Aircraft (n=114,599)
Hare (n=628)
Annual
Δ =0.58Δ =0.58
Figure 1. Overlap estimates of hare activity and aircraft movements at Dublin Airport. Shaded grey areas indicate times when activity
overlapped. Dotted and solid black lines indicate sunrise/sunset times on the shortest day and longest day of the sample period respectively.
Events are indicated along the x-axis for hares (blue) and aircraft movements (black).
ª2022 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. 37
S. Ball et al. Camera Traps Inform Airport Wildlife-Hazard
Strike patterns
Strike events across the year followed a crepuscular pat-
tern. The highest number of strikes was recorded simulta-
neously to peak hare activity in the morning (04:00–
07:59; 33% of strike events) and following the peak in the
evening (21:00–23:59; 27% of strike events). Strike event
times had a high (Δ>0.75) level of overlap with hare
activity patterns across the year (86%; CI 80–90%, Fig. 3),
as well as throughout the summer, autumn and winter
seasons. Spring was the only season where a moderate
overlap was recorded (66%; CI 54–74%).
Strike event times had a moderate overlap with aircraft
activity times across the year (58%; CI 51–61%), with
varying degrees of overlap across seasons. Highest overlap
was recorded during the autumn period (62%; CI 53–
73%) with the least amount of overlap recorded during
the summer (39%; CI 29–46%; Table 3). Significant dif-
ferences (P<0.001) in strike events relative to sun-
rise/sunset were recorded (F
3,234
=6.14, P<0.0001) for
winter compared with all other seasons (Fig. 2). Aircraft
movements generally picked up from approximately
06:00, with a high proportion of strike events recorded
until 08:00 across the year. A second peak in strike events
was recorded at night (21:00-23:59) when hourly aircraft
movements were declining (Fig. 4, Appendix 5). Overall,
12% of strikes occurred between 07:00–07:59 and a fur-
ther 12% between 23:00–23:59.
Discussion
Here we show that camera trap data can be used to iden-
tify the circadian and seasonal periods of increased strike
risk in the air transportation sector (see Carswell
et al., 2021). Prior research has investigated the suitability
of camera traps for use on airfields for understanding
species composition (Scheideman et al., 2017) and here
we demonstrate their suitability in determining the activ-
ity patterns of a terrestrial mammal species in relation to
aircraft movements. This study, using camera traps in a
transportation management setting, represents a valuable
addition to the growing literature on camera trap applica-
tions for wildlife conservation and management (e.g. Car-
avaggi et al., 2018; Garrote et al., 2019; Hofmeester
et al., 2020; Jachowski et al., 2015; Schwartz et al., 2018).
More importantly, it demonstrates the suitability of this
survey method for quantifying ecological phenomena of
management concern in busy, dynamic and heavily regu-
lated environments.
Previous studies have demonstrated that lagomorphs
alter activity patterns with increasing human influence on
the landscape (Wong & Candolin, 2015; Ziege
et al., 2016). However, the activity patterns of the Irish
hare recorded during this study did not appear to be
influenced by ongoing operations at Dublin Airport and
were similar to previously recorded seasonal and circadian
activity patterns for this sub-species (Caravaggi
et al., 2018). The hare population at Dublin Airport
Table 3. Annual and seasonal temporal overlap estimates for hare
activity (2019–2021), aircraft movement activity (2019–2021) and
reported strikes between hares and aircraft (2012–2021) at Dublin
Airport with bootstrapped confidence intervals (95%).
Season Overlap estimate (%) Upper CI Lower CI
i. Hare activity and aircraft activity temporal overlap (Fig. 1)
Annual 58.3 53.3 59.9
Spring 62.7 54.8 64.7
Summer 55.0 45.5 59.7
Autumn 52.0 41.9 58.3
Winter 59.2 51.6 64.8
ii. Aircraft activity and recorded hare strike events (2012–2021)
temporal overlap (Appendix 5)
Annual 57.6 51.3 61.4
Spring 61.3 53.1 70.8
Summer 38.4 29.0 46.0
Autumn 62.6 52.9 73.0
Winter 61.7 52.3 68.0
iii. Hare activity and recorded hare strike events (2012–2021)
temporal overlap (Fig. 3)
Annual 85.8 79.5 90.3
Spring 66.1 54.2 74.0
Summer 74.5 64.2 84.5
Autumn 78.2 69.5 91.1
Winter 79.5 72.0 91.9
Table 4. Annual and seasonal associations and dissociations in tem-
poral activity between hares and aircraft at Dublin Airport, estimated
using Cross-Correlation Functions (CCF). r=Pearson’s correlation
coefficient.
