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Original Research Article
Africa's apex predator, the lion, is limited by interference and
exploitative competition with humans
Kristoffer T. Everatt
a
,
b
,
*
, Jennifer F. Moore
c
, Graham I.H. Kerley
a
a
Centre for African Conservation Ecology, Department of Zoology, Nelson Mandela University, South Africa
b
Panthera, USA
c
University of Florida Department of Wildlife Ecology and Conservation, USA
article info
Article history:
Received 27 March 2019
Received in revised form 15 August 2019
Accepted 16 August 2019
abstract
Apex predators are crucial for maintaining ecological patterns and processes, yet humans
hinder their ability to fulfil this role by displacing them from the landscape. Many apex
predator species such as African lions (Panthera leo) are experiencing catastrophic declines
as a result of competition with growing human populations. Increasing our understanding
of the competitive interactions between lions and humans, as well as identifying thresh-
olds of lion tolerance to human activities are important both for lion conservation and our
understanding of apex predator ecology in the Anthropocene. We investigated the relative
and cumulative influences of anthropogenic pressures on lion occurrence across a
73 000 k m
2
multi-use landscape in southern Africa. We developed occupancy models from
replicated detection/non-detection spoor surveys across gradients of anthropogenic and
biotic features. We tested the two hypotheses that African lions were most limited by 1)
interference competition with humans or 2) exploitative competition with humans and
evaluated the relative contribution of individual anthropogenic and biotic variables to lion
occurrence. Our models predicted that lions occupied 49% of the landscape. The strongest
determinants of lion occupancy were negative associations with pastoralism and bush-
meat poaching, and a positive association with preferred prey. Thus, lions in this landscape
are limited by a combination of interference and exploitative competition with poachers
and pastoralists. However, interference competition with pastoralism was the biggest
driver limiting lion occupancy, with a clear disturbance threshold for lions cumulating in a
near complete loss of lions from the landscape when cattle surpass 21% occurrence. This
study provides a predictive understanding of the top-down impacts of humans on the
world's vulnerable apex carnivores.
©2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC
BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction
Apex predators have important ecological roles, exerting disproportionate influence over the structure and function of
ecosystems, such that the consequences of their disappearance can reverberate through trophic levels (Estes et al., 2011;
Ripple et al., 2014). Apex predators are extinction prone due to the rarity imposed on them by the energetic constraints of
their trophic position (Carbone and Gittleman, 2002;Cardillo et al., 2004). However, despite this natural rarity many apex
*Corresponding author. Centre for African Conservation Ecology, Department of Zoology, Nelson Mandela University, South Africa.
E-mail address: keveratt@panthera.org (K.T. Everatt).
Contents lists available at ScienceDirect
Global Ecology and Conservation
journal homepage: http://www.elsevier.com/locate/gecco
https://doi.org/10.1016/j.gecco.2019.e00758
2351-9894/©2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/
licenses/by-nc-nd/4.0/).
Global Ecology and Conservation 20 (2019) e00758
predators are in competition with humans (Treves and Karanth, 2003;Ripple et al., 2014). In the age of the Anthropocene,
these competitions have now pushed most species of apex predators into a conservation crisis (Dirzo et al., 2014;Ripple et al.,
2014). Thus,there is a need for a betterunderstanding of the nature of these competitiveinteractions including distinguishing
the relative effects of interference and exploitative competition on apex predator conservation.
For instance, African lions (Panthera leo) exert influence on lower trophic levels (Estes et al., 2011;Tambling et al., 2012),
with complex cascading effects (Le Roux et al., 2018) and are in competition with humans (Kissui, 2008;Khorozyan et al.,
2015). Lions across Africa have suffered an estimated 50% decline in abundance and 75% reduction in range over the past
20e50 years (IUCN, 2006:Riggio et al., 2012;Bauer et al., 2015). Many populations continue to decline (Bauer et al., 2015). The
proximate cause of declining African lion populations is increasing competition with humans. Interference competition can
take the forms of persecution of lions in retaliation to depredation or perceived depredation of livestock (Woodroffe and
Frank, 2005), unsustainable trophy hunting of lions (Loveridge et al., 2007), poaching (illegal hunting) of lions (Everatt
et al., unpublished) or behavioural exclusions (Oriol-Cotterill et al., 2015;Smith et al., 2017). Exploitative competition, where
resources required by lions are limited by humans, include habitat loss to expanding agricultural, settlement or other human
land uses (Riggio et al., 2012) and the loss of wild prey resources to hunting by humans for meat (Lindsey et al., 2013).
