Behavioral
Ecology
The official journal of the
ISBE
International Society for Behavioral Ecology
Behavioral Ecology (2016), 27(5), 1432–1440. doi:10.1093/beheco/arw052
Original Article
Effects of experimental human approaches
on escape behavior in Thomson’s gazelle
(Eudorcas thomsonii)
Tomas Holmern,a,b Trine Hay Setsaas,a Claudia Melis,c Jarle Tufto,d and Eivin Røskafta
aDepartment of Biology, Norwegian University of Science and Technology, Høgskoleringen 5,
Realfagbygget, NO-7491 Trondheim, Norway, bNorwegian Environment Agency, PO Box 5672
Sluppen, N-7485 Trondheim, Norway, cDepartment of Biology, Centre for Biodiversity Dynamics,
Norwegian University of Science and Technology, Høgskoleringen 5, Realfagbygget, NO-7491
Trondheim, Norway, and dDepartment of Mathematical Sciences, Centre for Biodiversity Dynamics,
Norwegian University of Science and Technology, N-7491 Trondheim, Norway
Received 4 April 2015; revised 18 February 2016; accepted 3 March 2016; Advance Access publication 22 April 2016.
Prey rely on making correct risk assessments when approached by potential predators in order to stay alive. We conducted experimental human approaches with different simulated threat levels toward solitary adult male Thomson’s gazelles, that were located
in open grassland. We measured individuals flight initiation distance (FID), distance fled, escape speed, and the distance between
the location where the focal individual had stopped to flee and where the human stopped the approach (termed safety distance).
Multivariate analyses revealed an overall significant effect of starting distance, alertness, and time of day, but no statistical effect
was found for approach speed on the multivariate response. The individual responses showed a significant positive effect of starting
distance on both FID and safety distance. We also found a novel unimodal effect of time on FID. Finally, alertness and approach speed
only had a significant effect on safety distance, where faster approaches and individuals that displayed alert behavior had shorter
safety distances. Together, these findings indicate support for the “flush early and avoid the rush” hypothesis, shows the necessity of
using starting distance, alertness, and time as covariates when testing the effects of threat level, and demonstrate the usefulness of
the new metric safety distance.
Key words: antipredator behavior, escape behavior, flight initiation distance, Serengeti, starting distance, Thomson’s gazelle.
INTRODUCTION
Flight is a common antipredator behavior among animals when
confronted with a predator and is a vital factor in enhancing individual fitness. What influences escape decisions in prey animals has
gained a lot of attention in the past decade, which can be seen as
part of a surge of interest to the potential role of behavior ecology
in wildlife conservation and management, and greater recognition
of predation as a key evolutionary force (Berger-Tal et al. 2015;
Cooper and Blumstein 2015). Escape behavior should be optimized
rather than maximized, so that animals should refrain from fleeing
until the cost and benefit of flight are equal (Ydenberg and Dill
1986). Studies on escape behavior mainly focus on preflight risk
assessment, mostly flight initiation distance (FID), because these
responses are easy to measure and the value it has for understanding
Address correspondence to T. Holmern. E-mail: [email protected].
© The Author 2016. Published by Oxford University Press on behalf of
the International Society for Behavioral Ecology. All rights reserved. For
permissions, please e-mail: [email protected]
risk perception (fish: Januchowski-Hartley et al. 2011; reptiles:
Samia et al. 2015; birds: Møller et al. 2014; mammals: Stankowich
2008). FID is the distance between the approacher and the focal
individual when it first moves away. Many of these studies use
human experimental approaches, which are generally regarded
more threatening than other types of disturbance (e.g., canids, cars,
aircrafts; Stankowich 2008).
In one of the first studies on preflight responses in ungulates,
Walther (1969) reported that in Thomson’s gazelles’ (Eudorcas thomsonii) FID was affected by vehicle approach speed. Later studies
have shown that there are a range of factors that may influence the
decision for an ungulate to flee from a potential predator (reviewed
in Stankowich 2008), such as predator type (e.g., Walther 1969),
approach speed (e.g., positive: Walther 1969; no effect: Hutson
1982), group size (e.g., positive: de Boer et al. 2004; Stankowich and
Coss 2006; no effect: Manor and Saltz 2005; negative Cederna and
Lovari 1985; Matson et al. 2005; Reimers et al. 2006), vegetation
Holmern et al. • Escape behavior in Thomson’s gazelle
1433
Yet, Samia and Blumstein (2015) tested this relationship with a
large and independent number of bird taxa and found strong support to FEAR using an appropriate metric (phi). Taken together,
these studies show that field methods need to take into account
both SD and alertness when studying preflight behavior and possible dynamic relationships with other aspects of escape behavior.
