Human Selection of Elk Behavioral Traits in a Landscape of Fear

Abstract

Among agents of selection that shape phenotypic traits in animals, humans can cause more rapid changes than many natural factors. Studies have focused on human selection of morphological traits, but little is known about human selection of behavioural traits. By monitoring elk (Cervus elaphus) with satellite telemetry, we tested whether individuals harvested by hunters adopted less favourable behaviours than elk that survived the hunting season. Among 45 2-year-old males, harvested elk showed bolder behaviour, including higher movement rate and increased use of open areas, compared with surviving elk that showed less conspicuous behaviour. Personality clearly drove this pattern, given that inter-individual differences in movement rate were present before the onset of the hunting season. Elk that were harvested further increased their movement rate when the probability of encountering hunters was high (close to roads, flatter terrain, during the weekend), while elk that survived decreased movements and showed avoidance of open areas. Among 77 females (2-19 y.o.), personality traits were less evident and likely confounded by learning because females decreased their movement rate with increasing age. As with males, hunters typically harvested females with bold behavioural traits. Among less-experienced elk (2-9 y.o.), females that moved faster were harvested, while elk that moved slower and avoided open areas survived. Interestingly, movement rate decreased as age increased in those females that survived, but not in those that were eventually harvested. The latter clearly showed lower plasticity and adaptability to the local environment. All females older than 9 y.o. moved more slowly, avoided open areas and survived. Selection on behavioural traits is an important but often-ignored consequence of human exploitation of wild animals. Human hunting could evoke exploitation-induced evolutionary change, which, in turn, might oppose adaptive responses to natural and sexual selection.

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doi: 10.1098/rspb.2012.1483
, 4407-4416 first published online 5 September 2012279 2012 Proc. R. Soc. B
Simone Ciuti, Tyler B. Muhly, Dale G. Paton, Allan D. McDevitt, Marco Musiani and Mark S. Boyce
fear
Human selection of elk behavioural traits in a landscape of
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Human selection of elk behavioural traits
in a landscape of fear
Simone Ciuti
1,
*
, Tyler B. Muhly
2
, Dale G. Paton
3
, Allan
D. McDevitt
3,4
, Marco Musiani
3
and Mark S. Boyce
1
1
Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada T6G 2E9
2
Alberta Innovates Technology Futures, Vegreville, Alberta, Canada T9C 1T4
3
Faculty of Environmental Design, University of Calgary, Calgary, Alberta, Canada T2N 1N4
4
School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland
Among agents of selection that shape phenotypic traits in animals, humans can cause more rapid changes
than many natural factors. Studies have focused on human selection of morphological traits, but little is
known about human selection of behavioural traits. By monitoring elk (Cervus elaphus) with satellite teleme-
try, we tested whether individuals harvested by hunters adopted less favourable behaviours than elk that
survived the hunting season. Among 45 2-year-old males, harvested elk showed bolder behaviour, including
higher movement rate and increased use of open areas, compared with surviving elk that showed less con-
spicuous behaviour. Personality clearly drove this pattern, given that inter-individual differences in
movement rate were present before the onset of the hunting season. Elk that were harvested further increased
their movement rate when the probability of encountering hunters was high (close to roads, flatter terrain,
during the weekend), while elk that survived decreased movements and showed avoidance of open areas.
Among 77 females (2–19 y.o.), personality traits were less evident and likely confounded by learning because
females decreased their movement rate with increasing age. As with males, hunters typically harvested
females with bold behavioural traits. Among less-experienced elk (2–9 y.o.), females that moved faster
were harvested, while elk that moved slower and avoided open areas survived. Interestingly, movement
rate decreased as age increased in those females that survived, but not in those that were eventually har-
vested. The latter clearly showed lower plasticity and adaptability to the local environment. All females
older than 9 y.o. moved more slowly, avoided open areas and survived. Selection on behavioural traits is
an important but often-ignored consequence of human exploitation of wild animals. Human hunting
could evoke exploitation-induced evolutionary change, which, in turn, might oppose adaptive responses
to natural and sexual selection.
