ArticlePDF Available

African ungulates and their drinking problems: Hunting and predation risks constrain access to water

  • CNRS & Nelson Mandela University

Abstract and Figures

Prey make several behavioural adjustments to minimize the risk of predation by their natural predators. When hunted, however, they may have to adjust their behaviour further or differently to cope with this additional mortality risk. Here, we investigated whether African large ungulates would adjust their behaviour in response to hunting risk (i.e. risk of being shot by human hunters). We predicted that they would shift their use of surface water, a key and scarce resource in African savannas, from day hours to night hours to reduce the risk of encountering human hunters. In Hwange National Park, Zimbabwe, we monitored waterholes to record the temporal drinking niche of three nonhunted ungulates (i.e. impala, Aepyceros melampus, greater kudu, Tragelaphus strepsiceros, sable antelope, Hippotragus niger). We also monitored waterholes in hunting areas in the vicinity of Hwange National Park. In Hwange National Park, the three species avoided waterholes at night, when the risk of natural predation was higher. Conversely, in the hunting areas, all three species visited waterholes more often at night. Impala and greater kudu, however, were less prone to switch towards night-time use of waterholes in hunting areas compared to sable antelope, although all three species were exposed to similar hunting risk. Our results suggest that hunting may force African ungulates to shift their visits at waterholes from day hours towards night hours, but that the magnitude of this shift may be constrained by the predation risk imposed by large nocturnal carnivores. We conclude that species preyed upon by natural predators adjust their antipredator behaviour in response to the additional risk of predation imposed by hunting.
Content may be subject to copyright.
African ungulates and their drinking problems: hunting and predation risks
constrain access to water
William-Georges Crosmary
, Marion Valeix
, Hervé Fritz
, Hillary Madzikanda
, Steeve D. Côté
Université de Lyon, CNRS Université Claude Bernard Lyon, Laboratoire Biométrie et Biologie Evolutive, Bât Gregor Mendel, Villeurbanne, France
Département de Biologie, Pavillon Alexandre-Vachon, Université Laval, Québec, Canada
CIRAD eEMVT, Integrated Wildlife Management Research Unit, Campus International de Baillarguet, Montpellier, France
Zimbabwe Parks and Wildlife Management Authority, Causeway, Harare, Zimbabwe
article info
Article history:
Received 27 April 2011
Initial acceptance 23 May 2011
Final acceptance 28 September 2011
Available online 16 November 2011
MS. number: A11-00345R
behavioural adjustment
ecology of fear
hunting risk
predation risk
Prey make several behavioural adjustments to minimize the risk of predation by their natural predators.
When hunted, however, they may have to adjust their behaviour further or differently to cope with this
additional mortality risk. Here, we investigated whether African large ungulates would adjust their
behaviour in response to hunting risk (i.e. risk of being shot by human hunters). We predicted that they
would shift their use of surface water, a key and scarce resource in African savannas, from day hours to
night hours to reduce the risk of encountering human hunters. In Hwange National Park, Zimbabwe, we
monitored waterholes to record the temporal drinking niche of three nonhunted ungulates (i.e. impala,
Aepyceros melampus, greater kudu, Tragelaphus strepsiceros, sable antelope, Hippotragus niger). We also
monitored waterholes in hunting areas in the vicinity of Hwange National Park. In Hwange National Park,
the three species avoided waterholes at night, when the risk of natural predation was higher. Conversely,
in the hunting areas, all three species visited waterholes more often at night. Impala and greater kudu,
however, were less prone to switch towards night-time use of waterholes in hunting areas compared to
sable antelope, although all three species were exposed to similar hunting risk. Our results suggest that
hunting may force African ungulates to shift their visits at waterholes from day hours towards night
hours, but that the magnitude of this shift may be constrained by the predation risk imposed by large
nocturnal carnivores. We conclude that species preyed upon by natural predators adjust their
antipredator behaviour in response to the additional risk of predation imposed by hunting.
Ó2011 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.
With ongoing increase of human disturbance on wildlife
habitats, there is growing concern about how disturbance stimuli
may affect animal behaviour (Geist 1970; Caro 1999a). Animals
respond to human disturbance similarly to predation risk, that is,
by adjusting their behaviour in order to reduce the disturbance, for
example, by increasing ight initiation distance or vigilance, or by
shifting habitats (reviewed in Stankowich & Blumstein 2005). There
may be medium- to long-term costs associated with behavioural
adjustments (e.g. lower resource intake rate, lower diet quality, or
decreased mating opportunities; Lima & Dill 1990; Lima 1998). The
repercussions of these nonlethal effects of predation or disturbance
(sensu Lima 1998) might therefore alter the tness of individuals,
affect population dynamics (Creel et al. 2007; Creel & Christianson
2008), and ultimately community structure and ecosystem
functioning (Brown et al. 1999; Ripple & Beschta 2004).
Hunting may be considered an extreme type of human distur-
bance as individuals from harvested populations experience
a direct risk of mortality. Hunting disturbance disrupts normal
activities, alters diurnal activity rhythms and increases ight
initiation distances in many waterbird species (reviewed in Madsen
& Fox 1995). In large ungulates, studies have reported increased
ight initiation distance in response to hunting disturbance
(reviewed in Stankowich 2008). However, much less is known
about long-term behavioural responses such as shifts in habitat use
(Swenson 1982) and feeding sites (Benhaiem et al. 2008), or
changes in activity patterns (Kufeld et al. 1988).
Here we address an example of long-term behavioural adjust-
ment to hunting in large game ungulates. We assessed whether
ungulates adjust their temporal use of key and scarce resources in
response to hunting risk (i.e. risk of being shot by human hunters).
A common behavioural response of prey to predation risk is to
avoid risky areas where predators are abundant (Ripple & Beschta
2004; Creel et al. 2005; Valeix et al. 2009a). In certain circum-
stances, however, spatial avoidance of predators may not be
possible, particularly when prey have no choice but to use risky
*Correspondence: W.-G. Crosmary, Département de Biologie, Pavillon
Alexandre-Vachon, 1045 avenue de la Médecine, Université Laval, Québec, Québec
G1V 0A6, Canada.
E-mail address: (W.-G. Crosmary).
Contents lists available at SciVerse ScienceDirect
Animal Behaviour
journal homepage:
0003-3472/$38.00 Ó2011 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.
Animal Behaviour 83 (2012) 145e153
areas to access rare and scattered resources where predators might
look for them. Under such situations, prey may shift their niche
along the time axis to reduce temporal overlap with predator
activity (reviewed in Kronfeld-Schor & Dayan 2003). While such
temporal adjustments have been illustrated in some predatoreprey
studies (Kotler et al. 1991; Fenn & Macdonald 1995; Roth & Lima
2007; Valeix et al. 2009b), much less has been reported on
hunted species (but see Kilgo et al. 1998; Sunde et al. 2009),
particularly on how hunting risk may affect resource utilization.
Semiarid African savannas are excellent ecosystems to investigate
how hunting risk may constrain temporal access of ungulates to
scarce water resources. First, hunting is very common in Africa, more
particularly sport hunting (Lindsey et al. 2007), or recreational
hunting practised by paying tourists. Yet, only few studies have
reported behavioural adjustments in response to hunting risk in
African ungulates (Caro 1999b; Matson et al. 2005; Setsaas et al.
2007) compared to ungulates from the northern hemisphere
(e.g. reindeer, Rangifer tarandus; reviewed in Reimers & Colman
2006). Second, surface water is limited in African semiarid
savannas. Hence, even though the risk of encountering predators
(Valeixet al. 2009a, b) and human hunters (e.g. Harrison & Bates 1991,
for ungulates in deserts of the Middle East) may be high, most
ungulates have to come to waterholes almost daily to satisfy their
drinking requirements. Third, the richness of African ungulate
communities provides the opportunity for original comparative
studies. While behavioural changes in harvested populations may be
used as indicators of species sensitivity to human hunting, Gill et al.
(2001) stressed that behavioural indicators might not always
adequately reect animal response to human disturbance. For
instance, animals of lower condition, or that undergo higher
constraints on resource requirements, are less likely to adjust their
behaviour in response to human disturbance (e.g. Beale & Monaghan
2004). Likewise, behavioural response to hunting risk may expose
ungulate species to their natural predators (e.g. Kilgo et al. 1998).
Species that are therefore more vulnerable to natural predators might
be less likely to adjust their behaviour to human hunting risk.
Multispecies studies are rare and greatly needed to understand how
specic ecological constraints may inuence behavioural responses
to human hunting.
We investigated how different African ungulate species (impala,
Aepyceros melampus, greater kudu, Tragelaphus strepsiceros,sable
antelope, Hippotragus niger) adjust their temporal use of waterholes
in response to hunting risk. We hypothesized that ungulates would
shift their temporal use of waterholes to night-time in hunting areas
to decrease temporal overlap with human hunters, in comparison to
protected areas where ungulates drink mainly during daytime
(Valeix et al. 2007a). We expected this shift to be partial, notably
because of the constraint exerted by predation risk at night, as the
main predators in our study area are mostly nocturnal (i.e. lion,
Panthera leo:Schaller 1972; spotted hyaena, Crocuta crocuta:Kruuk
1972). Furthermore, we predicted the magnitude of the shift to
vary according to species vulnerability to natural predation.
The species more often preyed upon (i.e. greater kudu) should
perform a shift of lower magnitude than the other species.
