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Ecology and Evolution. 2020;10:3605–3619.
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3605www.ecolevol.org
Received: 12 January 2020
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Revised: 16 January 2020
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Accepted: 20 Januar y 2020
DOI: 10.1002/ece3.6089
ORIGINAL RESEARCH
Unsustainable anthropogenic mortality disrupts natal dispersal
and promotes inbreeding in leopards
Vincent N. Naude1,2 | Guy A. Balme2 | Justin O'Riain1 | Luke T.B. Hunter3,4 |
Julien Fattebert2,4,5 | Tristan Dickerson2 | Jacqueline M. Bishop1
This is an op en access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original wor k is properly cited.
© 2020 The Authors. Ecolog y and Evolution published by John Wiley & Sons Ltd.
1Institute for Communities and Wildlife in
Africa, University of Cape Town, Cape Town,
South Africa
2Panthera, New York, NY, USA
3Wildlife Conservation Society, Bronx, NY,
USA
4Centre for Functional Biodiversity, School
of Life Sciences, University of KwaZulu-
Natal, D urban, South Africa
5Wyoming Cooper ative Fish and Wildlife
Research Unit, D epar tment of Zoology and
Physiology, Univer sity of Wyoming, Laramie,
WY, USA
Correspondence
Vincent Naude, Institute for Communities
and Wildlife in Africa, University of Cape
Town, Cape Town, Western Cape, South
Africa.
Email: vnaude@panthera.org
Funding information
Research on leopard ecology in the SSGR
is funded by Panthera in collaboration
with ecotourism lodges and the Sabi Sand
Wildtuin management. Research in the
PMC is funded by Panthera, Alber t, and
Didy Har tog; the Timbo Afrika Foundation;
the National Research Foundation (FA
2004050400 038 to GB); and the University
of KwaZulu-Natal Gay Langmuir Bursary
(to JF). VN is funded by Panthera and the
Institute for Communities and Wildlife in
Africa.
Abstract
Anthropogenic mortality of wildlife is typically inferred from measures of the ab-
solute decline in population numbers. However, increasing evidence suggests that
indirect demographic effects including changes to the age, sex, and social structure
of populations, as well as the behavior of survivors, can profoundly impact population
health and viability. Specifically, anthropogenic mortality of wildlife (especially when
unsustainable) and fragmentation of the spatial distribution of individuals (home-
ranges) could disrupt natal dispersal mechanisms, with long-term consequences to
genetic structure, by compromising outbreeding behavior and gene flow. We in-
vestigate this threat in African leopards (Panthera pardus pardus), a polygynous felid
with male-biased natal dispersal. Using a combination of spatial (home-range) and
genetic (21 polymorphic microsatellites) data from 142 adult leopards, we contrast
the structure of two South African populations with markedly different histories of
anthropogenically linked mortality. Home-range overlap, parentage assignment, and
spatio-genetic autocorrelation together show that historical exploitation of leopards
in a recovering protected area has disrupted and reduced subadult male dispersal,
thereby facilitating opportunistic male natal philopatry, with sons establishing terri-
tories closer to their mothers and sisters. The resultant kin-clustering in males of this
historically exploited population is comparable to that of females in a well-protected
reserve and has ultimately led to localized inbreeding. Our findings demonstrate
novel evidence directly linking unsustainable anthropogenic mortality to inbreeding
through disrupted dispersal in a large, solitary felid and expose the genetic conse-
quences underlying this behavioral change. We therefore emphasize the importance
of managing and mitigating the effects of unsustainable exploitation on local popula-
tions and increasing habitat fragmentation between contiguous protected areas by
promoting in situ recovery and providing corridors of suitable habitat that maintain
genetic connectivity.
KEYWORDS
home-range, kin-clustering, microsatellites, Panthera pardus, philopatry, relatedness
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1 | INTRODUCTION
When assessing the effects of anthropogenic mortality on wildlife
populations, managers and policymakers typically consider only di-
rec t numerical resp on se s of populati ons to human-mediate d mo rtal-
ity (poaching, retaliatory conflict, and unregulated trophy hunting;
Woodroffe & Ginsberg, 1998). Indirect demographic effects (age,
sex, and social structure) and the behavior of survivors have pro-
found impacts on the health and viability of remaining populations
(Ausband, Mitchell, Stansbury, Stenglein, & Waits, 2017; Ausband,
Stansbury, Stenglein, Struthers, & Waits, 2015; Rutledge et al.,
2010). For example, harvest can facilitate the spatial reorganization
of individuals within populations by creating home-range vacancies
that can be filled by neighboring or immigrant conspecifics through a
“vacuum effect” (Frank, Leclerc, et al., 2017a). This may increase the
probability of encounters between unfamiliar individuals leading to
elevated rates of conflict, sexually selected infanticide, and increased
local extinction risk (Creel et al., 2015, 2016; Gosselin, Zedrosser,
Swenson, & Pelletier, 2015; Whitman, Starfield, Quadling, & Packer,
2004). Moreover, directed harvest toward a specific sex, age, or size
cohort disrupts dispersal patterns (Frank, Ordiz, et al., 2017b; Milner,
Nilsen, & Andreassen, 2007).
By maintaining gene flow within and among populations, disper-
sal is critical to the persistence of spatially structured metapopula-
tions (Dolrenry, Stenglein, Hazzah, Lutz, & Frank, 2014; Gundersen,
Johannesen, Andreassen, & Ims, 2001; Hanski & Simberloff, 1997).
However, by increasing territorial turnover and providing opportu-
nities for subadults to settle locally, harvest limits natal dispersal (in
the absence of immigration), affecting both local and metapopulation
dynamics (Blyton, Banks, & Peakall, 2015; Newby et al., 2013). While
many studies highlight the demographic effects of unsustainable har-
vest, the behavioral mechanisms employed to counteract these effects
and subsequent consequences to population genetic structure remain
poorly understood, particularly in large carnivores (Milner et al., 2007).
While inbreeding susceptibility is documented in felids (e.g., Panthera
leo, Munson et al., 1996; Puma concolor, Ernest, Vickers, Morrison,
Buchalski, & Boyce, 2014), few monitoring studies have the requisite
longitudinal mortality (well-documented mortality for entire popula-
tions), spatial (fine-scale movement of known individuals), and genetic
(mult igener at io na l pe di gr ees of known individuals) dat a to enable com -
parison between populations and thereby demonstrate a tenable link
between high levels of mortality (often human-mediated), disrupted
dispersal, and inbreeding (Onorato, Desimone, White, & Waits, 2011).