Keywords: contemporary evolution; anti-predator behaviour; shy–bold continuum; hunting; elk;
Cervus elaphus; GPS telemetry
1. INTRODUCTION
Phenotypic traits of wild vertebrate and invertebrate popu-
lations are constantly shaped and reshaped by changes
in the environment and by numerous agents of natural
selection, including predators [1]. Among these countless
factors, modern humans have emerged as a dominant
evolutionary force [2]. Humans can cause more rapid phe-
notypic changes than many natural agents [3]. For several
animal species, Darimont et al. [4] suggested that rates of
phenotypic change driven by human harvest could outpace
those driven by other selective forces. Human influence on
phenotypes also might generate large and rapid changes in
population and ecological dynamics, including those that
affect population persistence [5,6].
By exploiting prey at high levels and targeting fundamen-
tally different age- and size-classes than natural predators
[7,8], humans can generate rapid phenotypic and genetic
changes in both morphological and life-history traits in
exploited prey [9]. However, while research has focused
on human-mediated selection of morphological traits in
wild populations (e.g. selection of large-antlered or large-
horned ungulate males [10,11]), little is known about
human-mediated selection of behavioural traits. Here we
predict that prey, depending on individual personality
traits, can adopt anti-predator behavioural strategies in
response to human hunting pressure, and thus humans
directly influence prey behavioural traits. The importance
of behaviourally mediated effects of humans requires greater
attention in the wild, as these effects have been shown only
in domesticated animals [12,13].
We tested whether elk (Cervus elaphus) that were even-
tually killed by human hunters (hereinafter referred to as
harvested) had less favourable behaviours than surviving
elk in southwest Alberta, Canada. Elk is a good model
species because of its high degree of behavioural plasticity
in response to predators [14,15]. We deployed global
positioning system (GPS) satellite-telemetry collars on
122 elk. GPS-radiotelemetry provides vast quantities of
high-quality relocation data that allow for disentangling
spatial anti-predator strategies adopted by large mammals
[16]. We investigated among-individual differences in
personality traits of males (n ¼ 45, age: 2 y.o.) all facing
the hunting season with the same experience level but
* Author for correspondence (ciuti@ualberta.ca).
Electronic supplementary material is available at http://dx.doi.org/
10.1098/rspb.2012.1483 or via http://rspb.royalsocietypublishing.org.
Proc. R. Soc. B (2012) 279, 4407–4416
doi:10.1098/rspb.2012.1483
Published online 5 September 2012
Received 27 June 2012
Accepted 16 August 2012
4407 This journal is q 2012 The Royal Society
on September 26, 2012rspb.royalsocietypublishing.orgDownloaded from

no longer bonded with their mothers, which can influence
anti-predator strategies of calves [17,18]. At the same
time, we studied females (n ¼ 77) from a range of experi-
ence levels (age: 2–19 y.o.) and thus different knowledge
of the environment [19].
We predict that harvested elk have higher movement
rates than those that survived, thus more likely to be
detected by hunters [20], particularly in less-steep terrain
that is more accessible to hunters, in open areas or close
to roads where there is increased detectability, and
during the weekend when human activity is higher.
Therefore, our general prediction is that individuals
choosing to increase movements as an anti-predator strat-
egy to avoid hunters, especially when they are more
visible, have a higher probability of being harvested than
animals that take a ‘hiding’ strategy by decreasing move-
ment rate as an anti-predator strategy. These patterns
should be more evident in males, as we studied young
males with low experience levels, whereas learning could
confound personality traits in older fe males.
In our study design, we first assessed the movement
strategies of harvested elk versus those that survived
before and during the hunting season. If behavioural
differences between elk represent personality differences
versus lear ning, those differences should be already pre-
sent before the onset of the hunting season. We then
calculated which spatial behaviour patterns affected the
probability of an elk being harvested.
2. MATERIAL AND METH ODS
(a) Study area
The study occurred within a montane ecosystem along the
easter n slopes of the Rocky Mountains in southwest Alberta,
Canada. Some monitored elk moved to southeastern British
Columbia and northwestern Montana during study (see the
electronic supplementary material, figure S1). This is a diverse
landscape, from flat agriculturally developed grasslands to
mixed conifer/hardwood forests and abrupt mountains.
Human activity was intense during the autumn hunting
season, especially during weekends. Hunters access hunt-
ing areas using forestry roads and trails, searching at dawn
and dusk until they detect prey, often using binocular s or
spotting scopes. We deployed 43 trail cameras along roads
and trails in the study area [21] to quantify human activity.