Study Site
Hwange National Park (nonHA) in northwestern Zimbabwe
S, 26
E) is approximately 15 000 km
. Sport hunting is
allowed and practised in governmental safari areas and private
concessions around nonHA: Matetsi Safari Area South (MSA South,
189 0 k m
) and Gwayi Intensive Conservation Area South (Gwayi ICA
South, ca. 880 km
)(Fig. 1). The hunting mode practised in these
hunting areas is sport hunting, and thus harvest usually represents
a small fraction of total population size, contrary to culling or
uncontrolled poaching. Harvest rates are traditionally set at about 2%
of population size for most ungulates in Zimbabwean hunting areas
(Cumming 1989). During the study period, we estimated per capita
hunting risk based on the ratio of hunting quotas over population
size estimates for the three species. We found, as did Cumming
(1989), that percentages of harvested individuals were about 2% of
population size (i.e. 2.5%, 1.7% and 1.4% for impala, greater kudu and
sable antelope, respectively). This implies similar per capita risk of
human predation for all ungulate species. These Hunting Areas (HA)
act as buffers between the National Park and human settlements
where subsistence agriculture is the principal activity and wildlife is
only present at low densities (Dunham 2002).
Vegetation is typical of southern African dystrophic wooded and
bushed savannas with patches of grasslands, dominated mainly by
Colophospermum mopane,Combretum spp., Acacia spp., Baikiaea
plurijuga and Terminalia sericea (Rogers 1993). The rainy season
ranges from November to April, and long-term annual rainfall
averages 613 mm (Chamaillé-Jammes et al. 2006). Surface water is
mainly found in natural and articially lled waterholes, and in
some rare rivers. During the dry season, natural waterholes and
rivers dry up and most of the surface water available to animals is
found in articially lled waterholes and river pools.
No sport hunting occurs in nonHA, but the Zimbabwe Parks and
Wildlife Management Authority (ZPWMA) allocates quotas to its
staff for food rations, mainly on elephants, Loxodonta africana, and
buffalos, Syncerus caffer, while other ungulates have not been
allocated since 2007. In the surrounding HA, hunting season ranges
from March to December (since the mid-1970s). Ungulates are
strictly hunted during daytime, from dawn (around 0600 hours) to
dusk (around1900 hours). Hunters are not allowed to hunt from
vehicles, so all hunts are performed on foot. Overall, the abundance
of large carnivores appears to be comparable between the study
HAs and their adjacent nonHA areas (Elliot 2007;Table 1). Hence,
the main difference between HA and nonHA is the predation risk
due to hunting by humans. We thus disposed of a contrasted
system, where the risk to ungulates in nonHA was exclusively
exerted by natural predators and the risk to ungulates in HA
originated both from natural predators and human hunters (further
information on the different study areas is provided in Table 1).
Our study area covered, in the peripheral HA, the hunting
concessions from which we had obtained authorization to conduct
wildlife surveys during the study period: Unit 3 in MSA South
(ca. 360 km
) and four hunting properties in Gwayi ICA South
(ca. 300 km
)(Fig. 1). In nonHA, we chose to cover the blocks that
were adjacent to those hunting concessions: Main camp
(ca. 1300 km
) and Robins (ca. 1000 km
) in the northern part of
Hwange National Park (Fig. 1). Therefore, the hunting concessions
and their adjacent blocks in nonHA presented similar vegetation
types and environmental conditions (i.e. rainfall, temperature, soil
characteristics; Ganzin et al. 2008; Peace Parks Foundation 2009).
We monitored waterholes in nonHA and surrounding HA in
2007 and 2008 during the hot dry season (AugusteOctober). In HA,
we selected waterholes on the basis of availability of standing water
and signs of recent ungulates presence (i.e. fresh spoors and faeces).
We monitored nine waterholes once in 2007 and seven waterholes
once in 2008. Only two waterholes were surveyed both years (i.e.13
different waterholes). In nonHA, each waterhole is surveyed once
a year (since the 1970s) by Wildlife and Environment Zimbabwe
(WEZ) and ZPWMA. Accounting for vegetation similarities with HA
as well as water availability, we randomly selected, from the
W.-G. Crosmary et al. / Animal Behaviour 83 (2012) 145e153146
existing database, 19 waterholes in 2007 and 13 waterholes in 2008
(i.e. 19 different waterholes).
Monitoring consisted in 24 h counts during full moon periods to
document temporal drinking niches of ungulates at waterholes. Full
moon nights guarantee good visibility conditions to carry out
observations, particularly in the areas surrounding waterholes,
which are characterized by the absence of vegetation. Observations
were conducted with binoculars or spotting scopes (15e45) from
platforms, tree hides or a car parked about 100 m away from the
waterholes to avoid disturbing animals. We recorded the time at
which ungulate groups arrived at waterholes, as well as species,
group size and presence/absence of young. We accounted for group
size and presence of young as cofactors in the decision to access
waterholes during daytime or at night, as both group size (many
eyes effect:Pulliam 1973;dilution effect:Hamilton 1971; Dehn
1990) and presence of young (Berger 1991; Burger & Gochfeld
1994) are known to inuence antipredator behaviours.
Study Species
We focused on two browsers: impala and greater kudu, and one
woodland grazer: sable antelope. These ungulates are strictly
dependent on surface water and they visit waterholes regularly,
mostly during daytime in nonHA (Valeix et al. 2007a). In the study
area, the main predators are lions and spotted hyaenas (Table 1).
Lions are ambush predators and many lion kills (w40%) occur close
to a waterhole (Valeix et al. 2009a, 2011). Hyaenas are cursorial
predators that commonly hunt in waterhole areas (Salnicki et al.
2001; N. Drouet-Hoguet, personal observations). Both carnivore
species are nocturnal and their visits to waterholes occur mainly at
night (70% and 80% of the lion visits recorded during the
Table 1
Site characteristics and densities of ungulates and large predators in nonhunting
areas (NonHA) in Hwange National Park, Zimbabwe, and in hunting areas (HA) in the
vicinity of Hwange National Park
Robins Gwayi ICA
Unit 3/MSA
Site characteristics
Area (km
) 1300 1000 300 360
Waterhole density
(nb/100 km
1.23 1.3 4.34 2.5
Road length (km) 325 400 172 263
Ungulate density (indiv./km
Impala 1.43 (0.47) 5.45 (0.31) e3.0 (0.15)
Greater kudu 1.59 (0.24) 1.52 (0.36) e2.5 (0.20)
Sable antelope 0.22 (0.44) ee1.0 (0.45)
Predator density
Lions (call-up surveys,
indiv./100 km
2.8e5.5 *2e3.9 5.3e7.7
Lions (spoor transects) 2.6 *2.2 4.5
Hyaena (call-up surveys,
indiv./100 km
11.3e22.1 *5.5e10.9 6.5e11.7
Hyaena (spoors/100 km
) 27.2 *11.6 26.5
Leopard (spoors/100 km
) 1.2 e4.2 1.3
Wild dog (spoors/100 km
) 0.4 e1.6 0.2
Cheetah (spoors/100 km
) 0.1 e0.5 1.5
Coefcients of variation associated with ungulate density estimates are given in
parentheses and were calculated by distance sampling (Buckland et al. 2001).
Density estimates were not available but were comparable to those at the Main
Camp (A. J. Loveridge & N. Elliot, personal communication).
For call-up surveys, the lower estimate corresponds to the 2.5 km response
range, and the higher estimate corresponds to the 3.2 km response range
(Elliot 2007).
Figure 1. Hwange National Park (nonHA) and adjacent hunting areas (Gwayi ICA South and MSA south, HA) in Zimbabwe. Study area is delineated by thick black lines.
W.-G. Crosmary et al. / Animal Behaviour 83 (2012) 145e153 147
Impala - nonHA
Kudu - nonHA
Sable - nonHA
Kudu - HA
Sable - HA
Impala - HA
00:00 00:00
06:00 06:00
12:00 12:00
18:00 18:00
25 16 9 16 25
16 944916
36 25 16 94
96,25 4 2,25 1
916 2536
4 6,25
12,25 4
6,25 9
12,25 4
0,25 0,25
Figure 2. Temporal visits at waterholes by groups of impala, greater kudu and sable antelope in 2007 and 2008 during daytime (0600e1900 hours) and night-time (1900e0600
hours) under two contrasted situations: natural predation and no sport hunting (nonHA, Hwange National Park, Zimbabwe) versus natural predation and sport hunting (HA). Each
bar length represents the number of groups that visited waterholes duringeach hour; the area of each bar is thus proportional to the frequency ofgroup visits. The black line in each
graph indicates mean arrival time, with 95% condence interval.
W.-G. Crosmary et al. / Animal Behaviour 83 (2012) 145e153148
monitoring occurred at night in nonHA and HA, respectively, and
90% and 100% of the hyaena visits occurred at night in nonHA and
HA, respectively; see also Supplementary Material, Fig. S1). Lions
were not hunted in HAs at the time of the study because of
a moratorium on lion hunting that took place in the study area
between 2005 and 2008, and hyaenas are very rarely targeted by
Impalas are more abundant than greater kudus and sable ante-
lopes in our study area (ca. 8800 impalas, 4700 greater kudus and
1200 sable antelopes), but they represent less than 5% of lionsdiet
(Loveridge et al. 2007). The four-fold difference in abundance
between greater kudu and sable antelope is reected in the diet of
lions, with greater kudu representing up to 24% and sable antelope
less than 5% (Loveridge et al. 2007). This is consistent with other
African systems where lions prey upon greater kudu and sable
antelope in accordance with the abundance of these prey species,
whereas impala is avoided (reviewed in Hayward & Kerley 2005).
However, in the nearby Kruger National Park, South Africa, 61.3% of
encounters between impala and lion lead to hunts (Funston et al.
2001). Therefore, despite impala not being a preferred prey species
for lion, impalas probably do not consider lions to be a low-level
threat. Hyaenas show no prey species preference (Hayward 2006).