Hours
Season Lag (from) Lag (to) Peak lag t-value r
Annual 12 89 3.14 0.557*
Annual 503 3.79 0.628*
Spring 534 2.42 0.603*
Spring 999 1.62 0.327
Summer 12 89 2.64 0.491*
Summer 401 3.45 0.592*
Autumn 11 89 2.28 0.438*
Autumn 512 3.59 0.608*
Winter 11 810 2.76 0.507*
Winter 4 2 0 3.30 0.576*
Winter 10 12 12 1.55 0.315
Negative lags indicate that hare activity preceded aircraft movements
and positive lags indicate that hare activity followed aircraft move-
ments. Zero indicates that activity was contemporaneous.
*Denotes significant lag.
38 ª2022 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.
Camera Traps Inform Airport Wildlife-Hazard S. Ball et al.
exhibited a bimodal, crepuscular activity pattern, with
clear peaks in activity at sunrise and sunset for the spring
and summer seasons when days are longer. A higher rate
of diurnal activity was also recorded during these seasons,
likely due to energetic requirements and the need to con-
tinue foraging beyond the hours of (semi-) darkness.
During the autumn and winter seasons, hares exhibited a
trimodal activity pattern, with peaks at sunrise, sunset
and at approximately midnight, as seen in some other
mammal species (Brivio et al., 2016; Ikeda et al., 2019).
Generally, activity times of the Irish hare at Dublin Air-
port preceded peak aircraft activity times, with winter
being the only season when a significant peak in hare
activity was contemporaneous with aircraft movements.
Given that the majority of aircraft movements occurred
in the daytime, this is to be expected as hares were pre-
dominantly active prior to the start of daily airfield oper-
ations.
The development of effective strike mitigation measures
requires an understanding of strike events and factors
driving these events. Overlap between hare activity and
aircraft movements was moderate (58%) but was high
between hare activity times and hare strike times at the
airfield (85%), with strikes typically occurring according
to a bimodal pattern with a peak at 04:00–07:59 and
another at 21:00–23:59. Identifying times of higher risk is
useful from a management execution and implementation
perspective. These, for instance, are the times during
which scaring and patrol efforts may be increased or air-
craft may receive additional alerts about possible hare
activity. Indeed, concentrating efforts for only 2 hours a
day (07:00–07:59 and 23:00–23:59) could be greatly bene-
ficial. However, as animals adjust activity seasonally with
changing sunrise and sunset times, actual intraspecies
activity peaks may change week to week based on natural
and artificial light levels (Hoffmann et al., 2018). Indeed,
−10
−5
0
5
10
Spring
Summer
Autumn
Winter
Detection time
relative to sunrise/set
A
−10
−5
0
5
10
Spring
Summer
Autumn
Winter
Strike time
relative to sunrise/set
B
Figure 2. (A) Detection time of hare activity (2019–2021) on the airfield using motion-activated camera traps and (B) time of reported hare strike
events (2012–2021) relative to sunrise and sunset for each season. Shaded areas indicate hours after sunset and lighter areas indicate hours after
sunrise. Mean SD are represented by boxplots (left) and the density and spread of the data are represented by raincloud plots (right; Allen
et al., 2021). The mean annual offset of events relative to sunrise and sunset across all seasons is represented by the dashed line.
ª2022 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. 39
S. Ball et al. Camera Traps Inform Airport Wildlife-Hazard
we have demonstrated that hare activity at Dublin
changes according to sunrise and sunset times and on a
seasonal basis. Consequently, airfield managers need to
operate a suite of mitigation measures that are flexible in
their response to changing animal activity patterns.
Despite several road ecology studies demonstrating that
increased volumes of road traffic are associated with
increased roadkill rates (e.g. Haigh, 2012), strikes
occurred at Dublin when aircraft movement numbers
were relatively low, indicating that hare activity is a better
indicator of strike risk than the volume of aircraft move-
ments. This has important considerations should aircraft
activity patterns be altered and suggests that strike risk
may remain high at specific times of the day (periods of
high hare activity) even during periods when aircraft
activity patterns are altered (e.g. such as during the
Covid-19 pandemic and economic crashes (Franke &
John, 2011)). This furthermore demonstrates the
importance of maintaining high-quality strike data on a
local and national scale (e.g. FAA, 2021).