Surprisingly, few studies have focused on how FID is matched
with other aspects of escape behavior, such as escape angle, escape
speed, and distance fled (Dill 1990; Stankowich and Coss 2006).
For example, FID can affect the type of escape responses because
perceived greater risk (e.g., short FID) may necessitate more
extreme escape measures. Animals can also compensate for greater
risk through adjusting escape speed. For instance, escape speed in
woodchucks (Marmota monax) was higher when the distance to refuge was greater (Kramer and Bonenfant 1997). Conversely, in mule
deer (O. hemionus), there was no difference in escape speed between
human and snowmobile approaches (Freddy 1986). These studies
suggest that postflight responses may be dependent on species, type
of threat, and access to safe refuges.
Distance to perceived safety is an important parameter influencing risk perception. In species that have access to safe refugia,
individuals seek to optimize distance and adjust speed according
to the perceived risk (Bonenfant and Kramer 1996). Stankowich
and Blumstein (2005) speculated that prey have a zone of safety
around refugia, and when they are located farther away, they have
an elevated perception of risk. In comparison, ungulate species living in open grassland habitats generally do not have safe refuges
(e.g., steep terrain, cliffs, or dense vegetation) and will therefore be
under evolutionary selection pressure to develop a greater variety
of behavioral responses in order to remain safe (Caro et al. 2004).
It may therefore pay off to be risk aversive under these circumstances and consequently move away early when a perceived threat
is detected and always attempt to maintain a safe distance to the
potential predator (Walther 1969). Nonetheless, in previous studies,
the distance fled is often used as the only postflight metric to gauge
the perceived threat. However, it does not accurately take into
account the actual distance to the threat (e.g., human approacher),
but measures only the distance between start and stop points of
type (e.g., Altmann 1958; Rowe-Rowe 1974; Stankowich and Coss
2007), time of day (e.g., Walther 1969; Taylor and Knight 2003),
and anthropogenic disturbance (e.g., Hamr 1988; Reimers et al.
2003; Setsaas et al. 2007). However, there is large heterogeneity
across studies both in terms of effect size and direction (Stankowich
and Blumstein 2005; Stankowich 2008). This is hardly surprising
because there has been no common method of how to conduct
approach experiments across studies and a lack of agreed variables
to include (e.g., Guay et al. 2013, but see Blumstein et al. 2015).
Previous studies have shown that when investigating flight decisions in experimental approaches, it is crucial to account for starting distance (SD) (see Figure 1 and Table 1 for definitions), as well
as alertness (or alert distance) (Blumstein 2003; Fernádez-Juricic
and Schröeder 2003). SD limits the potential range of response in
FID by focal individuals because short SDs will be associated with
greater risk for the prey (Samia and Blumstein 2015). Moreover,
animals that are alert prior to the approach by a potential predator,
or become alert early in the approach, will also choose to escape
earlier than individuals that are unaware of the approaching threat,
as shown in Columbian black-tailed deer (Odocoileus hemionus columbianus), impala (Aepycerus melampus), and giraffe (Giraffa camelopardalis) (Setsaas et al. 2007; Stankowich and Coss 2007; Marealle et al.
2010). These studies indicate that ungulates will move away earlier when detecting potential risks, probably in order to reduce or
minimize ongoing vigilance costs of monitoring the approaching
threat, as suggested by the flush early and escape the rush (FEAR)
hypothesis (Blumstein 2010). However, Dumont et al. (2012) voiced
concern over potentially spurious relationship between SD and
alert distance with FID, due to spontaneous movement of the study
object and statistical constraints. Nevertheless, Williams et al. (2014)
showed that the FEAR prediction remains even after accounting
for spontaneous behavior. Among several different taxa (reptiles,
birds, mammals), there is a positive relationship between SD and
alert distance with FID, which underscores its relevance (Samia
et al. 2013). The importance of monitoring costs in escape theory
was also underlined by the developments made by Cooper and
Blumstein (2014), who showed that dynamic increases in assessed
risk might increase as duration (e.g., SD) of approach increases.