Impala and greater kudu, which are among the commonest prey
species consumed by hyaenas, are taken in proportion to their
relative abundance. The sable antelope, however, is more likely to be
avoided. In our study area, greater kudu, impala and sable antelope
represent 8e12 %, 5e10% and less than 5% of hyaenasdiet, respec-
tively (Drouet-Hoguet 2007). However, about 80% of hyaenasfood is
acquired by scavenging (Drouet-Hoguet 2007), whereas about 16% is
obtained by hunting. Impalas represent 56% of the prey species
hunted, and greater kudus represent 19%, whereas sable antelopes
are not hunted (Drouet-Hoguet 2007). For the secondary large
carnivore species of the study area (i.e. leopard, Panthera pardus,wild
dog, Lycaon pictus, cheetah, Acinonyx jubatus), there is no equivalent
information on diet composition. Nevertheless, reviews on these
large carnivore diets (Hayward et al. 2006a, b, c)indicatethat
leopards preferentially prey upon impala, consume greater kudu in
proportion to their relative abundance, and tend to avoid sable
antelope. Wild dogs preferentially prey upon greater kudu and
impala (see Rasmussen 2009 for our study area), but they consume
sable antelope in proportion to their relative abundance. Cheetahs
preferentially prey upon impala, consume greater kudu in
accordance with their relative abundance and tend to avoid sable
antelope. Overall, these diet studies suggest that greater kudu, and
impala to a lesser extent, are more vulnerable to their natural
predators than are sable antelope.
The magnitude of behavioural adjustments in hunted
populations is likely to increase with hunting pressure (Caro 1999b;
Matson et al. 2005). During road counts carried out in HA in the late
dry season in 2007 and 2008, we encountered four groups of impala/
100 km, three groups of greater kudu/100 km and 1.4 groups of sable
antelope/100 km. In the meantime, the realized hunting quotas (i.e.
actual numbers of animals shot) were 1.2 times higher for impala
than for greater kudu, and four times higher for impala than for sable
antelope (ZPWMA, unpublished data). Therefore, considering the
dilution effect, the human hunting risk perceived by groups of
ungulates was comparable among the three species. Thus, the three
species differed mainly in their exposure to natural predators, and
we therefore expected impala and greater kudu to be less prone to
shift their visits at waterholes during night-time than sable
antelopes when hunted because of their higher exposure to natural
predators then. We observed 226 groups of impala (131 in nonHA, 95
in HA), 188 groups of greater kudu (149 in nonHA, 39 in HA) and 63
groups of sable antelope (41 in nonHA, 22 in HA).
Statistical Analyses
Drinking temporal niche at waterholes was approximated by
the arrival time of groups at waterholes. We plotted a frequency
distribution of group observations at waterholes by hour using
Oriana 2.0 software for circular data (Fisher 1993). We used the
group as the statistical unit. Mean arrival times per species were
compared with the WatsoneWilliams test for circular means
(Fisher 1993). To test whether waterholes were more frequently
visited at night in HA than in nonHA, we compared the respective
temporal distributions of visits at waterholes between the two
zones. To determine the expected number of visits in HA during
daytime (0600e1900 hours) and at night (1900e0600 hours), we
used the distribution of visits at waterholes in nonHA where there
was no sport hunting. We then compared the expected distribution
with the observed distribution in HA using a contingency table
(following Zar 1984), with the Pearsons chi-square test with Yates
continuity correction, and the Fishers exact test for frequencies
that were smaller than ve.
For each species, we investigated the effects of zone (nonHA or
HA), group size and presence/absence of young on the probability
of arriving at a waterhole at night with a logistic regression model.
The respective effects of group size and presence of young,
however, may be confounded for the three species because these
two factors were correlated (Wilcoxon signed-ranks test with
continuity correction: impala: V¼20301, r¼0.10, N¼201,
P<0.001, N¼201; greater kudu: V¼17020, r¼0.19, N¼184,
P<0.001; sable antelope: V¼177 0 , r¼0.64, N¼59, P<0.001).
We included all possible interactions between xed effects but
none was statistically signicant. To compare the probabilities of
visiting waterholes at night between species, we used a logistic
regression with species and zone as factors, and their interaction.
For all logistic models, we used the Akaike Information Criterion
(AIC) to select the best model (Burnham & Anderson 2002). The
model with the lowest AIC value (i.e. the best compromise between
accuracy and precision) was retained. When
AIC between two
models was less than two, we selected the simplest model
according to the parsimony rules (Burnham & Anderson 2002). All
statistical analyses were generalized linear mixed models, with
year and waterhole tted as random effects, using R lme4 package
Groups of all three species visited waterholes preferentially
during daytime, generally avoiding night hours (Fig. 2). However,
avoidance of waterholes during night-time was less marked in HA,
where proportionally more groups came at night, than in nonHA.
In nonHA, 6% of groups of impala, less than 1% of groups of greater
kudu and 10% of groups of sable antelope visited waterholes at night.
Conversely in HA, 24% of groups of impala, 20% of groups of greater
kudu and 50% of groups of sable antelope visited waterholes at night.
These differences between day and night were signicant for the
three species (impala, Pearsons chi-square test:
¼13.76, N¼226,
P<0.001; greater kudu, Fishers exact test: N¼188, P¼0.001; sable
antelope, Fishers exact test: N¼63, P¼0.001; Fig. 3).
Average arrival times at waterholes did not differ between
nonHA and HA for impala (WatsoneWilliams test: F
1, 221
P¼0.2; Fig. 2) or greater kudu (F
¼0.15, P¼0.7; Fig. 2). Sable
antelope arrived at waterholes later in HA than in nonHA
(WatsoneWilliams test: F
¼18.68, P<0.0001; average
arrival time in nonHA ¼1140 hours; average arrival time in
HA ¼1856 hours). Circular variances associated with average
arrival times were higher in HA than in nonHA (circular
W.-G. Crosmary et al. / Animal Behaviour 83 (2012) 145e153 149
standard deviation: impala: nonHA ¼65
; greater kudu:
nonHA ¼41
; sable antelope: nonHA ¼56
The distribution of arrival times was thus more scattered in HA than
in nonHA. Temporal distributions of arrival times were more
concentrated during daytime in nonHA than in HA, particularly
during the hottest hours of the day (between 1000 and 1600 hours):
for impala, 49.6% of groups were observed during the hottest hours
of the day in nonHA, as opposed to 35.9% in HA; for greater kudu, it
was 68.5% in nonHA versus 31.5% in HA; and for sable antelope, 51%
in nonHA versus 30% in HA.
Logistic regressions conrmed that the probability of visiting
waterholes at night was higher in HA than in nonHA for the three
species (estimate SE: impala: 2.3 1.0; greater kudu: 3.7 1.1;
sable antelope: 7.0 2.2; Table 2). Moreover, the probability of
visiting waterholes at night was lower for groups of impala with
young (estimate SE ¼2.6 1.2), but not for groups of greater
kudu with young (Table 2). For sable antelope, although the
presence of young was retained in the selected model (Table 2), its
effect on the probability of visiting waterholes at night was highly
variable (estimate SE ¼20.4 2322.1). Group size was not
retained in the selected models and, therefore, did not affect the
probability of visiting waterholes at night for any of the three
species (Table 2).
Sable antelope had a higher probability of visiting waterholes at
night compared to impala and greater kudu in HA, but not in nonHA
(Fig. 4). In HA, impala and kudu had a similar probability of visiting
waterholes during night-time, whereas in nonHA, greater kudu had
a lower chance of visiting waterholes at night than did the two
other species.
Prey may shift their niche along the time axis to reduce temporal
overlap with predator activity (reviewed in: Kronfeld-Schor & Dayan
2003; Caro 2005). Human hunters, similarly to natural predators,
may also induce a temporal adjustment of the niche in hunted
populations (e.g. Kilgo et al. 1998). Here, we observed African
ungulatesuse of waterholes in semiarid savannas to examine how
100 131
95 39
% Groups observed at waterholes
Impala Kudu Sable
Figure 3. Effect of sport hunting on attendance of groups of impala, greater kudu and
sable antelope at waterholes during daytime (grey bars; 0600e1900 hours) versus
night-time (black bars; 1900e0600 hours) in 2007e2008. Zones with natural
predation and no sport hunting (nonHA, Hwange National Park, Zimbabwe) versus
natural predation and sport hunting (HA) are compared. Numbers above bars indicate
number of groups present at waterholes in each zone. Statistical tests are from
comparisons of frequencies within contingency tables, but results are presented as
percentages for clarity. *P<0.001.
Table 2
Summary of the logistic regression modelling the probability of visiting waterholes
at night as a function of zone (hunting area, HA, versus nonhunting area, nonHA),
group size and status (i.e. presence/absence of young) for groups of impala, greater
kudu and sable antelope in Hwange National Park (Zimbabwe) and the surrounding
hunting areas in 2007 and 2008
Models K*AIC
Null 22 127.1 7.8
Zone 24 125.2 5.9
Status 24 121.6 2.3
Group size 24 128.7 9.4
Zone Dstatus 25 119.3 0
Zone þgroup size 25 127 7.7
Group size þstatus 25 123.3 4
Zone þgroup size þstatus 26 120.5 1.2
Null 22 55.4 5.4
Zone 24 51 1
Status 24 56.5 6.5
Group size 24 55.6 5.6
Zone þstatus 25 50 0
Zone þgroup size 25 50.8 0.8
Group size þstatus 25 57.5 7.5
Zone þgroup size þstatus 26 51.6 1.6
Null 15 58 21
Zone 16 49.7 12.7
Status 16 48.1 11.1
Group size 16 55.2 18.2
Zone Dstatus 17 37 0
Zone þgroup size 17 42.7 5.7
Group size þstatus 17 50.6 13.6
Zone þgroup size þstatus 18 38.9 1.9
The selected models (lowest AIC) are shown in bold font.