With the onset of the Covid-19 pandemic, national
lockdowns were implemented in Ireland at the end of
March 2020 with aircraft movement numbers being
severely impacted during the remainder of the year. As
with road-traffic data on collisions with mammals (e.g.
Łopucki et al., 2021), this reduction in air traffic coin-
cided with a reduction in the number of hare strikes
recorded at Dublin Airport. An average of 77% of
recorded hare strikes at Dublin Airport occurred during
April–December from 2012–2019. However, only 38% of
strike events were recorded during this time frame in
2020. Although there was a small decrease (~8%) esti-
mated in the size of the population once surveys resumed
(June 2020–July 2021) from pre-lockdown data (SB,
unpublished data), this is unlikely to explain the reduction
in strike numbers. Despite this, annual strike rates (per
0.00 0.06 0.12
Time
Density of Activity
0:00 6:00 12:00 18:00 24:00
Strikes (n=69)
Hare Activity (n=271)
Spring
Δ =0.66
0.00 0.06 0.12
Time
Density of Activity
0:00 6:00 12:00 18:00 24:00
Strikes (n=54)
Hare Activity (n=122)
Summer
Δ =0.75
0.00 0.04 0.08
Time
Density of Activity
0:00 6:00 12:00 18:00 24:00
Strikes (n=56)
Hare Activity (n=76)
Autumn
Δ =0.78
0.00 0.04 0.08
Time
Density of Activity
0:00 6:00 12:00 18:00 24:00
Strikes (n=59)
Hare Activity (n=159)
Winter
Δ =0.80
0.00 0.04 0.08
Time
Density of Activity
0:00 6:00 12:00 18:00 24:00
Strikes (n=238)
Hare Activity (n=628)
Annual
Δ =0.86
Figure 3. Overlap estimates of hare activity and hare strikes at Dublin Airport. Shaded grey areas indicate times when activity overlapped. Dotted
and solid black lines indicate sunrise/sunset times on the shortest day and longest day of the sample period respectively. Events are indicated
along the x-axis for hares (blue) and strikes (black).
40 ª2022 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.
Camera Traps Inform Airport Wildlife-Hazard S. Ball et al.
10,000 aircraft movements) with the Irish hare did not
decrease, notwithstanding a 65% reduction in aircraft
movements for 2020 compared with 2019 (daa, unpub-
lished data). Indeed, strikes increased from 0.54 strikes
per 10,000 aircraft movements in 2019 (Ball, Butler,
et al., 2021a), to 0.92 strikes per 10,000 aircraft move-
ments in 2020, indicating that the number of aircraft
movements are at least partially responsible for the num-
ber of strike events. However, strike rates in 2019 were
unusually low, potentially attributed to population man-
agement practices in 2018 (1.89 strikes per 10,000 aircraft
movements; Ball, Butler, et al., 2021a) and habitat distur-
bance in 2019 due to ongoing construction works for a
new runway. These observed changes in strike rate at the
airfield could potentially be attributed to the consequen-
tial changes in aircraft movement patterns, land use
changes by the hares or potentially due to increased naiv-
ety of the population (Mumme et al., 2000; Schwartz
et al., 2020). Despite the conservation status of the Irish
hare, strike events are unlikely to have population level
impacts (Ball, Butler, et al., 2021a). Dublin Airport was
not the only airfield to report an increased strike rate
with hares, with Italian airports reporting an 81%
increase in hare strike rate during the 2020 lockdown
periods (Montemaggiori, 2021).
