Estimating leopard density across the highly modified human-dominated landscape of the Western Cape, South Africa
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Estimating leopard density across the highly
modified human-dominated landscape of the
Western Cape, South Africa
CAROLYN H. DEVENS, MATT W. HAYWARD, THULANI TSHABALALA, AMY DICKMAN
J E A N N I N E S . M C M A N U S , B O O L S M U T S and M I C H A E L J . S O M E R S
Abstract Apex predators play a critical role in maintaining Keywords Camera trapping, carnivore conservation,
the health of ecosystems but are highly susceptible to habitat leopard, Panthera pardus, secr, SPACECAP, spatially
degradation and loss caused by land-use changes, and to explicit capture–recapture
anthropogenic mortality. The leopard Panthera pardus is
Supplementary material for this article is available at
the last free-roaming large carnivore in the Western
https://doi.org/./S
Cape province, South Africa. During –, we carried
out a camera-trap survey across three regions covering
c. , km of the Western Cape. Our survey comprised
camera sites sampling nearly , camera-trap nights,
Introduction
resulting in the identification of individuals. We used two
spatially explicit capture–recapture methods (R programmes
secr and SPACECAP) to provide a comprehensive density
analysis capable of incorporating environmental and an-
T he exponential growth of the human population is
threatening all levels of biodiversity, including large
carnivores, with % of species’ populations experiencing
thropogenic factors. Leopard density was estimated to be continuing declines (Estes et al., ; Ripple et al., ).
. and . leopards/ km, using secr and SPACECAP, Large carnivores are particularly at risk of extinction
respectively. Leopard population size was predicted to be because of their small population sizes, slow reproductive
– individuals for our three study regions. With these rates, complex social structures and requirement for
estimates and the predicted available leopard habitat for the large and contiguous habitats with sufficient prey (Cardillo
province, we extrapolated that the Western Cape supports an et al., ; Ripple et al., ). These characteristics, and
estimated – individuals. Providing a comprehensive their vulnerability to negative interactions with humans,
baseline population density estimate is critical to understand- have driven declines of some of the most wide-ranging
ing population dynamics across a mixed landscape and help- carnivores (Cardillo et al., ; Ray et al., ;
ing to determine the most appropriate conservation actions. Swanepoel et al., ; Wolf & Ripple, ).
Spatially explicit capture–recapture methods are unbiased Although the leopard is considered the most adaptable
by edge effects and superior to traditional capture–mark– large carnivore (Ripple et al., ), range declines of –%
recapture methods when estimating animal densities. We globally, –% in Africa and –% in southern
therefore recommend further utilization of robust spatial Africa indicate that leopard populations are not as re-
methods as they continue to be advanced. silient to anthropogenic influences as previously believed
(Jacobson et al., ). Major anthropogenic threats to
leopards include ongoing habitat loss and fragmentation
CAROLYN H. DEVENS* (Corresponding author, orcid.org/0000-0002-7539-
0969), MATT W. HAYWARD† and MICHAEL J. SOMERS¶ ( orcid.org/0000-0002- (Harcourt et al., ; Crooks, ; Swanepoel et al.,
5836-8823) Mammal Research Institute, Department of Zoology and ), depletion of prey resources (Woodroffe, ;
Entomology, University of Pretoria, Pretoria, South Africa
E-mail chdevens@gmail.com
Karanth & Chellam, ), unsustainable hunting levels
(Woodroffe, ; Harcourt et al., ) and direct perse-
THULANI TSHABALALA‡, JEANNINE S. MCMANUS§ and BOOL SMUTS§ Research
Department, Landmark Foundation, Riversdale, South Africa cution by people (Harcourt et al., ; Treves & Karanth,
AMY DICKMAN WildCRU, Oxford University, Abingdon, UK ; Treves et al., ; Treves & Naughton-Treves,
*Also at: Research Department, Landmark Foundation, Riversdale, South Africa
; McManus et al., ; Swanepoel et al., ).
†Also at: School of Environmental and Life Sciences, University of Newcastle, Increasing human population density and loss of habitat
Callaghan, Australia increase the likelihood of resource competition with people,
‡Also at: School of Agricultural, Earth and Environmental Sciences, University
of KwaZulu-Natal, Durban, South Africa often resulting in human–carnivore conflict (Woodroffe,
§Also at: Department of Biodiversity and Conservation Biology, University of ; Cardillo et al., ). Persecution often includes
the Western Cape, Cape Town, South Africa indiscriminate use of lethal methods to manage livestock
¶Also at: Centre for Invasion Biology, University of Pretoria, Pretoria, South
Africa depredation by carnivores (e.g. snares, poisoned carcasses,
Received December . Revision requested March . gin traps (leg-hold traps), gun traps, live trapping) and tar-
Accepted November . First published online September . geted (often retaliatory) hunting (McManus et al., ).
