Hunting exacerbates the response to human disturbance in large
herbivores while migrating through a road network
DALE G. PATON,1 SIMONE CIUTI,2, MICHAEL QUINN,3 AND MARK S. BOYCE4
1

Anatum Ecological Ltd., Crowsnest Pass, Alberta T0K 0E0 Canada
Department of Biometry and Environmental System Analysis, University of Freiburg, Freiburg 79106 Germany
3
Research, Scholarship & Community Engagement, Mount Royal University, Calgary, Alberta T3E 6K6 Canada
4
Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2E9 Canada

2

Citation: Paton, D. G., S. Ciuti, M. Quinn, and M. S. Boyce. 2017. Hunting exacerbates the response to human
disturbance in large herbivores while migrating through a road network. Ecosphere 8(6):e01841. 10.1002/ecs2.1841

Abstract. Migrations of large ungulates are globally threatened in environments affected by increasing
human disturbance, rising large carnivore predation, deteriorating habitat quality, and changing climate. Animals migrating outside of protected areas can be exposed to greater human pressure, and this effect can be
stronger when humans are perceived to be a predation risk, such as during hunting seasons. Using four consecutive years of satellite telemetry data (n = 138 migration events), we compared habitat selection, movement,
and behavior of a large partially migratory herbivore while migrating through a heterogeneous landscape in
spring and fall. We tested the hypothesis that fall hunting exacerbates the response of a large herbivore
exposed to human disturbance while migrating through a road network. All elk (Cervus elaphus) selected
greater forest cover, reduced movement rates, and avoided roads during fall-day than in any other season or
time of day. Avoidance of roads was reduced during spring at night, for example, the time period of no hunting with fewest people on roads. Elk using stopovers in fall displayed different seasonal and diurnal behaviors
between sexes in response to the disturbance. Females used steeper terrain during fall-day and males did not
use this strategy in fall. Male avoidance of roads was much stronger than females during fall-day and males
were less likely to cross a road during fall. Such responses are probably linked to higher hunting pressure on
males vs. females. Finally, we found that elk spent more time feeding during spring migration compared to the
fall migration and elk vigilance was >3 times higher in the fall hunting season. Our results provide insights into
the effect of fear of humans on the ecology of both sexes of a migrating large herbivore when using stopovers.
Such changes in behavior and stopover use might affect animal fitness by decreasing foraging, cause displacement from high-quality habitats, or affect the permeability of migration route stopovers.
Key words: Cervus elaphus; elk; hunting; migration; permeability; predation risk; radiotelemetry; resource selection
functions; stopover; vehicle traffic; vigilance.
Received 29 March 2017; accepted 19 April 2017. Corresponding Editor: James W. Cain III.
Copyright: © 2017 Paton et al. This is an open access article under the terms of the Creative Commons Attribution
License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
E-mail: simone.ciuti@biom.uni-freiburg.de

INTRODUCTION

declines of large ungulate migration on several
continents due to human developments and
disturbance (Berger 2004, Ito et al. 2006, Bolger
et al. 2008, Festa-Bianchet et al. 2011). To
conserve migration, researchers have begun to
investigate the effects of habitat loss, human disturbance (Hebblewhite et al. 2006, Voeten et al.
2010), and climate change (Walther et al. 2002,
Monteith et al. 2011) on migratory behavior.

Migratory species can be important drivers of
ecosystem processes (McNaughton 1994, Augustine and McNaughton 1998, Milner-Gulland
et al. 2011), provide economic and social values
to humans (Vors and Boyce 2009), and can serve
as flagship species for conservation (Thirgood
et al. 2004). There is growing concern over
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PATON ET AL.

on prey in multicarnivore ecosystems, indirectly
affecting both prey and carnivore movement and
behavior (Valeix et al. 2012, Schmidt and Kuijper
2015) and possibly compromising fitness (Hern

andez and Laundr
e 2005, Creel et al. 2007, Christianson and Creel 2010—but see White et al. 2011,
Middleton et al. 2013a, b). Such human-driven risk
effects on prey might be at least as significant as
effects of large carnivores (Creel and Christianson
2008), including changes in habitat use (Hebblewhite et al. 2006, Lone et al. 2015), foraging
behavior, group size, and physiology (Caro 2005).
Many have studied indirect antipredator risks
associated with human hunting, which occurs in
numerous regions throughout the world (Proffitt
et al. 2009, Ciuti et al. 2012a, b, Barnier et al. 2014,
Lone et al. 2014). None, however, have investigated these risks during animal migration. Our
study provides empirical data into how human
disturbance affects the spatial and behavioral ecology of migrating ungulates.
We analyzed a unique dataset that combines
habitat selection and movement rates of radiotracked elk migrating through an intensively used
road network, data on vehicle traffic on these
roads, and elk behavioral observations to test the
effect of human disturbance on elk behavior during migration. This partially migrating population
of elk travels from winter to summer ranges in
spring, when hunting is not permitted, and from
summer to winter ranges in fall, when hunting is
allowed (Ciuti et al. 2012a). We compared male
and female elk movements, habitat selection, and
their behavior during fall/spring seasons, and
day/night time periods, with hunting or no hunting activity, representing numerous or few
humans, respectively. Our study provided a
unique opportunity to investigate the effect of
human disturbance on elk behavior when
humans compose a real threat compared to when
they are not. We evaluated the hypothesis that
hunting intensifies the response to human disturbance by this large herbivore while migrating
through a road network in an unprotected area.
We predicted that when elk migrate during fall
hunting season, they would adopt more cautious
behavior. We also predicted that behavioral
responses to humans would be stronger during
the day, when humans are actually present on
roads, compared to night, when human presence
in the area is significantly lower.

Several recent studies have investigated the disruption to migratory behavior (Bolger et al. 2008,
Holdo et al. 2011, Lendrum et al. 2012) and the
primary mechanisms of animal migration (Mysterud et al. 2001, Hebblewhite and Merrill 2007,
Voeten et al. 2010, Milner-Gulland et al. 2011).
These combined stressors can result in negative
cumulative impacts to migrating animals by creating impermeable or semi-permeable movement
barriers, reduced use of migration routes, or
increased movement rates (Sawyer et al. 2013),
which ultimately might result in population
effects (Leblond et al. 2013, Middleton et al.
2013a). When animals migrate, they must balance access to essential resources with exposure
to risk from predation and anthropogenic disturbances (Fryxell 1991). In areas of increasing
human disturbance, it is a conservation challenge
to provide suitable landscape conservation and
management strategies for migratory populations (Bolger et al. 2008). One of these challenges
is a shortage of research regarding key elements
of herbivore behavioral responses to human disturbance (Alerstam 2006, Morales et al. 2010).
Our research contributes insights into how
human disturbance outside of protected areas
can affect ungulate behavior during migration.
Some of the most widespread modifications of
the natural landscape have been from the construction and maintenance of roads (Forman 2003,
Beckmann et al. 2012). On the one hand, large herbivores in national parks often seem undisturbed
by roads, habituated to traffic and people, and
attracted by roadside vegetation and the protection from predators in areas near roads and
human settlements (Rogala et al. 2011, Goldberg
et al. 2014). Protected areas, however, are only a
small fraction of wildlife habitat worldwide, and
for ungulate populations outside parks, particularly those in regions facing hunting pressure,
roads and high-traffic volumes have been shown
to alter animal spatial behavior and distribution
(Rowland et al. 2000, Frair et al. 2008, Bonnot
et al. 2013). Hunting and other sources of human
disturbance can cause large herbivores to respond
to human-caused stimuli such as approaching
vehicles as if these stimuli were predatory threats,
causing animals to exhibit risk avoidance behaviors (Remis and Kpanou 2011, Ciuti et al. 2012b,
Barnier et al. 2014). Hunting can be a significant
contributor to the cumulative predation pressure
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PATON ET AL.