Despite changes to air traffic volume at Dublin post
the implementation of Covid-19 lockdown measures, the
circadian activity of the hares at the airfield did not
change between seasonal sampling periods (i.e., summer
2019 vs. summer 2020). While hare activity data for the
spring prior to pandemic-related disruptions to aircraft
traffic were not available, we do not believe that reduced
aircraft movements impacted on hare activity at Dublin
Airport. Circadian activity patterns for spring followed
the same bimodal pattern as the summer sampling period
and were consistent with previously published activities of
the Irish hare (Caravaggi et al., 2018). While circadian
activity of the hares was unchanged by the reduction of
air traffic, camera traps documented a change in hare
grouping behaviour at the airfield. Prior to the pandemic,
more than one hare was recorded in a frame only 0.89%
of the time and group size did not exceed two. Multiple
hares were recorded 11% of the time during the same
seasons in 2020, with up to four hares recorded within a
single frame. Other mountain hare populations have been
recorded to spend long periods of time under canopy
0
4
8
12
0 6 12 18 24
Hour
Frequency of detection (%)
Activity Type
Aircraft
Hare
Strike
Figure 4. Activity times for the Irish hare (circadian), aircraft movements and recorded strike events (2012–2021) between hares and aircraft at
Dublin Airport, across the year. Hare and aircraft activity data were collected between 2019–2021. Light grey shaded areas demonstrate the
range of sunrise and sunset times through the study period (i.e., 21st June vs. 21st December). Dark grey shaded areas demonstrate hours of
darkness across the whole study.
ª2022 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. 41
S. Ball et al. Camera Traps Inform Airport Wildlife-Hazard
cover to avoid predation (Rehnus, 2014). However, by
design, Dublin Airport is devoid of diverse habitat which
could offer shelter to wildlife. With reports of increased
and altered bird and wildlife activity at airfields with
reduced air traffic (Schrimpf et al., 2021), the forming of
these groups could be a social response to perceived
increase in predation pressure at the airfield, with reduced
aircraft movements to deter predators. Alternatively,
reduced aircraft movements and consequential reduced
disturbance could have allowed for the formation of lei-
surely social groups.
Daily and seasonal activities are thought to be two beha-
vioural factors influencing mammal strike incidents with
aircraft (Schwarz et al., 2014). As mammal strike events are
becoming an increasing concern to the airline industry,
understanding these patterns could help to mitigate strike
risk at airfields by allowing for the targeted implementation
of strike mitigation measures. Such measures could include
increased runway patrols (e.g. Crain et al., 2015), species-
specific noise and light harassment (Biondi et al., 2011), or
giving prior warning to pilots operating during periods of
increased risk. While aircraft movement temporal activity
and volume are likely to play a role in strike patterns, we
found wildlife activity itself to be more closely associated
with strike patterns, highlighting the importance of under-
standing the ecology and life histories of the fauna using
the airfield environment. Hence, while we use data for the
Irish hare, these methods would be suitable for a cohort of
terrestrial mammals associated with airfields (e.g. canids,
Crain et al., 2015; ungulates, Biondi et al., 2011), assuming
that an adequate number of detections (~100; Lashley
et al., 2018) are obtained. High-quality data are vital to aid
wildlife strike prevention research on airfields and for other
modes of transportation within the sector (Steiner
et al., 2014). An added benefit of data collection through
the use of camera traps is the ability to capture the activities
of sympatric species using one piece of equipment which
can help inform risk and management decisions regarding
multiple species (Appendix 6).
Conclusion
Using predator–prey data analytical methods and relatively
inexpensive remote sensing equipment, we determined the
activity patterns of a mammal species inhabiting the airfield
in a large, international airport and identified periods of
increased risk. Strike events were more closely associated
with hare activity at the airfield rather than aircraft activity.
This demonstrates the importance of identifying and
understanding the wildlife populations utilising the airfield
environment. These data can be used to inform the devel-
opment of suitable mitigation strategies focussed on the
species of concern (as opposed to the near-impossible task
of altering aircraft activity) and to identify periods of
increased risk with other mammalian species at other air-
fields.
Acknowledgements
We gratefully acknowledge the contribution of the airfield
staff and airport authorities at Dublin Airport for providing
aircraft movement and hare strike data. Thanks to Gerry
Keogh, Jim Eviston, Bob Navan and Clemence Parneix of
the daa for project support and for providing bespoke data.
Thank you to the fire crew at Dublin Airport for field assis-
tance. Thank you to Thomas Kelly (UCC/daa) for the long-
term collation of hare strike data and as a project mentor.
To Allen Whitiker (UCC) for equipment support and tech-
nical guidance. This work was conducted as part of a PhD
studentship funded by the Irish Research Council (IRC) and
the daa in collaboration with University College Cork (pro-
ject EBPPG/2018/43). Open access funding provided by
IReL.