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use,
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https://doi.org/10.1017/S0030605318001473
Oryx, 2021, 55(1), 34–45 © The Author(s), 2019. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605318001473
Leopard density in South Africa’s Western Cape 35
Estimating a species’ density across various types of land Fewster, ), spatially explicit capture–recapture models
cover (i.e. habitat and vegetation type) and land uses (i.e. are becoming increasingly robust and comprehensive.
residential/urban land, cultivated land, commercial and These statistical and methodological advances provide
recreational land and areas protected for conservation) multiple options that can be applied to specific study ques-
can help determine how population numbers are affected tions while making population density estimates increasing-
by landscape features. Density estimates are not equally ly reliable. Inaccurate density estimates can lead to biased
robust, and under- or overestimating populations can have population estimates, with serious implications for conser-
substantial implications for conservation management and vation management (Soisalo & Cavalcanti, ).
policy (Foster & Harmsen, ; Hayward et al., ). For The estimation of a baseline population density for
example, Soisalo & Cavalcanti () demonstrated how leopards in South Africa’s Western Cape province is
overestimating jaguar Panthera onca populations by five fundamental for understanding how this regionally im-
individuals per km inflated the overall population portant population responds to landscape-scale threats
estimate by , individuals across their , km and changes. By estimating densities across landscapes of
study region. Density estimation also depends upon data varying resource availability and threats, we can examine
quality, with a large enough sample size and capture prob- drivers of regional density variation and improve conserva-
ability for the chosen analysis method (Foster & Harmsen, tion management for disjunct populations.
).
Conservationists and ecologists constantly seek to im-
prove the methodology for estimating animal population Study area
abundances and densities (Griffiths & van Schaik, ;
This study covers c. , km in three areas (Langeberg,
Karanth & Nichols, ; Karanth & Nichols, ). The
Overberg and Garden Route) in the Western Cape, South
use of camera traps and photo capture–recapture analysis
Africa (Fig. ). The topography varies from the Cape Fold
is a common and effective non-invasive practice for ob-
Mountain peaks with an altitude of . , m that extend
taining data on wildlife population dynamics, particularly
, km east to west, to coastal and low-lying valleys
of rare or elusive species (Foster & Harmsen, ). Leo-
at , m altitude (Thamm & Johnson, ). Biomes
pards, like many other large felids, are challenging to
included in our study are Thicket, Afro-temperate forest
monitor because of their large home ranges, low population
(Forest), Sandstone fynbos (Fynbos), Nama-Karoo, Succu-
density and primarily solitary and elusive nature (Karanth &
lent-Karoo and Savanna (Mucina & Rutherford, ).
Nichols, ; Treves & Karanth, ). Global population
In the Langeberg we deployed camera traps in the
estimates across the leopard’s vast geographical distribution
greater Riversdale/Heidelberg area on the southern slopes
do not account for the health and sustainability of smaller,
of Langeberg Mountain Range, in the greater Greyton area
isolated regional metapopulations across a variety of habitat
on the southern slope of Riviersonderend Mountain Range
types, countries and levels of human landscape modification
and in the Robertson Wine Valley (Breede River Valley)
and pressure.
situated between the Langeberg and Riviersonderend
Camera-trap surveys have become an important data
Mountain Ranges. In the Overberg we surveyed the greater
collection method for population and density studies
Hermanus area to the west, the greater Cape Agulhas and
because they are non-invasive, practical and affordable
Arniston areas to the south along the coast, and the greater
(Karanth, ; Karanth & Nichols, ; Foster &
De Hoop Nature Reserve area to the eastern extent of the
Harmsen, ). The capture–recapture method relies on
surveyed region. Along the Garden Route we surveyed
individuals being identifiable (Karanth, ; Silver et al.,
temperate forest along the southern slopes of the Outeniqua
) and has been the predominant approach used in
and Tsitsikamma Mountains from George in the west to
felid density studies.
the Bloukrans River in Plettenberg Bay in the east.