Number of parameters. It includes the random factor waterholes.
Probability of visiting a waterhole at night
nonHA HA
nonHA HA
nonHA HA
Figure 4. Probabilities of visiting waterholes at night (1900e0600 hours) for impala,
greater kudu and sable antelope in 2007 and 2008. Zones with natural predation and
no sport hunting (nonHA, Hwange National Park, Zimbabwe) versus natural predation
and sport hunting (HA) are compared. Estimates from logistic regression models and
their condence intervals (backtransformed) are shown. Estimates with different
letters were signicantly different at P<0.05.
W.-G. Crosmary et al. / Animal Behaviour 83 (2012) 145e153150
they adjust their temporal niche in response to both their risk of
natural predation by large carnivores mostly at night and their risk of
being hunted by human hunters during the day. Groups of impala,
greater kudu and sable antelope visited waterholes more often at
night in hunting areas than in nonhunting areas (Hwange National
Park), where visits at waterholes were almost exclusively diurnal.
The temporal pattern of access to water was concentrated around
the hottest hours of the day in the protected area, particularly for
greater kudu, while it was scattered throughout the day in hunting
areas. More interestingly, we observed that groups of impala and
greater kudu were less prone to switch their access to water towards
night-time in hunting areas compared to groups of sable antelope,
although they were exposed to similar hunting risk.
The diurnal use of waterholes by ungulates in African savannas
is largely dictated by the circadian rhythms and physiology of these
ungulates (Finch 1972; Ayeni 1977; Valeix et al. 2007b). It may also
be a tactic to decrease the risk of encountering the main natural
predators of these ecosystems (Valeix et al. 2009b), which visit
waterholes essentially at night to drink or to hunt their prey. The
activity of ungulates at waterholes was almost exclusively diurnal
in the protected area, similar to that observed in other protected
areas (Hwange National Park: Weir & Davison 1965; Valeix et al.
2007a; Tsavo National Park, Kenya: Ayeni 1975), whereas a large
proportion of the groups (24% for impala, 20% for greater kudu and
50% for sable antelope) visited waterholes at night in hunting areas.
We hypothesize that this signicant temporal shift towards
nocturnal use of waterholes in hunting areas is a behavioural
adjustment of ungulates to the risk of being hunted by
humans during daytime. The increase of nocturnal activities
(e.g. movements, foraging, social interactions) as a way to escape
human disturbance occurring during daytime has been largely
documented in birds (e.g. brent goose, Branta bernicla bernicla:
Riddington et al. 1996; sanderling, Calidris alba:Burger & Gochfeld
1991 ), but less so in ungulates (but see: white-tailed deer, Odocoi-
leus virginianus:Kilgo et al. 1998; red deer, Cervus elaphus:Sunde
et al. 2009).
The signicant temporal shift towards nocturnal use of
waterholes exposes ungulates to their natural predators. Hence,
ungulates in the hunting areas face a trade-off between the risk of
natural predation and the risk of being hunted. Such trade-off is
poorly understood because most studies on hunting are carried out
on ungulates of temperate ecosystems (moose, Alces alces, elk,
Cervus canadensis:Altmann 1958; roe deer, Capreolus capreolus:de
Boer et al. 2004; Benhaiem et al. 2008; fallow deer, Dama dama:de
Boer et al. 2004; caribou and reindeer, R. tarandus: reviewed in
Reimers & Colman 2006), where the diversity and densities of
predators have been largely reduced by human activities (Berger
1999; Andersen et al. 2006). In comparison, African savannas still
host a rich guild of large carnivores. Our results showed greater
circular variances associated with mean arrival time at waterholes
for the three species in hunting areas, whereas the great majorityof
observations were made during the hottest hours of the day
(1000e1600 hours) in the protected area. In African savannas, the
hottest hours of the day coincide with the period when large
carnivores are less active (Kruuk 1972; Schaller 1972).
Consequently, ungulates drinking at waterholes during this period
may benet from a reduced risk of natural predation. Conversely,
visits of ungulates at waterholes were more randomly distributed
over the day in hunting areas. As a consequence, their whereabouts
were less predictable to human hunters. Random antipredator
behaviour of prey may also be an efcient tactic against predators;
for instance, Scannell et al. (2010) recently demonstrated that prey
that scan at random are more difcult to catch by predators. The
decrease in the predictability of the temporal pattern of visits at
waterholes may be an alternative tactic adopted by ungulates to
partly reduce hunting risk during daytime without completely
shifting towards night-time, thus limiting increased exposure to
natural predators.
The magnitude of the temporal shift in the use of waterholes
was expected to vary according to species vulnerability to natural
predation and exposure to hunting. Because the three species
differed mainly in their exposure to natural predation, we predicted
that greater kudu, and to a lesser extent impala, should be less
prone to shift their visits at waterholes during night-time than
sable antelope. Interestingly, sable antelope showed the greatest
shift to night use of waterholes in hunting areas, whereas greater
kudu and impala showed the smallest shift. Such results are
consistent with the observation that greater kudu is the second
most frequent prey of lions (after buffalo) (Loveridge et al. 2007),
and that greater kudu and impala represent a greater share of
hyaena diet in Hwange National Park than do sable antelope
(Drouet-Hoguet 2007), while sable antelope has never been iden-
tied as a main prey for one of the large carnivores in Hwange
National Park. Previous studies in this ecosystem have shown that
greater kudus are particularly responsive to the immediate risk of
predation by lions (Valeix et al. 2009b; Périquet et al. 2010). Hence,
it is not surprising that greater kudu and impala showed less
shifting in their temporal niche at waterholes towards the night in
hunting areas, and that sable antelope, which appears less sensitive
to natural predation, shifted the most to night use of waterholes in
hunting areas. Our results indicate that the higher the risk of
natural predation, the lower the probability of shifting to night use
of waterholes to avoid human hunters.
Note that we have limited our conclusions to nights during
which visibility was good, since we carried out all of our observa-
tions during full moon nights. Lower visibility condition during
other nights may change prey perception of natural predation risk.
Ayeni (1975) observed that many herbivores in Tsavo National Park,
Kenya, drank later into the night on moonlit nights than on
moonless nights, probably because it is then much more difcult to
detect approaching predators. Under moonless or cloudy nights,
the situation of ungulates would be therefore even more compli-
cated than during full moon nights because natural predation risk is
probably enhanced because of lower visibility.
The propensity to shift to nocturnal use of waterholes was also
inuenced by presence of young in impala, and in sable antelope to
a lesser extent, with the probability of coming to waterholes at
night being lower for groups with young than for groups without
young. Because young are highly vulnerable to predators, groups
with young normally adjust their behaviour to compensate for
increased vulnerability (increased vigilance: Burger & Gochfeld
1994; avoidance of risky areas and risky hours: Hamel & Côté
2007). In greater kudu, however, the presence of young in groups
had no signicant inuence. Since greater kudu is the most
vulnerable prey species, it may not be less dangerous for groups
without young to come at night at waterholes than for groups with
young. This however remains to be investigated. We found no effect
of group size on the probability of coming to waterholes at night.
Perceived predation risk, however, was expected to be lower in
larger groups because more individuals are present to scan the
surroundings for approaching predators (many-eyes effect,
Pulliam 1973) and because of the dilution effect of large groups
(Hamilton 1971; Dehn 1990). At night, because of the lower light
conditions (even under full moon light), more individuals may not
sufciently offset the increased risk of predation in large groups
because of the presence of young. Moreover, the risk for groups
with young to be targeted by natural predators might be higher
than the risk to be targeted by human hunters, the latter usually
seeking trophy males, which are often solitary or found in small
bachelor groups.
W.-G. Crosmary et al. / Animal Behaviour 83 (2012) 145e153 151
We cannot fully exclude other potential alternative explanations
to the nocturnal shift observed in hunting areas (e.g. differences in
environmental conditions, tourism and natural predation). The
studied hunting areas and Hwange National Park were adjacent and
showed similar soil composition, vegetation structure, temperature
and rainfall characteristics (Ganzin et al. 2008; Peace Parks
Foundation 2009). Moreover, the diurnal preferential use of water-
holes by African ungulates has been shown in other ecosystems
(e.g. Tsavo National Park, Kenya: Ayeni 1975; Etosha National Park,
Namibia: Preez & Grobler 1977). Therefore, differences in environ-
mental conditions are unlikely to account for the difference in the
temporal pattern of visits at waterholes observed in our study
between Hwange National Park and the adjacent peripheral hunting
areas. Tourism was generally low in Zimbabwe during our study
(2007e2008) because of the economic and political crisis, and there
were very few tourists in Hwange National Park and the peripheral
hunting areas. Finally, densities of natural predators, proportion of
waterholes visited by these natural predators, and the temporal
pattern of these visits were similar between Hwange National Park
and the peripheral hunting areas (Table 1,Supplementary Material,
Fig. S1). Hence, we interpret the shift to night use of waterholes in
hunting areas as a response to the risk of being hunted by human
hunters during the day, which is the main difference between the
national park and the peripheral hunting areas.