Gazelle Stops
ce
fet
Sa
Vehicle
nc
e
e
sta
nc
sta
i
yD
Di
Sto
F le
d
tan
is
pD
Escape
Angle
Approacher
Flight Initiation Distance
Gazelle Origin
Starting Distance
Figure 1
Plan view of gazelle FID from an approaching human, escape angle, and distance fled during flight. The SD and safety distance are shown by thin black
lines, whereas the stop distance is indicated with thin dotted lines. The big black square represents the starting point for the approach, and the black triangle
indicates the direction of approach and where the approacher stops when the focal individual moves away. The locations of the gazelle before and after flight
are represented by small black dots.
Behavioral Ecology
1434
Table 1
Definitions and terms used in the text and figures
Term
Definition
SD
The distance between the focal individual’s original
location and the human approacher when starting walking
toward the animal
The distance between the human approacher and the focal
individual when it first moves away
The distance between the original location of the focal
individual and the location where the individual stops to
move
The distance between the location where the approaching
human stops as a response to the focal individual moving
away and the location where the focal individual stops after
fleeing
FID
Distance
fled
Safety
distance
the fleeing animal. The safety distance (see Figure 1 and Table 1),
defined as the distance from the fleeing prey’s ending point to where
the predator stopped the approach because the prey began to flee,
is a potentially new metric that defines the zone where the animal
judges itself to be safe and may thus improve the understanding of
how species respond to threats (i.e., animals that do not have safe
refugia). For example, when the focal individuals move away from
the approaching predator at an escape angle of 180°, the safety
distance equals FID and the distance fled. The consequences of
an ungulate’s risk assessment to a greater threat level will thus not
only manifest themselves in preflight behavior but will also likely be
closely associated to postflight behavior.
The objective of this study was to test how increased predation
threat affected different aspects of escape behavior, while controlling for SD and alertness. We also included a novel escape metric,
termed safety distance, which may better reflect postflight response
in species living in open habitats. We conducted experimental
human approaches on foot with different approach speed toward
solitary individual adult male Thomson’s gazelles in open grassland. These approaches were used as standardized models in order
to test the dynamic effects of predator behavior across preflight and
escape behavior (FID, distance fled, escape speed, safety distance)
because of the largely inconsistent results of sex, group size, and
vegetation type in previous studies on ungulates (Ydenberg and
Dill 1986; Stankowich and Blumstein 2005; Stankowich and Coss
2007; Stankowich 2008). We hypothesized that greater perceived
threat, through rapid human approaches, would elicit larger distances in preflight and escape behavior because ungulates respond
to increased perceived risk by adjusting the distance between
themselves and the potential predator (Stankowich 2008). We predicted that 1) animals would flee soon after detecting the threat
(i.e., positive relationship between SD and FID), 2) FID would be
greater with increasing approach speed in accordance with greater
perceived predation risk, 3) escape speed would be greater under
increased predation risk, and 4) distance fled and safety distance
would increase with increasing approach speed in accordance with
greater perceived predation risk.
METHODS
Subject and location
This study was conducted on the short grass plains in Serengeti
National Park, which is located in north-eastern Tanzania.
Thomson’s gazelles are small migratory grazers (ca. 20 kg) that live
in open savannah grassland and had, in 2003, an estimated population size of about 170 000 within the Serengeti ecosystem (IUCN
2008). The herds are not stable, and there is considerable emigration and immigration. During the dry season, territory activity is
limited, where territorial males revert to bachelor status and the
sexes mingle (Estes 1993), and several medium- and large-sized
carnivores prey on Thomson’s gazelles. Hunting is strictly prohibited within the national park, but illegal bushmeat hunting, which
is mostly conducted by local people on foot with bows and arrows
or are caught in wire snares, is widespread (Maddock 1979; Arcese
et al. 1995). Thomson’s gazelle is one of the species that is actively
targeted (Holmern et al. 2004).
Experimental procedures
Approaches by a single observer were conducted between August
to mid-December 2003 on solitary adult males (>1 years old, horns
reaching well beyond the ears and with a clear S shape) located
in open vegetation (grassland or wooded grasslands with 2–20%
tree cover) with short grass (<30 cm) and level topography between
06:00 AM and 07:00 PM. We sampled a very large area along
roads and tracks within the central plain areas (~3000 km2). An
area was never resampled before at least a week had gone by, and
we excluded identified territorial males used in previous trials.
We used a vehicle to scout along the established tracks and roads
for potential individuals that could be used in the approach trials.