More recently, spatial capture–recapture or spatially
explicit capture–recapture methods have become popular
because they provide comprehensive analyses that can in- Methods
corporate environmental and anthropogenic factors when
estimating animal densities. These methods enable density Camera-trap surveys
analyses to include spatio-temporal data from capture
histories, providing direct estimates of population density We undertook seven large-scale camera-trap surveys during
that remain unbiased by edge effects (Chase Grey et al., June –March , focusing on likely presence of leo-
). By allowing for flexibility in individual heterogeneity pards inside and outside protected areas and across different
with the consideration of capture probability relative to trap agricultural land-use zones (livestock, crops, forestry). We
location, spatial covariates such as habitat, and intrinsic fac- used Cuddeback Attack IR (Cuddeback, Green Bay, USA)
tors such as sex and age (Foster & Harmsen, ; Efford & digital infrared cameras and selected camera-trap locations
Oryx, 2021, 55(1), 34–45 © The Author(s), 2019. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605318001473
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https://doi.org/10.1017/S0030605318001473
36 C. H. Devens et al.
FIG. 1 (a) Location of the study areas in the Western Cape province, South Africa. (b) Camera-trap surveys conducted across the
Langeberg (, km), Garden Route (, km) and Overberg (, km) areas. Data from camera stations with identified
leopards were analysed with programmes SPACECAP and secr with various buffers.
based on the highest likelihood of leopard activity as deter- density estimates than non-spatial methods (Obbard et al.,
mined by physical evidence (scat, spoor and territorial scent ; Gerber et al., ; Gopalaswamy et al., ; Noss
and scratch markings on trees), as well as habitat type and et al., ; Braczkowski et al., ). We calculated density
topography (Karanth & Nichols, ). At each camera- estimates using two spatially explicit capture–recapture
trap station, we positioned two cameras to capture both methods within the programming environment R ..
flanks of a passing leopard. The stations were placed – (R Development Core Team, ): () the maximum
km apart to achieve even coverage of the sampling area likelihood based estimator programme secr .. (Efford
and camera stations remained active for a minimum of et al., ; Efford, , ), which is a more robust
months to ensure maximum likelihood of leopard activity version of programme DENSITY (Efford, ), and () the
being captured without violating the closed population as- Bayesian estimator programme SPACECAP .. (Gopalaswamy
sumption (Karanth & Nichols, ; Devens et al., ). et al., ). Previous studies have applied these two pro-
grammes to compare density estimates (Kalle et al., ;
Thapa et al., ) and to compare results of spatially explicit
Capture–recapture identification capture–recapture with non-spatial methods (Noss et al.,
; Braczkowski et al., ). We used these programmes
Individual leopards can be identified from clear photo-
for comparison of two spatially explicit capture–recapture
graphs of both flanks by their unique rosette markings.
methods and to determine the most robust and inclusive
Identification from single flank photos is possible, but
density estimate range for each study region.
needs to be based upon the observation of characteristics
For the analysis with secr we categorized camera traps as
such as the animal’s overall size, neck girth, injuries or
count detectors, which allows repeat detections. Count data
scars. Although such characteristics may be useful, incor-
can result from devices such as automatic camera traps.
rectly identified individuals could bias density estimates,
Count detectors record the presence of an animal at a trap
resulting in overestimation of the population if flank photos
location without restricting movement and allow . detec-
of the same animal were erroneously assigned to two indi-
tion of an individual at a particular site on any occasion. The
viduals. We therefore only utilized double flank capture
secr.fit function was run for each regional survey phase
events for identification.
using models of time (g*T) and behavioural response
(g*b), as well as buffers of , , , and km
Data analysis surrounding the minimal convex polygon around camera
stations in each camera-trapping area. These buffers
Spatially explicit capture–recapture methods for density ensured inclusion of all leopard home ranges within reach
analyses are more comprehensive and reliable than tra- of camera traps (Kalle et al., ) and made density estima-
ditional capture–recapture analyses (Borchers & Efford, tion more reliable by determining the point at which the
; Kalle et al., ; Gopalaswamy et al., ; Chase density estimate stabilized (Kalle et al., ; Chase Grey
Grey et al., ; Thapa et al., ) and produce lower et al., ).