References
Allen, M., Poggiali, D., Whitaker, K., Marshall, T.R., van
Langen, J. & Kievit, R.A. (2021) Raincloud plots: a multi-
platform tool for robust data visualization [version 2; peer
review: 2 approved]. Wellcome Open Research,4, 63. https://
doi.org/10.12688/wellcomeopenres.15191.2
Altringer, L., Navin, J., Begier, M.J., Shwiff, S.A. & Anderson,
A. (2021) Estimating wildlife strike costs at US airports: a
machine learning approach. Transportation Research Part D:
Transport and Environment,97, 102907. https://doi.org/10.
1016/j.trd.2021.102907
Arrondo, E., Garc
ıa-Alfonso, M., Blas, J., Cortes-Avizanda, A.,
De la Riva, M., Devault, T.L. et al. (2021) Use of avian GPS
tracking to mitigate human fatalities from bird strikes
caused by large soaring birds. Journal of Applied Ecology,58,
1411–1420. https://doi.org/10.1111/1365-2664.13893
Askren, R.J., Dorak, B.E., Hagy, H.M., Eichholz, M.W.,
Washburn, B.E. & Ward, M.P. (2019) Tracking Canada
geese near airports: using spatial data to better inform
management. Human-Wildlife Interact.,13, 344–355. https://
doi.org/10.26077/yv1k-dg31
Ball, S., Butler, F., Caravaggi, A., Coughlan, N.E., Keogh, G.,
Callaghan, M.J.A.O. et al. (2021a) Hares in the long grass :
increased aircraft related mortality of the Irish hare (Lepus
timidus hibernicus) over a 30- year period at Ireland’s largest
civil airport. European Journal of Wildlife Research,67–80,
1–10. https://doi.org/10.1007/s10344-021-01517-y
Ball, S., Caravaggi, A. & Butler, F. (2021b) Runway roadkill: a
global review of mammal strikes with aircraft. Mammal
Review,1–16, 420–435. https://doi.org/10.1111/mam.12241
Biondi, K., Belant, J., Martin, J., DeVault, T. & Wang, G.
(2011) White-tailed deer incidents with U.S. civil aircraft.
42 ª2022 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.
Camera Traps Inform Airport Wildlife-Hazard S. Ball et al.
Wildlife Society Bulletin,35, 303–309. https://doi.org/10.
1002/wsb.46
Bolger, R. & Kelly, T. (2008) Wildlife Management Plan 2008.
Dublin, Ireland: Dublin Airport Authority.
Brivio, F., Bertolucci, C., Tettamanti, F., Filli, F., Apollonio,
M. & Grignolio, S. (2016) The weather dictates the rhythms:
alpine chamois activity is well adapted to ecological
conditions. Behavioral Ecology and Sociobiology,70, 1291–
1304. https://doi.org/10.1007/s00265-016-2137-8
Caravaggi, A., Gatta, M., Vallely, M., Hogg, K., Freeman, M.,
Fadaei, E. et al. (2018) Seasonal and predator-prey effects
on circadian activity of free-ranging mammals revealed by
camera traps. Peer J,6,1–27. https://doi.org/10.7717/peerj.
5827
Caravaggi, A., Montgomery, I. & Reid, N. (2017) Management
and control of invasive brown hares (Lepus europaeus):
contrasting attitudes of selected environmental stakeholders
and the wider rural community. Biology Environment,117B,
53–63. https://doi.org/10.3318/BIOE.2017.08
Caravaggi, A., Zaccaroni, M., Riga, F., Schai-Braun, S.C., Dick,
J.T.A., Montgomery, W.I. et al. (2016) An invasive-native
mammalian species replacement process captured by camera
trap survey random encounter models. Remote Sensing in
Ecology and Conservation,2,45–58. https://doi.org/10.1002/
rse2.11
Carswell, B.M., Rea, R.V., Searing, G.F. & Hesse, G. (2021)
Towards building a species-specific risk model for mammal-
aircraft strikes. Journal of Air Transport Management,15,
288–303.
Clair, C.C.S., Backs, J., Friesen, A., Gangadharan, A., Gilhooly,
P., Murray, M. et al. (2019) Animal learning may contribute
to both problems and solutions for wildlife-train collisions.
