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Anthropogenic mortality of wildlife is typically inferred from measures of the absolute 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 investigate 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 territories 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 consequences underlying this behavioral change. We therefore emphasize the importance of managing and mitigating the effects of unsustainable exploitation on local populations and increasing habitat fragmentation between contiguous protected areas by promoting in situ recovery and providing corridors of suitable habitat that maintain genetic connectivity. Unsustainable anthropogenic mortality of wildlife and fragmentation of the spatial distribution of individuals disrupts natal dispersal mechanisms, with long‐term consequences to genetic structure, by compromising outbreeding behavior and gene flow. Our study investigates this threat in African leopards, demonstrating novel evidence directly linking unsustainable anthropogenic mortality to inbreeding through disrupted dispersal in a large, solitary felid and exposes the genetic consequences underlying this behavioral change.
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Ecology and Evolution. 2020;10:3605–3619.
Received: 12 January 2020 
Revised: 16 January 2020 
Accepted: 20 Januar y 2020
DOI: 10.1002/ece3.6089
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,
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,
Vincent Naude, Institute for Communities
and Wildlife in Africa, University of Cape
Town, Cape Town, Western Cape, South
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
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.
home-range, kin-clustering, microsatellites, Panthera pardus, philopatry, relatedness
   NAUDE Et Al.
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.
2.1 | 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
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.,
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
   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
South Africa
Study areas
Protected areas
Urban areas
10 25 50
Naonal borders
Road network
Conservaon area
Indian Ocean
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
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,
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
(1.52.5mM), 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.
   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).
3.1 | 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
= 336 ± 7.20 [SE];
= 367 ± 14.20 [SE];
= 361 ± 11.7
[SE]; F2 = 2.99; p = .05). Male home-ranges were markedly larger
than that of females in both the SSGR (
= 26.93 ± 2.37
= 50.02 ± 5.43 [SE]; t32 = 3.90; p < .001) and PMC
= 31.54 ± 1.34 [SE];
= 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
to genotyping success” (number of repeats required per sample) was
significantly lower in the PMC than in the SSGR (
= 1.0 4 ± 0.03
= 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 (
= 68.05 ± 0.39
= 63.52 ± 1.57 [SE]; t40 = 2.80; p = .008; CI = 1.26, 7.79).
SSGR supports greater heterozygosit y (
= 0.78 ± 0.03 [SE];
= 0.65 ± 0.03 [SE]; t40 = 3.01; p < .005; CI = 0.04, 0.22), allelic
richness (
= 6.01 ± 0.31 [SE];
= 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 (
= 2.86 ± 0.37 [SE];
= 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 (
= −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 (
= 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 (
= 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
= −0.08 ± 0.02 [SE];
= 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 (
= −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
= 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 (
= 0.16 ± 0.00 [SE];
= 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 (
= 0.15 ± 0.01
= 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
= 0.31 ± 0.10 [SE];
= 0.61 ± 0.06 [SE]; t9 = 2.49; p = .034)
and breeding pair home-range overlap being nearly 20% greater
in SSGR than PMC (
= 0.63 ± 0.05 [SE];
= 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
= 0.55 ± 0.06 [SE];
= 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 malemale (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.
   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.
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
74 72
17 15
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)
SSGR PMC Comparison
(±SE) %
(±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
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
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
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
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.
   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)
20 010 20
20 10
Longitudinal distance from natal centroid (km)
010 20
20 10
(a) (b)
(c) (d)
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
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
noitalerrocotuA (rM)
Geographical distance class (km)
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*
   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).
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.
None declared.
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.
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.
Vincent N. Naude
Anderson, P. K. (1989). Dispersal i n rodents: A reside ntial fitness hypothesis.
Salt Lake City, UT: American Society of Mammalogists.
Athreya, V., Odden, M., Linnell, J. D. C., & Karanth, U. (2010). Translocation
as a tool for mitigating conflict with leopards in human-dominated
landscapes of India. Conservation Biology, 25(1), 133–141. https ://doi.
Ausband, D. E., Mitchell, M. S., Stansbury, C. R., Stenglein, J. L., & Waits, L.