Oryx, 2021, 55(1), 34–45 © The Author(s), 2019. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605318001473
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https://doi.org/10.1017/S0030605318001473Leopard density in South Africa’s Western Cape 37
SPACECAP was used to estimate abundance and density ‘Spatial Capture–Recapture’ option runs a spatially explicit
using spatially-explicit capture–recapture models to derive capture–recapture analysis. The Markov-Chain Monte
spatial Bayesian estimates with trap response. A grid of Carlo settings varied between study areas and buffers.
equally spaced points km apart was generated in QGIS Markov-Chain Monte Carlo iterations were set between
. (QGIS Development Team, ) and clipped to the sur- , and , with a burn-in period between ,
veyed area containing the camera trap array combined with and , iterations and a thinning rate of . The data aug-
an extended surrounding area, known as the state-space, at mentation numbers were c. – times the number of ani-
distances of , , , and km. These points represent mals identified in each regional survey and varied between
all potential home range centres of all leopards within the and ,. We assessed chain convergence with the
survey. The potential home range centres file included the Geweke diagnostic test produced within the SPACECAP out-
geographical coordinates and habitat suitability of each of put in the form of z-score values. Z-scores between −. and
these points within the state-space. We determined habitat +. implied adequate convergence and confirmed that the
suitability using Maxent .. (Phillips et al., ; Phillips Markov-Chain Monte Carlo analysis was run with a suffi-
et al., ) model output comprising environmental vari- ciently long burn-in period. The SPACECAP output also pro-
ables, including eight WorldClim bioclimatic variables duces a Bayesian P-value to provide additional assessment of
obtained from WorldClim website (Hijmans et al., ), the model fit where P-values close to or imply that the
human footprint index (WCS & CIESIN, ), altitude, an- model is inadequate. The adequacy of the data augmentation
thropogenic biomes (Ellis & Ramankutty, ), Globcover number can be checked in the ‘density plot for psi’ and
(FAO, ), South African National Bio-diversity ‘density plot for N’ files included within the output files.
Institute ecosystem status of vegetation types (Rouget All densities obtained with SPACECAP produced z-scores
et al., ), and a subset of location data obtained from that achieved convergence, as well as sufficient model fit
two leopards resident in the area that were equipped with and data augmentation number. The output also included
GPS collars. We used the Natural Breaks function (Jenks, pixel-specific density estimates (animals per km), which
) in ArcMap .. to code the model output as either we used to create a pixel density map of the study areas
(not suitable = –.) or (suitable = .–.). and compare leopard densities across various land covers
The habitat suitability generated in this way (Jenks and uses to illustrate relationship with anthropogenic factors.
method) was recorded for each point and a home range cen-
tre input file was generated for each area’s buffer distances
(Fig. ). Results
We ran SPACECAP with a km pixel area, and set
the model definitions to ‘Trap response present’, ‘Spatial Camera-trap data and sampling effort
Capture–Recapture’, ‘half normal’ detection function and
the capture encounters ‘Bernoulli’s process’. The ‘Trap The total sampling effort for the Overberg (November –
response present’ option implements a ‘trap-specific’ behav- March ) was , camera-trap nights across a total
ioural response in which the probability of capture at a spe- of camera-trap locations ( cameras). This yielded
cific trap increases (or decreases) after the initial capture. The leopard photographs (captures), with eight individuals
FIG. 2 Maxent probability
distribution model of leopard
habitat suitability in the Western
Cape and considering this study’s
km buffers around camera-trap
areas. Distribution model is
reclassified with Natural Breaks
function.
Oryx, 2021, 55(1), 34–45 © The Author(s), 2019. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605318001473
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https://doi.org/10.1017/S003060531800147338 C. H. Devens et al.
identified in of them. These individuals were detected at
Identified
leopards
nine of the camera-trap sites (Table ).
TABLE 1 Summary of capture–recapture camera-trap survey sampling effort and leopard Panthera pardus capture results across three study areas in the Western Cape, South Africa.
The total sampling effort for the two phases of the
8
21
42
24
71
Langeberg survey (April –December ) consisted
of , camera-trap nights across a total of camera-trap
Total leopard pictures
locations ( cameras). This yielded leopard captures,
from which individual leopards were identified in
enabling leopard
photographs, and these individuals were detected at of
identification
the camera-trap sites (Table ).