Large carnivore species differ in their degree of competitive interactions with humans (Cardillo et al., 2004) and therefore
in their ability to persist in human-impacted landscapes. For instance, leopards (Panthera pardus) and cougars (Puma con-
color), both mid-sized, elusive and solitary generalists, can exist at relatively high densities even in unprotected human-
dominated landscapes by subsisting on domestic animals or smaller prey (Athreya et al., 2013;Moss et al., 2016). In
contrast, the abundance of grizzly bears (Ursus arctos), a larger carnivore which is more prone to conflicts with humans is
more limited by human disturbance (Apps et al., 2004;Linke et al., 2013). Lions are among the largest of terrestrial carnivores,
are more gregarious and less cryptic then other felids, and display a preference for larger prey species whose weight category
overlaps with domestic livestock and wild species also sought after by human hunters (Schaller, 1972;Hayward et al., 2007).
Lions are also dominant over sympatric apex predators, including leopards, cheetah (Acinonyx jubatus), spotted hyenas
(Crocuta crocuta) and African wild dogs (Lycaon pictus)(Palomares and Caro, 1999;Durant, 2000) and hence unlikely to have
evolved appropriate strategies for mitigating competition with syntopic dominant predators. Lions are also larger and more
vocal, making their presence more obvious than the other syntopic predators. These characteristics may increase their
competitive interactions with humans relative to other apex predators and make humans less inclined to tolerate their
presence, thus reducing their ability to persist in anthropogenically-impacted landscapes.
In this study, we explore the roles of interference and exploitative competitive interactions between lions and humans,
and specifically the relative importance of the various anthropogenic pressures on lion occurrence across the landscape. We
tested the hypotheses that a) lions were most limited byinterference competitionwith humans or b) lions were most limited
by exploitative competition with humans. We predicted that if lions were more limited by interference competition with
humans, then the occupancy of lions would be best predicted by a negative relationship with pastoralism and poaching
activities, and if lions were more limited by exploitative competition with humans then the occupancy of lions would be best
predicted by a positive relationship with prey abundance and a negative relationship with bushmeat poaching. Additionally,
we sought to identify thresholds of occurrence for lions along an increasing gradient of limiting anthropogenic pressures.
Thus, we add to the body of knowledge about the effects of competition by (1) describing anthropogenic effects on apex
predator ecology, and (2) providing information on competitive abilities of lions, which is crucial for predicting habitat
suitability and determining conservation needs of lions.
2. Methods
2.1. Study area and population
This study was carried out between 2014 and 2016 in the Greater Limpopo Lion Conservation Unit (GLLCU) of South Africa,
Mozambique and Zimbabwe (IUCN, 2006). The GLLCU covers approximately 73 00 0 km
2
, and includes South Africa's Kruger
National Park (KNP) (19 485 km
2
), Zimbabwe's Gonarezhou National Park (GNP) (5 053km
2
), and Mozambique's Limpopo
National Park (LNP) (11 233 km
2
), Banhine National Park (BNP) (7 250 km
2
) and Zinave National Park (ZNP) (4 000 km
2
),
several private game reserves, community hunting, grazing, farming and logging areas (Fig. 1). The study area includes a
relatively discrete lion population whose distribution can be defined by a hard edge along the fenced western boundary of the
greater KNP and the fenced northern boundary of GNP, and the eventual effective absence of lions along the eastern and
southern reaches of the GLLCU (this study).
Based on the large amount of available lion habitat and the size of the lion population, the GLLCU is considered one of
Africa's ten remaining population strongholds (Riggio et al., 2012). At the onset of this study (2014), KNP supported a stable
and protected population of >1600 lions (Ferreira and Funston, 2010), and GNP, LNP and BNP supported small populations
estimated at 33, 66 and 10 lions, respectively, with the latter three all below their respective ecological carrying capacities
(Groom et al., 2014;Everatt et al., 2014; K. Everatt, unpublished). Throughout the majority of the Mozambican portion of the
GLLCU lions are faced with the compounded challenges of prey depletion caused by bushmeat poaching, mortalities as by-
catch in bushmeat snares, poaching for body parts, retaliatory or pre-emptive killing by pastoralists and loss of habitat to
agricultural land conversion (Everatt et al., 2014; K. Everatt, unpublished). All three of the Mozambican National Parks are
inhabited by people who practice a variety of subsistence activities (cropping, pastoralism, hunting and gathering) as well as
K.T. Everatt et al. / Global Ecology and Conservation 20 (2019) e007582
high levels of commercial poaching of ungulates for bushmeat and of elephants (Loxodonta africana), rhino species (Cera-
totherium simum and Diceros bicornis) and lions for body parts (Everatt et al., 2015;Everatt et al., 2016; Everatt et al., un-
published; this study). Vegetation across the region is classified as mixed savanna, woodland and grasslands (Stalmans et al.,
2004). Wild ungulate densities across the GLLCU are spatially heterogeneous, largely due to the heterogeneous nature of
wildlife protection. For instance, impala (Aepyceros melampus) occur at densities >8/km
2
in KNP, 1.21/km
2
in GNP, 0.10/km
2
in
LNP and 0.07/km
2
in BNP (Redfern et al., 2002;Dunham et al., 2010;Grossmann et al., 2014;Stalmans and Peel, 2009) and
buffalo (Syncerus caffer) at densities of 1.50/km
2
in KNP, 0.46/km
2
in GNP, 0.12/km
2
in LNP and 0.002/km
2
in BNP (Seydack
et al., 2012;Dunham et al., 2010;Grossmann et al., 2014;Stalmans and Peel, 2009).