METHODS

and hunting. However, during the last decade,
there has been increasing human disturbance from
recreational activities and resource extraction
resulting in an increase in infrastructure, human
activity, road density, and traffic volume on roads.
Road densities in the Castle-Carbondale are at
levels known to affect elk movements (0.55 km/
km2; Rowland et al. 2000, Frair et al. 2008). In the
Castle-Carbondale, predation risk is from a variety
of carnivores including grizzly bears (Ursus arctos),
black bears (Ursus americanus), cougars (Puma concolor), wolves (Canis lupus), lynx (Lynx canadensis),
and coyotes (Canis latrans). Cougars and wolves in
this area are responsible of preying upon approximately 5% of the elk (figure estimated by collecting data on mortality causes in 182 radiocollared
animals monitored by the montane elk research
program in this area, http://montaneelk.com/),
mostly during winter. Grizzly bears and black
bears also pose a predation risk, especially to
calves during spring (Griffin et al. 2011). For an
overview on predator distribution and predator
pressure on elk, see previous research conducted
in the study area (Morehouse and Boyce 2011,
Muhly 2011, Muhly et al. 2011). However, human
hunting is the largest source of elk mortality in this
region (Ciuti et al. 2012a, Paton 2012). Roughly
50% of males but <20% of females were shot in
our long-term monitoring program, the former
being more heavily targeted than the latter (Ciuti
et al. 2012a). The 110-d male elk-hunting season
(archery and rifle, early September until December) is controlled by hunting regulations, with few
access restrictions to the public land where elk
migratory corridors are located. Hunting of female
elk occurs for 51 d with harvest managed by
lottery for a limited number of permits.

Animal care and compliance
Our data collection complied with all relevant
federal laws of Canada and provincial laws of
Alberta. Procedures adopted in this study were
reviewed and approved by the University of
Alberta Animal Care and Use Committee—Biosciences (Animal care protocol no. 536-1003 AR
University of Alberta, Edmonton, Canada), by all
jurisdictions of the Alberta Government (Permit Numbers: BI-2008-19, RC-06SW-001, and
23181CN), and by Parks Canada (Permit Numbers: WL-2010-7292, WL-2010-5755).

Study site and elk population
Our study occurred during four consecutive
years (2007–2010) within a montane ecosystem
along the eastern slopes of the Rocky Mountains in
southwest Alberta, Canada. This is a diverse landscape, ranging from flat agricultural grasslands in
the east, to mixed conifer/deciduous forests and
mountains in the west. Based on location of winter
ranges, spatial distribution of migratory corridors,
and summer range locations, elk sub-populations
are commonly identified as “herds” by local managers (Paton 2012). Seven main herds are known
and have been monitored in this region by our
large-scale telemetry program (Ciuti et al. 2012a,
Paton 2012). Here, we focused on the Castle-Carbondale herd, that is, a herd that we monitored
intensively from 2007 to 2010 and for which we
gathered detailed information about human use
and vehicle traffic on roads (Ciuti et al. 2012b).
The annual home range of the Castle-Carbondale herd of southwest Alberta is an area under
multiple jurisdictions administered by the provinces of Alberta and British Columbia. The
winter range includes both private and provincial
(i.e., public) land of Alberta, whereas migratory
corridors and summer home ranges are on
provincial lands in Alberta and to a lesser extent
in British Columbia. Cattle ranching constituted
the dominant land use on private land. Activities
in the public land also included cattle grazing and
natural gas extraction as well as recreational use
including camping, all-terrain vehicle (ATV) use,
hunting, fishing, and hiking (Ciuti et al. 2012b).
Elk in this region have experienced decades of
disturbance by timber harvesting, natural gas
extraction, cattle grazing, off-highway vehicle use,
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Vehicle traffic data
We deployed 52 traffic counters (Diamond Traffic Products; Apollo, Oakridge, Oregon, USA)
randomly distributed on trails and a variety of
road types (i.e., paved roads, gravel roads,
unimproved roads, truck trails, and ATV trails).
Moreover, 21 trail cameras (Silent Image RM30;
RECONYX, Creekside, Wisconsin, USA) were
deployed at randomly selected locations on roads
and trails. Trail cameras provided time-stamped
photographs of motorized use that triggered the
camera’s infrared sensor. Using both traffic counter and trail-camera data, Northrup et al. (2012)
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PATON ET AL.

and Ciuti et al. (2012b) modeled traffic volume for
the entire road network in our study site. We
obtained estimates of traffic volumes for spring
and fall migration, during the day and night.
According to Ciuti et al. (2012b), nighttime was
calculated on a monthly basis and was the time
between the average sunset and average sunrise
for each month. Likewise, daytime was the time
between the average sunrise and average sunset.
Based on trail-camera data, 95% of motorized traffic recorded within public lands, that is, where
migratory corridors were located, was related to
recreational activities (cars, trucks, Recreational
Vehicles: 80%; ATVs such as quads and motorbikes: 15%), whereas only 5% was linked to industrial activities (ranching and natural gas extraction).

higher than fall migration because animal numbers were reduced by fall hunting. We used a
Brownian bridge movement model (BBMM;
Horne et al. 2007) to estimate individual elk utilization distribution for each seasonal migration. A
sequence pathway of two-hour radiocollar GPS
relocation data was collected between winter and
summer home ranges during each spring and fall
migration (Sawyer et al. 2009) to produce a
BBMM graphic of stopovers for each elk.
Using the BBMMs, we were able to isolate stopover sites from steps taken to move from a stopover site to another. However, our data differed
from, for example, mule deer migration and stopover use in Wyoming (Sawyer et al. 2009), which
is characterized by stopover sites connected by
long-distance movement paths. The Castle-Carbondale elk migration is a relatively slow process
(20 d, on average), and the distance between elk
stopovers was relatively short (<4 km distant
from one location to the next one, on average)
with 99% of relocations clustered in stopovers.
During our study, the two-hour GPS telemetry
relocation schedule was not able to capture a sufficient sample size for analyses of step-by-step
movements connecting two consecutive stopover
sites. Therefore, we primarily describe habitat
selection within stopovers, that is, resting/feeding
hotspots along the migratory corridors.
We estimated fine-scale resource selection
functions (RSFs) for individual elk (Manly et al.
2002). We matched relocations (used) with random points within a 660 m radius buffer
(matched-pair or conditional logistic regression
approach; Duchesne et al. 2010). This allowed us
to consider true available habitats for elk migrating along migratory corridors. Each observed
location was paired with 10 random locations
within the 660 m radius circle (a distance >90%
of observed distances traveled between two-hour
relocations). Based on the spatial location, elk
used and random points were associated with a
set of environmental predictors: canopy cover
(cc, percentage), aspect (aspect, cos-transformed),
terrain ruggedness (r, in meters sensu (Riley
et al. 1999), distance to the closest road (d, in m),
traffic on the closest road (tr, vehicle/h). We
screened covariates for collinearity using the
Pearson correlation coefficient, and for multicollinearity using the variance inflate factor
(|rp| < 0.7; VIF 
 3).