The total sampling effort for the three phases of the
40
86
250
125
376
Garden Route survey area (May –December ) was
, camera-trap nights across a total of camera-trap
locations ( cameras). This yielded leopard captures,
Total leopard
of which individual leopards were identified in photo-
pictures
graphs, and these individuals were detected at of the
118
142
454
238
714
camera-trap sites (Table ). The total sampling effort was
, camera-trap nights across camera-trap locations
( cameras), with leopard captures and identified
Total camera-
individuals (Table ).
trap nights
2,880
3,600
7,110
4,530
13,590
Density estimates
The spatially explicit capture–recapture density estimates
Camera-trap stations
varied by region and between regional phases, and estimates
with leopard IDs
from secr were lower than those from SPACECAP. The effect
of using various buffers within the two programmes, par-
ticularly SPACECAP, demonstrated the sensitivity to buffer
width and data augmentation size (Kalle et al., ). We cal-
9
33
58
33
100
culated mean density estimates from stabilized buffer values
to ensure the study area was large enough to avoid capturing
Camera-trap
any individuals residing outside the buffered region during
stations
the survey.
In the Overberg, secr density estimates stabilized at
32
40
79
42
151
. leopards/ km (CI .–.). Both phases in the
Langeberg had density estimates that were stable across all
Regional
phases
five buffers with a phase one density estimate of . leo-
pards/ km (CI .–.) and a phase two estimate of
1
3
2
6
. leopards/ km (CI .–.). The Garden Route’s
first phase had an estimated density of . leopards/ km
(active nights)
Survey phase
(CI .–.) with densities stable across all buffers, whereas
duration
phase two had an estimated density of . leopards/ km
(CI .–.) and phase three produced a density estimate of
90
90
90
90
270
. leopards/ km (CI .–.; Table ). We calculated
Oct. 2012–Mar. 2015
July 2011–Mar. 2015
Apr. 2012–Jan. 2013
the mean density for multi-phase study areas resulting in
July 2011–Sep. 2012
. leopards/ km in the Overberg, compared to
. leopards/ km (CI .–.) in the Langeberg and
Survey period
. leopards/ km (CI .–.) along the Garden
Route. The highest estimated density was Langeberg’s
phase two with . leopards/ km, and the lowest was
. leopards/ km in the Overberg (Table ).
Garden Route
SPACECAP produced an Overberg density estimate of
Study area
Langeberg
. leopards/ km (CI .–.), Langeberg phase one
Overberg
and two density estimates of . (CI .–.) and . (CI
Mean
Total
.–.) leopards/ km, respectively, and Garden
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TABLE 2 Density estimates from programmes secr and SPACECAP, with standard error (SE), standard deviation (SD), % confidence intervals (CI), range of buffers that achieved mean
density estimate stabilization, area of regional km buffer, number of leopards estimated within the surveyed area, and the Bayesian P-value for model fit.
Density estimate Buffer range of means’
(leopards/100 km2) stabilization (km) Estimated no. of leopards P-value
secr SPACECAP secr SPACECAP
mean ± SE (95% CI) mean ± SD (95% CI) secr SPACECAP 35 km buffer area (km2) (95% CI) (95% CI) SPACECAP
Langeberg
Phase 1 0.29 ± 0.05 (0.20–0.41) 1.67 ± 0.26 (1.21–2.18) 15–35 15–30 0.86
Phase 2 0.70 ± 0.15 (0.57–1.76) 2.11 ± 0.35 (1.46–2.82) 15–35 25–35 0.55
Area mean 0.50 ± 0.10 (0.39–1.09) 1.89 ± 0.30 (0.89–2.50) 19,063.42 93.41 360.30 0.71
(74.35–205.88) (169.70–476.60)
Garden Route
Phase 1 0.50 ± 0.26 (0.19–1.32) 1.44 ± 0.58 (0.55–2.54) 15–35 20–30 0.47
Phase 2 0.34 ± 0.11 (0.18–0.63) 0.92 ± 0.16 (0.65–1.22) 25–35 20–35 0.73
Phase 3 0.29 ± 0.13 (0.13–0.67) 0.51 ± 0.10 (0.36–0.70) 20–35 15–35 0.60
Area mean 0.38 ± 0.17 (0.17–0.87) 0.96 ± 0.28 (0.52–1.49) 6,680.34 25.39 64.13 0.60
(11.36–58.12) (34.74–99.54)
Overberg
Leopard density in South Africa’s Western Cape
Phase 1 0.17 ± 0.10 (0.06–0.48) 0.69 ± 0.30 (0.39–1.28) 30–35 25–35 7,910.04 13.45 54.58 0.63
(4.75–37.97) (30.85–101.25)
Overall mean 0.35 ± 0.12 (0.21–0.81) 1.18 ± 0.29 (0.60–1.76) 11,217.93 44.08 159.67 0.65
(30.15–100.66) (78.42–225.79)
Total area without overlap1 29,258.43 132.25 479.01
(90.46–301.97) (235.25–677.38)
The area total is without km buffer land overlap between survey areas.