Fig. 1. Location of the study area (dark area in inset) in southern Africa and the survey area overing the Greater Limpopo Lion Conservation Unit of South Africa,
Mozambique and Zimbabwe, with the overlaid grid of 200 km
2
cells. Grid cells that were randomly selected for sampling are shaded.
K.T. Everatt et al. / Global Ecology and Conservation 20 (2019) e00758 3
2.2. Study design
Interspecific competition can be inferred by examining the dynamics of multi-species' occupancy assumed to be resultant
of one species’numerical or behavioural response to the presence of another (Linnell and Strand, 2000;Haynes et al., 2014).
We examined the heterogeneity in lion occupancy in relation to gradients of wild prey, domestic livestock and bushmeat
poaching pressures across the Greater Limpopo Lion Conservation Unit of southern Africa. In this study we define bushmeat
poaching as the sum of all illegal or unregulated hunting of wildlife for meat (see Appendix 1 for examples). There was no
legal regulated hunting occurring in our study area during the surveys. We developed single-season occupancy models from
spatially replicated presence-absence walking spoor surveys of lions, their prey and threats, with an interest in two pa-
rameters, the probability of occupancy (
j
) and the probability of detection (p). Hierarchical modelling of covariates was used
to make inferences on the ecological factors limiting lion occupancy and detectability (MacKenzie et al., 2002). We made the
following assumptions for the estimator occupancy ѱto be interpreted as the proportion of area occupied: 1) sites were
closed to changes in occupancy; 2) species were not falsely identified; 3) detections were independent, or, if not then such
dependencies were modelled (Hines et al., 2010); and 4) all heterogeneity in occupancy or detection probability was modelled
using covariates (MacKenzie et al., 2002). To estimate the proportion of area occupied by lions, our sample units (sites) were
defined as 200 km
2
grid cells. Lion prides are known to occupy home ranges of between 30 km
2
(Kissui et al., 2009) and 1
450 km
2
(Funston, 2011) in size, with the size inversely related to the availability of prey (Van Orsdol et al., 1985). The average
home range in southern Kruger National Park, an area in the region with some of the highest relative prey densities (Ferreira
and Funston, 2010), is 100 km
2
(Funston et al., 2003). We therefore assumed that using grid cells of twice this size would be
large enough to reduce spatial autocorrelation between sites but small enough to assume that entire grid cells are occupied,
thus reducing the chance of occupancy over-estimation. A grid of 14.142 km 14.142 km (200 km
2
) cells was placed over the
entire landscape and 150 of these were randomly chosen for sampling, excluding any cells that consisted of over 50% set-
tlement areas. The resulting survey area covered the full gradients of anthropogenic disturbances and wild prey and livestock
densities from across the GLLCU (Fig. 1). A second level of randomness was incorporated by dividing each grid cell into four
quadrats (50 km
2
) and selecting one of these for obligate sampling.
In order to reduce the likelihood of changes in occupancy during sampling (closure) individual grid cells were sampled
within 36-h periods and adjoining grid cells were surveyed in succession (Hines et al., 2010). We acknowledge that a single
survey at this landscape scale however does not consider seasonal variations. Surveys were conducted by the same expe-
rienced tracker. All animal detections were represented by unambiguously identified tracks and bushmeat poaching de-
tections were identified by unambiguously identified sign, including snares, traps, shotgun shells, butchered carcasses, active
poaching camps and encounters with poachers (see Appendix 1)(Stuart and Stuart, 2013). Within each 200 km
2
cell an
average of 40 sequential 1 km detection non-detection samples were walked searching for lion tracks, other animaltracks and
bushmeat poaching sign, with at least one sample reaching into the randomly selected quadrat and the first sample selected
upon access to the cell (Karanth et al., 2011;Thorn et al., 2011;Whittington et al., 2015). While this approach had not yet been
used for lions the survey effort used here was comparable to the required survey effort determined by Karanth et al. (2011) for
sampling the landscape occupancy of tigers (Panthera tigris). Due to poor tracking substrate and a lack of roads throughout
much of the study area, all transects were surveyed on foot. Sampling transects were chosen to maximize detection of rare
carnivores by searching along game trails and habitat edges, thereby minimizing the likelihood of false absences (MacKenzie
et al., 2006).