Across southern Africa, large felids have a long history of both
legal and illegal exploitation. African leopards (Panthera pardus par-
dus) have been heavily harvested throughout this region for their
economic value as trophies in legal hunts (Balme, Slotow, & Hunter,
2010; Braczkowski et al., 2015; Swanepoel, Lindsey, Somers, Hoven,
& Dalerum, 2011) and for mostly illegal use in traditional practices
(Harries, 1993; Kumalo & Mujinga, 2017; Williams, Loveridge, Newton,
& MacDonald, 2017). Many leopards are also removed in retaliatory
conflict due to their real or perceived threat to livestock (Loveridge,
Wang, Frank, & Seidensticker, 2010). In this study, we investigate how
such anthropogenic mortality and persecution disrupt individual dis-
persal in leopards, altering spatial patterns of kinship, which ultimately
promotes inbreeding in this solitary species. Previous telemetry stud-
ies suggest that leopards, like many polygynous mammals, generally
exhibit female philopatry and male-biased natal dispersal (Balme,
Robinson, Pitman, & Hunter, 2017a; Fattebert, Balme, Dickerson,
Slotow, & Hunter, 2015a; Fattebert et al., 2016). Subadult females are
thus predicted to compete for philopatry and attempt to breed within
or adjacent to their natal ranges, forming spatially defined kin-clusters
(Lambin, Aars, & Piertney, 2001). In contrast, subadult male leopards
typically disperse in order to avoid competition with larger, conspecific
adult males, thereby reducing the probability of mating with related
females (Dobson, 1982; Wolff, 1994). In heavily harvested populations,
young male leopards are released from local male–male competition
and may exhibit “opportunistic natal philopatry” to avoid the substan-
tial costs of dispersal, undertaking shorter dispersal distances and
establishing home-ranges nearer their mothers and sisters (Fattebert,
Robinson, Balme, Slotow, & Hunter, 2015b). In such a scenario, the so-
cio-spatial structure of males is expected to approximate the kin-clus-
tered spatial structure of females, which, in the absence of active
inbreeding avoidance, ultimately promotes increased levels of localized
inbreeding (Støen, Bellemain, Sæbø, & Swenson, 2005).
Here, we use home-range estimates together with parentage and
relatedness analyses to explore dispersal dynamics and the consequent
fine-scale genetic structure of two leopard populations with markedly
different histories of anthropogenically linked mortality: a well-pro-
tected population at ecological carrying capacity (Balme, Pitman, et
al., 2017b; Balme et al., 2019) and a population recovering from a re-
cent history of extensive anthropogenic mortality (Balme, Slotow, &
Hunter, 2009; Balme et al., 2010). Under the premise of density-de-
pendent male-biased dispersal and female philopatry, we predict that
(a) female leopards with overlapping home-ranges will support higher
levels of relatedness than males in both populations, this being par-
ticularly evident in the recovering population where mothers can
adjust their home-ranges to accommodate daughters—whereas this
would not always be possible in a population at capacity (Fattebert
et al., 2016); (b) levels of relatedness between overlapping males and
females will be higher in the recovering population due to reduced
dispersal distances of sons (Fattebert, Robinson, et al., 2015b); and
(c) reduced dispersal distances exhibited by both sexes in the recov-
ering population will result in higher levels of inbreeding. We discuss
our findings in the context of local population fitness and the broader
implications of disrupted dispersal on persistence and functional con-
nectivity across leopard metapopulations throughout protected areas.
2 | MATERIALS AND METHODS
2.1 | Study areas
The study was undertaken in two protected area complexes of
South Africa that differ markedly in their historical rates of an-
thropogenic mortality. The Sabi Sand Game Reserve (SSGR) is a
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NAUDE Et A l.
privately owned conservancy (est. 1948) in the Lowveld region
of the Mpumalanga province (Figure 1a). It covers 625 km2 but
is contiguous along its southern and eastern boundaries with the
Kruger National Park and Manyeleti Game Reserve in the north.
The SSGR thus forms part of a much larger (~22,000 km2) pro-
tected system. Although the western boundary of the reserve is
adjacent to a densely populated community, the border fence is
impermeable to leopards and the population seems unaffected by
det rimental edge effects (Balme et al., 2019). The re is also no legal
offtake of leopards inside the SSGR and levels of poaching are
very low; anthropogenic mortality accounted for <2% of leopard
deaths in the SSGR between 1975 and 2015 and the population
appears at capacity (Balme, Pitman, et al., 2017b).
The Phinda-uMkhuze Complex (PMC) is situated in the
Maputaland region of the KwaZulu-Natal province (Figure 1b)
and comprises two neighboring reserves: Phinda Private Game
Reserve (est. 1991) and the public uMkhuze Game Reser ve (est.
1912), forming a contiguous protected landscape of 660 km2.
The PM C is surr ou nded by a mosa ic of comme rcial game ranches,
li vest o ck f arms , an d Zulu com m unit ies; the s e land t ypes are of ten
hostile to leopards (Thorn, Green, Dalerum, Bateman, & Scott,
20 12) . Un like th e SS G R , th e bo u ndar y fence of th e PMC is per me-
able to leopards and individuals move freely between protected
and unprotected land (Balme et al., 2010). The PMC, particularly
uMkhuze, also suffers high levels of wire-snare poaching, which
can have a marked effect on large carnivores such as leopards
(Becker et al., 2013). Accordingly, leopards in the PMC face far
greater mortality risk than those in the SSG R; bet ween 2002 and
201 2, huma n-re la te d mor tal it y acc ou nt ed for >50% of all leopard
deaths in the PMC (Balme et al., 2009). Nonetheless, recent pol-
icy changes have allowed the PMC leopard population to recover:
from a disturbance period (pre-2004), when the population was
in decline (λ = 0.978); through a recovery period (2005–2008),
following the implementation of sustainable harvest protocols
and other conservation interventions (λ = 1.136); to a stabiliza-
tion period (2009–2012), when the population density reached
putative carrying capacity (λ = 1.010; Fattebert, Robinson, et al.,
2015b).
Historically, the SSGR and PMC populations were possibly linked
via dispersal (Fattebert, Hunter, Balme, Dickerson, & Slotow, 2013).
The two study sites also have similar habitats (open to semi-wooded
savannah), climates (mean monthly temperatures ranging from 19 to
33°C and average annual rainfall of ~60 0 mm), levels of prey abun-
dance, and similar leopard densities (SSGR: 11.81 ± 2.56 leopards
100/km2, Balme et al., 2019; PMC: 9.51 ± 1.22 leopards 100/km2
following recovery, Rogan et al., 2019), forming contiguous leopard
habitat with no physical barriers to dispersal (Figure S1). Accordingly,
the observed differences in spatial behavior and genetic structure
are assumed to be the result of human interference rather than due
to other environmental or ecological factors, such as competitor
presence or density which does not differ between these reserves
(Balme, Pitman, et al., 2017b; Balme et al., 2019; Fattebert et al.,
2016; Rogan et al., 2019).