Elk captures and collection of GPS fixes
We used a helicopter and net-gun to capture 50
Castle-Carbondale elk during winter. Males
(n = 12) were 1½-year-old at captures, whereas
female (n = 38) aged 1–18-year-old (Ciuti et al.
2012a). Elk were blindfolded and hobbled to allow
collaring and sampling with low impact to the
elk. Female elk were equipped with 4400M GPS
(Lotek, Newmark, Ontario, Canada) and GEN4GPS (Telonics, Mesa, Arizona, USA) collars,
whereas males were fitted with 4400 GPS/Argos
collars (Lotek). GPS units were programmed to
obtain location fixes every two hours. Female relocations were remotely downloaded in the field,
whereas male data were received via email
through the ARGOS system. All radiocollars were
outfitted with a remote drop-off device programmed to disengage after 104 weeks. If the
device failed, elk were recaptured by helicopter
using a net-gun to retrieve the collars. All of the
50 radiocollars deployed to monitor the CastleCarbondale elk were successfully retrieved.

Analysis of habitat selection by elk during
migration
All data management and analyses were done
using R (R Core Team 2015). During 2007–2010,
we obtained 31,332 GPS fixes from the 50 migratory elk (n = 12 males and n = 38 females) traveling along migratory corridors (excluded summer
and winter ranges) on public land. We eventually
analyzed 138 migration events (73 spring and 65
fall, of which 111 were from females and 27 were
from males). Spring-migration sample sizes were
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PATON ET AL.

RSF ¼ wðxÞ ¼ expðb1 x1 þ b2 x2    þ bn xn Þ

We generated a set of a priori resource selection models established on biological assumptions (Table 1, two-first columns from left).
Based on exploratory analyses and screening for
non-linear relationships, we allowed for quadratic effects in canopy cover and ruggedness. For
each individual, the resource selection model
was fit using conditional logistic regression
(binary response variable: 0 = available, 1 =
used) using the survival package (Therneau and
Grambsch 2000). Models were fit during spring
(day, night) and fall (day, night). Using Akaike’s
information criterion (AIC), we selected the topranked model for each individual. Once we
obtained parameter estimates for each individual
conditional logistic regression, we averaged parameters for males and females separately to make
inference on selection patterns at the population
level (Fieberg et al. 2010). Some of the individual
models did not include one or more parameter estimate, because excluded by model selection, and
thus contributed with b = 0 to the mean. Following
these procedures, we obtained averaged male and
female beta (b) coefficients to estimate the final
RSFs (Manly et al. 2002). In the parametric case, a
RSF is an exponential function given samples of
used and available resource units of the form

which corresponds to the frequency of used
resource units divided by the frequency of
available resource units for any environmental
covariate, xi.

Analysis of movement and behavioral data of elk
during migration
Step length (i.e., distance between two-hour
telemetry relocations) is a proxy of elk mobility
(Ciuti et al. 2012a). We derived step length using
ARCMAP (ESRI, Redlands, California, USA)
combined with GME (http://www.spatialecol
ogy.com/gme/) and R (R Core Team 2015), and
we used these data to model movement rate during spring and fall migration. We modeled the
variation in step lengths (log-transformed, in
meters) by fitting a linear mixed model (lme4
package, Bates et al. 2015) with individual elk as
random intercept. We fitted the following predictors as fixed effects and interactions terms: sex
(males, females), season (spring, fall), time period
(day, night), terrain ruggedness (including quadratic effect), canopy cover (including quadratic
effect), distance to the closest road (log-transformed, including quadratic effect), traffic on the

Table 1. A priori model structures used to model male (M, n = 12) and female (F, n = 38) elk individual-level
resource selection during spring (daytime, nighttime) and fall (daytime, nighttime) migrations.

Candidate model name
mainly environment
mainly antipredator
humans only (1)
humans only (2)
environment and humans (1)
environment and humans (2)
antipredator and humans (1)
antipredator and humans (2)
environment, antipredator,
and humans (1)
environment, antipredator,
and humans (2)
“no humans” directly
involved
“humans” involved

Spring
day (%)

Spring
night (%)

Fall-day
(%)

Fallnight (%)

Candidate model structure

F

M

F

M

F

M

F

M

cc + cc2 + aspect
r + r2 + cc + cc2
d
d + tr
cc + cc2 + aspect + d
cc + cc2 + aspect + d + tr
r + r2 + cc + cc2 + d
r + r2 + cc + cc2 + d + tr
r + r2 + cc + cc2 + aspect + d

0
38
5
0
0
0
24
17
11

8
33
0
0
0
8
18
25
8

0
35
3
0
5
0
11
21
17

0
33
8
0
0
0
25
17
17

3
16
8
0
5
3
32
19
11

0
12
0
0
0
0
25
25
38

3
27
0
3
13
5
16
23
5

12
13
0
0
0
12
38
25
0

r + r2 + cc + cc2 + aspect + d + tr

5

0

8

0

3

0

5

0

d and/or tr not included in
the model
d and/or tr included in the model

38

41

35

33

19

12

30

25

62

59

65

67

81

88

70

75

Notes: Figures represent the percentages of a priori structures selected as top-ranked models (using the Akaike’s Information Criterion) in elk individual modeling. Bold lines summarize the percentages of selected model structures that included
or did not include road-related predictors, respectively. cc, canopy cover (%); cc2, quadratic effect for canopy cover (%);
aspect, aspect (cos-transformed); r, terrain ruggedness (m); r2, quadratic effect for terrain ruggedness (m); d, distance from the
closest road (m); tr, traffic on the closest road (vehicle/h).

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PATON ET AL.

until they were no longer visible, or they began to
rest, whichever came first. Behavioral states (feeding, vigilance, traveling, grooming, and aggression) were recorded using a voice recorder (Ciuti
et al. 2012b). We calculated vigilance rate (vigilance bouts/min), the proportion of time being vigilant (time vigilance/time active), the proportion of
time traveling (time traveling/total time), and the
proportion of time feeding (time feeding/total time
active, i.e., foraging efficiency) for each individual.
Data used in this study are a subsample from our
previous research (Ciuti et al. 2012b) and represent
154 focal individuals (n = 59 adult females without calf, n = 40 adult females with calf, n = 25
yearling males, and n = 30 yearling females).
Our observations were designed to record vigilance behavior, that is, elk standing with the head
at or above the shoulder level while looking for
potential threats. Given the distance between
observers and focal elk, we cannot exclude that
part of the vigilance behavior might have been
spent in chewing activities, enabling the elk to
optimize the time budget by combining antipredator behavior with feeding-related activity. However, the vigilance bouts recorded for our sample
population were always longer than a simple
scanning event of the landscape, suggesting an
actual loss of opportunities for feeding activities.
Indeed, mean length of vigilance bouts was 89.4 s
(standard error [SE] = 7.3 s), much longer than
the time that would be needed (e.g., few seconds)
by the animal to chew the grass and scan the landscape prior to taking a new food bite.
We tested for differences in the four behavioral
metrics (vigilance rate; the proportion of time
being vigilant, arcsin-square-root-transformed;
the proportion of time traveling, arcsin-squareroot-transformed; and the proportion of time
feeding, arcsin-square-root-transformed) by fitting
four linear models with season (spring, fall), herd
size, and age–sex class (four levels) as predictors.