Following Caro (1986), we first excluded potential trials where possible dangerous animals where located close by, and approaches
were not conducted if a potential predator was in sight. Second,
the selected individuals had to be at least 50 m from other individuals or groups (including individuals from other large wildlife
species). Third, there had to be an unobstructed view along the
trajectory between the approacher and the focal individual (e.g.,
no large rocks, bushes, and trees). Fourth, the focal individual had
to be closer to the vehicle than other animals around it (along the
line of approach). Fifth, trials were not conducted if the animals
reacted to the vehicle, if vegetation or terrain restricted the choice
of escape angle, as well as in close proximity to water courses and
tourist facilities. Finally, trials were abandoned if the individual suddenly changed its behavior (i.e., both in pre- and post-flight behavior) due to disturbances other than the approaching human, such
as: another vehicle was nearby, if it was raining, due to interaction
with other wildlife located nearby or if individuals had visual contact with previous trials.
When a suitable gazelle had been identified, we gently stopped
and switched off the car engine. We then noted initial behavior of
the individual before commencing the approach (behavior having
the longest duration during the first 60 s). The first author (T.H.)
and second author (T.H.S.) together scored these as alert (head held
high with outstretched neck, standing with ears erect, and facing the
vehicle) or not alert (i.e., feeding, sleeping/resting). Before the experimental approach commenced, the distance from the vehicle to the
individual (i.e., SD) was measured with a Leica geovid 7 × 42 BDA
rangefinder, which was accurate to the nearest 1 m (<366 m) (with
an integrated electronic compass). Whereupon, a single observer
(T.H., wearing khaki colored t-shirt and pants) exited the vehicle
and started walking in a slow steady pace directly toward the individual. We conducted 10 speed trials of each approach type, where
slow approaches (n = 94) had a speed of 1.7 ± 0.2 m/s (standard
error) and fast approaches 4.4 ± 0.7 m/s (n = 59). The 2 approach
speeds were selected on the basis of providing a relevant difference
Holmern et al. • Escape behavior in Thomson’s gazelle
1435
in threat level, and the fast approach also had to consider the speed
that the approacher could maintain over a relevant distance.
Immediately, when the focal gazelle took flight (defined as movement away from the individuals original position, ranging from a
walk to trotting or sprinting away), the observer stopped the approach
and using a stopwatch kept track of the time the individual used to
run away before stopping again (flight time). After the approach had
been terminated the second observer (driver) recorded the distance
to the first observer and the distance to where the animal had terminated its flight (i.e., stop distance). Angles to the focal gazelle’s origin
and stop position were also noted. The distance fled and safety distance were calculated by using the law of cosines (Figure 1), whereas
escape speed was calculated as follows: distance fled/flight time.
Analyses
We used a multivariate analysis of variance (Manova) approach
that allows the dependent variables to be grouped and analyzed as
one multivariate response, in addition to looking at the individual
responses separately. We fitted the model using the lm function in
the “base” R-package and conducted type II Pillai’s trace tests of
significance using Manova in the “car” package (Fox and Weisberg
2011) because we had multiple dependent variables (FID, distance
fled, escape speed, and safety distance) that were found to be moderately correlated with each other (i.e., Table 2). Taking into account
the correlations between the response variables, the Pillai’s trace tests
if the independent variables have an overall effect on the multivariate response. This results in more power, in particular, if the variance
of the multivariate response is small in the direction of the multivariate effect. We selected independent variables for explaining pre flight
and escape behavior based on the review done by Stankowich and
Blumstein (2005), these were SD, alertness, approach speed, and
time of day. Instead of modeling time as linear, we included 2 terms:
β1cos(2π t/24) + β2sin(2π t/24), where 0 < t < 24, so that we avoided
a discontinuity at 24. We chose to include 2 interactions, SD × alertness and approach speed × alertness, to investigate if the distance to
the starting point affected alertness and if behavior depended on the
different threat levels in the experiment. Prior to analyses, all response
variables were inspected for normality through quantile–quantile
plots, and it was necessary to square root transform FID, distance fled,
escape speed, and safety distance to improve normality of the residual
errors. The full model was investigated through a step down approach
by omitting independent variables with no significant effect based on
the Pillai’s trace test at a significance level of α = 0.05. The considered significance value was 2 tailed at P < 0.05 throughout, and all
data are presented in mean ± standard error. The analyses were done
using R 3.2.1 Software (R Development Core Team 2014).
RESULTS
A total of 153 approach experiments were conducted, where
the focal individuals all reacted by fleeing from the approaching
human. Several of the predictor variables were found to correlate,
where FID had a positive correlation with both SD (rp = 0.589,
P < 0.001) and safety distance (rp = 0.529, P < 0.001).