2.2 | Data collection and sampling
In the SSGR, individual location data were collected through direct
observation of leopards, using methods detailed in Balme, Pitman,
et al. (2017b). Briefly, the SSGR hosts several ecotourism lodges that
operate high-end photographic safaris. Clients are taken on “game-
drives” twice daily led by an experienced guide and tracker. The high
density of vehicles (98 ± 2 per game drive) and extensive road net-
work (mean road density of 3.2 km per km2) ensures that most of the
reserve is traversed daily at a high expected vehicle encounter rate
(0.17 ± 0.05 vehicles per km). Drives are not limited to roads, as skilled
trackers pursue charismatic species by vehicle or on foot until the ani-
mal is located, or the tracks are lost. This intensive search effort results
in frequent sightings; on average, 6,428 ± 914 unique leopard sight-
ings are recorded per annum with individual leopards being seen on
average every 2.74 ± 0.04 days. Leopards in the SSGR are highly ha-
bituated to vehicles and guides are familiar with the individuals resid-
ing in their traversing area (individual leopards can be distinguished by
their unique vibrissae patterning; Miththapala, Seidensticker, Phillips,
Fernando, & Smallwood, 1989). Data captured include the identity
of the individual leopard (if known), GPS location of the sighting, the
presence and number of offspring, as well as other notable behavior
(e.g., intra- and interspecific interactions). Although multiple guides
sometimes submitted data from the same sighting, we retrospectively
filtered the data to ensure that each unique sighting was captured only
once, that is, an individual leopard was included in only a single sight-
ing per game drive. To assess the accuracy of the guides' ability to
distinguish individuals, we asked them to submit photographs with the
putative identity of the animal from a random subset of sightings; they
correctly identified the individual leopard (n = 121) in all photographs.
We also cross-referenced data submitted by guides from different
lodges to assess the consistency of the information captured, and we
found no significant discrepancies (as in Balme et al., 2013). Samples
for DNA analysis were obtained from leopard fecal deposits collected
by guides in the SSGR. Only samples where the guide observed the
leopard defecating (and they were therefore confident of its identity)
were used in analyses. In total, 145 samples from 81 individuals were
collected between 2015 and 2018. Fecal samples were dry-stored on
silica beads at −80°C.
Spatial data in the PMC were collected using telemetr y, fol-
lowing methods detailed in Fatteber t et al. (2016). Leopards were
captured using a combination of free-darting, cage-trapping, and
soft-hold foot-snaring and fitted with either a VHF (250 g, Sirtrack
Ltd., Havelock North, New Zealand, 0.5% of adult female body
mass) or GPS (420 g, Vectronic-Aerospace, Berlin, Germany, 1.2%
of adult female body mass) collar. VHF-collared individuals (n = 41)
were located ever y three days on average to within ~100 m using
ground homing or triangulation across the PMC (mean road density
of 2.6 km per km2), whereas GPS collars (n = 28) were programmed
to record 2–6 fixes daily. Ear-punch biopsy samples from 69 individ-
uals were collected for genetic analyses during captures from 20 02
to 2012. Tissue samples were stored in >90% ethanol at −20°C.
Capture and collaring of leopards were approved (research permit
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NAUDE Et Al.
FIGURE 1 Maps showing the position of the two study areas within the existing matrix of land use and habitat type. SSGR: Sabi Sand
Game Reserve (a) and PMC: Phinda-uMkhuze Complex (b) indicated in black
SSGR
Mozambique
A
B
South Africa
Swaziland
PMC
Study areas
Protected areas
Agriculture
Forest/Thicket
Scrub/Grassland
Urban areas
Water
0km
10 25 50
N
Naonal borders
Road network
Conservaon area
Indian Ocean
(a)
(b)
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3609
NAUDE Et A l.
HO/4004/07) by the provincial conservation authority, Ezemvelo
KwaZulu-Natal Wildlife and by the Animal Ethics Subcommittee
of the University of KwaZulu-Natal Ethics Committee (approval
051/12/Animal).
2.3 | Home-range estimation
To determine the spatial distribution and dispersal patterns of in-
dividuals in the two study populations, we calculated home-range
estimates (size, centroid, utilization density, and overlap) for all sexu-
ally mature (≥3 years) leopards postdispersal (Balme et al., 2013),
using autocorrelated kernel density estimates (ADKEs; Fleming et
al., 2015), where an ANOVA was used to identify significant differ-
ences in relocation counts between individuals of different spatial
sampling types (observation, GPS, and VHF). These 95% AKDEs
are considered robust for comparisons between different spatial
data types (Fleming et al., 2015). All pairwise comparisons of spatial
overlap between individuals were restricted to periods of temporal
co-occurrence (over a continuous four-year sampling period of three
generations). Subsequent analyses were focussed on home-range
overlap (HRO; Bhattacharyya coefficient), as the most relevant met-
ric with regard to inbreeding opportunity, as this relates directly to
encounter potential and is not affected by between-site variance in
home-range size (km2). Variogram calculations, movement model fits,
and home-range estimations were implemented in the ctmm package
(Calabrese, Fleming, & Gurarie, 2016; Fleming et al., 2015). Home-
range centroids were estimated as the geometric mean of coordi-
nates used to fit the AKDE contours.
Estimated semi-variance was plotted as a function of time-lag
to visually inspect the autocorrelative structure of the location
data (Fleming et al., 2014). Brownian motion (BM) or Ornstein–
Uhlenbeck (OU) movement models were used at zero to short
time lags, where a linear increase in the semi-variance corre-
sponded with uncorrelated velocity, whereas integrated OU (IOU)
or OU with foraging (OUF) was used where upward curvature at
these time lags indicated autocorrelation in the velocity. If plotted
semi-variance did not approach an asymptote, individuals were not
considered to be range residents; these leopards were either not
monitored for long enough or did not exhibit behaviors that meet
the definition of range residents and were removed from further
analyses. Thereafter, space use was investigated by assessing be-
havior across longer time lags, where range residents are expected
to reach an asymptote on a timescale that corresponds to the
home-range crossing time (Calabrese et al., 2016; Fleming et al.,
2014). Maximum-likelihood model fits (Fleming et al., 2014) were
ranked by AICc (Calabrese et al., 2016). Home-ranges were esti-
mated conditionally on the fitted and selected model per individ-
ual. OU models are described using two parameters (home-range
crossing time in days and variance in km2), while OUF models are
described using three (home-range crossing time in days, veloc-
ity autocorrelation timescale in hours, and variance in km2). OU
models provided home-range and crossing time estimates, where
OUF models provided these metrics as well as the velocity auto-
correlation timescale and average distance travelled per individual.