Humans counted per day during weekends was 20.7 + 5.7
(mean + s.e.), and significantly lower (12.4 + 3.3 humans
per day) during weekdays (paired sample t-test: n ¼ 43, t ¼
3.112, p ¼ 0.003) [21].
The elk rifle hunting season was from early September
until the end of November. Wolf (Canis lupus) cougar
(Puma concolor) and grizzly bear (Ursus arctos) are the main
natural predators in the area [21].
(b) Elk data
Male (n ¼ 45) and female (n ¼ 77) elk were captured
(animal care protocol no. 536-1003 AR University of
Alberta) during the winters of 2007–2011 using helicopter
net-gunning. Males were fitted with Lotek ARGOS GPS-
radiotelemetry collar s, whereas females were fitted with
Lotek GPS-4400 radiotelemetry collars (Lotek wireless
Inc., Ontario, Canada). All collars were programmed with
a 2-h relocation schedule. Satellite transmitted data of
males were received weekly via email, whereas data of females
were remotely downloaded in the field. A total of 635 700
GPS relocations collected from January 2007 to December
2011 were used in this study. A vestibular canine was taken
using dental lifters during the capture to assess age through
cementum analysis (Matson’s Laboratory, MT, USA). All
males were aged 1.5 y.o. during the winter capture, and con-
sequently they faced the following hunting season at the age
of 2.5 y.o. (greater than equal to three-point antlers). Age of
females ranged from 2 to 19 y.o. By the last day of the hunt-
ing season, 97 elk were still alive and 25 had been harvested
(table 1; see the electronic supplementary material, figure S2
for details on monitoring period). Age of females that were
harvested ranged from 2 to 9 y.o. The majority (93%) of
hunting mortalities occurred between early September and
early November.
(c) Ecological factors affecting elk mobility
We calculated step length (i.e. distance between 2 h teleme-
try relocations, in metre) as a proxy of elk mobility [22]
using ARCMAP v. 9.2 (ESRI Inc., Redlands, CA) with the
Hawth’s Tools extension (http://www.spatialecology.com/
htools/). We report in table 2 the complete list of ecological
factors that have been predicted to affect elk mobility (i.e.
step length) based on previous studies on ungulates
[19,20,22– 31] and our own predictions (see the electronic
supplementary material, table S1 for further details on GIS
data). To distinguish migration from other movement beha-
viours, we used a single measurement, the net-squared
displacement (NSD) that measures the straight line distances
between the starting location and the subsequent locations for
the movement path of a given individual. On the basis of
shape of NSD patterns, we split the monitored sample into dis-
perser, migratory and resident elk [32] (see table 1 and
electronic supplementary material, figure S2). Dawn and dusk
periods were assessed each month as the 4 h period around twi-
light start and twilight end (sun 68
below horizon) for which we
o
btained the daily occurrence using the sunrise/sunset calcula-
torforthegeographicalcentreofthestudysite(http://www.
nrc-cnrc.gc.ca/eng/services/hia/sunrise-sunset.html).
Table 1. Elk monitored using GPS radio telemetr y in southwest Alberta and southeast British Columbia, Canada and
northwest Montana, USA from 2007 to 2011. Sample size was split according to sex, individual movement strategy
(migratory, disperser or resident) and individual fate during hunting season.
males n ¼ 45 females n ¼ 77
grand totalmigratory disperser resident total migratory disperser resident total
harvested 11 3 1 15 8 0 2 10 25
survived 22 7 1 30 59 1 7 67 97
total 33 10 2 45 67 1 9 77 122
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We modelled variation in step length (natural log-
transformed, hereinafter referred to as step length) using
generalized additive mixed models (GAMMs) [33]inR
v. 2.14.1 [34], with individual elk fitted as a random intercept
[35]. Following Burnham et al. [36], we constructed four
sets of a priori GAMMs (see the electronic supplementary
material, table S2 –S5).