Philosophical Transactions of the Royal Society B,374,
20180050. https://doi.org/10.1098/rstb.2018.0050
Crain, A., Belant, J. & DeVault, T. (2015) Carnivore incidents
with U.S. civil aircraft. Transportation Research Part D,36,
160–166. https://doi.org/10.1016/j.trd.2014.12.001
Dolbeer, R. & Begier, M.J. (2021) Strikes to civil aircraft in the
United States, 1990–2019. Washington, DC: Federal Aviation
Administration. U.S Department of Agriculture, Animal and
Plant Health Inspection Service, Wildlife Services.
Dorsey, B.P., Clevenger, A. & Rew, L.J. (2017) Relative risk
and variables associated with bear and ungulate mortalities
along a railroad in the Canadian Rocky Mountains. In:
Borda-de-
Agua, L., Barrientos, R., Beja, P. & Pereira, H.M.
(Eds.) Railway Ecology. Cham, Switzerland: Springer, pp.
135–155.
FAA. (2021) National wildlife strike database. Washington, DC:
Federal Aviation Administration. https://wildlife.faa.gov/
home
Franke, M. & John, F. (2011) What comes next after
recession? - Airline industry scenarios and potential end
games. Journal of Air Transport Management,17,19–26.
https://doi.org/10.1016/j.jairtraman.2010.10.005
Garrote, G., P
erez De Ayala, R.,
Alvarez, A., Mart
ın, J.M.,
Ruiz, M., De Lillo, S. et al. (2019) Improving the random
encounter model method to estimate carnivore densities
using data generated by conventional camera-trap design.
Oryx,1–6,99–104. https://doi.org/10.1017/
S0030605318001618
Haigh, A. (2012) Annual patterns of mammalian mortality on
irish roads. Hystrix,23,58–66. https://doi.org/10.4404/
hystrix-23.2-4747
Hoffmann, J., Palme, R. & Eccard, J.A. (2018) Long-term dim
light during nighttime changes activity patterns and space
use in experimental small mammal populations.
Environmental Pollution,238, 844–851. https://doi.org/10.
1016/j.envpol.2018.03.107
Hofmeester, T.R., Young, S., Juthberg, S., Singh, N.J.,
Widemo, F., Andr
en, H. et al. (2020) Using by-catch data
from wildlife surveys to quantify climatic parameters and
timing of phenology for plants and animals using camera
traps. Remote Sensing in Ecology and Conservation,6, 129–
140. https://doi.org/10.1002/rse2.136
Ikeda, T., Takahashi, H., Igota, H., Matsuura, Y., Azumaya,
M., Yoshida, T. et al. (2019) Effects of culling intensity on
diel and seasonal activity patterns of sika deer (Cervus
nippon). Scientific Reports,9,1–8. https://doi.org/10.1038/
s41598-019-53727-9
Jachowski, D.S., Katzner, T., Rodrigue, J.L. & Ford, W.M.
(2015) Monitoring landscape-level distribution and
migration Phenology of Raptors using a volunteer camera-
trap network. Wildlife Society Bulletin,39, 553–563. https://
doi.org/10.1002/wsb.571
Jaren, V., Andersen, R., Ulleberg, M., Pedersen, P. & Wiseth,
B. (1991) Moose-train collisions: the effects of vegetation
removal with a cost-benefit analysis. Alces,27,93–99.
Kelly, T.C., Sleeman, P., Coughlan, N.E., Dillane, E. &
Callaghan, M.J. (2017) Bat collisions with civil aircraft in
the Republic of Ireland over a decade suggest negligible
impact on aviation safety. European Journal of Wildlife
Research,63,23–26. https://doi.org/10.1007/s10344-017-
1081-x
Kitowski, I. (2016) The impact on aviation operations at
Polish civil airfields caused by mammals. Bezpiecze
nstwo i
Technika Po_
zarnicza,42,57–63. https://doi.org/10.12845/
bitp.42.2.2016.5
Laist, D.W., Knowlton, A.R., Mead, J.G., Collet, A.S. &
Podesta, M. (2001) Collisions between ships and whales.
Marine Mammal Science,17,35–75. https://doi.org/10.1111/
j.1748-7692.2001.tb00980.x
Lashley, M.A., Cove, M.V., Chitwood, M.C., Penido, G.,
Gardner, B., Deperno, C.S. et al. (2018) Estimating wildlife
activity curves: comparison of methods and sample size.