A statistical significant effect was obtained for the 3 main effects,
SD, alertness, and time, on the multivariate response, which contributed significantly to the difference between groups. There was
no significant effect of approach speed on the multivariate response
of escape behavior (Table 3).
The individual response showed a strong positive effect of SD on
FID. Interestingly, there was a unimodal effect of time of day on
FID, where the effect was at a maximum at 11:59 AM (amplitude:
15.5 m) (Figure 2). None of the variables included in the model for
distance fled were significant (Table 4). For the dependent variable
escape speed, only time had a statistical significant effect, where
the effect increased toward dusk (maximum: 07:30 PM, amplitude:
1.1 m/s) (Figure 3). Finally, there was a significant effect of SD,
alertness, and approach speed on safety distance. SD had a positive
effect. Gazelles that displayed alert behavior prior to approaches
had shorter safety distances. Also, fast approaches caused shorter
safety distances in the focal gazelles (Figure 4).
DISCUSSION
Overall, the results from our multivariate analyses support that
Thomson’s gazelle choose to escape early when detecting a potential threat, through our finding of a strong positive relationship
between SD and FID, supporting the FEAR hypothesis (Blumstein
2010). However, contrary to expectations, increased threat level did
not have any significant effect on preflight nor in the direction we
predicted for postflight behavior. Moreover, we found a unimodal
effect of time on FID, where FID peaked when the sun was at its
highest on the Serengeti plains. Additionally, the novel escape metric, safety distance, revealed that increased threat level and alertness caused focal gazelles to flee shorter distances than gazelles that
were not alert to the approaching threat.
Using humans as experimental predators in order to gauge antipredator behavior is a common experimental method (Stankowich
and Blumstein 2005) and is often used in combination with a
vehicle (e.g., Bildstein 1983; Caro 1986; Stankowich and Coss
2006). Nevertheless, it is not without drawbacks because it may
be challenging to separate responses due to the vehicle from that
of the human approacher. In our study, if the individual showed
alert behavior prior to the approach, this was probably a response
to the disturbance of the vehicle. For not-alert individuals, the
response was likely only caused by the human approacher (SD >
alert distance). However, the individuals chosen were located relatively far away from the car, which gave them time to assess the
approaching human as a separate threat and respond accordingly
in pre- and post-flight behavior. Nevertheless, we cannot rule out
a possible small effect because of the presence of the car on FID
(e.g., through an effect of silhouette; Walther 1969), but considering
Table 2
Pearsons correlation matrix of the best Manova model, mean, SD, and range for the dependent variables explaining escape behavior
1. FID
2. Distance fled
3. Escape speed
4. Safety distance
1
2
1.0
−0.027
−0.027
0.529
1.0
0.587
0.201
3
1.0
0.077
4
Mean
SD
Range
1.0
81.1
57.7
6.2
125.9
28.64
38.80
3.17
45.94
28–188 m
8–218 m
0.5–18.1 m/s
47–304 m
Behavioral Ecology
1436
Table 3
Estimates for the Manova test describing the vector of the 4
dependent variables on escape behavior in Thomson’s gazelle
Variable
df
Pillai’s trace
F
P value
SD
Alert
Approach speed
Time (sin + cos term)
4, 144
4, 144
4, 144
8, 144
0.384
0.105
0.059
0.127
22.484
4.212
2.272
2.455
<0.001
0.003
0.064
0.014
50
Flight initiation distance (m)
100
150
df, degrees of freedom.
8
10
12
Time
14
16
18
Figure 2
FID of Thomson’s gazelles in relation to the time of day.
the wide range of distances used in the study, we judge this to be
of minor importance. Moreover, in some of the previous studies,
escape behavioral parameters are taken from mixed—species flocks,
where a focal individual approach is used and where it is assumed
that no species interactions occur (Weston et al. 2012). In our study,
we strove to select isolated individuals (see Methods for details),
but considering the abundant number of wildlife on the Serengeti
plains in some cases there were other wildlife groups located in the
vicinity. Nevertheless, we think that this likely had little influence on
pre- and post-flight behavior because we excluded cases were there
were obvious interactions with conspecifics or heterospecifics. We
speculate that it might have a potential small effect on escape angle
because it would increase the survival chances of an individual if it
could hide or seek refuge among other wildlife (Fitzgibbon 1990).