The first two sets of models (one for each sex, electronic
supplementary material, table S2 and S4) were built to pre-
dict the variation of step length from January, i.e. after the
end of the hunting season, through the next autumn hunting
season. This approach allowed us to verify whether (i) har-
vested and survived elk had different movement rates
before the onset of the hunting season, and (ii) elk were
Table 2. Candidate ecological factors that influence elk mobility (step length) before and during the hunting season.
group of factors
included in
model selection factors
variables associated
with elk step length
predicted link with individual
movement rate (step length)
supporting
examples
a
individual
behaviour
hunting season fate survived or harvested higher movement rates are expected
in elk that are eventually shot by
hunters (through increased
encounters with humans)
[20]
Julian date Julian date elk mobility could flexibly fluctuate
through time (Julian date), e.g.
depending on movement
behaviour, period of the year
(rut) and hunting pressure
[23– 25]
movement behaviour migratory, disperser,
resident
higher movement rates are expected
in dispersers or young migratory
individuals owing to exploratory
behaviour within unknown
grounds
[26]
individual
experience
(age)
age age home ranges and, arguably,
movement rates decrease with
age (as a result of increased
experience and/or knowledge of
the habitat)
[19]
environment day period night, dawn, day,
dusk
higher movement rates are expected
at dawn and dusk as a result of
crepuscular activity
[27]
terrain ruggedness
b,c
terrain ruggedness r lower movement rates are expected
as higher energy expenditure for
locomotion is required due
terrain ruggedness, and,
consequently, elevation and
snow cover.
[28]
open areas (anti-predator
behaviour)
elk step length
recorded outside
or inside open
areas (un-forested)
higher movement rates are expected
within open areas because of
higher perceived risk
[29]
open areas (foraging
behaviour)
lower movement rates are expected
if animals forage in open habitat
[22]
humans land use (human
disturbance on a large
spatial scale)
national park, private
land, public land
different movement rates are
expected within national park,
private and public land, but the
direction of such an effect is
still unclear
[20,30]
distance from gravel roads
c
(human disturbance on a
small spatial scale)
distance from the
nearest gravel
road d
grv
higher movement rates are expected
close to roads
[31]
week period (human
disturbance on a temporal
scale)
weekday or Sat.–
Sun.
higher movement rates are expected
when human disturbance
increases (i.e. during the
weekend)
[31]
two-way
interactions
different response to
humans between elk that
are harvested or survive
during the hunting season
two-way interactions elk that are harvested are expected
to move faster (higher
detectability) when and where
hunter activity is higher (i.e.
flatter terrain, open areas, close
to roads, during weekends)
none
a
In ungulates.
b
Collinear with elevation and snow cover in winter time.
c
Computed for the telemetry relocation prior to the step length.
Human hunters select elk behaviour S. Ciuti et al. 4409
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sensitized (e.g. suddenly changed their movement rate) at the
onset of the hunting season. Using GAMMs allowed us to
flexibly model step length through time (Julian date) by fit-
ting smoothing splines [33]. We also fit smoothing splines
for elk that survived and harvested elk separately (Julian
date by hunting season fate), and smoothing splines to
allow for a nonlinear effect of age on step length of females
(see the electronic supplementary material, table S4).
We built two more sets of models (one for each sex, elec-
tronic supplementary material, tables S3 and S5) to predict
variation in step length during the hunting season. We
included four two-way interactions between hunting season
fate (survived, harvested) and terrain ruggedness r, open
areas (outside, inside), distance from gravel roads d
grv
and
week period (weekday, Sat. –Sun.) to verify the different indi-
vidual responses to human presence between elk that
survived or were harvested. To test whether experience
might affect the response of females to the presence of hun-
ters, we fit smoothing splines for the effect of age on step
length for survived and harvested elk separately.
The use of AIC to select the best model could be proble-
matic when using mixed models, given that AIC penalizes
models according to the number of predictor variables
[37], which is not clear because of the random effect. We
thus examined our four sets of GAMMs using the deviance
information criterion [38,39]. Parameters were estimated
for top-ranked models.
We verified whether harvested and survived elk in our final
top-ranked models were spatially autocorrelated with each
other. Heterogeneity in hunting pressure could lead to spatial
segregation between survived and harvested elk. Autocorre-
lated step length could also be expected among individuals
using the same areas. We did not find any pattern in the
spatial distribution of elk that survived and were harvested
(see the electronic supplementary material, figure S1), nor
in the distribution of residuals of top-ranked GAMMs
plotted versus their spatial coordinates (see the electronic
supplementary material, figure S3 [40]). Inspections of var-
iograms allowed us to exclude spatial autocorrelation of
residuals in top-ranked models (Moran’s I-test: p  0.353
in all cases; see electronic supplementary material, figure S3).