closest road (log-transformed, including quadratic effect), the three-way interaction time period 9
season 9 sex, and related two-way interactions.
All numerical predictors were scaled prior to fitting the model [(x  mean)/standard deviation]
to achieve model stability and convergence in the
mixed modeling (which also applies to the mixed
model described below).
The likelihood or road crossing was modeled
by fitting a generalized linear mixed-effect model
(binomial distribution of error; binary response
variable: 1 = step walked by elk every two hours
crossing a road, and 0 = step not crossing a road)
with individual elk as random intercept. We fitted the following as fixed effects and interaction
terms: sex (males, females), season (spring, fall),
time period (day, night), terrain ruggedness
(including quadratic effect), canopy cover (including quadratic effect), traffic on the closest road
(log-transformed, including quadratic effect), the
three-way interaction time period 9 season 9
sex, and related two-way interactions.
Elk behavioral observations were carried out
during a more comprehensive field study carried
out across herds in southwest Alberta (Ciuti et al.
2012b). During spring and fall migration, elk
observations were carried out at dawn and dusk
using binoculars (10 9 50) and spotting scopes
(25–40 9 60) to observe elk within open areas of
the Castle-Carbondale area. Observations were
performed by the same two observers from roads
without leaving the vehicle at a distance always
>500 m. We recorded date, time, location, group
size, and sex and age class composition for each
group observed within the Castle-Carbondale
herd. We collected data on female-dominated
herds only (i.e., males <50%; Childress and Lung
2003). The position of each group was assessed
with the combined use of a GPS (eTrex Legend;
Garmin International, Olathe, Kansas, USA), a
compass, and a rangefinder (Elite 1600 ARC;
Bushnell, Overland Park, Kansas, USA). We
divided elk into four age–sex classes (adult
females with calf, adult females without calf, male
and female yearling). Individual vigilance in randomly selected elk was estimated by a focal animal sampling rule and a continuous recording
rule (Martin et al. 1993). To avoid observation of
the same individual elk more than once, only one
to four individuals were observed in each group.
Each focal individual was observed for 15 min,
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RESULTS
Elk migratory patterns
We analyzed 138 migration events (73 spring
and 65 fall) in 50 elk (n = 12 males and n = 38
females, Table 2). Pooled data from all years
resulted in a mean duration for migration of
roughly 3 weeks in both spring and fall, although
there was high individual variability (Table 2).
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PATON ET AL.
Table 2. Characteristics of the migratory behavior in 50 elk (38 females, 12 males) monitored with satellite
telemetry from 2007 to 2010 in the Castle-Carbondale region, southwest Alberta.
Season

Migration
length† (days)

Walked
distance‡ (km)

Linear
displacement§ (km)

Number of
stopovers¶

Sojourn linear
distance†† (km)

Fall
Spring

21.3  2.0 (1–68)
19.7  1.5 (2–65)

60.1  4.4 (11–163)
72.5  4.7 (9–252)

18.0  0.7 (5–32)
20.0  0.7 (6–34)

7.2  0.4 (1–17)
7.7  0.4 (2–17)

3.6  0.4 (0.4–21.3)
3.4  0.3 (0.7–13.3)

Notes: Mean  standard error are shown along with range of variability within parentheses (minimum–maximum). Fall migrations started on average on 1st November (range: 23rd August–15th December) and ended on 19th November (4th September–1st
January). Spring migrations started on average on 3rd May (19th March–17th June) and ended on 23rd May (2nd April–29th June).
† Defined as the time (in days) occurring between the first and the last satellite relocations recorded along the migration
corridor, thus excluding relocations inside the winter and the summer ranges.
‡ Defined as the sum of the linear distances connecting consecutive satellite relocations recorded along the migratory corridor.
§ Defined as the linear distance between the first and the last relocations recorded along the migratory corridor.
¶ Identified by fitting Brownian bridge movement models on satellite relocations during migration.
†† Defined as the distance between two successive stopover sites.

High among-individual variability in migration
duration was likely linked to high variability in
the distance between individual winter and summer ranges (walked distance ranging from 9 to
252 km; linear displacement ranging from 5 to
34 km, Table 2). Likewise, the number of stopovers ranged from 1, for the shortest migration, to
17 for longer migrations. Distance between stopover sites was roughly 3.5 km on average both in
spring and in fall (Table 2).

outputs S1–S4). Percentages of a priori model
structures selected as top-ranked are presented
(sensu AIC) in Table 1, showing a large amongindividual variation in which model was supported. Antipredator-only models prevailed in
spring, whereas models including human-related
disturbance covariates (i.e., distance to and traffic
on roads) prevailed in the fall (Table 1), with strikingly similar patterns between males and females
(Table 1). The highest number of models including human disturbance predictors was selected
during day period for the fall migration.
Parameter estimates for male and female elk
were obtained by averaging individual modeling
estimates (reported in Appendix S2: Table S1),
which we used in RSFs (depicted in Figs. 1–5).

Habitat selection by elk during migration

We fitted individual conditional logistic regression models for spring (day, night) and fall (day,
night) migrations, which we have reported in full
as supplementary information (Appendix S1: R

Fig. 1. Relative probability of selection for canopy cover (in percentage) by female and male elk during fall
(daytime, nighttime) and spring (daytime, nighttime) migration, as predicted by resource selection functions.

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PATON ET AL.

Fig. 2. Relative probability of selection for terrain ruggedness (in meters) by female and male elk during fall
(daytime, nighttime) and spring (daytime, nighttime) migration, as predicted by resource selection functions.

and fall, during the day (with males showing the
highest degree of avoidance during daytime in
fall), and during the fall at night (females: Fig. 4;
males: Fig. 5). However, avoidance of roads was
reduced in spring at night by females (Fig. 4),
when males actually showed the strongest selection for roads (Fig. 5). During spring nighttime,
elk of both sexes were more likely to select for
open areas (sensu Fig. 1) along primary roads with

Inter-individual variability in parameter estimates was reported in full as supplementary
information (Appendix S3: Figs. S1–S7).
Both sexes selected for more forest cover (Fig. 1),
females (but not males) selected for steeper terrain
(Fig. 2), and both sexes (especially males) avoided
roads (Fig. 3) to a greater extent during fall-day
than any other season/time of the day. Both sexes
avoided roads irrespective of traffic during spring

Fig. 3. Relative probability of selection for distance to roads (in meters) by female and male elk during fall
(daytime, nighttime) and spring (daytime, nighttime) migration, as predicted by resource selection functions.

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PATON ET AL.