As an apex predator, lions are naturally limited by bottom up resources (Hayward et al., 2007), but also by top-down
anthropogenic pressures (Everatt et al., 2014) due to their wide exposure to human-driven ecological processes (Dirzo
et al., 2014). In order to model heterogeneity in lion occupancy, we collected covariate data for each 1 km sample which
we assumed to be biologically relevant to lion habitat use at the home range scale (Mitchell and Hebblewhite, 2012). These
covariates included the presence/absence of wild prey species, domestic livestock (cattle) and bushmeat hunting, and the
landscape variables (Table 1,Appendix 2). We combined important lion prey species into two categories: “Preferred prey”,
with the species preferentially selected by lions across Africa and in our study area and “All prey”with the combination of all
wild ungulates species which are predated on by lions in our study area (including preferred prey species) (Hayward and
Kerley, 2005; K Everatt, unpublished) (See Appendix 1 for the full list of species). The landscape variables that we
assumed to be important in determining lion occupancy (the distance from the centre of Kruger and Gonarezhou National
Parks (areas offering greater wildlife protection), the distance from settlement edges and the amount of riparian habitat) were
extracted for each 1 km sample from raster layers (www.peaceparks.co.za) in ArcGIS 10.3.1 (www.esri.com)(Table 1).
The variables that may influence the detectability of tracks in this landscape include the observer skill, recent rains, angle
of sun, substrate quality and livestock and human densities (Funston et al., 2010;Stuart and Stuart, 2013). We approached the
sampling such that these biases were minimized or modelled as covariates. Surveys were conducted at least four days post
heavy rains, and surveys were not conducted at mid-day when the angle of the sun makes detecting tracks more difficult. In
addition, we rated the quality of substrate foreach 1 km sample as good, medium or poor based on the likelihoodof being able
to see a carnivore track if it was there and recorded the transect type. These were then considered as detection covariates
along with relative livestock and human impacts (Table 1). All detection/non-detection data and transect positions were
recorded by the Cyber tracker (www.cybertracker.org) program on a smart phone.
K.T. Everatt et al. / Global Ecology and Conservation 20 (2019) e007584
2.3. Analytical methods
2.3.1. Occupancy modelling
Site occupancy
j
and detection probability pwere estimated using the maximum likelihood functions (MacKenzie et al.,
2006) of the single season correlated detections option in PRESENCE v. 11.6 (Hines, 2006). The correlated detections model
deals with the positive spatial autocorrelation of detections when sampling is not random with replacement and the
detection in one sample translates to an increased probability of detection in the next sample. Such situations are faced when
sampling for a mobile species along a trail (Hines et al., 2010). General notations for the correlated detections model are
provided in Hines et al. (2010). Continuous site covariates were standardized using a z-scale. Multi-collinearity between
variables was tested for using a Pearson correlation coefficient with an exclusion criterion of r >0.6 in MS Excel v. 1906 (Clare
et al., 2015).
2.4. Model selection procedures
We were interested in the relative contribution of anthropogenic (bushmeat poaching, pastoralism, protected area
management) and natural covariates (preyavailability) on the landscape-scale occupancy of lions. Models were ranked based
on Akaike Information Criterion (AIC) (Hines et al., 2010) and considered tobe strongly supported if
D
AIC 2. A candidate set
was developed which considered all models with
D
AIC ˂7 whose combined model weights 0.95, excluding the models that
did not reach numerical convergence. AIC weights were used to determine the weight of evidence for each model and they
were summed for each covariate in the 95% confidence set (Burnham and Anderson, 2002). Variables with high summed
model weights were considered more important in explaining heterogeneity in lion occupancy. The sign of the
b
-coefficients
was used to determine the direction of influence of covariates (MacKenzie et al., 2006). Covariates were considered robust if
the 90% confidence interval
b
±1.65 standard error SE did not include zero (Burnham and Anderson, 2002). Overall
parameter estimates for detection probability pand occupancy ѱwere calculated using a weighted model averaging tech-
nique (Burnham and Anderson, 2002).