3940 C. H. Devens et al.
Route phase one, two and three of . leopards/ km (CI Analysis with secr and SPACECAP resulted in an
.–.), . leopards/ km (CI .–.) and . leo- overall estimated leopard density of . leopards/ km
pards/ km (CI .–.), respectively. Again, we calcu- (CI .–.) and . leopards/ km (CI .–.) re-
lated the mean density for multi-phase areas; the Overberg spectively. The output from SPACECAP includes a pixel
was . leopards/ km, compared to . leopards/ density file, which we converted into a fine-scale map
km (CI .–.) in the Langeberg and . leopards/ using QGIS, showing the variation of estimated animal
km (CI .–.) along the Garden Route (Table ). densities across each of the potential home range centres
FIG. 3 Pixelated ( km) SPACECAP
leopard density maps showing the
(a) Overberg, (b) Langeberg and
(c) Garden Route study areas.
Camera-trap sites shown are sites
with individually identified leopards.
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https://doi.org/10.1017/S0030605318001473Leopard density in South Africa’s Western Cape 41
estimate of leopards (CI .–.) in the total study
area (Table ).
With the regional SPACECAP mean density estimates
there would be c. leopards (CI .–.) in the Garden
Route, leopards (CI .–.) in the Overberg and
leopards (CI .–.) in the Langeberg (Table ), yield-
ing a total estimate of leopards (CI .–.; Table ).
Comparatively, using the total area of all three regions’
km buffers (without overlap) and the overall SPACECAP
mean density provides a population estimate of leopards
(CI .–.) for the entire study area (Table ).
Our study area of ,. km comprises % of the
Western Cape’s total area (, km). To provide a
population outlook at the provincial level we extrapolated
an ecologically useful Western Cape abundance estimate
using our secr and SPACECAP mean density estimates
and Swanepoel et al.’s () estimated , km of
remaining suitable leopard habitat in Western Cape (%
of the province). This suggests that the entire Western
Cape could harbour as few as (CI .–.) and as
FIG. 4 Sum of SPACECAP leopard pixel density estimates many as (CI .–.) leopards (Table ).
(leopards/km) for different land-cover types in the Western
Cape study areas. ‘Agriculture’ includes cultivated commercial
fields, orchards and plantations, ‘urban’ includes residential and
Discussion
urban commercial land, and ‘other’ includes dams, roads and
railways. This study covered c. , km and is one of the most
extensive leopard camera-trap density estimate surveys con-
ducted in the Western Cape province of South Africa. We
(Fig. ). Using these pixel density values, we also created
compared the use of two spatially explicit capture–recapture
study area graphs depicting the sum of pixel densities across
methods for a regionally imperilled disjunct leopard popu-
land cover categories (CapeNature, ) and protected
lation. Our comprehensive density analysis investigated
areas (Department of Environmental Affairs, ; Fig. ).
leopard persistence in a predominately human-modified
and privately-owned landscape with varying degrees of
Estimated population numbers conflict and persecution.
It is important to reiterate that our study area incorpo-
Combining this study’s total area with each region’s rates variation across vegetation types, landscape topography
km buffer width (Garden Route ,. km; Overberg and degree of landscape modification and fragmentation.
,. km; Langeberg ,. km) with the regional Because of its highly cultivated agricultural landscape
secr mean density estimates results in an estimate of leo- (mainly grain production, as well as livestock farming), the
pards (CI .–.) in the Garden Route, leopards (CI Overberg is commonly considered as the breadbasket of the
.–.) in the Overberg and leopards (CI .–.) Cape. The Langeberg’s Breede River Valley is encircled by
in the Langeberg (Table ). The total estimate for all areas the Cape Fold Mountain ranges and is dominated by
combined was leopards (CI .–.; Table ). In fynbos, vineyards and orchards, whereas the Garden Route
comparison, combining the total non-overlapping area is a long stretch of the south-western coast situated between
of all three study area’ km buffers (,. km) with mountains to the north and the Indian Ocean to the south,
the overall secr mean density produces a population which harbours a unique mixture of fynbos, indigenous
TABLE 3 Comparison of estimated population size of leopards using the total area within this study’s km buffer distance (without regional
survey overlap) and Swanepoel et al.’s () estimate for suitable leopard habitat in the Western Cape.