Fig. 4. Relative probability of selection for distance to roads (in meters) by female elk during spring (daytime,
nighttime) and fall (daytime, nighttime) migration, as predicted by resource selection functions. Predictions were
depicted for different vehicle traffic intensities on the closest road (six scenarios, ranging from 1 vehicle every
15 min to 1 vehicle every 8 h).

in the daytime during the spring migration,
whereas daily movement was more cautious
during fall migration.
The likelihood of road crossing by male or
female elk was significantly lower in the fall than
in the spring (Fig. 7; generalized linear mixed-effect
model; Appendix S2: Table S3), being the day-fall
the period of the year when the lowest probability
for an elk to cross a road was recorded. Road crossing was more likely in roads with lower traffic volumes (Fig. 8), with the greatest avoidance of roads
depending on traffic recorded in the fall (Fig. 8).
We report results of elk direct behavioral
observations (Fig. 9) carried out during daytime
within open areas along the migratory corridors

high-traffic (1 vehicle/15 min) rather than along
secondary forest roads (1 vehicle/8 h, Figs. 4, 5 for
females and males, respectively).

Elk movement rate and vigilance behavior during
migration
During spring migration, we found that elk of
both sexes had a significantly faster movement
rate (m/2 h) during the day than during the night
(Fig. 6; linear mixed-effect model, Appendix S2:
Table S2). This was not true during fall
migration, when elk significantly decreased their
day movement rate to the level of that recorded
at night (Fig. 6; linear mixed-effect model; Appendix S2: Table S2). Elk traveled longer distances
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PATON ET AL.

Fig. 5. Relative probability of selection for distance to roads (in meters) by male elk during spring (daytime,
nighttime) and fall (daytime, nighttime) migration, as predicted by resource selection functions. Predictions were
depicted for different vehicle traffic intensities on the closest road (six scenarios, ranging from 1 vehicle every
15 min to 1 vehicle every 8 h).

vigilance rate was >3 times higher in the fall
compared to spring (Fig. 9d, effect of season,
b = 0.590, SE = 0.097, t = 6.026, P < 0.001).
All linear models included herd size as a confounding factor (P < 0.05), as well as age–sex
classes (P > 0.05).

(observations on 154 randomly selected individuals). We found that animals spent more time
feeding during spring migration than in the fall
migration (Fig 8a; linear model, effect of season:
b = 0.199, SE = 0.068, t = 13.409, P < 0.001, with
fall being the reference level), whereas elk were
more vigilant in fall compared to spring (Fig. 9b,
effect of season: b = 0.125, SE = 0.046, t =
2.689, P = 0.008). When located in an open
area, elk spent more time traveling during the
fall migration compared to spring migration
(Fig. 9c, effect of season: b = 0.177, SE = 0.054,
t = 3.248, P = 0.001), with the end result that
elk were less likely to persist within open areas
when hunting was allowed. We found that
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DISCUSSION
Throughout spring and fall, humans use the
landscape, but when fall hunting is associated
with human presence, disruption of elk behavior
during migration increases. Few studies have
presented such a detailed analysis and quantification of the behavioral modifications occurring

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PATON ET AL.

Fig. 6. Step lengths (distance walked every two hours, in meters, log-transformed) predicted by the linear
mixed-effect model for female and male elk during fall (daytime, nighttime) and spring (daytime, nighttime)
migration. Error bars are marginal 95% confidence intervals.

populations in areas of increasing human disturbance is a challenging conservation issue in
North America (Bolger et al. 2008). Understanding mechanisms affecting migration is critical for
avoiding human disturbances from blocking or
reducing permeability of migration routes (Sawyer et al. 2012, Benz et al. 2016). The lack of stopover permeability (e.g., reduced likelihood of
crossing the road network) could reduce the
benefits of migration, and influence the ability of
species to modify range due to climate change
(Minor and Urban 2008). Low permeability
might affect the degree to which migratory individuals would be required to or able to alter their
traditional migratory paths and effect the rate of
migration to seasonal ranges (Lendrum et al.

in a large herbivore during its migration. Our
results identify the mechanisms and the related
elk behavioral response by which fall hunting
(compared to spring) affects ungulates using
migration stopovers, that is, (1) increased selection for safer habitats; (2) reduced selection for
areas close to roads depending on their vehicle
traffic, reduced likelihood of road crossing, and
landscape permeability; (3) overall decreased
daily movement rates; (4) decreased time within
open areas to avoid predation risk, decreased
time foraging, and increased vigilance. Results of
this study are important for the conservation of
mammals that undertake partial migration in
jurisdictions where hunting occurs. Conserving
suitable landscapes for migratory ungulate

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PATON ET AL.

Fig. 7. Likelihood of road crossing predicted by the generalized linear mixed-effect model for female and male
elk during fall (daytime, nighttime) and spring (daytime, nighttime) migration. Error bars are marginal 95%
confidence intervals.

sexes. Females used steeper terrain during fallday and males did not use this strategy in hunting season. Male avoidance of roads was much
stronger than females during fall-day, a possible
reaction to the higher hunting pressure on males
vs. females. These shifts in elk distribution from
disturbance to areas away from roads can reduce
access to high-value habitats near roads (Edge
and Marcum 1991, Rowland et al. 2000). However, both sexes appeared to compensate for displacement by moving closer to roads at night,
possibly accessing higher-quality forage sites, a
choice they made more often in spring. Spring
elk movement was diurnal, with greater movement during the day than during the night,
whereas fall migration switched to more cautious movements, moving similarly during the

2012). Disruption of stopovers during migration
has potential to be an important management
and conservation risk for migratory species outside parks and protected areas.
Resource selection function results of elk
migration indicate, during fall-day, all elk
selected forested areas to find cover from human
hunters (Edge and Marcum 1991, Ciuti et al.
2012a). Migrating elk using stopovers also
avoided roads in all seasons, but increased their
response to roads and traffic volume during fall
hunting season. The only time that elk were
located near to high-traffic roads was at night
during the spring, when humans were not perceived as a threat because hunting was not
allowed. Most interestingly, there were differences in responses to human hunters between
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PATON ET AL.

Fig. 8. Road crossing probability as a function of vehicle traffic, as predicted by the generalized linear mixedeffect model fitted to explain the likelihood of road crossing (binary response: 1 = road crossed, 0 = road not
crossed) along steps walked every two hours by male and female elk during spring and fall migration. Shaded
areas are marginal 95% confidence intervals.

Fig. 9. Proportion of time spent in feeding (time feeding/total time active, i.e., foraging efficiency), vigilance
(time being vigilant/time active), traveling (time traveling/total time), and vigilance rate (the number of vigilance
bouts/min) in 154 focal individuals observed along spring and fall migratory corridors.