2.5. Identification of ecological thresholds
We were interested in identifying ecological thresholds in lion occupancy in relation to important natural and anthro-
pogenic variables. We plotted the model averaged estimate of lion occupancy against the highest ranking significant
explanatory variables. Using the “segmented”package in R (R Core Team, 2017)wefit piecewise “broken stick”regression
models to the data to identify breakpoints where the relationship between the response and explanatory variables changes to
estimate ecological thresholds (Toms and Villard, 2015).
Table 1
Variables assumed to describe lion occupancy and detectability, their ecological relevance and metrics recorded from each 1 km samples of spoor surveys or
from GIS maps.
Variable Ecological relevance Metric recorded Source
Occupancy
Preferred
prey
Species preferentially selected by lions across Africa and in our study area (Hayward and
Kerley, 2005; K. Everatt, unpublished)
Summed presence/absence of each
species
Spoor
survey
All prey All prey species taken by lions in our study area (Hayward and Kerley, 2005; K. Everatt,
unpublished)
Summed presence/absence of each
species
Spoor
survey
Bushmeat
poaching
Direct persecution of lions and/or depletion of lion prey Presence/absence Spoor
survey
Cattle Persecution of lions by pastoralists/potential lion prey Presence/absence Spoor
survey
Protected
area
Increased protection of lions and prey Distance (km) from centre of Kruger
of Gonarezhou NP
GIS
Settlement Increased human impact Distance (km) from nearest
settlement edge
GIS
Riparian
habitat
Habitat feature known to be selected by some lion prey (Estes, 1991) and selected by
lions (Hopcraft et al., 2005)
Number of 30 30 m riparian pixels
per grid cell
GIS
Detectability
Substrate Influence on the detectability of tracks. Scoring based on assumed likelihood of detecting
lion tracks if lions were present.
Good/medium/bad Spoor
survey
Transect type Lions may show selection/avoidance of transect types Road/track/trail/river/bush Spoor
survey
See Appendix 1 for full description of prey species and bushmeat poaching sign recorded.
K.T. Everatt et al. / Global Ecology and Conservation 20 (2019) e00758 5
3. Results
3.1. Occupancy by lions
A total of 3759 1 km sampling occasions were walked across 103 grid cells (range 10e45 km/cell, mean ¼34.5 km/cell)
resulting in 26 000 km
2
of sampled habitat. Lions were detected in 215, poaching in 485, cattle in 783 and one or more of
preferred prey species in 2608 of the samples (Appendix 3).
The covariate combination of “Preferred prey”and “All prey”(r ¼0.81) as well as “All prey”and “Cattle”(r ¼0.71) were
found to be correlated and were not included in models together leaving 24 candidate models of statistically uncorrelated
variables (Appendix 4).
3.2. Model selection
We first accounted for the probability of detection pby ranking all covariates for pincluding the two detection only
covariates (see Table 1). The top ranking model included the covariates substrate, cattle occurrence, and distance to nearest
settlement on the probability of detection (
J
,th0(),t.h1(.),p(Sub. þCþS),th0pi(.);Table 2). Given our sampling effort, the
probability of detecting lions in a grid cell from the top-ranking model was calculated at b
p¼0.344 ±SE ¼0.092. This model
was then held constant for all further analysis and only the covariates describing occupancy ѱwere left to vary. We then
compared 24 models describing the influence of the covariates, in isolation and in combinations, on lion occupancy. From all
top-ranking models, the weighted average occupancy estimate was b
j
¼0.488 ±SE ¼0.079.
Heterogeneity in the occupancy of lions was best explained by the significantly negative relationship with the occurrence
of cattle, which occurred in all the highest-ranking models (
b
¼16.486, SE ¼8.788, w¼1.0). Lion occupancy was also
explained by the significantly positive relationship with the occurrence of preferred prey species (
b
¼1.199, SE ¼0.723,
w¼0.516). A negative relationship with bushmeat poaching (
b
¼0.455, SE ¼0.725, w¼0.753) and positive relationship
with proximity to protected area centres (
b
¼0.021, SE ¼0.039, w¼0.178) also contributed to explaining lion occupancy
(Table 2,Fig. 2).
3.3. Identification of ecological thresholds
A negative non-linear response was identified between the weighted average estimates of lion occupancy b
j
and the
occurrence of cattle with a threshold at the value of cattle occurrence of 0.208 (Fig. 3a). Mean site estimates were b
j
¼0.677 ±SE ¼0.105 at sites with cattle occurrence values of less than 0.208 (74 sites) and were b
j
¼0.005 ±SE ¼0.013 at
sites with cattle occurrence values of greater than 0.208 (29 sites). A positive non-linear response was identified between lion
occupancy and the occurrence of preferred lion prey with a threshold occurring at the value of preferred prey occurrence of
0.218 (Fig. 3b). Mean site estimates were b
j
¼0.063 ±SE ¼0.012 at sites with preferred prey occurrence values of less than
0.218 (11 sites)and were b
j
¼0.539 ±SE ¼0.087 at sites with preferred prey occurrence values of greater than 0.218 (92 sites).