Finally, volumetric space-time UD and HRO (Bhattacharyya's coef-
ficient) were estimated based on these selected models (Fieberg &
Kochanny, 2005; Winner et al., 2018). All analyses were conducted
in R (R Core Team, 2018) and QGIS (QGIS Development Team,
2018).
2.4 | DNA extraction, PCR, and genotyping
DNA was successfully extracted for 81 individuals from SSGR and
69 individuals from PMC. DNA was extracted from fecal samples
using the QIAamp DNA Stool Mini Kit and from tissue using the
DNEasy Blood and Tissue Kit (Qiagen, Inc., Valencia, CA, USA).
Individuals were genot yped at 22 microsatellite loci (Table S1)
previously shown to be polymorphic in leopards (McManus et
al., 2014; Ropiquet et al., 2015; Uphyrkina et al., 20 01) together
wit h a Zn-finger linked sex ing marker (Pilgrim, McKelvey, Rid dle, &
Schwartz, 2004). PCRs contained ~50–100 ng/µl DNA, 200 ng/μl
bovine albumin serum (BSA), a locus-specific MgCl2 concentration
(1.5–2.5mM), 2.0 μM each of for ward-lab eled an d rever se pr im ers ,
5 μl DreamTaq™ Green PCR Master Mix (Thermoscientific), and
deionized water to a total reaction volume of 25 μl. PCRs were
pe rfor med on an Appli e d Bi osyste ms Veriti ® Thermal Cycler. Given
the generally lower qualit y DNA extracted from fecal samples, all
samples were amplified in singleplex and in triplicate (from ex-
traction to amplification) to ensure reproducibility. Locus-specific
thermal profiles were developed following Menotti-Raymond et
al. (1999), and PCR products were pooled according to size and
fluorescent labeling for visualization (Table S1). A positive control
was use d for si ze sc ori ng bet ween ru ns, an d a negat ive cont rol was
included throughout. Genot ypes were analyzed on a 3100-Avant
Genetic Analyzer (Applied Biosystems) at the Central Analytical
Facility, Stellenbosch University, South Africa. Genotypes were
sized using the LIZ® 600 internal size standard and alleles were
scored in GENEIOUS R10 (Biomatters Limited). Automated allele
calls were manually checked for accuracy. Genotyping error was
assessed per triplicate sample run on each individual and ≥2/3
consensus alleles used in subsequent analyses, where no such
consensus was achieved or genotypes failed (≤15/22 loci ampli-
fied), whole genotypes were removed. Where available, known
parent–offspring relationships were used to find mismatches.
Stutter errors, large allele dropouts, short allele dominance, and
significant departures from Hardy–Weinberg equilibrium (HWE)
were examined across loci for each population using a chi-square
test for goodness of fit and sequential Bonferroni corrections
performed on the resulting P-values (Rice, 1989). FSTAT 2.9 was
used to test for linkage disequilibrium (LD) between pairs of loci
(Goudet, 2002). The significance of sex ratio estimates for each
population was assessed with a binomial distribution test, calcu-
lated as the probability of the observed number of males and fe-
males given an expected sex ratio of 0.5.
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NAUDE Et Al.
2.5 | Kinship, relatedness, and inbreeding
Parentage assignment and relatedness indices were used to confirm
kinship and augment our observed pedigrees for both populations.
Individual parentage assignments were estimated within a maximum-
likelihood framework implemented in CERVUS 3.0 (Kalinowski, Taper,
& Marshall, 2007). Simulations were generated at a given level of con-
fidence for all offspring analyzed. Parameters included the following:
100,000 offspring, 2% mistyped loci, 89% typed loci for SSGR, and
93% typed loci for PMC, as determined by CERVUS for the dataset.
Assignment was only tested if a minimum of 15 loci were successfully
genotyped, while candidate parents were limited to adults (≥3 years
old) and pairs that were alive at the same time. Parents were assigned
based on likelihood-of-difference (LOD) scores calculated at both 95%
(strict) and 80% (relaxed) confidence levels. The strict assignment (95%)
was used to build whole pedigrees, whereas the more relaxed assign-
ment (80%) was used to provide further insight into likely relationships
between individuals when not strictly assigned. Where no 95% assign-
ment was supported and a clear 80% assignment was available, this
was used to assign parentage. Pairwise relatedness between all indi-
viduals in both populations was estimated using the Wang relatedness
metric (rw) in SPAGeDI 1.0 (Hardy & Vekemans, 2002; Wang, 2002).
This estimator was chosen for its apparent desirable properties among
reviewed relatedness indices, namely, low sensitivity to the sampling
error that results from estimating population allele frequencies and a
low sampling variance that decreases asymptotically to the theoretical
minimum with increasing numbers of loci and alleles per locus (Blouin,
2003). For each population, the frequency distribution of relatedness
coefficients was summarized for defined kin-categories (unknown,
parent–offspring, full-sibling, half-sibling, and breeding pairs) based
on field observations (e.g., mothers with offspring, siblings), parentage
analysis, and relatedness scores. Observed pedigrees were suppor ted
and expanded for both populations and inbreeding events recorded.
In addition, the adegenet (Jombart, 2008) and ape (Paradis & Schliep,
2018) R-packages were used to estimate per locus and population-
level inbreeding coefficients (FIS).
2.6 | Spatio-genetic structure
To test for evidence of restricted or disrupted dispersal, we exam-
ined the fine-scale genetic structure of offspring (mother–daughter
[M-D]; mother–son [M-S]) and sex-based dyads (female–female [F-
F]; female–male [F-M]; male–male [M-M]) per unit distance from the
natal range in our two study populations. We first superimposed as-
signed maternal home-range centroids with a concentric ring (the
average maternal home-range area) surrounded by three concentric
rings representing: the nearest-neighboring maternal home-range
(1st order); the next peripheral neighboring maternal home-range
(2nd order); and all other maternal home-range areas beyond this
periphery. The width of each band represents the average maternal
home-range radius by population. Offspring (those assigned through
parentage analyses) home-range centroids were then plotted in
relative x-y proximity to their natal centroid and their frequencies
plotted by concentric ring so as to schematically represent the dif-
ferences in philopatric home-range establishment relative to the
natal home-range by sex for each population.