Our study illustrates how sport hunting inuences the ecology
of ungulates in a system where ungulates have to face a trade-off
between the risk of natural predation by carnivores and the risk
of being hunted, the former being higher at night and the latter
higher during the day. To our knowledge, this is one of the rst
attempts to explore such trade-off, and one of the rare studies that
contrasts different species. The three study species were charac-
terized by a gradient in the risk of natural predation, leading to
a gradient in the shift made to night use of waterholes in hunting
areas. The species more often preyed upon (i.e. greater kudu) per-
formed the shift of the lowest magnitude, whereas the species less
preyed upon (i.e. sable antelope) performed the shift of the highest
magnitude. Because our study did not measure the potential costs
associated with the shift of visits to waterholes towards night-time
in hunting areas, we cannot assume that this behavioural response
to hunting risk has any impact on individual tness. To better
understand how sport hunting and induced behavioural adjust-
ments inuence ungulate population dynamics, there is a need to
investigate whether the differences that we observed in ungulate
behaviour between hunting-free areas and hunting areas actually
lead to measurable costs in terms of physiology and survival
(e.g. greater exposure to natural predators, alteration of thermo-
regulation, or changes in time budget).
We acknowledge the Director General of the Zimbabwe Parks
and Wildlife Management Authority for providing the opportunity
to carry out this research and for permission to publish this
manuscript. W.G.C. was supported by scholarships from the French
Agence Nationale de Recherche and the Natural Sciences and
Engineering Research Council of Canada Discovery Grant (to S.D.C.),
and by the Soutien aux cotutelles internationales de thèses de
lUniversité Claude Bernard Lyon 1. This research was carried out
within the framework of the HERD project (Hwange Environmental
Research Development), as well as with the Research Platform
Production and Conservation in Partnership RP-PCP, funded by
the French Ministère des Affaires Etrangères, Ambassade de
France au Zimbabwe, CIRAD, CNRS and the IFB Global Change and
Biodiversity. We thank S. Le Bel, CIRAD representative in Zimbabwe,
and S. Chamaillé-Jammes and T. Tarakini for insightful comments
on previous drafts of the manuscript. We are indebted to Wildlife
Environment Zimbabwe for providing waterhole census data, as
well as rangers, students and volunteers who participated in eld
work. Finally, we sincerely thank Tim Caro and two anonymous
referees for their very fruitful comments on the manuscript.
Supplementary Material
Supplementary material for this article is available, in the online
version, at doi:10.1016/j.anbehav.2011.10.019.
Altmann, M. 1958. The ight distance in free ranging big game. Journal of Wildlife
Management,22, 207e209.
Andersen,R., Linnell, J. D. C. & Solberg, E. J. 2006. The future roleof large carnivores in
terrestrial trophic interactions: the northern temperate view. In: Large Herbivore
Ecology, Ecosystem Dynamics and Conservation (Ed. by K. Danell, P. Duncan,
R. Bergström & J. Pastor), pp. 413e424. Cambridge: Cambridge University Press.
Ayeni, J. S. O. 1975. Utilization of waterholes in Tsavo National Park (East). East
African Wildlife Journal,13, 305e323.
Ayeni, J. S. O. 1977. Waterholes in Tsavo National Park, Kenya. Journal of Applied
Ecology,14, 369e378.
Beale, C. M. & Monaghan, P. 2004. Behavioural responses to human disturbance:
a matter of choice? Animal Behaviour,68, 1065e1069.
Benhaiem, S., Delon, M., Lourtet, B., Cargnelutti, B., Aulagnier, S.,
Hewison, A. J. M., Morellet, N. & Verheyden, H. 2008. Hunting increases
vigilance levels in roe deer and modies feeding site selection. Animal Behav-
Berger, J. 1991. Pregnancy incentive and predation constraints in habitat shifts:
experimental and eld evidence for wild bighorn sheep. Animal Behaviour,41,
Berger, J. 1999. Anthropogenic extinction of top carnivores and interspecic animal
behaviour: implications of the rapid decoupling of a web involving wolves,
bears, moose and ravens. Proceedings of the Royal Society B,266, 2261e2267.
de Boer, H. Y., van Breukelen, L., Hootsmans, M. J. M. & van Wieren, S. E. 2004.
Flight distance in roe deer (Capreolus capreolus) and fallow deer (Dama dama)
as related to hunting and other factors. Wildlife Biology,10,35e41.
Brown, J. S., Laundré, J. W. & Gurung, M. 1999. The ecology of fear: optimal
foraging, game theory, and trophic interactions. Journal of Mammalogy,80,
Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L. &
Thomas, L. 2001. Introduction to Distance Sampling. Estimating Abundance of
Biological Populations. Oxford: Oxford University Press.
Burger, J. & Gochfeld, M. 1991. Human activity inuence and nocturnal foraging of
sanderlings (Calidris alba). Condor,93, 259e265.
Burger, J. & Gochfeld, M. 1994. Vigilance in African mammals: differences among
mothers, other females and males. Behaviour,131,154e274.
Burnham, K. P. & Anderson, D. R. 2002. Model Selection and Multimodel Inference:
a Practical Information-theoretical Approach. New York: Springer.
Caro, T. 1999a. The behavioureconservation interface. Trends in Ecology & Evolution,
14, 366e369.
Caro, T. 1999b. Demography and behaviour of African mammals subject to
exploitation. Biological Conservation,91,91e97.
Caro, T. 2005. Antipredator Defenses in Birds and Mammals. Chicago: Chicago
University Press.
Chamaillé-Jammes, S., Fritz, H. & Murindagomo, F. 2006. Spatial patterns of the
NDVIerainfall relationship at the seasonal and interannual time scales in an
African savanna. International Journal of Remote Sensing,27, 5185e5200.
Creel, S. & Christianson, D. 2008. Relationships between direct predation and risk
effects. Trends in Ecology & Evolution,23,194e201.
Creel, S., Winnie, J. A., Maxwell, B., Hamlin, K. & Creel, M. 2005. Elk alter habitat
selection as an antipredator response to wolves. Ecology,86, 3387e3397.
Creel, S., Christianson, D., Liley, S. & Winnie, J. 2007. Predation risk affects
reproductive physiology and demography of elk. Science,315, 960.
Cumming, D. H. M. 1989. Commercial and safari hunting in Zimbabwe. In: Wildlife
Production Systems: Economic Utilisation of Wildlife ( R. J. Hudson, K. R. Drew
& L. M. Bodkin), pp. 147e169. Cambridge: Cambridge University Press.
Dehn, M. M. 1990. Vigilance for predators: detection and dilution effects. Behavioral
Ecology and Sociobiology,26,337e342.
Drouet-Hoguet, N. 2007. Inuenc e des activités anthropogéniques sur le régime
alimentaire et la réponse numérique de la hyène tachetée en savane arborée dys-
trophique dominée par léléphant. Ph.D. thesis, University Claude Bernard Lyon 1.
Dunham, K. M. 2002. Aerial Census of Elephants and Other Large Herbivores in North-
west Matabeleland, Zimbabwe: 2001. WWFeSARPO Occasional Paper Series.
Harare: WWFeSARPO.
Elliot, N. 2007. A comparison of two methods for estimating population densities of
lion (Panthera leo) and other large carnivores using spoor transects and call-up
stations. M.S. thesis, Oxford University.
Fenn, M. G. P. & Macdonald, D. W.1995. Use of middens by red foxes: risk reverses
rhythms of rats. Journal of Mammalogy,76,130e136.
W.-G. Crosmary et al. / Animal Behaviour 83 (2012) 145e153152
Finch, V. A. 1972. The effects of solar radiation, of temperature regulation, and heat
balance in 2 East African antelopes, the eland and the hartebeest. American
Journal of Physiology,222,1374e1379.
Fisher, N. I. 1993. Statistical Analysis of Circular Data. Cambridge: Cambridge
University Press.
Funston, P. J., Mills, M. G. M. & Biggs, H. C. 2001. Factors affecting the hunting
success of male and female lions in the Kruger National Park. Journal of Zoology,
253, 419e431.
Ganzin, N., Crosmary, W.-G. & Fritz, H. 2008. A Simplied Vegetation Map of
Hwange National Park and Surrounding Areas Using Landsat ETMþSatellite
Imagery. Hwange Environmental Research and Development Programme, Report
2008. Harare: Zimbabwe Parks and Wildlife Management Authority.
Geist, V. 1970. A behavioral approach to the management of wild ungulates. In:
Scientic Management of Animal and Plant Communities for Conservation.
Eleventh Symposium, British Ecological Society (Ed. by E. Duffey & A. S. Watt), pp.
413e424. Oxford: Blackwell Scientic.
Gill, J. A., Norris, K. & Sutherland, W. J. 2001. Why behavioural responses may not
reect the population consequences of human disturbance. Biological
Conservation,97, 265e268.
Hamel, S. & Côté, S. D. 2007. Habitat use patterns in relation to escape terrain: are
alpine ungulate females trading-off better foraging sites for safety? Canadian
Journal of Zoology,85, 933e943.
Hamilton, W. D. 1971. Geometry for the selsh herd. Journal of Theoretical Biology,
31, 295e311.
Harrison, D. L. & Bates, P. J. J. 1991. The Mammals of Arabia. 2nd edn. Seven Oaks:
Harrison Zoological Museum.
Hayward, M. W. 2006. Prey preferences of the spotted hyaena (Crocuta crocuta) and
degree of dietary overlap with the lion (Panthera leo). Journal of Zoology,270,
Hayward, M. W. & Kerley, G. I. H. 2005. Prey preferences of the lion (Panthera leo).
Journal of Zoology,267, 309e322.
Hayward, M. W., Henschel, P., OBrien, J., Hofmeyr, M., Balme, G. & Kerley, G. I. H.
2006a. Prey preferences of the leopard (Panthera pardus). Journal of Zoology,
270, 298e313.
Hayward, M. W., Obrien, J., Hofmeyr, M. & Kerley, G. I. H. 2006b. Prey preferences
of the cheetah (Acinonyx jubatus)(Felidae: Carnivora): morphological limita-
tions or the need to capture rapidly consumable prey before kleptoparasites
arrive? Journal of Zoology,270,615e627.