The relationship between lion occupancy and the occurrence of bushmeat poaching displayed a weak negative correlation of
r
2
¼0.0939 (Fig. 3c).
4. Discussion
Many apex carnivore species now find themselves in a conservation crisis, being limited through competitive interactions
with humans (Ripple et al., 2014). In this study we sought to identify what limits lions in human-impacted landscapes by
considering the relative roles of interference and exploitative competition between lions and humans, and the relative
importance of different anthropogenic pressures on lion occupancy. We further identified important thresholds for the oc-
cupancy of lions across the landscape.
Table 2
The summary of the best models describing the effects of covariates on lion occupancy ѱand detectability p. Number of sites ¼109. Covariates include Cattle
(C), Preferred prey (PP), Bushmeat poaching (BP), Protected areas (PA), Settlements (S), Substrate (Sub) and Transect type (T).
Models
D
AIC AIC weight Model likelihood Number of parameters
Detection
J
,th0(.),th1(.),p(Sub þCþS),th0pi(.) 0.00 0.952 1 8
J
,th0(.),th1(.),p(Sub þCþSþT),th0pi(.) 5.99 0.048 0.4795 7
Occupancy
J
(C þPP), th0(.),th1(.),p(Sub þCþS),th0pi(.) 0.00 0.3180 1 10
J
(C),th0(.),th1(.),p(Sub þCþS),th0pi(.) 0.74 0.2196 0.6907 9
J
(C þPP þBP),th0(.),th1(.),p(Sub þCþS),th0pi(.) 1.60 0.1429 0.4493 11
J
(C þBP),th0(.),th1(.),p(Sub þCþS),th0pi(.) 1.96 0.1193 0.3753 10
J
(C þPA),th0(.),th1(.),p(Sub þCþS),th0pi(.) 2.45 0.0934 0.2938 10
J
(C þBP þPP þPA),th0(.),th1(.),p(Sub þCþS),th0pi(.) 3.50 0.0553 0.1738 12
K.T. Everatt et al. / Global Ecology and Conservation 20 (2019) e007586
We found that lions occupied approximately 49% of the full 26 0 00 km
2
area of sampled habitat. The presence of cattle was
the single strongest explanatory variable of lion occupancy, with the cattle covariate appearing in all the top-ranking models
and having a significant negative influence on lion occurrence. Because lions are known to predate on cattle (Kissui, 2008;
Valeix et al., 2012;Tumenta et al., 2013;Loveridge et al., 2017) this significant negative relationship between cattle presence
and lion occupancy likely indicates that the costs associated with cattle depredation, including persecution by pastoralists,
must be higher than the energetic rewards gained by lions byconsuming cattle. It is important to note that the occurrence of
cattle is used here as a proxy for the impacts of likely retaliatory actions taken towards lions by cattle owners.
The influence of cattle on lion occupancy could indicate that lions are individually aware of the risks associated with
occupying pastoralist areas and exhibit a behavioural avoidance of these landscapes or of threats within these landscapes
(Oriol-Cotterill et al., 2015;Loveridge et al., 2016;Suraci et al., 2019), or that lions are excluded from cattle areas through
persecution of lions by pastoralists (Woodroffe and Frank, 2005;Kissui, 2008). Behaviour avoidance of high-risk areas is a
common strategy among species under predation pressure (Thaker et al., 2011) and even apex predators are increasingly
having to adjust their behaviour in response to top-down pressures exerted by humans (Oriol-Cotterill et al., 2015;Smith
et al., 2015,2017). Alternatively, the influence of cattle on lion occupancy documented here could also indicate a numeri-
cal response where lions are excluded from cattle-occupied lands through higher levels of persecution. An analysis of the
sources of lion mortalities in our study area found that 41.2% of documented lion mortalities were attributed to persecution by
pastoralists (K Everatt, unpublished), confirming that at least some of the heterogeneity in lion occupancy described here
should be attributed to a numerical response to this anthropogenic pressure rather than a behavioural response.