SD is often used as a surrogate for alert distance (Blumstein
2003; Cooper et al. 2015), and in accordance with our first prediction, we found that SD had a strong positive effect on FID, thereby
further supporting the FEAR hypothesis (Blumstein 2010; Cooper
and Blumstein 2014). The costs of fleeing a patch are partly dependent on resource availability according to the model of Ydenberg
and Dill (1986). On the plains, grass and herbs are abundant, and a
grazer will sustain a minimal opportunity cost by temporarily leaving a patch. However, remaining will not only incur a higher monitoring cost, but as the distance closes the more likely it is that the
ensuing attack by the predator will demand a energetically costly
escape, increase chances of injury, or in the worst case be successful. It will therefore, as pointed out in the life-dinner principle
(Dawkins 1982), pay off to be risk aversive and move away from an
approaching potential threat soon after it is detected. For example,
results in Walther (1969) illustrate that the risk of capture is taken
into account when gazelles initiate flight at much greater distances
for carnivores that prefer gazelles as prey (e.g., cheetah Acinonyx
jubatus) than more generalist predators.
Table 4
Coefficient estimates, SE, t values, and P values for estimates explaining the 4 dependent variables in escape behavior in Thomson’s
gazelle (transformed values)
Dependent
Independent
β
SE
t
P value
FID
Adjusted R2: 0.384, F5,147 = 19.95, P < 0.001
Intercept
SD
Alert
Approach speed
Sin
Cos
Intercept
SD
Alert
Approach speed
Sin
Cos
Intercept
SD
Alert
Approach speed
Sin
Cos
Intercept
SD
Alert
Approach speed
Sin
Cos
5.650
0.016
0.370
−0.151
0.006
−1.006
6.298
0.004
0.154
0.018
0.069
−0.299
2.212
0.001
0.072
0.134
−0.173
0.089
9.520
0.011
−0.661
−0.755
−0.204
−0.572
0.39
0.002
0.22
0.21
0.16
0.41
0.80
0.004
0.449
0.42
0.33
0.822
0.19
0.00089
0.105
0.099
0.077
0.193
0.58
0.003
0.327
0.309
0.239
0.598
14.260
8.754
1.682
−0.718
0.039
−2.481
7.838
1.092
0.343
0.041
0.212
−0.363
11.741
1.599
0.68
1.349
−2.237
0.463
16.283
3.922
−2.022
−2.443
−0.849
−0.956
<0.001
<0.001
0.095
0.474
0.969
0.014
<0.001
0.277
0.732
0.967
0.832
0.717
<0.001
0.112
0.497
0.179
0.027
0.644
<0.001
<0.001
0.045
0.016
0.397
0.341
Distance fled
Adjusted R2: −0.021, F5,147 = 0.36, P = 0.8736
Escape speed
Adjusted R2: 0.032, F5,147 = 1.99, P = 0.08
Safety distance
Adjusted R2: 0.128, F5,147 = 5.47, P < 0.001
SE, standard error.
Holmern et al. • Escape behavior in Thomson’s gazelle
0
5
Escape speed (m/s)
10
15
1437
8
10
12
Time
14
16
18
Figure 3
The escape speed of Thomson’s gazelles in relation to the time of day.
0
50
Safety distance (m)
100 150 200 250
300
Slow
Fast
50
100
150
200
Starting distance (m)
250
300
Figure 4
The relationship between approach speed with safety distance. Slow
approaches (solid circles) are shown with heavy solid line, whereas fast
approaches (open circles) are shown with thin dotted line.
Interestingly, our results revealed a unimodal effect of time on
FID, which is to our knowledge the first time that this has been
reported in mammals. FID showed a peak in amplitude at midday. This coincides with the time the sun and temperature is at
its highest on the plains (rising from about 15 °C in the morning
to above 30 °C at noon). In other taxa, such as ectotherms, the
effect of temperature on escape decisions has been reported in
several studies (Cooper 2000; Cooper and Wilson 2008). Recently,
Lattanzio (2014) also reported a top in FID during midday in the
lizard Ameiva festiva. He suggested that this coincided with the peak
foraging or mate searching period, where A. festiva would have a
higher perception of risk due to elevated predation risk. In contrast,
predation risk for Thomson’s gazelles during the day is thought
to be highest in the morning and late evening (Schaller 1972).
Nevertheless, Cooper et al. (2007) reported that for the major day
time hunter of Thomson’s gazelles, the cheetah, the risk was not
influenced by the time of day. However, in addition to the risk of
predation, large mammals living in hot and arid environments face
a risk of overheating and dehydration, and they have made several behavioral and physiological adaptations in order to conserve
energy (Fuller et al. 2014). On the plains we observed that during
midday hours Thomson’s gazelles sought shade and kept movement to a minimum. We therefore speculate that high temperature
might be a physiological cost and that focal gazelle’s chose to move
away earlier, through longer FIDs, in order to conserve energy and
deter a potential attack.