We then quantified the association between matrices of pair-
wise genetic and spatial distances (Peakall, Ruibal, & Lindenmayer,
2007; Smouse & Peakall, 2001) through direct correlation, spatial
autocorrelation analysis and mantel tests implemented in the ecodist
package (Goslee & Urban, 2007). Under a restricted or disrupted
dispersal model, autocorrelograms yield positive correlations at
short spatial distances (classes represent the average home-range
diameter per sex and population), followed by a gradual decrease to
zero with increasing geographical distance and a subsequent ran-
dom fluctuation of positive and negative values of the correlation
coefficient (Smouse & Peakall, 2001). The first x-intercept estimates
the extent of nonrandom genetic structure or defines the point at
whic h rand om st och ast ic dr ift re pla ces gene flow as the key determi-
nant of genetic structure (Vangestel, Mergeay, Dawson, Vandomme,
& Lens, 2011). As this intercept is dependent upon the true scale
of genetic structure, the chosen distance class size, and the sam-
ple size per distance class (Peakall et al., 2007), we also performed
a second autocorrelation analysis in which we plotted pairwise ge-
netic distances against increasing inclusive distance classes. Here,
the distance class at which the autocorrelation coefficient no longer
remains significant (999 bootstraps) approximates the true extent of
identifiable genetic structure between groups of individuals (Peakall
et al., 2007).
3 | RESULTS
3.1 | Home-range estimates
Home-ranges (km2) were successfully estimated for all 142 adult
leopards for which we had genetic data (SSGR: females = 49;
males = 24; total = 73; PMC: females = 31; males = 38; total = 69).
Due to high sampling intensity, rare forays or peripheral movements
were witnessed (mostly among young males) and accounted for in all
three datasets, where home-range relocation count s did not differ
significantly between individuals of different spatial sampling types
(
xobserved
= 336 ± 7.20 [SE];
xGPS
= 367 ± 14.20 [SE];
xVHF
= 361 ± 11.7
[SE]; F2 = 2.99; p = .05). Male home-ranges were markedly larger
than that of females in both the SSGR (
xfemale
= 26.93 ± 2.37
[SE];
xmale
= 50.02 ± 5.43 [SE]; t32 = 3.90; p < .001) and PMC
(
xfemale
= 31.54 ± 1.34 [SE];
xmale
= 50.32 ± 5.01 [SE]; t44 = 3.57;
p < .001). Female and male home-range size did not differ between
study populations (Tables S1 and S3).
3.2 | Genotyping and genetic diversity
The final dataset (Table S4) consisted of 15–21 loci successfully typed
for 142 known individuals in our two study populations. “Extraction
|
3611
NAUDE Et A l.
to genotyping success” (number of repeats required per sample) was
significantly lower in the PMC than in the SSGR (
xPMC
= 1.0 4 ± 0.03
[SE];
xSSGR
= 2.56 ± 0.09 [SE]; t81 = 15.69; p < .001; CI = −1.71, −1.33)
and genotyping failed (<15/22 loci amplified successfully) for only
eight leopards (SSGR = 8; PMC = 0). Locus FCA096 was removed
from all further analyses due to poor amplification success (14%–30%
of individuals). There was no evidence of LD or scoring errors due
to large allele dropout and stutter in either population. Mean geno-
type coverage was higher in SSGR than in PMC (
xSSGR
= 68.05 ± 0.39
[SE];
xPMC
= 63.52 ± 1.57 [SE]; t40 = 2.80; p = .008; CI = 1.26, 7.79).
SSGR supports greater heterozygosit y (
xSSGR
= 0.78 ± 0.03 [SE];
xPMC
= 0.65 ± 0.03 [SE]; t40 = 3.01; p < .005; CI = 0.04, 0.22), allelic
richness (
xSSGR
= 6.01 ± 0.31 [SE];
xPMC
= 4.89 ± 0.26 [SE]; t38 = 2.78;
p = .008; CI = 0.30, 1.94), and mean number of private alleles per
locus than PMC (
xSSGR
= 2.86 ± 0.37 [SE];
xPMC
= 0.52 ± 0.13 [SE];
t24 = 5.90; p < .001; CI = 1.52, 3.15). With the exception of some
locus-level deviations, the SSGR population was in HWE (see Table
S4), whereas PMC was not, with 12 out of the 21 markers out of
HWE. The SSGR showed significant female bias (z = 2.81, p = .003),
with no significant sex bias in PMC (z = −0.72, p = .235). As there are
likely very few unknown individuals in both populations, these sex
ratios are assumed to reflect absolute sex ratios and are thus not
expected to create a bias in overlap measures.
3.3 | Parentage analysis, relatedness, and inbreeding
Formal computational assignment of parentage, via a likeli-
hood framework, was successful for both populations (Table S5).
Maternity (no paternity known) was assigned for 63% of offspring in
SSGR and for 48% of offspring in PMC, corroborating all 30 putative
field-based maternal assignments in SSGR and 25/31 maternal as-
signments in PMC. Paternity (given known maternity) was assigned
for a 64% of offspring in PMC and 54% in SSGR, confirming all 20
putative sires in SSGR and 16/28 in PMC. Biparental assignment was
not possible for 30% of offspring in SSGR and 19% in PMC, while
the predicted resolving power (95% confidence) of loci sampled was
higher in SSGR (99%) than in PMC (96%), with 1% and 4% assigned
with 80% confidence in SSGR and PMC, respectively.
In both populations, kinship pairs showed mean relatedness co-
efficients within the limits of their expected distributions (Figure 2),
including confirmed breeding pairs in SSGR which were significantly
less related than random (
x
= −0.05; t12 = 4.27; p = .001; CI = −0.08,
−0.03). Mean relatedness of confirmed breeding pairs in PMC, how-
ever, did not fall within the limits of their expected distribution (
x
= 0.31; t11 = 6.07; p < .001; CI = 0.19, 0.42). Instea d, these were more
similar to that expected of the half-sibling distribution (
x
= 0.31;
t11 = 1.09; p = .297; CI = −0.06, 0.17).
Pedigree reconstruction provided no evidence of direct in-
breeding in SSGR, whereas in PMC, one father–daughter and two
half-sibling mating events were identified. The inbreeding coef-
ficient (FIS) was significantly greater in PMC than in SSGR (Table
S4;
xSSGR
= −0.08 ± 0.02 [SE];
xPMC
= 0.06 ± 0.03 [SE]; t40 = 4.93;
p < .001; CI = −0.20, 0.08), with evidence of significant outbreeding
in SSGR with FIS scores significantly less than 0 (
xSSGR
= −0.08 ± 0.02
[SE]; t20 = 5.27; p < .0001; CI = −0.11, −0.05) and significant lev-
els of inbreeding in PMC with FIS scores significantly greater than 0
(
xPMC
= 0.06 ± 0.03 [SE]; t20 = 2.58; p < .05; CI = 0.01, 0.12).