Large carnivores are known to respond to thresholds of human density beyond which the species or populations become
locally extinct (Woodroffe, 2000). We found a non-linear continuous response between lion occupancy and cattle occurrence
cumulating in a near absence of lions at sites with cattle occurrence greater than 21%. This threshold indicates a clear point
beyond which the relative occurrence of cattle and associated retaliatory or pre-emptive killing of lions for livestock
depredation prohibits the occurrence of lions in the landscape. In this case the 21% cattle occurrence threshold can be
interpreted as meaning that 21% of a given area is currently being used by cattle (within an approximate 14-day window (see
methods)). We interpret the resultant low occupancy rate of lions in cells with more than 21% cattle occurrence to mean that
lions are only able to occasionally use a landscape with this level of pastoralist impact. Beyond this level of pastoralist impact
lions may disperse into such areas but are unlikely to become resident; an interpretation which is validated by the author's
observations that many of the lions found in livestock areas, and which are involved in livestock conflict, were of dispersal age
and were often subsequently killed in retaliation of real or perceived livestock depredation (K Everatt, unpublished). Similar
human disturbance thresholds have been identified in other large predator systems including thresholds of resource
extraction beyond which grizzly bears cease to occur in western Canada (Lindsey et al., 2013) and thresholds of housing
density beyond which cougars cease to occur in the western US (Maletzke et al., 2017).
Knowledge of these thresholds is helpful for guiding conservation (Foley et al., 2015). For instance, assuming that the
response of lion occupancy to this threshold of cattle occurrence indicates a numerical response to persecution by pastoralists
Fig. 2. a) The relative contribution of important covariates (Pw>0.95), and b) the strengths and directions of the covariates explaining lion occupancy. The
covariates marked by * have
b
-coefficients with greater than 90% significance.
K.T. Everatt et al. / Global Ecology and Conservation 20 (2019) e00758 7
and assuming that lions select for cattle as prey (Hayward and Kerley, 2005), we can predict that sites within this study area
that have greater than 20% cattle occurrence could be acting as ecological traps for lions in the region (Battin, 2004).
The disturbance threshold we report here builds on the substantial body of knowledge describing the influences of
pastoralism on lion ecology (Valeix et al., 2012;Schuette et al., 2013;Oriol-Cotterill et al., 2015;Loveridge et al., 2016;Suraci
et al., 2019) and aligns with continent-scale meta-analyses indicating a negative influence of pastoralism on lion conservation
(Packer et al., 2013;Lindsey et al., 2017).
These data provide evidence of the negative impact of pastoralist activities on lion occupancy considering the response of
lions across a spatial gradient of anthropogenic pressures, analogous to a response of increasing pressures at a single site. This
‘space for time’approach is useful when studying ecological relationships between long-lived species which exhibit slower
and thus more difficult to detect responses to environmental changes (Estes et al., 2010). The response of lions to changes in
cattle occurrence identified here could help predict the species' response to Africa's increasing cattle herds and pastoralist
footprint (Wittemyer et al., 2008). It is interesting to note, however, that because our data originates from surveys across the
spatial gradients in a time snapshot it is possible that the occupancy threshold of lions in relation to cattle may be hysteretic,
that is being dependent on the direction of change in cattle occurrence (Scheffer et al., 2001). Cattle in our study area are
mostly restricted to Mozambique, where the abundance of cattle has generally been on the increase following economic
recovery after the end of country's civil war (Grossmann et al., 2014) and it is therefore possible that the threshold of lion
occurrence might be placed at much lower cattle occurrence in a system that is moving from higher to lower cattle impacts
(see Estes et al., 2010). For instance, a cattle-disturbance threshold in parts of East and West Africa may be expected to be
lower due to extreme degradation by overgrazing coupled with climate change causing, at least localized, declines of humans
and livestock (Wittig et al., 2007;Mganga et al., 2018). A successful carnivore-livestock conflict mitigation scheme (for
example Hazzah et al., 2014;Lichtenfeld et al., 2015) that reduces the number of lions killed by pastoralists in retaliation to
depredation could also shift the disturbance thresholds shown here.
The occupancy of lions across our study area had a significant positive relationship with the occurrence of their preferred
prey. The importance of the availability of prey resources for determining apex carnivore habitat use is well known and it is
expected to be the most important variable in the natural functioning of predator-prey systems (Hopcraft et al., 2005;
Mitchell and Hebblewhite, 2012;Everatt et al., 2015). Interestingly, the relationship between lion occupancy and the
occurrence of their preferred prey was nonlinear with a stronger relationship found at sites below a threshold of
Fig. 3. Relationships between the weighted average estimates of lion occupancy and the top-ranking significant explanatory covariates; a) cattle, b) preferred
prey and c) bushmeat poaching occurrence.
K.T. Everatt et al. / Global Ecology and Conservation 20 (2019) e007588
approximately 20% preferred prey occurrence. This stronger response may indicate the absence of density-dependant social
mechanisms regulating lion occupancy in these sites and indicates lion population or behavioural instability at these sites.