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PATON ET AL.

migration (Sawyer et al. 2013). A loss or major
decline of this partially migratory population
from migratory to the resident segment of the
herd would result in a change in elk distribution
(Hebblewhite et al. 2005, 2006). A change in elk
distribution could impact vegetation dynamics
by overgrazing on the population’s winter range
and undergrazing on other ranges, resulting in
changes to plant productivity and biodiversity.
Predators using elk as prey might in turn change
their distribution to overlap with the changed elk
distribution (Nelson et al. 2012). If a permanent
year-round shift of elk and predators to current
elk winter range occurred, it could result in
increased negative human/wildlife interactions
(both carnivores and ungulates) in agriculture or
ranch lands located on or nearby the existing
winter range (Morehouse and Boyce 2011).
Vehicle traffic and human disturbance have
much higher effects on elk because of the
increased fear of humans due to hunting. To
reduce these effects to stopover permeability,
access management (i.e., gating roads) can be
highly effective at reducing traffic volume and
minimizing disturbance effects on wildlife, particularly elk and grizzly bears (Northrup et al.
2012). Restricted access regulations and gating of
roads within 500–1000 m to important stopover
sites during migration could be used to manage
traffic activity. Restricting new roads outside of
the preferred buffer distance would be beneficial
to elk and other species by controlling road densities (Frair et al. 2008, Boulanger and Stenhouse
2014). Alternatively, human hunting could be a
tool for displacing ungulates from high human–
wildlife conflict areas by spatially and temporally changing hunting opportunities in hunting
units (Proffitt et al. 2009, Apollonio et al. 2010).

night and day. In highly disturbed areas, migratory animals may increase their travel rate along
preferred paths, moving through the area
quickly in spring (Lendrum et al. 2012) or be
more cautious in their movements in fall (Ciuti
et al. 2012a).
For spring migration, stopover habitat selection was mainly driven by non-hunting-related
environmental conditions, which represented
foraging and resting activities of migrating elk,
plus their response to potential risk from natural
predators. Similar to hunted elk in other jurisdictions of North America (Brodie et al. 2013), predation by natural predators in this study is
greater in winter and spring, with the majority of
elk mortality related to hunting harvest in fall
(Ciuti et al. 2012a, b).
Understanding herbivore behavior due to
hunting pressure is important for management
because these responses may outweigh the effect
of direct predation (Janis and Clark 2002, Creel
and Christianson 2008, P
eriquet et al. 2010). In
response to disturbance, our observation data
showed that elk increased vigilance and traveled
quickly within open areas during fall hunting
season. A quicker traveling rate within open
areas could also reduce time spent in the open,
thus reducing detectability to lessen hunter mortality risk (Mysterud 1999, Sih and McCarthy
2002, Hochman and Kotler 2007, Ciuti et al.
2012a) and create a potential loss of feeding
opportunities due to shorter persistence within
meadows (Mysterud 1999, Creel et al. 2005,
Hern

andez and Laundr
e 2005, Hochman and
Kotler 2007). Elk adjusted behavioral strategies
during migration, when risk due to hunting created a landscape of fear greater than “natural”
predation risk (Proffitt et al. 2009, Ciuti et al.
2012b, Bonnot et al. 2013, Norum et al. 2015).
There was a reduction in feeding activity during
fall migration possibly affecting the ability of elk
to further increase body condition for winter
(Cook 2011, Middleton et al. 2013a, b)—particularly if foraging efficiency and intake does not
offset the energetic demands of moving (Bender
et al. 2008, Seidel and Boyce 2016) caused by a
lower-quality diet (Barnier et al. 2014).
Comparable to reduced habitat quality or
increased predation, the disturbance effects of
semi-permeable barriers on migration route function could potentially reduce the benefits of
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ACKNOWLEDGMENTS
We thank the Natural Sciences and Engineering
Research Council of Canada (NSERC-CRD), Shell
Canada Limited, Alberta Conservation Association
(Grant Eligible Conservation Fund), Alberta Sustainable Resource Development, Safari Club International,
Alberta Parks, and Canada Parks (Waterton Lakes
National Park) for funding and support. The funders
had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. DGP, SC, and MSB conceived and designed the
experiments. DGP and SC performed the experiments.

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PATON ET AL.
Brodie, J., et al. 2013. Relative influence of human harvest, carnivores, and weather on adult female elk
survival across western North America. Journal of
Applied Ecology 50:295–305.
Caro, T. 2005. Antipredator defenses in birds and mammals. University of Chicago Press, Chicago, USA.
Childress, M. J., and M. A. Lung. 2003. Predation risk,
gender and the group size effect: Does elk vigilance
depend upon the behaviour of conspecifics? Animal Behaviour 66:389–398.
Christianson, D., and S. Creel. 2010. A nutritionally
mediated risk effect of wolves on elk. Ecology
91:1184–1191.
Ciuti, S., T. B. Muhly, D. G. Paton, A. D. McDevitt,
M. Musiani, and M. S. Boyce. 2012a. Human selection of elk behavioural traits in a landscape of fear.
Proceedings of the Royal Society of London B: Biological Sciences 279:4407–4416.
Ciuti, S., J. M. Northrup, T. B. Muhly, S. Simi,
M. Musiani, J. A. Pitt, and M. S. Boyce. 2012b.
Effects of humans on behaviour of wildlife exceed
those of natural predators in a landscape of fear.
PLoS ONE 7:e50611.
Cook, R. C. 2011. A multi-regional evaluation of nutritional condition and reproduction in elk. Dissertation. Washington State University, Pullman,
Washington, USA.
Creel, S., and D. Christianson. 2008. Relationships
between direct predation and risk effects. Trends in
Ecology & Evolution 23:194–201.
Creel, S., D. Christianson, S. Liley, and J. A. Winnie.
2007. Predation risk affects reproductive physiology and demography of elk. Science 315:960.
Creel, S., J. Winnie Jr., B. Maxwell, K. Hamlin, and
M. Creel. 2005. Elk alter habitat selection as an
antipredator response to wolves. Ecology 86:3387–
3397.
Duchesne, T., D. Fortin, and N. Courbin. 2010. Mixed
conditional logistic regression for habitat selection
studies. Journal of Animal Ecology 79:548–555.
Edge, W. D., and C. L. Marcum. 1991. Topography
ameliorates the effects of roads and human disturbance on elk. Pages 132–137 in Proceedings Elk
Vulnerability Symposium. Montana State University, Bozeman, Montana, USA.
^t
e, and A.
Festa-Bianchet, M., J. Ray, S. Boutin, S. Co
Gunn. 2011. Conservation of caribou (Rangifer
tarandus) in Canada: an uncertain future. Canadian
Journal of Zoology 89:419–434.
Fieberg, J., J. Matthiopoulos, M. Hebblewhite, M. S.
Boyce, and J. L. Frair. 2010. Correlation and studies
of habitat selection: Problem, red herring or
opportunity? Philosophical Transactions of the
Royal Society of London B: Biological Sciences 365:
2233–2244.

DGP, SC, and MSB contributed materials/analysis tools
and prepared data for analyses. SC analyzed the data.
All authors edited and approved the first draft written
by DGP and SC.