(d) Behaviours affecting probability of being harvested
during hunting season
We investigated behavioural choices that affected the prob-
ability of an elk being harvested during the hunting season.
For these analyses, we excluded those animals (n ¼ 4
males, n ¼ 5 females) that were partially located within
National Parks during the hunting season (where no hunting
is allowed) or within management units where the hunting of
elk males greater than equal to three points was not allowed.
For these animals, the probability of mortality was negatively
affected by local harvest management restrictions. For all
other animals, we fit generalized linear models (GLMs) in
R v. 2.14.1 [34], with binomial error distribution with hunt-
ing season fate (survived ¼ 0, harvested ¼ 1) as a response
variable. Following Bur nham et al. [36], we constructed
two sets of a prior i mixed models (seven for males, 15 for
females) using the following explanatory variables: mean dis-
tance from gravel roads (d
grv
), mean terrain ruggedness (r),
mean step length, elk age during the hunting season (for
female models only) and selection ratios for open areas
(w
oa
). To calculate selection ratios, we generated 5000
random points within each hunting season 95 per cent
kernel elk home range. We calculated selection ratios
for open areas (w
oa
) as the frequency of used locations
(within open areas) divided by the frequency of random
locations within open areas [41]. For each sex, parameter
estimates were reported for the top-ranked model identified
by minimum AIC model ranking and weighting [42].
3. RESULTS
(a) Ecological factors affecting male mobility
Selection and parameter estimates of the best GAMM pre-
dicting step length of males from Januar y through the
hunting season are reported in the electronic supple-
mentary material, table S2. Males that were harvested
moved faster (mean step length recorded every 2 h + s.e.:
328.7 + 3.1 m) than elk that survived (292.5 + 2.0 m)
the hunting season. Predictions of the top-ranked
GAMM for the variation of step length of harvested
versus survived elk are reported in figure 1a. Elk that
were harvested during the hunting season moved faster
before the onset of the hunting season than elk that survived
(figure 1a and electronic supplementary material, table S2).
Elk showed pronounced crepuscular activity while moving
faster at dawn (474.2 + 4.9 m) and dusk (378.1 + 4.6 m)
than during the day (273.6 + 2.5 m) and night (175.4 +
2.4 m). Males moved faster in areas of low terrain rug-
gedness and open areas (323.4 + 2.4 m) than outside of
them (287.7 + 2.3 m). Males also moved faster when
closer to gravel roads and during Sat.–Sun. (309.8 +
3.2 m) compared with weekdays (302.3 + 2.0 m). Move-
ment behaviour (migratory, resident, disperser) and land
use were factors retained in the best model.
Selection and parameter estimates of the best GAMM
predicting step length of males specifically during the
hunting season are reported in the electronic supplemen-
tary material, table S3 and table 3. Predictions of the
top-ranked GAMM for variation in step length of har-
vested elk versus those that survived are reported in
figure 2a. Harvested males always moved faster
(321.7 + 9.9 m) than elk that survived (269.4 + 4.4 m)
the hunting season (table 3 and figure 2a). In general,
variation of step length in males depending on environ-
mental and human factors (e.g. f aster movements at
dawn and dusk, in flatter terrain, within open areas and
closer to roads) recorded during the hunting season
(table 3) were similar to those recorded throughout the
year. The two-way interactions between hunting season
fate and environmental f actors were retained by the top-
ranked model (table 3). Elk that were harvested moved
faster than elk that survived as terrain ruggedness
decreased (i.e. flatter terrain) and when closer to roads
(table 3). When located within 1 km from the closest
road, harvested elk walked 58 m every 2 h more than
those than survived (harvested: 333.5 + 15.6 m; survived
275.9 + 6.5 m). We also found a strong interaction
between hunting season fate and week period in affecting
elk step length (table 3). Elk that were harvested increased
movement during weekends (weekday: 310.2 + 11.2 m;
Sat.– Sun. 350.0 + 20.4 m), whereas survived elk did
not (weekday: 270.7 + 5.1 m; Sat.–Sun. 266.0 + 8.4 m).