3.4 | Spatio-genetic structure
The mean proportion of home-range overlap (Table 1) among all in-
dividuals was higher in PMC than SSGR (
xSSGR
= 0.16 ± 0.00 [SE];
xPMC
= 0. 20 ± 0.0 0 [SE ]; t2088 = 2.90; p < .0 01). Whi le the propor t ion of
home-range overlap was not significant bet ween populations for fe-
male–female and male–male dyads, female–male home-range over-
lap was significantly higher in PMC than in SSGR (
xSSGR
= 0.15 ± 0.01
[SE];
xPMC
= 0.20 ± 0.01 [SE]; t1036 = 3. 22; p = .001). Home-range
overlap between kin-related pairs was not significant, with the
exception of mother–son pairs in PMC being twice that of SSGR
(
xSSGR
= 0.31 ± 0.10 [SE];
xPMC
= 0.61 ± 0.06 [SE]; t9 = 2.49; p = .034)
and breeding pair home-range overlap being nearly 20% greater
in SSGR than PMC (
xSSGR
= 0.63 ± 0.05 [SE];
xPMC
= 0.45 ± 0.07
[SE]; t18 = 2.11; p = .049). Home-range overlap between mother–
daughter pairs was slightly greater than mother–son pairs in SSGR
(
xFD
= 0.55 ± 0.06 [SE];
xMS
= 0.31 ± 0.10 [SE]; t9 = 2.03; p = .073).
Of the 27 daughters (Figure 3a) and 16 sons (Figure 3c) assigned
in SSG R , 37 % of daug ht ers and no son s est abl ished home -r ang e ce n-
troids within their mean maternal home-ranges, 30% of daughters
and 25% of sons within the 1st order mean peripheral home-range,
3% of daughters and 19% of sons within the 2nd order, and 30% of
daughters and 56% of sons beyond. In contrast, of the 14 daughters
(Figure 3b) and 18 sons (Figure 3d) assigned in PMC, 43% of daugh-
ters and 22% of sons established home-range centroids within their
mean maternal home-ranges, 43% of daughters and 50% of sons
within the 1st order mean peripheral home-range, 14% of daughters
and 11% of sons within the 2nd order, and no daughters and 17% of
sons beyond.
Mantel tests showed population-level spatio-genetic structuring
in both populations (Figure 4). Pairwise relatedness (rw) decreased
significantly as the proximity (km) between individuals increased
within female–female dyad pairs in SSGR (R2 = −.16; p < .001) and
within female–female (R2 = −.23; p < .001), female–male (R2 = −.25;
p < .001), and male–male (R2 = −.12; p = .025) dyad pairs in PMC
(Figure 4; Table S3). Autocorrelograms revealed fine-scale spa-
tio-genetic structure by pairwise proximity between individuals in
each dyad. Female kin-clustering was observed in both populations
(Figure 4a), where significantly positive autocorrelation occurred
over three female home-range radii in SSGR (0–6 km) and four in PMC
(0–8 km). This female kin-clustering effect was stronger over these
distances in the PMC. Significant clustering of females that were less
related than expected at random occurred for a range beyond this
distance in both populations (SSGR = 16–24 km; PMC = 14–24 km),
while all spatio-genetic structure showed no significant autocorrela-
tion (spatio-genetic independence) beyond 24 km in both reserves.
3612
|
NAUDE Et Al.
While this relationship is stronger in PMC than SSGR, significantly
positive female–male kin-clustering occurred over five home-range
radii in SSGR and four in PMC (Figure 4b). Significant clustering of
unrelated individuals then occurred for a range beyond this dis-
tance in both populations (SSGR = 12–26 km; PMC = 12–34 km),
while all spatio-genetic structure attenuated beyond 24–28 km in
both reser ves. The spatio-genetic structure of male–male dyad pairs
was largely independent throughout both populations (Figure 4c),
with some isolated incidents of significant autocorrelation. Spatio-
genetic structure in SSGR was independent, while bimodal structure
occurred in PMC (8–14; 21–25 km) which attenuated beyond 26 km.
4 | DISCUSSION
In this study, we contrast leopard populations from SSGR (a well-
protected population at carrying capacity) and PMC (a postharvest
population in recovery) to reveal the fine-scale genetic conse-
quences of disrupted dispersal due to these markedly different his-
tories of anthropogenic mortality. As predicted, mothers shared
>50% of their home-ranges with their daughters in both populations.
A consequence of this female philopatry is the spatial formation of
adult female kin-clusters, a phenomenon evidenced by the strong
spatio-genetic autocorrelation in female–female dyads in both SSGR
and PMC. Matrilineal assemblages are typical among large, solitary
carnivores, having been observed in brown bears (Ursus arctos;
Støen et al., 2005), pumas (Sweanor, Logan, & Hornocker, 2001), and
tigers (Panthera tigris; Smith, 1993; Goodrich et al., 2010; Gour et al.,
2013). Strategies to deal with the costs of increased resource com-
petition (for food and mates) implicit in this conservative dispersal
by females are assumed to have evolved because of the increased
inclusive fitness benefits that accrue—the so-called “resident fitness
hypothesis” (Anderson, 1989; Lambin et al., 2001). This is clearly
evident in the recovering PMC , where daughters do not establish
beyond the 2nd-order mean peripheral home-range of mothers.
Here, historical anthropogenic mortality may have created “gaps”
in the spatial matrix allowing mothers to accommodate daughters
within their home-ranges (Balme, Robinson, et al., 2017a). Female
kin-clustering and natal philopatry are evident in SSGR; however,
unexpectedly, 30% of daughters appear to have dispersed beyond
their maternal home-ranges. As this population is considered to be
at ca pacity (Balme , Pitman, et al., 2017b), this may be novel evide nce
of density-dependent female dispersal, as postulated by Fattebert,
Robinson, et al. (2015b).
Subadult males disperse from their natal range at sexual maturity
(~3 years old) to avoid conflict with resident adult males (Fattebert
et al., 2016; Fattebert, Robinson, et al., 2015b) and inbreeding with
related females (Balme et al., 2019). While kin-recognition mecha-
nisms have evolved in many species to limit close inbreeding, sex-bi-
ased natal dispersal is the primary outbreeding mechanism of most
FIGURE 2 Pairwise relatedness estimates (rw) of confirmed kinship categories. Expected theoretical relatedness coefficients for parent–
offspring/full siblings (0.5), half-sibling (0.25), and unrelated/random pairs (0) are indicated by dashed lines. The distribution for each kinship
category and number of pairs (below boxes) is indicated for Sabi Sand Game Reserve (gold) and the Phinda-uMkhuze Complex (black)
ssendetaleR (r
W
)
–0.60
–0.50
–0.40
–0.30
–0.20
–0.10
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
PMC
SSGR
Unknown-
pairs
Parent-
offspring
Full-
sibling
Half-
sibling
Breeding-
pair
2,515
2,239
74 72
9
8
17 15
13
12
|
3613
NAUDE Et A l.
polygynous mammals and is essential to maintaining gene flow
within and among populations (Greenwood, 1980). Sexually mature
male leopards in SSGR conformed to this inbreeding avoidance/
mate competition paradigm with no sons establishing within their
maternal home-ranges. However, in PMC 22% of sons established
home-ranges overlapping with their maternal home-range, suggest-
ing a disruption in the proximate drivers of male dispersal, leading
to reduced dispersal and opportunistic philopatry, in congruence
with Fattebert, Robinson, et al. (2015b). This is further supported
by a strong negative correlation between relatedness and distance
in female–male dyads in PMC, resulting in population-level male
kin-clustering similar to that of females. While this phenomenon of
disrupted dispersal has been reported in large carnivores with co-
operative breeding strategies (Loveridge, Searle, Murindagomo, &
Macdonald, 2007), it has rarely been documented in solitary species
(Riley et al., 2014), and to our knowledge, this is the first evidence of
population-level male kin-clustering in a large solitary felid.