LITERATURE CITED
Alerstam, T. 2006. Conflicting evidence about longdistance animal navigation. Science 313:791–794.
Apollonio, M., R. Andersen, and R. Putman. 2010.
European ungulates and their management in
the 21st century. Cambridge University Press,
Cambridge, UK.
Augustine, D. J., and S. J. McNaughton. 1998. Ungulate effects on the functional species composition of
plant communities: herbivore selectivity and plant
tolerance. Journal of Wildlife Management 62:
1165–1183.
Barnier, F., M. Valeix, P. Duncan, S. Chamaill
e-Jammes,
P. Barre, A. J. Loveridge, D. W. Macdonald, and
H. Fritz. 2014. Diet quality in a wild grazer declines
under the threat of an ambush predator. Proceedings of the Royal Society of London B: Biological
Sciences 281:20140446.
Bates, D., M. Maechler, B. Bolker, and S. Walker. 2015.
Fitting linear mixed-effects models using lme4.
Journal of Statistical Software 67:1–48.
Beckmann, J. P., A. P. Clevenger, M. Huijser, and J. A.
Hilty. 2012. Safe passages: highways, wildlife, and
habitat connectivity. Island Press, Washington
D.C., USA.
Bender, L. C., J. G. Cook, R. C. Cook, and P. B. Hall.
2008. Relations between nutritional condition and
survival of North American elk Cervus elaphus.
Wildlife Biology 14:70–80.
Benz, R. A., M. S. Boyce, H. Thurfjell, D. G. Paton,
M. Musiani, C. F. Dormann, and S. Ciuti. 2016. Dispersal ecology informs design of large-scale wildlife corridors. PLoS ONE 11:e0162989.
Berger, J. 2004. The last mile: how to sustain longdistance migration in mammals. Conservation
Biology 18:320–331.
Bolger, D. T., W. D. Newmark, T. A. Morrison, and
D. F. Doak. 2008. The need for integrative
approaches to understand and conserve migratory
ungulates. Ecology Letters 11:63–77.
Bonnot, N., N. Morellet, H. Verheyden, B. Cargnelutti,
B. Lourtet, F. Klein, and A. J. M. Hewison. 2013.
Habitat use under predation risk: Hunting, roads
and human dwellings influence the spatial behaviour of roe deer. European Journal of Wildlife
Research 59:185–193.
Boulanger, J., and G. B. Stenhouse. 2014. The impact of
roads on the demography of grizzly bears in
Alberta. PLoS ONE 9:e115535.

❖ www.esajournals.org

15

June 2017 ❖ Volume 8(6) ❖ Article e01841

PATON ET AL.
Leblond, M., C. Dussault, and J.-P. Ouellet. 2013.
Avoidance of roads by large herbivores and its
relation to disturbance intensity. Journal of Zoology 289:32–40.
Lendrum, P. E., C. R. Anderson, R. A. Long, J. G. Kie,
and R. T. Bowyer. 2012. Habitat selection by mule
deer during migration: effects of landscape
structure and natural-gas development. Ecosphere
3:1–19.
Lone, K., L. E. Loe, T. Gobakken, J. D. C. Linnell,
J. Odden, J. Remmen, and A. Mysterud. 2014.
Living and dying in a multi-predator landscape of
fear: Roe deer are squeezed by contrasting pattern
of predation risk imposed by lynx and humans.
Oikos 123:641–651.
Lone, K., L. E. Loe, E. L. Meisingset, I. Stamnes, and
A. Mysterud. 2015. An adaptive behavioural
response to hunting: Surviving male red deer shift
habitat at the onset of the hunting season. Animal
Behaviour 102:127–138.
Manly, B. F., L. McDonald, D. Thomas, T. L. McDonald, and W. P. Erickson. 2002. Resource selection by
animals: statistical design and analysis for field
studies. Springer Science & Business Media,
Dordrecht, The Netherlands.
Martin, P., P. P. G. Bateson, and P. Bateson. 1993.
Measuring behaviour: an introductory guide.
Cambridge University Press, Cambridge, UK.
McNaughton, S. J. 1994. Biodiversity and function of
grazing ecosystems. Pages 361–383 in E.-D. Schulze
and H. A. Mooney, editors. Biodiversity and
ecosystem function. Springer Berlin Heidelberg,
Berlin, Heidelberg, Germany.
Middleton, A. D., M. J. Kauffman, D. E. McWhirter,
J. G. Cook, R. C. Cook, A. A. Nelson, M. D. Jimenez, and R. W. Klaver. 2013a. Animal migration
amid shifting patterns of phenology and predation:
lessons from a Yellowstone elk herd. Ecology 94:
1245–1256.
Middleton, A. D., M. J. Kauffman, D. E. McWhirter,
M. D. Jimenez, R. C. Cook, J. G. Cook, S. E. Albeke,
H. Sawyer, and P. J. White. 2013b. Linking
anti-predator behaviour to prey demography
reveals limited risk effects of an actively
hunting large carnivore. Ecology Letters 16:1023–
1030.
Milner-Gulland, E. J., J. M. Fryxell, and A. R. E. Sinclair. 2011. Animal migration: a synthesis. Oxford
University Press, Oxford, UK.
Minor, E. S., and D. L. Urban. 2008. A graph-theory
framework for evaluating landscape connectivity
and conservation planning. Conservation Biology
22:297–307.
Monteith, K. L., V. C. Bleich, T. R. Stephenson, B. M.
Pierce, M. M. Conner, R. W. Klaver, and R. T.

Forman, R. T. 2003. Road ecology: science and solutions. Island Press, Washington D.C., USA.
Frair, J. L., E. H. Merrill, H. L. Beyer, and J. M. Morales.
2008. Thresholds in landscape connectivity and
mortality risks in response to growing road
networks. Journal of Applied Ecology 45:1504–
1513.
Fryxell, J. M. 1991. Forage quality and aggregation
by large herbivores. American Naturalist 138:
478–498.
Goldberg, J. F., M. Hebblewhite, and J. Bardsley. 2014.
Consequences of a refuge for the predator-prey
dynamics of a wolf-elk system in Banff National
Park, Alberta, Canada. PLoS ONE 9:e91417.
Griffin, K. A., et al. 2011. Neonatal mortality of elk driven by climate, predator phenology and predator
community composition. Journal of Animal Ecology 80:1246–1257.
Hebblewhite, M., and E. H. Merrill. 2007. Multiscale
wolf predation risk for elk: Does migration reduce
risk? Oecologia 152:377–387.
Hebblewhite, M., E. H. Merrill, L. E. Morgantini, C. A.
White, J. R. Allen, E. Bruns, L. Thurston, and T. E.
Hurd. 2006. Is the migratory behavior of montane
elk herds in peril? The case of Alberta’s Ya Ha
Tinda elk herd. Wildlife Society Bulletin 34:1280–
1294.
Hebblewhite, M., C. A. White, C. G. Nietvelt, J. A.
McKenzie, T. E. Hurd, J. M. Fryxell, S. E. Bayley,
and P. C. Paquet. 2005. Human activity mediates a
trophic cascade caused by wolves. Ecology
86:2135–2144.
Hern
andez, L., and J. W. Laundr
e. 2005. Foraging in
the ‘landscape of fear’ and its implications for habitat use and diet quality of elk Cervus elaphus and
bison Bison bison. Wildlife Biology 11:215–220.
Hochman, V., and B. P. Kotler. 2007. Patch use, apprehension, and vigilance behavior of Nubian Ibex
under perceived risk of predation. Behavioral Ecology 18:368–374.
Holdo, R. M., J. M. Fryxell, A. R. E. Sinclair, A. Dobson, and R. D. Holt. 2011. Predicted impact of barriers to migration on the Serengeti wildebeest
population. PLoS ONE 6:e16370.
Horne, J. S., E. O. Garton, S. M. Krone, and J. S. Lewis.
2007. Analyzing animal movements using Brownian bridges. Ecology 88:2354–2363.
Ito, T., N. Miura, B. Lhagvasuren, D. Enkhbileg,
S. Takatsuki, A. Tsunekawa, and Z. Jiang. 2006.
Satellite tracking of Mongolian gazelles (Procapra
gutturosa) and habitat shifts in their seasonal
ranges. Journal of Zoology 269:291–298.
Janis, M. W., and J. D. Clark. 2002. Responses of Florida
panthers to recreational deer and hog hunting.
Journal of Wildlife Management 66:839–848.