While the occupancy of lions was positively determined by their prey, it was also equally negatively determined by the
occurrence of bushmeat poaching activities (each contributing to 75.3% of the combined model weights). Prey populations
are severely depleted throughout the Mozambique portions of the study area where bushmeat poaching is widespread
(Everatt et al., 2019;Lindsey et al., 2017;Baghai et al., 2018). For instance, wild ungulate biomass was estimated at less than
20% of carrying capacity in the Limpopo National Park (Baghai et al., 2018) where bushmeat poaching occurred across 80% of
sites surveyed (Everatt et al., 2014). Everatt et al. (2014) showed that bushmeat poachers in Limpopo National Park select for
areas closer to settlements (as they typically travel by foot) and with medium-sized ungulates. Areas closer to settlements in
this landscape have become denuded of most wild ungulates other than the smallest of species such as common duikers
(Sylvicapra grimmia) (K Everatt, unpublished) which fall outside of lion's preferred prey range. The influence of the bushmeat
poaching variable on the occupancy by lions in this study area likely includes lions' selection against these prey-depleted
lands. The impact of competition with bushmeat poachers for prey on the occurrence of an apex predator would be ex-
pected to differ according to the degree of niche overlap between the predator and humans. For instance, a difference in
competitive ability with humans has been documented between tigers and leopards in India (Athreya et al., 2013). In Africa,
leopards which exploit smaller species (Hayward et al., 2007) could be expected to be more successful in wildlife depleted
landscapes than lions. A previous analysis found lion habitat use in Limpopo National Park to be strongly negatively asso-
ciated with bushmeat poaching activities (Everatt et al., 2014), while in contrast, leopard habitat use in the same area and time
was positively associated with both bushmeat poachers and lions (Strampelli et al., 2018). It is likely, however, that leopards
were not selecting for areas with lions or bushmeat poachers per se but rather selecting for the same hunting opportunities as
lions and bushmeat poachers. However, the bushmeat poaching variable in this study may also indicate other drivers in
addition to the depletion of prey. In this system, bushmeat poachers are often accompanied by large packs of domestic dogs
whose presence can be avoided by lions. Domestic dogs have been used successfully to deter lions from livestock (Bauer et al.,
2010). Dogs are loud, mobile and conspicuous, all of which are characteristics of a threat that would be relatively easy to
detect and avoid. Avoiding bushmeat hunters who use snares and traps, which are also commonly employed hunting tools in
this study area, however, would be very different in that respect from avoiding hunters who use dogs. Snares and traps are set
and left in such a way that they are meant to remain undetected by wildlife and it would thus be more difficult for lions to
adapt an avoidance behaviour to this type of cryptic activity. In fact, during this and other surveys in the Limpopo and Banhine
National Parks we have recorded lions scavenging fromsnares (K Everatt, unpublished) suggesting that lions do not recognize
this threat or show avoidance of snares.
The third explanation of a strong negative effect of bushmeat hunting on lion occupancy is that lions are persecuted and
spatially excluded by poachers. For instance, bushmeat poaching bycatch accounts for 18% of all known human-caused lion
mortalities in Limpopo National Park (K Everatt, unpublished). On some of the scavenging events mentioned above the lions
have themselves then been caught and killed in adjoining snares, suggesting that bushmeat poaching can also be acting as an
ecological trap to lions. There is also an overlap between bushmeat poaching and a rise in the targeted poaching of lions
where bushmeat poachers as well as rhino and elephant poachers are also increasingly poaching lions for body parts (K
Everatt, unpublished).
Apex carnivores are facing massive declines around the world as a consequence of their interactions with humans (Ripple
et al., 2014). The theory of intra-guild competition is applicable to understanding the conservation crisis facing apex carni-
vores, such as African lions, when humans are recognized as an apex predator. This study adds to the emerging body of
empirical results and a predictive understanding of the top-down impacts of humans on the world's vulnerable apex
carnivores.
Acknowledgements
We thank the following institutions for providing necessary permits: Administraç~
ao Nacional das
Areas de Conservaç~
ao,
South African National Parks, Zimbabwe Parks, Parque Nacional de Limpopo, Parque Nacional de Banhine, Parque Nacional de
Zinave, Kruger National Park, Gonarezhou National Park, Karangani Reserve and Maunge Conservancy. We thank Panthera,
the Natural Sciences and Engineering Research Council of Canada, Nelson Mandela University and Wilderness Foundation for
funding. We thank Leah Andresen and Eden Everatt for help in the field and three anonymous reviewers for their input on the
manuscript.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.gecco.2019.e00758.
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