Scientific Reports,8,1–11. https://doi.org/10.1038/s41598-
018-22638-6
Lendrum, P.E., Crooks, K.R. & Wittemyer, G. (2017) Changes
in circadian activity patterns of a wildlife community post
ª2022 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. 43
S. Ball et al. Camera Traps Inform Airport Wildlife-Hazard
high-intensity energy development. Journal of Mammalogy,
98, 1265–1271. https://doi.org/10.1093/jmammal/gyx097
Łopucki, R., Kitowski, I., Perli
nska-Teresiak, M. & Klich, D.
(2021) How is wildlife affected by the covid-19 pandemic?
Lockdown effect on the road mortality of hedgehogs.
Animals,11,1–8. https://doi.org/10.3390/ani11030868
Meijer, J.H., Michel, S., VanderLeest, H.T. & Rohling, J.H.T.
(2010) Daily and seasonal adaptation of the circadian clock
requires plasticity of the SCN neuronal network. The
European Journal of Neuroscience,32, 2143–2151. https://doi.
org/10.1111/j.1460-9568.2010.07522.x
Meredith, M. & Ridout, M. (2021) Overlap: estimates of
coefficient of overlapping for animal activity patterns.https://
cran.r-project.org/web/packages/overlap/overlap.pdf
MET Eireann. (2021). Dublin airport monthly data [WWW
Document]. https://www.met.ie/climate/available-data/
monthly-data (accessed 9.20.21).
Montemaggiori, A. (2021) Wildlife strike hazard and lockdown
at Italian airports. In: Proceedings of the Presented at the
World Bird Strike Virtual Conference. Virtual: World
Birdstrike Association.
Monterroso, P., Alves, P.C. & Ferreras, P. (2014) Plasticity in
circadian activity patterns of mesocarnivores in
Southwestern Europe: Implications for species coexistence.
Behavioral Ecology and Sociobiology,68, 1403–1417. https://
doi.org/10.1007/s00265-014-1748-1
Mumme, R.L., Schoech, S.J., Woolfenden, G.E. & Fitzpatrick,
J.W. (2000) Life and death in the fast lane: demographic
consequences of road mortality in the Florida Scrub-Jay.
Conservation Biology,14, 501–512. https://doi.org/10.1046/j.
1523-1739.2000.98370.x
Pereira, J.A. (2010) Activity pattern of Geoffroy’s cats
(Leopardus geoffroyi) during a period of food shortage.
Journal of Arid Environments,74, 1106–1109. https://doi.org/
10.1016/j.jaridenv.2010.03.017
Phillips, A.J.K., Fulcher, B.D., Robinson, P.A. & Klerman, E.B.
(2013) Mammalian rest/activity patterns explained by
physiologically based modeling. PLoS Computational Biology,
9, e1003213. https://doi.org/10.1371/journal.pcbi.1003213
Pokorny, B., Cerri, J. & Bu
zan, E. (2022) Wildlife roadkill and
COVID-19: a biologically significant, but heterogeneous,
reduction. Journal of Applied Ecology,59(5) 1291–1301.
Popp, J.N. & Boyle, S.P. (2017) Railway ecology:
Underrepresented in science? Basic and Applied Ecology,19,
84–93. https://doi.org/10.1016/j.baae.2016.11.006
R Core Team. (2021) R: a language and environment for
statistical computing. Vienna, Austria: R Foundation for
Statistical Computing.
Rehnus, M. (2014) The 24-hour cycle of the mountain hare
Lepus timidus Linnaeus, 1758. Zoological Garten,83, 140–
145. https://doi.org/10.1016/j.zoolgart.2014.10.001
Reid, N., McDonald, R.A. & Montgomery, W.I. (2010)
Homogeneous habitat can meet the discrete and varied
resource requirements of hares but may set an ecological
trap. Biological Conservation,143, 1701–1706. https://doi.
org/10.1016/j.biocon.2010.03.041
Ross, J., Hearn, A.J., Johnson, P.J. & Macdonald, D.W. (2013)
Activity patterns and temporal avoidance by prey in
response to Sunda clouded leopard predation risk. Journal of
Zoology,290,96–106. https://doi.org/10.1111/jzo.12018
Scheideman, M., Rea, R., Hesse, G., Soong, L., Green, C.,
Sample, C. et al. (2017) Use of wildlife camera traps to aid
in wildlife management planning at airports. Journal of Air
Transport Management,11, 408–419.