Male kin-clustering in polygynous mammals increases the like-
lihood of opportunistic mating events with close female relatives
(sisters, mothers, aunts, and cousins) which, without kin-recognition
(Støen et al., 2005), may result in local inbreeding (Matocq & Lacey,
2004; Perrin & Mazalov, 2000). In our study, mean relatedness
scores among confirmed breeding pairs in SSGR were essentially
random (Figure 2), with low population-level FIS scores indicative
of significant outbreeding. This result was expected, as there is
likely outbreeding and effective gene flow throughout the contig-
uous Kruger National Park landscape. The high degree of related-
ness (half-sibling) among breeding pairs in PMC however suggests
that historically high levels of anthropogenic mortality promote
opportunistic male philopatry and kin-clustering with translates
into significant population-level inbreeding (high FIS scores). While
behavioral avoidance alone does not seem to be a strong enough
driver of dispersal, as local inbreeding was observed (father–daugh-
ter and half-sibling) in PMC, it may be muting even stronger popu-
lation-level inbreeding signals. Similar findings of reduced dispersal
and outbreeding benefits linked to sustained harvest have been
documented in pumas (Logan & Sweanor, 2000; Sweanor, Logan, &
Hornocker, 2001), bobcats (Lynx rufus; Johnson, Walker, & Hudson,
2010), and black bears (Ursus americanus; Moore, Draheim, Etter,
Winterstein, & Scribner, 2014). While the PMC leopard population
is currently recovering from high levels of anthropogenically-linked
mortality (Rogan et al., 2019), demographic-based metrics alone
do not reveal the loss of genetic diversity and the consequences
this may have for the future health and viability of the population
(Kendall et al., 2009). Our results thus further highlight the impor-
tance of population connectivity to ensure gene flow and genetic
diversit y through immigration (Fattebert, Robinson, et al., 2015b;
Frankham, 2003; Hauenstein et al., 2019).
TABLE 1 Pairwise home-range overlap of 95% autocorrelated kernel density estimates (AKDE), described as the utilization density
(Bhattacharyya coefficient) per dyad, confirmed kin-relationships, breeding pairs, and across all individuals for 142 known leopards within
the Sabi Sand Game Reserve (SSGR) and Phinda-uMkhuze Complex (PMC), South Africa, 2002–2018. Parameter estimates are presented
as the percentage of population pairs with overlap (%); mean proportion of home-range utilization overlap (x ); standard errors (SE); and
associated P-values are based on the t-statistic for independent variables (two-tailed), with Welch correction for unequal variance, where
confidence intervals are presented (CI)
Category
SSGR PMC Comparison
%
x
(±SE) %
x
(±SE) tdf p-value CI
All Individuals 45.51 0.16 (0.00) 43.22 0.20 (0.00) 4.542088 <.001*** −0.07; 0.03
Dyads
Female–Female 48.13 0.15 (0.00) 44.52 0.18 (0.02) 1.48334 .140 −0.07; 0.00
Female–Male 44.47 0.15 (0.01) 44 .14 0.20 (0.01) 3.221036 . 001** −0.08; −0.02
Male–Male 38.79 0.18 (0.02) 40.83 0.22 (0.01) 1.27200 .203 −0.09; 0.02
Kin-relationships
Father–Daughter 63.16 0.49 (0.08) 76. 47 0.34 (0.06) 1. 6118 .1241 −0.04; 0.33
Father–Son 33.33 0.32 (0.14) 3 9.1 3 0.39 (0.11) 0 .416.694 −0.51; 0.36
Mother–Daughter 70.37 0.55 (0.06) 92.86 0.59 (0.07) 0.4427 .666 −0.24; 0.15
Mother–Son 48.73 0.31 (0.10) 81.15 0.61 (0.06) 2.499.034* −0.55; −0.05
Breeding Pairs 100 0.63 (0.05) 83.33 0.45 (0.07) 2.1118 .049* 0.00; 0.37
SSGR
Father–Daughter/Father–Son (N = 19/12) 1.074.344 −0.27; 0.61
Mother–Daughter/Mother–Son (N = 27/16) 2.039.073˙ −0.03; 0.52
PMC
Father–Daughter/Father–Son (N = 17/23) 0.3910 .706 −0.31; 0.22
Mother–Daughter/Mother–Son (N = 14/18) 0.1425 .893 −0.21; 0.18
˙p ≤ 0.10; *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001.
3614
|
NAUDE Et Al.
Potential alternative explanations for this observed pattern
include within-reserve habitat fragmentation and/or typical den-
sity-dependent dispersal contributing to the differences between
these populations, because they are at different stages of “de-
velopment.” The former posits that high levels of human-caused
mortality (such as in the PMC) are correlated with anthropogenic
barriers to movement (Tucker et al., 2018); however, there is no
evidence to suggest that anthropogenic barriers limit leopard dis-
persal in either of these populations (Figure 1), and if this were the
pri mary force behind limited disp er sal, it would still not exp lain why
these barriers are sex-specific (Figure 4). The latter suggests that
the SSGR has been stable for some time, while the PMC is recov-
ering and has only recently stabilized, such that increasing density
to parity with SSGR (+2.3 leopards per km2) might correct male dis-
persal and ultimately outbreeding. Though demographic recovery
is plausible, this does not mitigate the “genetic scaring” (evident in
reduced heterozygosity and increased inbreeding) accrued by the
PMC population and many small reserves like it, when undergoing
fluctuations of extreme harvest. Without immigration and effec-
tive connectivity between these reserves (an increasingly scarce
alternative), genetic recovery through drift alone may not be rapid
enough, as the ongoing genetic resilience of these populations is
compromised and at risk of stochastic effects. Moreover, mortality
ne e d not be un sust ain able to induc e these ef f ects, as it is not kn own
whether “sustainable mortality” by humans would necessarily elim-
inate the patterns observed. Certainly, less mor tality would have a
mitigating effect, but it is not known to what degree. Our study is
limited by the comparison of only two reserves on the wide spec-
trum of an thropogen ic morta li ty an d its impact s. Unfor tun atel y, the
fine-scale genetic structure of African leopard populations remains
understudied. This hinders the use of heterozygosity, relatedness
scores, and conventional inbreeding coefficients as a means of
FIGURE 3 Spatial distribution of postdispersal offspring relative to their natal home-range. Postdispersal centroids for daughters (circles)
and sons (triangles) are shown relative to their superimposed maternal centroids (white circles) for SSGR (gold) and PMC (black). Rings of
gray indicate the area of successional average female home-range (95% ADKE) radii around the natal centroid; three levels are shown: the
maternal home-range (dark gray), the 1st-order peripheral home-range (gray), and the 2nd-order peripheral home-range. A linear summary of
the proportion of individuals in each categor y is provided (bar graph bottom left)
20
10
0
10
20
)mk(diortneclatanmorfecnatsidlanidutitaL
10
0
10
20 010 20
20 10
Longitudinal distance from natal centroid (km)
010 20
20 10
PMC
SSGR
km2
25
50
Sons
Daughters
(a) (b)
(c) (d)
|
3615
NAUDE Et A l.