Contrary to our second prediction and in contrast to results
reported in previous studies in other taxa (e.g., Stankowich and
Blumstein 2005), approach speed was in our results not statistical significant. This is somewhat surprising, given that Walther
(1969) found an effect of approach speed by driving in a vehicle
toward focal gazelles. This effect is also widely reported in other
taxa such as in lizards and birds (Stankowich and Blumstein 2005).
A positive effect in ungulates has only been reported in a handful
of studies where a human approacher has been used as a stimuli
(Stankowich 2008). However, comparisons across studies is made
difficult by the fact that studies have used different protocols and
have not controlled for the dynamic effects of SD and alertness
(e.g., Walther 1969). A similar result as reported in this study was
found in Columbian black-tailed deer, where running approaches
did not elicit greater responses at the 0.05 level, when controlling
for SD and alertness (Stankowich and Coss 2006). Compared with
the study of Walther (1969), the approach speed was even slightly
larger in this study and should therefore be sufficient to represent
2 different threat levels (1.3–3.6 m/s vs. 1.7–4.4 m/s in our study).
We speculate that the relative size difference of the experimental
approacher (vehicle vs. human) might have an influence on the
focal individual. The loom rate of an object has also in other studies been reported to influence on escape responses, where the relative size of the approaching threat in respect to the focal individual
might matter (Walther 1969; Frid and Dill 2002). Further studies
on other species are required to elucidate the effect of approach
speed, while using the recommended research protocol (Blumstein
et al. 2015).
Previous studies have found both a positive relationship between
FID and the distance fled (Taylor and Knight 2003; Stankowich
and Coss 2007) and some report a negative relationship (Hamr
1988; Andersen et al. 1996). However, our results on distance fled
included the null model. The F test for the linear regression tests
whether the fit using a nonzero slope is better than the null model
with zero slope, which for this postflight model means that we cannot reject the null hypothesis. Our result thus indicates that distance fled may be a poor metric for escape behavior in some species
because it does not accurately reflect the actual distance between
the predator and focal individual. Ungulates, such as Thomson’s
gazelle, show great flexibility in adjusting their responses to the
perceived threat level, and because the focal gazelles in these types
of experiments were located in open habitat and were never pursued, it is likely that they were able to continuously monitor the
predator and quickly perceived a reduced risk and stopped fleeing.
Nevertheless, escape behavior depends on perceived risk, where,
for example, resident impalas that experience heavy illegal hunting pressure outside the Serengeti National Park fled at greater
distances compared with those inside the national park when
Behavioral Ecology
1438
approached by a human (Setsaas et al. 2007). Moreover, Parker
et al. (1984) reported that greater distance fled occurs in response to
skiers or individuals on foot compared with snowmobiles. Likewise,
Freddy et al. (1986) and Freddy (1986) reported that responses by
mule deer to persons on foot, when compared with snowmobiles,
were longer in duration, more often involved running, and required
greater energy expenditure.
In contrast to our third prediction, we did not find any effect of
threat level on escape speed in Thomson’s gazelle. In woodchucks
that use borrows, a heightened perception of risk has earlier been
reported to influence escape speed in woodchucks where the slope
of escape speed on FID was steeper when the burrow was located
between the observer and the woodchuck than when the woodchuck was located between the observer and the burrow (Kramer
and Bonenfant 1997). However, finding an effect might be particularly challenging in open habitats because they allow continuous
monitoring of the predator. In support of this, we also observed
that in some instances individual’s escape speed declined rapidly
after an initial burst when the focal gazelle realized that it was not
being pursued (Holmern T, personal observation). Considering
including experimental pursuits while registering pursuit deterrent signals, such as stotting, might prove a better indicator of
perceived threat level in gazelles (Fitzgibbon and Fanshawe 1988).
Time of day has earlier been reported to affect escape behavior
in ungulates (Altmann 1958; Taylor and Knight 2003). We found
time of day to be positively related to escape speed, where there
was a higher speed toward the end of the day. This result supports the findings of Walther (1969), who noted that Thomson’s
gazelles on the Serengeti plains take more easy flight toward the
end of the day, especially after sun down, and he suggested that
this might be related to the time periods when many predators
are active. Conversely, in zebra (Equus quagga) and wildebeest
(Connochaetes taurinus), Scheel (1993) found higher scan rates at
night, whereas in impala, Matson et al. (2005) found higher vigilance toward the end of the day. Both attributed this to the activity pattern of predators.