(b) Ecological factors affecting female mobility
Selection and parameter estimates of the best GAMM
predicting step length of females from January through
4410 S. Ciuti et al. Human hunters select elk behaviour
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the hunting season are reported in the electronic sup-
plementary material, table S4. Hunting season fate was
not retained in the top-ranked model. Females moved
faster in spring and decreased their movement rate in
summer (figure 1b). Inter-individual variability in step
length in females was higher in summer and during hunting
season whether compared with earlier periods of the year
(figure 1b). Younger females moved faster than older ones
(figure 1c). Females showed pronounced crepuscular
activity moving faster at dawn (389.2 + 2.4 m) and dusk
(309.8 + 2.3 m) than during the day (244.3 + 1.2 m) and
night (160.9 + 1.3 m). Females moved faster in low terrain
ruggedness and open areas (281.7 + 1.2 m) than outside of
them (234.6 + 1.2 m), and they moved faster when closer
to gravel roads and during Sat.–Sun. (265.8 + 1.6 m)
compared with weekdays (256.3 + 1.0 m). Movement be-
haviour (migratory, resident, disperser) and land use were
factors retained in the best model.
Selection and parameter estimates of the best GAMM
predicting step length of females specifically during the
hunting season are reported in the electronic supplementary
material, table S5. Hunting season fate was retained in the
best model. Predictions for the variation of step length of
harvested versus survived elk are reported in figure 2b.
Although females that were harvested sharply decreased
their movement rate at the onset of the hunting season,
they moved faster (304.2 + 8.4 m) than females that
survived (242.0 + 2.2 m) throughout the hunting season
(see the electronic supplementary material, table S5 and
figure 2b). Step length recorded during the hunting season
decreased as age increased in females that survived
( figure 2c), while this was not true for females that were
harvested (figure 2c). Females that were harvested (age
less or than equal to 9 y.o.) moved faster (304.1 + 8.4 m)
than females younger (245.5 + 2.9 m) or older (236.7 +
3.5 m) than 9 y.o. that survived the hunting season.
6.4
(a)
(b)(c)
5.5
all females all females
5.55
5.50
5.45
5.40
5.35
ln (step length)
ln (step length)
5.30
5.25
5.4
5.3
5.2
hunting
season
5.1
harvested
survived
hunting season
6.2
6.0
ln (step length)
5.8
5.6
50
(19 February
)
50
(19 February
)
0 150
(30 May)
250
(7 September)
100
(10 April)
150
(30 May)
100 200
510
age
15
300
Julian date (date)
Julian date (date)
200
(19 July)
250
(7 September)
300
(27 October
)
Figure 1. Predicted variation of step length over the time (from January through the hunting season) in male elk that survived or
were harvested during the hunting season (a), in female elk irrespective of their hunting season fate (b), and estimated smoother
predicting the effect of age on the variation of step length in female elk (c). Smoothed predicted values and approximate point-
wise 95% CIs were calculated by adding the intercept value to the contribution of both fixed and random effects in GAMMs.
Human hunters select elk behaviour S. Ciuti et al. 4411
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Two-way interactions were retained in the top-ranked
model (see the electronic supplementary material, table
S5) but without a clear effect in females, with the excep-
tion of distance from roads. Females that were harvested
moved faster than survived elk closer to roads. When
located within 1 km from the closest road, harvested
females walked 55 m every 2 h more than survived elk
(harvested: 317.0 + 11.5 m; survived 262.4 + 3.1 m).
(c) Behaviours affecting probability of being
harvested
Selection and parameter estimates of the most parsimo-
nious GLM predicting the probability of an elk being
harvested are repor ted in table 4 ((a) males, (b) females).
Males were more likely to be harvested if they selected
open areas, increased their movement rate and used flat-
ter terrain. Indeed, males that survived avoided open
areas (w
oa
¼ 0.65 + 0.04) more than harvested ones
(w
oa
¼ 0.82 + 0.07). Females were more likely to be har-
vested if they selected open areas and their movement rate
increased. While harvested fe males selected open areas
(w
oa
¼ 1.13 + 0.07), survived ones avoided them (w
oa
¼
0.87 + 0.05). Younger females (effect of age) using
areas closer to roads (effect of d
grv
) had a higher chance
of being shot by hunters.