P. (2017) Harvest and group effects on pup sur vival in a cooperative
breeder. Proceedings of the Royal Society B, 284(1855), e28539521.
https ://
Ausband, D. E., Stansbury, C. R., Stenglein, J. L., Struthers, J. L., & Waits,
L. P. (2015). Recruitment in a social carnivore before and af ter har-
vest. Animal Conservation, 18(5 ), 415–423. ht tps ://doi.or g/10.1111/
Balme, G. A., Batchelor, A., de Woronin Britz, N., Seymour, G.,
Grover, M., Hes, L., Hunter, L. T. B. (2013). Reproductive suc-
cess of female leopards Panthera pardus: The importance of top-
down processes. Mammal Review, 43(3), 221–237. https ://doi.
org /10.1111/j .1365-29 07.2012.0 0219.x
Balme, G. A., Pitman, R . T., Robinson, H. S., Miller, J. R . B., Funston, P. J.,
& Hunter, L. T. B. (2017b). Leopard distribution and abundance is un-
affected by interference competition with lions. Behavioural Ecology,
28(5), 1348–1358. https :// o/arx098
Balme, G. A ., Robinson, H. S., Pitman, R. T., & Hunter, L. T. B. (2017a).
Flexibility in the duration of parental care: Female leopards priori-
tise cub survival over reproductive output. Journal of Animal Ecology,
86(5), 1224–1234. https ://
Balme, G. A ., Rogan, M., Thomas, L., Pitman, R., Mann, G., Whittington-
Jones, G., … Hunter, L. (2019). Big cats at large: Density, structure,
and spatio-temporal patterns of a leopard population free of anthro-
pogenic mortality. Population Ecology, 61(3), 256–267. https ://doi.
org/10.1002/1438-390X .1023
Balme, G. A., Slotow, R., & Hunter, L. T. B. (2009). Impact of conserva-
tion interventions on the dynamics and persistence of a persecuted
leopard (Panthera pardus) population. Biological Conservation, 142(11),
2681–2690. https ://
Balme, G. A., Slotow, R., & Hunter, L. T. B. (2010). Edge effects and the
impact of non-protected areas in carnivore conservation: Leopards
in the Phinda–Mkhuze Complex, South Africa. Animal Conservation,
13(3), 315–323. https :// 09.00342.x
Becker, M., McRobb, R ., Watson, F., Dröge, E., Kanyembo, B., Murdoch,
J., & Kakumbi, C. (2013). Evaluating wire-snare poaching trends
and the impacts of by-catch on elephants and large carnivores.
Biological Conservation, 158, 26–36 . https ://
Blouin, M. S. (2003). DNA-based methods for pedigree recon-
structions and kinship analysis in natural populations. Trends in
Ecology and Evolution, 18 (10), 503–511. https ://
Blyton, M. D. J., Banks, S. C., & Peakall, R. (2015). The effect of sex-biased
dispersal on opposite-sexed spatial genetic struc ture and inbreeding
risk. Molecular Ecology, 24(8), 1681–1695. https ://
Braczkowski, A. R., Balme, G. A., Dickman, A., Macdonald, D. W.,
Johnson, P. J., Lindsey, P. A ., & Hunter, L. T. (2015). Rosettes, rem-
ingtons and reputation: Establishing potential determinants of leop-
ard (Panthera pardus) trophy prices across Africa. African Journal of
Wildlife Research, 45(2), 158–169.
Calabrese, J. M., Fleming, C. H., & Gurarie, E. (2016). ctmm: An R package
for analysing animal relocation data as a continuous-time stochastic
process. Metho ds in Ecolog y and Evolution, 7(9), 1124–1132. https ://
doi .org/10.1111/20 41-210X.12559
Couvet, D. (2002). Deleterious effects of restricted gene flow in frag-
mented populations. Conservation Biology, 16(2), 369–376. https :// 46/j.1523-1739.2002.99518.x
Creel, S., Becker, M., Christianson, D., Dröge, E., Hammerschlag, N.,
Hayward, M. W., Schuette, P. (2015). Questionable policy for
large carnivore hunting. Science, 350(6267) , 1473–1475. htt ps : //doi.