Hayward, M. W., Obrien, J., Hofmeyr, M. & Kerley, G. I. H. 2006c. Prey preferences
of the African wild dog Lycaon pictus (Canidae: Carnivora): ecological require-
ments for conservation. Journal of Mammalogy,87,1122e113 1.
Kilgo, J. C., Labisky, R. F. & Fritzen, D. E. 1998. Inuences of hunting on the
behavior of white-tailed deer: implications for conservation of the Florida
panther. Conservation Biology,12,1359e1364.
Kotler, B. P., Brown, J. S. & Hasson, O. 1991. Factors affecting gerbil foraging
behavior and rates of owl predation. Ecology,72, 2249e2260.
Kronfeld-Schor, N. & Dayan, T. 2003. Partitioning of time as an ecological resource.
Annual Review of Ecology, Evolution, and Systematics,34,153e181.
Kufeld, R., Bowden, D. & Schrupp, D. 1988. Inuence of hunting on movements of
female mule deer. Journal of Range Management,41,70e72.
Kruuk, H. 1972. The Spotted Hyena: a Study of Predation and Social Behaviour.
Chicago: Chicago University Press.
Lima, S. L. 1998. Nonlethal effects in the ecology of predatoreprey interactions.
Lima, S. L. & Dill, L. M. 1990. Behavioural decisions made under the risk of
predation: a review and synthesis. Canadian Journal of Zoology,68,619e640.
Lindsey, P. A., Roulet, P. A. & Romañach, S. S. 2007. Economic and conservation
signicance of the trophy hunting industry in sub-Saharan Africa. Biological
Conservation,134, 455e469.
Loveridge, A. J., Davidson, Z., Hunt, J. E., Valeix, M., Elliot, N. & Stapelkamp, B.
2007. Hwange Lion Project Annual Report 2007. Harare: Zimbabwe Parks and
Wildlife Management Authority.
Madsen, J. & Fox, A. D. 1995. Impacts of hunting on waterbirds: a review. Wildlife
Matson, T., Goldizen, A. & Putland, D. 2005. Factors affecting the vigilance and
ight behaviour of impalas: research article. South African Journal of Wildlife
Peace Parks Foundation 2009. Integrated Development Plan: Zimbabwean Compo-
nent of the KAZA TFCA. Harare: Zimbabwe Parks and Wildlife Management
&Fritz,H.2010. Individual vigilance of African herbivores while drinking:
the role of immediate predation risk and context. Animal Behaviour,79,
Preez, J. S. & Grobler, I. D. 1977. Drinking times and behaviour at waterholes of
some game species in the Etosha National Park. Madoqua,10,61e69.
Pulliam, H. R. 1973. On the advantages of ocking. Journal of Theoretical Biology,38,
Rasmussen, G. S. A. 2009. Anthropogenic factors inuencing biological processes of
the painted dog Lycaon pictus. Ph.D. thesis, Oxford University.
Reimers, E. & Colman, J. E. 2006. Reindeer and caribou (Rangifer tarandus)
response towards human activities. Rangifer,26,5
Riddington, R., Hassall, M., Lane, S. J., Turner, P. A. & Walters, R. 1996. The impact
of disturbance on the behaviour and energy budgets of brent geese (Branta b.
bernicla). Bird Study,43, 269e279.
Ripple, W. & Beschta, R. 2004. Wolves and the ecology of fear: can predation risk
structure ecosystems? BioScience,54, 755e766.
Rogers, C. M. L. 1993. A Woody Vegetation Survey of Hwange National Park. Harare:
Department of National Parks and Wildlife Management.
Roth, T. C. & Lima, S. L. 2007. The predatory behavior of wintering Accipiter hawks:
temporal patterns in activity of predators and prey. Oecologia,152,169e178.
Salnicki, J., Teichmann, M., Wilson, V. J. & Murindagomo, F. 2001. Spotted
hyaenas Crocuta crocuta prey on new-born elephant calves in Hwange National
Park, Zimbabwe. Koedoe,44,79e83.
Scannell, J., Roberts, G. & Lazarus, J. 2010. Prey scan at random to evade observant
predators. Proceedings of the Royal Society B,268,541e547.
Schaller, G. B. 1972. The Serengeti lion: a Study of PredatorePrey Relations. Chicago:
Chicago University Press.
Setsaas, T., Holmern, T., Mwakalebe, G., Stokke, S. & Røskaft, E. 2007. How does
human exploitation affect impala populations in protected and partially
protected areas? A case study from the Serengeti Ecosystem, Tanzania. Biolog-
ical Conservation,136, 563e570.
Stankowich, T. 2008. Ungulate ight responses to human disturbance: a review
and meta-analysis. Biological Conservation,141,2159e2173.
Stankowich,T.&Blumstein,D.T.2005. Fear in animals: a meta-analysis and
review of risk assess ment. Proceedings of the Royal Society B,272,
Sunde, P., Olesen, C. R., Madsen, T. L. & Haugaard, L. 2009. Behavioural responses
of GPS-collared female red deer (Cervus elaphus) to driven hunts. Wildlife
Biology,15, 454e460.
Swenson, J. E. 1982. Effects of hunting on habitat use by mule deer on mixed-grass
prairie in Montana. Wildlife Society Bulletin,10,115e120.
Valeix, M., Chamaillé-Jammes, S. & Fritz, H. 2007a. Interference competition and
temporal niche shifts: elephants and herbivore communities at waterholes.
Oecologia,153,739e74 8.
Valeix, M., Fritz, H., Matsika, R., Matsvimbo, F. & Madzikanda, H. 2007b. The role of
water abundance, thermoregulation, perceived predation risk and interference
competition in water access by African herbivores. African Journal of Ecology,46,
Valeix, M., Fritz, H., Loveridge, A. J., Davidson, Z., Hunt, J. E., Murindagomo, F. &
Macdonald, D. W. 2009a. Does the risk of encountering lions inuence African
herbivore behaviour at waterholes? Behavioral Ecology and Sociobiology,63,
Valeix, M., Loveridge, A., Chamaillé-Jammes, S., Davidson, Z., Murindagomo, F.,
Fritz, H. & Macdonald, D. W. 2009b. Behavioral adjustments of African
herbivores to predation risk by lions: spatiotemporal variations inuence
habitat use. Ecology,90,23e30.
Valeix, M., Chamaillé-Jammes, S., Loveridge, A. J., Davidson, Z., Hunt, J. E.,
Madzikanda, H. & Macdonald, D. W. 2011. Understanding patch departure rules
for large carnivores: lion movements support a patch-disturbance hypothesis.
American Naturalist, doi:10.1086/660824, published online 27 June 2011.
Weir, J. & Davison, E. 1965. Daily occurrence of African game animals at waterholes
during dry weather. African Zoology,1,353e368.
Zar, J. H. 1984. Biostatistical Analysis. London: Prentice Hall.
W.-G. Crosmary et al. / Animal Behaviour 83 (2012) 145e153 153
... We suggest that the strong human disturbance occurring in the mountainous areas of INP are perceived as top-down constraints preventing zebras from occupying its preferred habitats (Wilson and Mittermeier 2011;Muntifering et al. 2019). The recent history and contemporary hunting practices in INP may have led to this scenario, as the persistence of these activities may shift the species' behaviour through learning (Crosmary et al. 2012). Moreover, livestock can act as dominant competitors for the best grazing areas (Sirot et al. 2016), relegating zebras to suboptimal habitats. ...
... Springboks have lower surface water dependence than grazer species (Kihwele et al. 2020), and are described as being able to balance their hydric needs if they can obtain plant food containing at least 67% water (Nagy and Knight 1994). Additionally, in dry regions, waterholes are areas of predictable high predation risk, which constrain access to and behaviour at these sources of surface water (Valeix et al. 2008;Crosmary et al. 2012;Davidson et al. 2013). Taken together, these patterns suggest that springbok might be able to meet their nutritional and hydric requirements across large segments of INP's landscape, even tolerating some level of human disturbance, therefore they tend to use the space according to top-down constraints potentially imposed by competitors and/or predators (Valeix et al. 2008;Sirot et al. 2016). ...
Full-text available
Deserts are typically governed by bottom-up forces and are predicted to be further depleted of their resources, exacerbating extinction risk for local wildlife populations. Additionally, human populations living in these ecosystems are predicted to increase, exposing wildlife to additional human-induced top-down constraints and intensifying human-wildlife conflicts. We aim to investigate how surface water availability, forage availability and other landscape factors shape the spatial arrangement of large herbivore populations in a desert region, and to explore wildlife-livestock co-occurrence patterns to inform coexistence strategies that maximize conservation outputs. We fitted Bayesian zero-inflated binomial N-mixture models (Kéry and Royle 2015) to group count data collected over a 4 year period in the northern Namib desert (Iona National Park, Angola), and found that Hartmann’s mountain zebra and gemsbok preferentially forage in suboptimal low productivity flat areas, away from human activities. Conversely, springbok preferentially occurred in more productive and relatively rugged terrain. We also found a reliance of Hartmann’s mountain zebra on natural water sources (βDistWater=-1.04±0.26\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{DistWater}=-1.04\pm 0.26$$\end{document} and βDistWater=-0.77±0.20,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{DistWater}=-0.77\pm 0.20,$$\end{document} for dry and wet seasons, respectively), and a weaker reliance by gemsbok (βDistWater=0.20±0.10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{DistWater}=0.20\pm 0.10$$\end{document} and βDistWater=-0.15±0.10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{DistWater}=-0.15\pm 0.10$$\end{document}, respectively for dry and wet seasons). Conversely, we found springbok to forage further from available water (βDistWater=0.43±0.05\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{DistWater}=0.43\pm 0.05$$\end{document} and βDistWater=0.26±0.06\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{DistWater}=0.26\pm 0.06$$\end{document}, for dry and wet seasons, respectively), suggesting this species may be able to balance hydric requirements from dietary water. Our results support that human activities (inc. livestock herding) induce broad scale top-down regulation in landscape use by our target species, which are then susceptible to resource-driven bottom-up forces at a finer scale. These constraints reflect differences between the realized and expected conservation value of Iona National Park, because human-occupied areas force wildlife to suboptimal habitats. Additionally, we found significant stretches of the landscape to be co-occupied by wildlife and livestock, increasing competition for already limited resources. Our results are useful for informing conservation actions, namely through protected area zonation. Securing exclusive access to key resources by wildlife could be of utmost importance to ensure the long-term survival of these species, and to foster sustained human-wildlife coexistence.