4. DISCUSSION
(a) ‘Shy hiders’ versus ‘bold runners’
We substantiated our main prediction that individuals choos-
ing to move faster (i.e. a ‘running’ strategy, thus increasing
detectability sensu Frair et al. [20]) as an anti-preda tor strat-
egy to escape from hunters have a higher probability of being
harv ested than those animals that decrease movement as an
anti-predator strategy (i.e. a ‘hiding’ strategy). Patterns were
stronger in young inexperienced males facing their first hunt-
ing season compared with females. Males with higher
mov ement rate and weaker avoidance of open areas were
eventually harvested compared with shy individuals with
less conspicuous behaviour that survived. Personality clearly
drove this pattern, given that inter-individual differences in
movement rate w ere already present before the onset of the
hunting season. Males that wer e harves ted responded to
hunters by moving faster than elk that survived, especially
during weekends, close to roads and in flatter terrain. Flatter
terrain is generally more accessible to hunters, while using
sloped terrain gives an ungulate a better vantage point from
which to watch for predators [43]. Thus, males that were
harvested had adopted exactly the movement strategy
that would increase their detectability where and when
the probability of being spotted by a hunter was higher.
We did not detect a significant increase in activity in
males during the rut, which was likely confounded by the
overlapping hunting season.
P ersonality tr aits were less evident in females, likely con-
founded by learning. Indeed, females adjusted their
behaviour by decreasing movement rate with increasing
age, perhaps as a result of increased experience and/or
knowledge of the habitat [19]. However, our results
showed that hunters harvested female elk based on behav-
ioural traits. Among younger females (age 2–9 y.o.),
females that moved faster and selected open areas during
the hunting season were harvested, whereas females that sur-
vived moved more slowly and avoided open areas. Females
that were harvested moved faster than those that survived
when closer to roads, as recorded for males. Interestingly,
movement rate decreased as age increased in survived
females, but not among those that were eventually harvested.
The latter clearly sho w ed a lower plasticity and adaptability
to the local environment. Older and more experienced
females (10 –19 y.o .) decreased detectability by mo ving
slower, avoiding open areas, and consequently they all sur-
vived the hunting season.
Harvested elk could be defined as ‘runners’, while survi-
ved elk as ‘hiders.’ A noise, a car approaching or a person
walking likely evoked opposite behavioural responses in
eventually harvested and survived elk. Over the past few
years, concepts of personality and temperament in wildlife
have received increased attention [44]. In many vertebrates,
including birds, fishes and rodents, individuals differ
in aggressiveness, sociability, level of activity, reaction to
nov elty and fearfulness [45,46]. Such personality traits
have been used to characterize behavioural types and gave
rise to the concept of ‘bold’ and ‘shy’ individuals. The
‘shy– bold continuum’ is now recognized as a fundamental
axis of behavioural variation in animals [44,47], and is
associated with the response of an individual to risk-taking
and novelty [48]. The cautious behaviour of elk that sur-
vived in our study (shy hiders) is certainly the end result
of an extreme individual plasticity, resulting in the ability
to adapt behaviour to more people on a weekend. An impor-
tant question is whether the behavioural differences among
individuals are highly repeatable (i.e. depending on person-
ality traits) or if they are a consequence of recent experience?
Hunters appear to create a ‘landscape of fear’ [49], but
apparently individual elk respond to that stimulus very
differently, significantly affecting their survival.
(b) Huma ns selecting behavioural traits: three-way
community-level inte ractions
The occurrence of two contrasting alternative strategies
(runners versus hiders) increases the probability that a
Table 3. Coefficients (
b
) and standard errors (s.e.) estimated
by the best general additive mixed model (GAMM)
predicting step length (ln-transformed) of male elk (n ¼ 45)
in southwest Alberta, southeast British Columbia and
northwest Montana during the hunting season.
b
s.e.
intercept 6.464 0.390
hunting season fate (harvested) 2.189 1.220
movement behaviour (migratory) 0.095 0.086
movement behaviour (resident) 0.153 0.164
day period (day) 2 0.834 0.041
day period (dusk) 2 0.434 0.043
day period (night) 2 1.098 0.038
terrain ruggedness r 2 0.013 0.002
open areas (inside) 0.115 0.036
land use (private land) 0.184 0.086
land use (public land) 0.033 0.085
log-distance from gravel roads d
grv
2 0.043 0.023
week period (Sat.– Sun.) 2 0.055 0.033
hunting season fate (harvested)  r 2 0.005 0.003
hunting season fate (harvested)  open
areas (inside)
0.018 0.076
hunting season fate (harvested)  d
grv
2 0.056 0.043
hunting season fate (harvested)  week
period (Sat.– Sun.)