inter pr et in g population-l evel effect s, as there is no bas el in e data on
allelic frequency and diversity of “natural” (outbred or panmictic)
populations. Despite this, we are encouraged that multiple lines of
evidence derived from both spatial and genetic data provide con-
sistent support for anthropogenic mor tality driving limited disper-
sal in males which in turn results in kin-clustering and ultimately
inbreeding.
Given time and adequate protection, territorial turnover among
male leopards in PMC may slow and stabilize, re-establishing
male-biased dispersal and restoring the typical in situ outbreed-
ing effect of genetic drift as capacit y is reached (Couvet, 2002;
Fatteber t, Robinson, et al., 2015b). An alternative is leopard trans-
location under a metapopulation management approach; however,
translocations have been largely unsuccessful to date, as leopards
FIGURE 4 Spatial autocorrelation of pairwise relatedness estimates (rw) over geographical distance (km) are indicated for the Sabi Sands
Game Reserve (gold triangles) and the Phinda-uMkhuze Complex (black circles) by female–female (a), female–male (b), and male–male (c)
dyads, respectively. Depicted as a function of geographical distance (left) and as the effect of different distance class sizes on the extent of
genetic autocorrelation (right). Significant spatio-genetic autocorrelation is indicated by solid shapes and its direction determined above or
below the dashed 0-line. Hollow shapes indicate nonsignificance or an independent spatio-genetic pattern within the distance class
010203
04
0
010203040
–0.04
–0.02
0.00
0.02
0.04
0.06
–0.04
–0.02
0.00
0.02
0.04
0.06
0.08
–0.04
–0.02
0.00
0.02
0.04
0.06
–0.04
–0.02
0.00
0.02
0.04
0.06
–0.04
–0.02
0.00
0.02
0.04
0.06
0.08
–0.04
–0.02
0.00
0.02
0.04
0.06
0.08
noitalerrocotuA (rM)
Geographical distance class (km)
F-F
F-M
M-M
PMC
SSGR
M-M
F-M
F-F
0 – 0 – 0 – 0 –
R2= –.16; pMantel < .001***
R2= –.23; pMantel < .001***
R2= –.03; pMantel = .34 ns
R2= –.25; pMantel < .001***
R2= .04; pMantel = .58 ns
R2= –.12; pMantel = .025*
(a)
(b)
(c)
3616
|
NAUDE Et Al.
are wide-ranging, have complex social dynamics, and are costly to
contain (Athreya, Odden, Linnell, & Karanth, 2010; Ropiquet et al.,
2015; Weilenmann, Gusset, Mills, Gabanapelo, & Schiess-Meier,
2010; Weise, Stratford, & Vuuren, 2014). Genetic recovery and ulti-
mate sustainability could more likely be managed and fast-tracked by
formally maintaining connectivity between PMC and its surround-
ing reserves (e.g., Makhasa Nature Reserve, Ubombo Mountain
Nature Reserve, Isimangaliso Wetland Park, Manyoni Private Game
Reserve, Thanda Safari—Big 5 Game Reserve and Hluhluwe-iMfolozi
Park). Wildlife corridors have proven successful in maintaining func-
tional gene flow between populations in la rge, so litary felids like jag-
uar (Wultsch et al., 2016) and tiger (Sharma et al., 2013), despite the
political (e.g., land ownership, conservation priorities) and logistical
(e.g., road networks, suitable habitat) challenges.
Few protected areas sufficiently encompass the wide range of
these species and large, solitary carnivores effectively confined to
small reserves often suffer edge effects and even localized extinc-
tion (Woodroffe & Ginsberg, 1998). Our study demonstrates novel
genetic consequences underlying this process and emphasizes the
importance of managing and mitigating the effects of increasingly
threatened protected areas and fragmented corridors of structur-
ally suitable habitat that maintain effective connectivity (Fatteber t,
Balme, et al., 2015a; Hauenstein et al., 2019; Kaiser, 2001).
ACKNOWLEDGMENTS
The research on leopard ecology in the SSGR is funded by Panthera
in collaboration with the ecotourism lodges and the Sabi Sand
Wildtuin management. The authors thank David Wood and Mike
Grover for their help in the data collection and the Sabi Sand Wildtuin
management for their support. Research in the PMC is funded by
Panthera, Albert, and Didy Hartog; the Timbo Afrika Foundation;
the National Research Foundation (FA 200405040 0038 to GB); and
the University of KwaZulu-Natal Gay Langmuir Bursary (to JF). VN is
funded by Panthera and the Institute for Communities and Wildlife
in Africa (iCWild). The authors are deeply grateful to all the guides,
lodge owners, staff, and management of both the SSGR and the PMC
who assisted in the study.
CONFLICT OF INTEREST
None declared.
AUTHORS' CONTRIBUTIONS
VN, GB, and JB conceived the study and designed the experimen-
tal protocols with input from JOR. Data were collected by VN, GB,
LH, JF, and TD. Analyses were conducted by VN and JB. All authors
discussed and interpreted the data. VN, GB, LH, JF, TD, JOR, and JB
contributed critically to the writing of the manuscript and gave final
approval for publication.
DATA AVAILAB ILITY STATE MEN T
Summaries of calculated metrics used in analyses are reported in the
appendix, and any further information can be made available upon
request from the corresponding author.
ORCID
Vincent N. Naude https://orcid.org/0000-0002-0275-1727
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SUPPORTING INFORMATION
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Supporting Information section.
How to cite this article: Naude VN, Balme GA, O'Riain J, et al.
Unsustainable anthropogenic mortality disrupts natal dispersal
and promotes inbreeding in leopards. Ecol Evol. 2020;10:3605–
3619. https ://doi.org/10.1002/ece3.6089
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