... Individuals of many prey species congregate around waterholes (Valeix et al., 2008), which attracts predators (Trinkel et al., 2004;Valeix et al., 2009) and aggregates parasites (Titcomb et al., 2021). In the vicinity of waterholes, competition can be high and may alter interactions (Crosmary et al., 2012;Sirot et al., 2016;Valeix et al., 2010). These include aggressive interactions for access to water, for example among elephants (O'Connell-Rodwell et al., 2011), other ungulates (Ferry et al., 2020), or between predators . ...
Full-text available
Proclaimed in 1907, Etosha National Park in northern Namibia is an iconic dryland system with a rich history of wildlife conservation and research. A recent research symposium on wildlife conservation in the Greater Etosha Landscape (GEL) highlighted increased concern of how intensification of global change will affect wildlife conservation based on participant responses to a questionnaire. The GEL includes Etosha and surrounding areas, the latter divided by a veterinary fence into large, private farms to the south and communal areas of residential and farming land to the north. Here, we leverage our knowledge of this ecosystem to provide insight into the broader challenges facing wildlife conservation in this vulnerable dryland environment. We first look backward, summarizing the history of wildlife conservation and research trends in the GEL based on a literature review, providing a broad-scale understanding of the socioecological processes that drive dryland system dynamics. We then look forward, focusing on eight key areas of challenge and opportunity for this ecosystem: climate change, water availability and quality, vegetation and fire management, adaptability of wildlife populations, disease risk, human-wildlife conflict, wildlife crime, and human dimensions of wildlife conservation. Using this model system, we summarize key lessons and identify critical threats highlighting future research needs to support wildlife management. Research in the GEL has followed a trajectory seen elsewhere reflecting an increase in complexity and integration across biological scales over time. Yet, despite these trends, a gap exists between the scope of recent research efforts and the needs of wildlife conservation to adapt to climate and land-use changes. Given the complex nature of climate change, in addition to locally existing system stressors, a framework of forward-thinking adaptive management to address these challenges, supported by integrative and multidisciplinary research could be beneficial. One critical area for growth is to better integrate research and wildlife management across land-use types. Such efforts have the potential to support wildlife conservation efforts and human development goals, while building resilience against the impacts of climate change. While our conclusions reflect the specifics of the GEL ecosystem, they have direct relevance for other African dryland systems impacted by global change.
... The animals walked more in the morning and late afternoon (Figures 2 and 3). Diurnal activity patterns are affected by temperature, humidity, predators, competitors, age, nutritious grass availability (Kanda & Cote, 2012;Ruckstuhi & Neuhaus, 2009 ...
... We expected that deer would avoid all forms of risk along their movement path, however, avoidance of the risk of predation by mountain lions would be strongest because risk of direct mortality should promote the greatest behavioral response (Crosmary et al., 2012;Thaker et al., 2011;Valeix et al., 2009). Alternatively, deer may not be able to avoid risk within their home range, and instead selection for risk should vary according to the amount of risk they are exposed to within their home range (Figure 1c). ...
Understanding factors that influence animal behavior is central to ecology. Basic principles of animal ecology imply that individuals should seek to maximize survival and reproduction, which means carefully weighing risk against reward. Decisions become increasingly complex and constrained, however, when risk is spatiotemporally variable. We advance a growing body of work in predator‐prey behavior by evaluating novel questions where a prey species is confronted with multiple predators and a potential competitor. We tested how fine‐scale behavior of female mule deer (Odocoileus hemionus) during the reproductive season shifted depending upon spatial and temporal variation in risk from predators and a potential competitor. We expected female deer to avoid areas of high risk when movement activity of predators and a competitor were high. We used GPS data collected from 76 adult female mule deer, 35 adult female elk, 33 adult coyotes, and six adult mountain lions. Counter to our expectations, female deer exhibited selection for multiple risk factors, however, selection for risk was dampened by the exposure to risk within home ranges of female deer, producing a functional response in habitat selection. Furthermore, temporal variation in movement activity of predators and elk across the diel cycle did not result in a shift in movement activity by female deer. Instead, the average level of risk within their home range was the predominant factor modulating the response to risk by female deer. Our results counter prevailing hypotheses of how large herbivores navigate risky landscapes, and emphasize the importance of accounting for the local environment when identifying effects of risk on animal behavior. Moreover, our findings highlight additional behavioral mechanisms used by large herbivores to mitigate multiple sources of predation and potential competitive interactions.
... In impala in the Serengeti, faecal glucocorticoid metabolite (FGM) concentration is affected more by forage quality than human disturbance [28], and although impala may be more vigilant in tourist areas, there is some evidence of habituation to human presence [29]. Other research has reported diurnal changes in African ungulate behaviour as a result of human disturbance: in areas where human hunting is allowed, three large African ungulates (impala (Aepyceros melampus), greater kudu (Tragelaphus strepsiceros) and sable antelope (Hippotragus niger)) are more likely to drink at night [30], presumably due to hunting pressures. Diurnal and seasonal changes in grazing behaviour were observed by Schuette et al. [31] as ungulates selected a foraging strategy that was optimal for the level of human disturbance, the risk of predation and the vegetation quality. ...
Full-text available
In Africa, wildlife-watching experiences create substantial revenue from tourists that can finance wildlife conservation. Horseback safaris, where an experienced guide takes guests through the bush on horseback to observe plains game species, are a popular activity. Close encounters between ridden horses and game species are unnatural and potentially stressful situations, and horseback safaris may have adverse impacts on both the horses and the wildlife they have come to observe. This study aims to provide a preliminary insight into the behavioural responses of horses and herbivorous plains game species, including giraffe, zebra and impala, as a proxy measure of the potential welfare implications of horseback safaris. Seventeen group safari rides were observed encompassing 72 encounters with plains game species. Game species differed in their response to encounters with the horseback safari ride. Equine response behaviour appeared to be influenced by the species of game encountered. Horses seemed more wary of giraffe than other species, with a higher percentage of horses showing stationary and retreat behaviour at the start of giraffe encounters. They were also most likely to shy at giraffe. The behavioural responses suggest that game encounters can elicit a stress response in both animal groups, although it is not usually extreme, potentially indicating that some degree of habituation has occurred. Balancing the welfare of both the horses and the plains game species along with tourist preferences may be challenging in this context.
... dogs and coyotes), whose presence is directly and indirectly favoured by humans. This can have very important consequences for the long-term viability of wildlife populations, for instance, physiological stress caused by a reduction of water intake might affect animal's reproductive success (Crosmary et al. 2012, Tuomainen & Candolin 2011, Wakefield & Attum 2006, Zanette et al. 2011. Additionally, it is known that domestic dogs can harass and prey upon native wildlife, forcing them to avoid usual feeding areas or periods of activity (Hughes & Macdonald 2013, Lenth et al. 2008). ...
Great attention has been drawn to the impacts of habitat deforestation and fragmentation on wildlife species richness. In contrast, much less attention has been paid to assessing the impacts of chronic anthropogenic disturbance on wildlife species composition and behaviour. We focused on natural small rock pools (sartenejas), which concentrate vertebrate activity due to habitat’s water limitation, to assess the impact of chronic anthropogenic disturbance on the species richness, diversity, composition, and behaviour of medium and large-sized birds and mammals in the highly biodiverse forests of Calakmul, southern Mexico. Camera trapping records of fauna using sartenejas within and outside the Calakmul Biosphere Reserve (CBR) showed that there were no effects on species richness, but contrasts emerged when comparing species diversity, composition, and behaviour. These effects differed between birds and mammals and between species: (1) bird diversity was greater outside the CBR, but mammal diversity was greater within and (2) the daily activity patterns of birds differed slightly within and outside the CBR but strongly contrasted in mammals. Our study highlights that even in areas supporting extensive forest cover, small-scale chronic anthropogenic disturbances can have pervasive negative effects on wildlife and that these effects contrast between animal groups.
... For example, water sources drive large elephant aggregations 11 and increase contact rates among cattle herds 12 during dry periods. However, the degree to which different herbivores gather at water may vary by diet 13 , physiology 14 , and predation risk 14,15 . Camera trapping work of animal overlaps at watering holes and at baited food stations has shown speciesspecific increases in contact rates around resources [16][17][18] suggesting potential implications for disease transmission. ...