0.179 0.070
4412 S. Ciuti et al. Human hunters select elk behaviour
Proc. R. Soc. B (2012)
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behavioural trait will be selected by humans. Indeed,
among the most ubiquitous recent impacts on vertebrate
predator–prey dynamics are the global dissemination
and explosive growth of humans in all but high Arctic
landscapes [50]. As a consequence, the strength of the
interaction that once involved native prey and native pre-
dators is now modulated by a complex, three-way
community-level interaction involving people, predators
and prey [51]. Hunting mortality is often substantially
higher than natural mortality for game animals [52].
Selection of behavioural traits is an important but often-
ignored the consequence of human exploitation of wild
animals. Adaptation to exploitation might produce unde-
sirable evolutionary change [52]. Such a change may not
be unde sirable if environmental conditions and selection
pressures generate new evolutionary trajectories reflecting
new conditions experienced by animals. For instance, if
hunters are producing shyer elk that are harder to find,
it may be undesirable for the hunters but not for the
elk population. However, evolutionary change could
become undesirable when previous selection pressures
and the new ones are antagonistic, and the combination
of both pressures is leading to a decrease in population
viability [2 –4]. Empirical studies showed how human
harvest of ungulates may drive wolf– elk or wolf– caribou
population trends [53– 55], with special regards to ungu-
late populations subjected to multiple predators [56].
Human hunters might cause even more rapid changes if
they are selecting elk anti-predator strategies differently
than those selected by wolves. Increases in mobility
could be the natural response of elk against their natural
predator [57], but this strategy is clearly not favourable
for avoiding human predation, as shown by our data.
Many species such as elk have been hunted by humans
for centuries [58]; so human selection on prey is not new.
Hunting pressure, though, might have been increased
6.6
(a)
6.4
6.2
6.0
5.8
250
(7 September)
250
(7 September)
260
(17 September)
270
(27 September)
270
(27 September)
280
(7 October
)
290
(17 October
)
290
(17 October
)
300
510
age
15
280260
300
(27 October
)
(b)
(c)
6.0
5.7
5.6
5.5
ln (step length) ln (step length)
ln (step length)
5.8
5.6
5.4
5.2
Julian date (date)
Julian date (date)
harvested
survived
survived
harvested
harvested
survived
Figure 2. Predicted variation of step length in male (a) and female elk (b) that survived or were harvested during the hunting
season, and estimated smoothers for the effect of age on step length in female elk depending on hunting season fate (c).
Smoothed predicted values and approximate point-wise 95% CIs were calculated by adding the intercept value to the
contribution of both fixed and random effects in GAMMs.
Human hunters select elk behaviour S. Ciuti et al. 4413
Proc. R. Soc. B (2012)
on September 26, 2012rspb.royalsocietypublishing.orgDownloaded from

where human population has exploded in the recent
past. However, the main difference between the pre-
Columbian era and present day is technology. Modern
hunters have high-powered rifles for hunting, and this
favours different behaviours than when hunters were
hunting with spears or with a bow. High-technology hunt-
ing is certainly introducing very different selection
pressures, and this could explain why elk have not already
evolved a consistent strategy to deal with modern hunting.
Wildlife managers have typically placed primary emphasis
on the demographic consequences of hunting, with little
consideration of potential evolutionary effects [52].
If humans are indeed becoming the most powerful
evolutionary force in the environment [2 –4], wildlife man-
agers might need to modify harvest regulations and policies
to ensure that hunting is sustainable. Human-mediated
evolutionary changes could reduce fitness [59– 61] with
the potential to affect future yield and population viability
[52]. Species with a relatively high degree of individual
behavioural plasticity (such as elk) are more likely to survive
these new human selection pressures, but there could be
direct trait-mediated consequences for the population
as well as indirect consequences for other species that
interact with elk (e.g. wolves). Furthermore, species with
little behavioural plasticity might be in greater danger of
extirpation or extinction.
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
Parks Canada for funding and support. We thank three
anonymous reviewers and the editors for invaluable
comments on the manuscript.
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