Area secr (0.35 leopards/100 km2) SPACECAP (1.18 leopards/100 km2)
Total buffered area of this study (29,258.43 km2) 102.4 (95% CI 61.4–237.0) 345.3 (95% CI 175.6–515.0)
Suitable leopard habitat in the Western Cape (49,850 km2; 174.5 (95% CI 104.7–403.8) 588.2 (95% CI 299.1–877.4)
Swanepoel et al., 2013)
Oryx, 2021, 55(1), 34–45 © The Author(s), 2019. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605318001473
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https://doi.org/10.1017/S003060531800147342 C. H. Devens et al.
predominantly non-conflict crop cultivation (vineyard and
orchard) in a valley surrounded by natural mountain vege-
tation. Additionally, the majority (%) of the estimated
leopard pixel density on agricultural land across all study
areas occurs in cultivated commercial fields and % occurs
in forestry plantations, which can generally be considered
non-conflict agriculture.
All three areas demonstrate a discrepancy between suit-
able habitat and land designated as protected for the conser-
vation and sustainability of biodiversity (Figs & ). The
vast majority of each area’s estimated leopard pixel density
occurs on non-protected land with the highest percentage
occurring in the Overberg (%) (Fig. ; Supplementary
Table ).
Our secr and SPACECAP estimates are some of the lowest
national density estimates for leopards and are comparable
to three other studies in the Western Cape (Martins, ;
Mann, ; Devens et al., ; Table ). The difference be-
tween the estimates from the two programmes was greater
than expected, which could be attributed partially to the
FIG. 5 Sum of SPACECAP leopard pixel density estimates extra Maxent habitat mask covariate modelling incorpo-
(leopards/km) for suitable leopard habitat within protected and rated into the SPACECAP analyses. The likelihood method
non-protected areas for each study area in the Western Cape. requires substantially less computation time than the
Bayesian approach (seconds or minutes vs hours or days),
temperate forest and forestry plantations. The agricultural is less sensitive to buffer width and data augmentation size
land in all three areas is utilized by leopards, as evidenced and has greater convenience and versatility for customiza-
by capture and recapture locations and suitable habitat tion with covariates and models. SPACECAP entails checks
extending beyond natural vegetation (Fig. ) and protected to verify that convergence is achieved, model fit is sufficient,
area boundaries (Fig. ). None of our identified territorial and data augmentation is adequate. Hence, it is more
adult leopards remained strictly within protected areas, appropriate and robust for small sample sizes. Our mean
and most did not utilize any protected land within their SPACECAP density estimate of . leopards/ km is
home range. Although protected areas play a crucial role directly comparable to results from Devens et al. (),
in the conservation of natural landscapes, they do not who reported mean density estimates of . and .–.
encompass the entirety of remaining natural vegetation or leopards/ km for SPACECAP and two GPS methods,
existing leopard habitat. In the Western Cape % of conser- respectively (Table ). The GPS methods used data from
vation areas contain suitable leopard habitat but only % of collared leopards and incorporated home range size
leopard habitat occurs within conservation areas (Swanepoel (home range density estimate) and home range overlap of
et al., ). same-sex neighbouring leopards (socially considerate dens-
These findings, together with estimated leopard densities ity estimate). Therefore, we suggest our SPACECAP results
within highly modified, cultivated and non-protected land are more accurate and reliable for determining the spatial
(Figs & ), suggest that non-protected, mostly privately requirements of species. We recommend spatially explicit
owned land plays an important role in sustaining the capture–recapture methods for future research, with the
Western Cape leopard population. Both spatially explicit specific incorporation of GPS collar data, because these
capture–recapture methods suggest that the Langeberg methods are the most robust at capturing a species’ spatial
winelands region supports the highest leopard density and ecology within a population.
the Overberg’s agricultural landscape the lowest. The sum We extrapolated an ecological density (number of indivi-
of each study area’s pixel densities is consistent with these duals per useable area) using our density estimates and a
findings (Fig. ). Although densities estimates vary between prediction of , km suitable leopard habitat remaining
study areas, the composition of land-cover and protected in the Western Cape (Swanepoel et al., ). Our findings
land utilized within each area is comparatively consistent suggest that there are – leopards remaining (Table ).