Schrimpf, M.B., des Brisay, P.G., Johnston, A., Smith, A.C.,
S
anchez-Jasso, J., Robinson, B.G. et al. (2021) Reduced
human activity during COVID-19 alters avian land use
across North America. Science Advances,7,1–12. https://doi.
org/10.1126/sciadv.abf5073
Schwartz, A.L., Shilling, F.M. & Perkins, S.E. (2020) The value
of monitoring wildlife roadkill. European Journal of Wildlife
Research,66(1), 1–12. https://doi.org/10.1007/s10344-019-
1357-4
Schwartz, A.L., Williams, H.F., Chadwick, E., Thomas, R.J. &
Perkins, S.E. (2018) Roadkill scavenging behaviour in an
urban environment. Journal of Urban Ecology,4(1), juy006.
https://doi.org/10.1093/jue/juy006
Schwarz, K., Belant, J., Martin, J., DeVault, T. & Wang, G.
(2014) Behavioral traits and airport type affect mammal
incidents with U.S. civil aircraft. Environmental
Management,54, 908–918. https://doi.org/10.1007/s00267-
014-0345-4
Steiner, W., Leisch, F. & Hackl€
ander, K. (2014) A review on
the temporal pattern of deer-vehicle accidents: impact of
seasonal, diurnal and lunar effects in cervids. Accident;
Analysis and Prevention,66, 168–181. https://doi.org/10.
1016/j.aap.2014.01.020
UKCAA Safety Regulation Group CAP. (2008) CAP 772
Birdstrike Risk Management for Aerodromes. London, UK.
Viviano, A., Mori, E., Fattorini, N., Mazza, G., Lazzeri, L.,
Panichi, A. et al. (2021) Spatiotemporal overlap between the
European brown hare and its potential predators and
competitors. Animals,11, 562. https://doi.org/10.3390/
ani11020562
Wolfe, A. & Hayden, T.J. (1996) Home range sizes of Irish
mountain hares on coastal grassland. Biology Environment,
96, 141–146.
Wong, B.B.M. & Candolin, U. (2015) Behavioral responses to
changing environments. Behavioral Ecology,26, 665–673.
https://doi.org/10.1093/beheco/aru183
Wright, P.G.R., Coomber, F.G., Bellamy, C.C., Perkins, S.E. &
Mathews, F. (2020) Predicting hedgehog mortality risks on
British roads using habitat suitability modelling. PeerJ,2020,
1–22. https://doi.org/10.7717/peerj.8154
York, D.L., Cummings, J.L. & Wedemeyer, K.L. (2000)
Movements and distribution of radio-collared canada geese
in Anchorage, Alaska. Northwest Nature,81, 11. https://doi.
org/10.2307/3536894
44 ª2022 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.
Camera Traps Inform Airport Wildlife-Hazard S. Ball et al.
Zanni, M., Brivio, F., Grignolio, S. & Apollonio, M. (2021)
Estimation of spatial and temporal overlap in three ungulate
species in a Mediterranean environment. Mammal Research,
66, 149–162. https://doi.org/10.1007/s13364-020-00548-1
Ziege, M., Babitsch, D., Brix, M., Kriesten, S., Straskraba, S.,
Wenninger, S. et al. (2016) Extended diurnal activity
patterns of European rabbits along a rural-to-urban
gradient. Mammalian Biology,81, 534–541. https://doi.org/
10.1016/j.mambio.2016.07.002
Supporting Information
Additional supporting information may be found online
in the Supporting Information section at the end of the
article.
Appendix 1. Images demonstrating that camera traps
were effective at capturing activity during hours of day-
light (top) and darkness (bottom).
Appendix 2. Minimum Metadata Standards for camera
trap deployment. This is in in Excel format attached sepa-
rately.
Appendix 3. camera trap deployment log.
Appendix 4. Overlap estimates of hare and aircraft activ-
ity for sampling periods prior to the Covid-19 pandemic.
Appendix 5. Overlap estimates of aircraft activity and
recorded hare strike events (2012–2021) temporal overlap.
Appendix 6. An example of how the methodology is
applicable to other terrestrial mammal species.
ª2022 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. 45
S. Ball et al. Camera Traps Inform Airport Wildlife-Hazard
Available via license: CC BY-NC 4.0
Content may be subject to copyright.