Full-text available
Shifts in landscape heterogeneity and climate can influence animal movement in ways that profoundly alter disease transmission. Water sources that are foci of animal activity have great potential to promote disease transmission, but it is unknown how this varies across a range of hosts and climatic contexts. For fecal-oral parasites, water resources can aggregate many different hosts in small areas, concentrate infectious material, and function as disease hotspots. This may be exacerbated where water is scarce and for species requiring frequent water access. Working in an East African savanna, we show via experimental and observational methods that water sources increase the density of wild and domestic herbivore feces and thus, the concentration of fecal-oral parasites in the environment, by up to two orders of magnitude. We show that this effect is amplified in drier areas and drier periods, creating dynamic and heterogeneous disease landscapes across space and time. We also show that herbivore grazing behaviors that expose them to fecal-oral parasites often increase at water sources relative to background sites, increasing potential parasite transmission at these hotspots. Critically, this effect varies by herbivore species, with strongest effects for two animals of concern for conservation and development: elephants and cattle.
... Our studies in some of these photographic areas reveal that wildfowl species generally increased their wariness in presence of humans but the current levels of disturbance do not seem to affect their probability of occupying these pans (Tarakini et al., 2020a). Pans in the hunting areas probably experience less pressure from human disturbances (when compared to those in photography areas) as hunting is seasonal (Crosmary et al., 2012) and fewer tourists visit them. Also, there is less effort directed to providing artificial water in these areas and waterbird abundance is generally lower on seasonal pans. ...
Wetlands in southern Africa. The formation and ecology of pans in southern Africa. Waterbird communities and breeding in the pan ecosystem. The pan ecosystem in western Zimbabwe - protected areas and nonprotected areas. Trends and drivers of waterbird communities. Threats to waterbirds inside and outside protected areas. - Avian Influenza Virus (AIV) - Avian malaria - West Nile Virus (WNV). Benefits of waterbirds to local people. Measures for the conservation of waterbirds in the pan wetland system.
The landscape of fear (LOF) concept posits that prey navigate spatial heterogeneity in perceived predation risk, balancing risk mitigation against other activities necessary for survival and reproduction. These proactive behavioral responses to risk can affect individual fitness, population dynamics, species interactions, and coexistence. Yet, antipredator responses in free-ranging prey often contradict expectations, raising questions about the generality and scalability of the LOF framework and suggesting that a purely spatial, static LOF conceptualization may be inadequate. Here, we outline a ‘dynamic’ LOF framework that explicitly incorporates time to account for predictable spatiotemporal variation in risk–resource trade-offs. This integrated approach suggests novel predictions about predator effects on prey behaviors to refine understanding of the role predators play in ecological communities.
Interface areas in Southern Africa that consist of communal lands located at the edge of protected areas face a growing number of human/wildlife coexistence related issues and among them, the risk of pathogen transmission between wild and domesticated species.In this context, the present thesis aims to 1) Characterize the environmental variables, at a landscape scale at three different interfaces located in Southern Africa (hwange National park, Gonarezhou National Park, North Kruger National Park), that potentially influence the movements of two focal species, one wild ungulates species (the buffalo – Syncerus caffer caffer) and one domesticated ungulates species (the cattle – Bos taurus & Bos indicus) 2) Simulate the movements of the focal species, at the individual and herd scales, in relation with their respective environments, 3) Determine the nature, frequency and localization of the contacts between the focal species to better apprehend the risks of pathogen transmission.A temporal series of Sentinel-2 satellite images have been classified to produce monthly surface water and landcover maps at 10 meters spatial resolution. These environmental variables have then been integrated into a spatialized mechanistic movement model based on a collective motion of self-propelled individuals to simulate buffalo and cattle movements and contacts in response to the surface water seasonality and the type of landcover. To spatialized the movements and contacts models, the domain specific language Ocelet has been used. Telemetry data collected in previous studies have been used as reference data to design, calibrate and validate the movement and contact models.Results highlighted strong space and time variabilities of water availability in the three study areas. Landcover classified maps accurately reproduced the specificities in landscape compositions of the three study areas. By only taking surface water into account, the mechanistic movement models showed a positive and significant correlation between observations/simulations movements and space-use of buffalo’s and cattle herds despite overestimating the presence of buffalo individuals at proximity of the surface water. The addition of the landcover increased the overall accuracy of the movement models. Contacts patterns and their accuracies have been reproduced but their respective accuracy differed according to the study area.Overall, combining remote sensing and spatial modeling offers possibilities to develop simple models to simulate animal movements and contacts in direct relation with the environment. This methodology has the advantage of being scalable and reproducible. It subsequently offers the possibility of integrating an epidemiological model to estimate the risk of pathogen transmission in direct link with the movements and contacts of animal species.
Full-text available
Safari hunting is economically and ecologically attractive. Like game ranching for meat production, safari hunting is a relatively recent development in Zimbabwe dating to the early 1960s. Safari hunting is conducted under a variety of arrangements on a variety of land bases. Landowners are responsible for wildlife management on their lands. Public land available for hunting is leased to Safari Operators or Hunters' Associations, or individual hunts are sold. Three types of safari are distinguished: big game safaris, plains game safaris, and ranch hunts. -from Author
Full-text available
Waterbird hunting is a widespread activity in wetlands throughout Europe and constitutes one of the most significant sources of disturbance during autumn and winter. The biological evidence for effects of hunting disturbance on the behaviour and distribution of migratory and wintering waterbirds and its possible impacts on population dynamics is reviewed. Most of the literature has been concerned with local effects of disturbance, focussing on quarry geese and dabbling ducks. Comparatively little is known about effects on diving ducks and waders, while there is no direct evidence for impacts at the population level for any waterbird species. Hunting disturbance can cause temporary disruption of normal activities of waterbirds, alter their diurnal rhythms and increase escape flight distances. It can displace waterbirds from preferred feeding and roosting habitats at local or regional level and increase turnover, so that the carrying capacity of a site is not reached. Quarry waterbirds, and those occurring in large inshore concentrations, such as many dabbling ducks, geese and waders, are potentially most sensitive to disturbance. Hunting disturbance can disrupt pair-bonds and family structures which may affect reproductive output. Evidence is provided that many waterbird populations are limited by winter conditions and that the majority of studied waterbird species lose body reserves during winter. Because hunting disturbance causes under-exploitation of potential feeding grounds where population limitation is considered to occur, such disturbance will, by definition, have an impact at the population level. However, the magnitude of this impact has not been quantified and requires a modelling approach.
The effects of deer hunting by humans on deer population dynamics and behavior may indirectly affect the population dynamics and behavior of deer predators. We present data on the effects of hunting on the behavior of white-tailed deer (Odocoileus virginianus) on the Osceola National Forest, a potential reintroduction site for the endangered Florida panther (Felis concolor coryi). We then use this information to formulate and recommend testable hypotheses to investigate whether these changes in deer behavior influence panther movements, mortality, and hunting success. We monitored 14 radio-collared deer from June 1990 through July 1991 to compare movement, activity, and habitat-use patterns between the hunting and nonhunting seasons. Mean distance of deer to the nearest road, mean distance of activity centers of diel home ranges to the nearest road, and mean nocturnal rate of activity were greater during the hunting than the nonhunting seasons. During the hunting season, deer avoided clearcuts, young pine plantations (4-10 years old), and other open habitats and preferred swamp and mature pine forests, both of which provided cover. These results suggest that deer responded to hunter disturbance by moving away from roads and increasing nocturnal activity. Although recreational deer hunting may reduce the prey base for panthers, the changes we observed in deer behavior during the hunting season may benefit panthers in the following ways: (1) an increase in nocturnal activity and movement away from roads by deer into areas frequented by panthers may increase prey availability for panthers; (2) the movement of deer away from roads may in turn draw panthers away from roads, which may decrease the chance of panthers being killed by vehicular traffic or poachers.
Flight distances in roe deer Capreolus capreolus and fallow deer Dama dama with respect to a human observer on foot were measured in four nature reserves in the Netherlands: two dune reserves in the western part (the Amsterdam Water Supply Dunes (AWD) and Kennemerduinen (KD)) and two forested areas in the eastern part of the country (Hoge Veluwe (HV) and Kootwijk (KO)). In the four areas there is a gradient in hunting pressure from almost none in the AWD, via an increase in KD, to KO and HV. Fallow deer occur in both of the dune reserves and are not hunted. Of all the factors studied, hunting regime and habitat structure were most strongly related to flight distance. Although the number of individuals per group and most weather conditions also showed some relation to flight distances, their influence was relatively unimportant compared to that of hunting regime and habitat structure. When walking down wind, deer (both roe and fallow deer) flee at longer distances (64.7 ± 5.8 m) than when walking upwind (41.7 ± 3.3 m) or in calm wind (44.2 ± 1.8 m). In the roe deer population of the AWD, flight distances were the shortest among all the studied areas. In both of the dune areas, the flight distances in dense vegetation structures were shorter than in open field. Fallow deer flight distances did not differ between the dune reserves AWD and KD.
Hunting is a fundamentally important tool for wildlife managers. We examined the null hypothesis that hunting does not influence deer movement and their use of habitat types. Seventeen radio-collared, adult, female Rocky Mountain mule deer (Odocoileus hemionus hemionus) were located 1 day before the 1983 first Colorado deer season, and during day 2 of the first and day 3 of the second deer seasons in the foothills west of Fort Collins, Colorado. Distance from the preseason location to each location during hunting seasons were calculated for each deer. There were no differences between mean distance from pre-hunting season location to hunting season location for 10 deer that had all 3 locations in the area closed to hunting, and 4 deer that had 3 locations in the area open to hunting (P = 0.34 and 0.52). All 17 deer had all 3 locations in the interior of their minimum convex polygon home ranges. Those home ranges had a mean size of 226 ha and range of 117 to 323 ha. However, deer in the section open to hunting generally moved to vegetation types with increasingly better escape cover as the hunting seasons progressed. We conclude that hunting pressure did not cause deer movement in terms of distance or cause them to leave their normal home ranges, but did cause deer to move into more adequate cover.