We found that safety distance caused an escape response contrary to our last prediction. Thomson’s gazelle responded by
decreasing safety distance under the heightened predation risk
approach. Focal gazelles that were alert prior to the experiment commenced also reduced safety distances. However, nonalert focal gazelles kept longer distances between themselves and
the approacher. Similarly, recent work by Dumont et al. (2012)
reported that in alpine marmots (Marmota marmota) behavior prior
to the start of the experiment had an effect on FID. The authors
suggested that this was a result of differences in scanning rates that
cause delays in detection, and might partly explain the variance
in pre- and post-flight metrics seen in previous studies. We speculate that when gazelles’ have suboptimal information about the
threat, such as not being alert, the best strategy may be to be risk
aversive when escaping. Thereby, inflicting a greater energetic cost
to the predator through keeping a greater safety distance, which
will allow the gazelle time to monitor and improve the risk assessment. Our finding that sudden unanticipated disturbances cause
greater escape responses is also in agreement with other studies
(Parker et al. 1984; Hamr 1988; Taylor and Knight 2003; de Boer
et al. 2004). The effect of approach speed on preflight response
has earlier been reported in several taxa (Samia et al. 2013), but
this study is among the few who have reported an effect in a postflight metric (Stankowich and Blumstein 2005; Stankowich 2008).
In animals that use refuges, the margin of safety (i.e., predators
distance to refuge when the prey enters the refuge) and distance
fled is clearly the more relevant variable when investigating escape
behavior. However, in open landscapes with no refuges, our study
indicates that safety distance may be a more relevant measurement. Not only does it more accurately reflect the distance to the
threat, but it also indirectly takes into account the escape angle
of the prey as opposed to distance fled. For instance, the distance
fled might indicate a movement directly away from the predator (180°), which would be the safest choice, but animals may
also move sideways. For example, in Columbia black-tailed deer,
Stankowich and Coss (2006) found that the escape angle in the
majority of cases was less than 180° and in some instances even
less than 90°; thereby, taking the deer toward the potential predator instead of moving away from it. Mammals show many forms
of escape, where behavior often serve multiple functions (Caro
et al. 2004). Interestingly, several studies report that ungulates may
circle back during flight when approached by a predator (Walther
1969; Baskin and Skogland 1997). Olfactory and auditory cues are
also important for ungulates when deciding to flee (Altmann 1958).
Moving sideways might thus be a complementary escape tactic to
gain an accurate assessment of the predator’s motivational state
for appropriate choice of escape response that maximizes the individual’s survival chances. Not controlling for such a behavioral
effect could therefore introduce large variance in the response and
may partly explain the considerable variation among studies in the
direction of distance fled (Stankowich 2008). We suggest that by
reflecting the distance from the predator rather than the point the
prey started its escape (i.e., distance fled), safety distance is a more
refined postflight metric, which better considers ungulates dynamic
escape decisions.
In summary, we demonstrated that increased threat level did
not cause greater pre- and post-flight responses as predicted
in Thomson’s gazelle, when using SD, alertness, and time as
covariates. The results support the FEAR hypothesis through a
positive effect of SD on FID. Furthermore, as pointed out by
Lattanzio (2014), the unimodal effect of time opens up new
opportunities to investigate if this holds in other environments
and taxa, and emphasizes that time should be considered used
as a covariate. Increased threat level had an effect contrary to
the direction we predicted on safety distance. When alerted to
the danger, prey on the Serengeti plains can continuously monitor predators, where our results indicate that gazelles adjust
safety distance dynamically in order to compensate for perceived
risk. We therefore propose that future studies on escape behavior should carefully select covariates, consider using a multivariate approach, and use safety distance instead of distance fled
when investigating postflight metrics in animals that do not have
access to safe refugia.
FUNDING
This work was supported by a Norwegian Research Council grant
(RCN: 140865/720 to E.R.)
We thank N. Alfred, E. Kalumbwa, G. Mwakalebe, B. Røskaft, and
S. Stokke for assistance during the study and 2 anonymous reviewers for
very constructive comments. We are further grateful to the Tanzania Wildlife
Research Institute (TAWIRI), Tanzania National Parks, Norwegian Institute
for Nature Research (NINA), and Tanzanian Commission for Science and
Technology for logistic support and permission to conduct the study.
Handling editor: Marc Thery
Holmern et al. • Escape behavior in Thomson’s gazelle
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