❖ www.esajournals.org

16

June 2017 ❖ Volume 8(6) ❖ Article e01841

PATON ET AL.
Proffitt, K. M., J. L. Grigg, K. L. Hamlin, and R. A. Garrott. 2009. Contrasting effects of wolves and
human hunters on elk behavioral responses to predation risk. Journal of Wildlife Management 73:
345–356.
R Core Team. 2015. R: a language and environment for
statistical computing. R Foundation for Statistical
Computing, Vienna, Austria.
Remis, M. J., and J. B. Kpanou. 2011. Primate and
ungulate abundance in response to multi-use zoning and human extractive activities in a Central
African Reserve: human impacts on wildlife. African Journal of Ecology 49:70–80.
Riley, S. J., S. D. DeGloria, and R. Elliot. 1999. A terrain
ruggedness index that quantifies topographic
heterogeneity. Intermountain Journal of Sciences
5:23–27.
Rogala, J. K., M. Hebblewhite, J. Whittington, C. A.
White, J. Coleshill, and M. Musiani. 2011. Human
activity differentially redistributes large mammals
in the Canadian Rockies National Parks. Ecology
and Society 16:16.
Rowland, M. M., M. J. Wisdom, B. K. Johnson, and
J. G. Kie. 2000. Elk distribution and modeling in
relation to roads. Journal of Wildlife Management
64:672–684.
Sawyer, H., M. J. Kauffman, A. D. Middleton, T. A.
Morrison, R. M. Nielson, and T. B. Wyckoff. 2013.
A framework for understanding semi-permeable
barrier effects on migratory ungulates. Journal of
Applied Ecology 50:68–78.
Sawyer, H., M. J. Kauffman, R. M. Nielson, and J. S.
Horne. 2009. Identifying and prioritizing ungulate migration routes for landscape-level conservation. Ecological Applications 19:2016–2025.
Sawyer, H., C. Lebeau, and T. Hart. 2012. Mitigating
roadway impacts to migratory mule deer – A case
study with underpasses and continuous fencing.
Wildlife Society Bulletin 36:492–498.
Schmidt, K., and D. P. J. Kuijper. 2015. A “death trap”
in the landscape of fear. Mammal Research 60:
275–284.
Seidel, D. P., and M. S. Boyce. 2016. Varied tastes:
home range implications of foraging-patch selection. Oikos 125:39–49.
Sih, A., and T. M. McCarthy. 2002. Prey responses to
pulses of risk and safety: testing the risk allocation
hypothesis. Animal Behaviour 63:437–443.
Therneau, T. M., and P. M. Grambsch. 2000. Modeling
survival data: extending the Cox model. Springer,
New York.
Thirgood, S., A. Mosser, S. Tham, G. Hopcraft,
E. Mwangomo, T. Mlengeya, M. Kilewo, J. Fryxell,
A. R. E. Sinclair, and M. Borner. 2004. Can parks

Bowyer. 2011. Timing of seasonal migration in
mule deer: effects of climate, plant phenology, and
life-history characteristics. Ecosphere 2:1–34.
Morales, J. M., P. R. Moorcroft, J. Matthiopoulos, J. L.
Frair, J. G. Kie, R. A. Powell, E. H. Merrill, and
D. T. Haydon. 2010. Building the bridge between
animal movement and population dynamics.
Philosophical Transactions of the Royal Society of
London B: Biological Sciences 365:2289–2301.
Morehouse, A. T., and M. S. Boyce. 2011. From venison
to beef: seasonal changes in wolf diet composition
in a livestock grazing landscape. Frontiers in Ecology and the Environment 9:440–445.
Muhly, T. B. 2011. Direct, indirect and predatormediated effects of humans on a terrestrial food
web: implications for conservation. Thesis. University of Calgary, Calgary AB, Canada.
Muhly, T. B., C. Semeniuk, A. Massolo, L. Hickman,
and M. Musiani. 2011. Human activity helps prey
win the predator-prey space race. PLoS ONE 6:
e17050.
Mysterud, A. 1999. Seasonal migration pattern and
home range of roe deer (Capreolus capreolus) in an
altitudinal gradient in southern Norway. Journal of
Zoology 247:479–486.
Mysterud, A., R. Langvatn, N. G. Yoccoz, and C. N.
Stenseth. 2001. Plant phenology, migration and
geographical variation in body weight of a large
herbivore: the effect of a variable topography. Journal of Animal Ecology 6:915–923.
Nelson, A. A., M. J. Kauffman, A. D. Middleton, M. D.
Jimenez, D. E. McWhirter, J. Barber, and K. Gerow.
2012. Elk migration patterns and human activity
influence wolf habitat use in the Greater Yellowstone Ecosystem. Ecological Applications 22:2293–
2307.
Northrup, J. M., J. Pitt, T. B. Muhly, G. B. Stenhouse,
M. Musiani, and M. S. Boyce. 2012. Vehicle traffic
shapes grizzly bear behaviour on a multiple-use
landscape. Journal of Applied Ecology 49:1159–
1167.
Norum, J. K., K. Lone, J. D. Linnell, J. Odden, L. E.
Loe, and A. Mysterud. 2015. Landscape of risk to
roe deer imposed by lynx and different human
hunting tactics. European Journal of Wildlife
Research 61:831–840.
Paton, D. G. 2012. Connectivity of elk migration in
southwestern Alberta. Thesis. University of
Calgary, Calgary AB, Canada.
P
eriquet, S., M. Valeix, A. J. Loveridge, H. Madzikanda, D. W. Macdonald, and H. Fritz. 2010. Individual vigilance of African herbivores while
drinking: the role of immediate predation risk and
context. Animal Behaviour 79:665–671.

❖ www.esajournals.org

17

June 2017 ❖ Volume 8(6) ❖ Article e01841

PATON ET AL.
Vors, L. S., and M. S. Boyce. 2009. Global declines of
caribou and reindeer. Global Change Biology 15:
2626–2633.
Walther, G.-R., E. Post, P. Convey, A. Menzel,
C. Parmesan, T. J. C. Beebee, J.-M. Fromentin,
O. Hoegh-Guldberg, and F. Bairlein. 2002. Ecological responses to recent climate change. Nature 416:
389–395.
White, P. J., R. A. Garrott, K. L. Hamlin, R. C. Cook,
J. G. Cook, and J. A. Cunningham. 2011. Body condition and pregnancy in northern Yellowstone elk:
Evidence for predation risk effects? Ecological
Applications 21:3–8.

protect migratory ungulates? The case of the
Serengeti wildebeest. Animal Conservation 7:113–
120.
Valeix, M., G. Hemson, A. J. Loveridge, G. Mills, and
D. W. Macdonald. 2012. Behavioural adjustments
of a large carnivore to access secondary prey in a
human-dominated landscape. Journal of Applied
Ecology 49:73–81.
Voeten, M. M., C. A. D. M. Van De Vijver, H. Olff, and
F. Van Langevelde. 2010. Possible causes of decreasing migratory ungulate populations in an East African savannah after restrictions in their seasonal
movements. African Journal of Ecology 48:169–179.

SUPPORTING INFORMATION
Additional Supporting Information may be found online at: http://onlinelibrary.wiley.com/doi/10.1002/ecs2.
1841/full

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