(Supplementary Tables & ). The Langeberg’s leopard However, this may be an optimistic estimation. Habitat
pixel densities on agricultural land are higher than in the modelling is not infallible and identified suitable habitat is
other two study areas, and equal to the Garden Route’s not necessarily useable for wide-ranging species such as the
natural vegetation (Fig. ). This can be attributed to the leopard. The available habitat estimate included fragmented
Oryx, 2021, 55(1), 34–45 © The Author(s), 2019. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605318001473
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https://doi.org/10.1017/S0030605318001473Leopard density in South Africa’s Western Cape 43
TABLE 4 Published South African leopard population density estimates for data collected after , with the analysis programme used.
Region Source Density estimate (leopards/100 km2) Programme1
Various (Western Cape) This study 0.17–0.50 (mean 0.35) secr
0.69–1.89 (mean 1.18) SPACECAP
Various (Western Cape, Eastern Cape) Devens et al. (2018) 0.24–1.89 (mean 0.95) SPACECAP
0.90 HRDE
1.11 SCDE
Little Karoo (Western Cape) Mann (2014) 0.50 CAPTURE
1.18 DENSITY
Cederberg Mountains (Western Cape) Martins (2010) 1.80–2.30 CAPTURE
Phinda-Mkhuze Complex (Kwazulu-Natal) Balme et al. (2010) 2.49–11.11 CAPTURE
Zululand Rhino Reserve (Kwazulu-Natal) Chapman & Balme (2010) 2.50–7.00 CAPTURE
Phinda Private Game Reserve (Kwazulu-Natal) Braczkowski et al. (2016) 3.40 secr
3.65 SPACECAP
7.28–9.28 CAPTURE
Waterberg Mountains (Limpopo) Swanepoel et al. (2015) 4.56–6.59 secr
Phinda Private Game Reserve (Kwazulu-Natal) Balme et al. (2009a) 6.97 CAPTURE
Phinda Private Game Reserve (Kwazulu-Natal) Balme et al. (2009b) 7.17–11.21 CAPTURE
Soutpansberg Mountains (Limpopo) Chase Grey et al. (2013) 10.70 SPACECAP
N’wanetsi Concession (Kruger National Park) Maputla et al. (2013) 12.70 CAPTURE
HRDE, home range density estimate; SCDE, socially considerate density estimate.
habitat, small isolated pockets of habitat and areas of habitat Iris Englund Foundation, National Lotteries Distribution Trust
subject to edge effects and anthropogenic pressures. These Fund, Mones Michaels Trust, Arne Hanson and the Deutsche Bank
South Africa Foundation for assisting in funding this research. MJS
factors can lead to habitat areas being unable to accommo-
was supported by the National Research Foundation. We acknowledge
date viable populations (Woodroffe & Ginsberg, ). In Landmark Foundation for providing the resources to enable this
addition, our maximum Western Cape population estimate research, and thank private landowners and CapeNature for their
of individuals is well below the minimum viable popu- cooperation and assistance.
lation size necessary to maintain genetic diversity (Traill
Author contributions Study conception: CHD, BS, JM; method-
et al., ). The genetic population structure of leopards
ology design, data collection and writing: CHD; data analysis: CHD,
in the Western Cape and Eastern Cape provinces indicates TT; revisions: MS, MH, AD, TT, BS.
very low to moderate gene flow between the three subpopu-
lations (McManus et al., ). Conflicts of interest None.
Although highly adaptable, the leopard is a widely per-
secuted species that experiences varying levels of anthro- Ethical standards All authors have abided by the Oryx guidelines on
ethical standards, and fieldwork was conducted with the necessary
pogenic threats and habitat loss. Our results suggest that approvals and permits from appropriate institutions and statutory
protected areas are inadequate to secure the long-term con- authorities. Cape Nature Research Permit to collect fauna specimens
servation of leopards in the Western Cape. By establishing for scientific research in the Western Cape was granted to CHD in
density and population size estimates in an increasingly August 2014 (permit no.: AAA007-00130-0056) and prior camera-
fragmented and modified landscape, we increase the under- trap data was covered under a Cape Nature Permit for JM. These per-
mits were granted for the purposes of collaring leopards. CHD was also
standing of how leopards can persist in human-dominated
granted an Animal Ethics Committee Approval Certificate from the
areas, influencing conservation planning for the species. For University of Pretoria in February 2014–December 2016 (project num-
adaptive and wide-ranging species such as large carnivores, ber: EC005-14). We did not collect specimens for this study, and all
non-protected and human-dominated areas are becoming fieldwork was conducted ethically with the cooperation of local conser-
increasingly important for genetic dispersal and land- vation authorities and landowners.
scape connectivity (Boron et al., ). The threats and con-
servation conflicts affecting the leopard in South Africa’s
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