org/10.1126/scien ce.aac4768
Creel, S., M'soka, J., Dröge, E., Rosenblat t, E., Becker, M. S., Matandiko,
W., & Simpamba, T. (2016). Assessing the sustainability of African
lion trophy hunting, with recommendations for polic y. Ecological
Applications, 26(7), 2347–2357. https ://
Dobson, F. S. (1982). Competition for mates and predominant juvenile
male dispersal in mammals. Animal Behaviour, 30(4), 118 3–1192.
https ://
Dolrenry, S., Stenglein, J., Hazzah, L., Lutz, R. S., & Frank, L. (2014).
A metapopulation approach to African lion (Panthera leo) con-
servation. PLoS ONE, 9(2), 1–9. ht tps ://
Ernest , H. B., Vickers, T. W., Morrison, S. A., Buchalski, M. R., & Boyce,
W. M. (2014). Fractured genetic connectivity threatens a south-
ern California puma (Puma concolor) population. PLoS ONE, 9(10),
e107985. https :// urn al.pone.0107985
Fattebert, J., Balme, G., Dickerson, T., Slotow, R., & Hunter, L. (2015a).
Density-dependent natal dispersal patterns in a leopard population
recovering from over-harvest. PLoS ONE, 10(4), e0122355. https :// al.pone.0122355
Fattebert, J., Balme, G., Robinson, H. S., Dickerson, T., Slotow, R., &
Hunter, L. (2016). Population recovery highlights spatial organization
dynamics in adult leopards. Journal of Zoo logy, 299(3), 153–162. https
:// .1111/jzo.123 44
Fattebert, J., Hunter, L., Balme, G., Dickerson, T., & Slotow, R. (2013).
Long-distance natal dispersal in leopard reveals potential for a
three-country metapopulation. South African Journal of Wildlife
Research, 43(1), 61–67. https ://
Fattebert, J., Robinson, H. S., Balme, G ., Slotow, R., & Hunter, L. (2015b).
Structural habitat predicts functional dispersal habitat of a large
carnivore: How leopards change spots. Ecological Applications, 25(7),
1911–1921. https ://
Fieberg, J., & Kochanny, C. O. (20 05). Quantifying home-range overlap:
The importance of the utilization distribution. Journal of Wildlife
Management, 69(4), 1346–1359. https ://doi.or g/10.2193/0022-
541x(2005)69[1346:qhoti o];2
Fleming, C. H., C alabrese, J. M., Mueller, T., Olson, K. A., Leimgruber, P.,
& Fagan, W. F. (2014). From fine-scale foraging to home ranges: A
semi variance approach to identifying movement modes across spa-
tiotemporal scales. The American Natu ralist, 183(5), 154–167. https :// 86/67550 4
Fleming, C. H., Fagan, W. F., Mueller, T., Olson, K. A., Leimgruber, P., &
Calabrese, J. M. (2015). Rigorous home range estimation with move-
ment data: A new autocorrelated kernel densit y estimator. Ecology,
96(5), 1182–1188. https ://
Frank, S. C., Leclerc, M., Pelletier, F., Rosell, F., Swenson, J. E., Bischof,
R., Zedrosser, A . (2017a). Sociodemographic factors modu-
late the spatial response of brown bears to vacancies created by
hunting. Journal of Animal Ecology, 87(1), 247–258. https ://doi.
org /10.1111/1365-2656.12767
Frank, S. C., Ordiz, A ., Gosselin, J., Hertel, A., Kindberg, J., Leclerc, M.,
… Swenson, J. E. (2017b). Indirect effects of bear hunting: A review
from Scandinavia. Ursus, 28(2), 150–164. https ://
Frankham, R. (2003). Genetics and conservation biology. Comptes
Rendus Biologies, 326(1) , 22–2 9. ht tps ://d oi. org/ 10 .1 01 6/
Goodrich, J. M., Miquelle, D. G., Smirnov, E. N., Kerley, L. L., Quigley,
H. B., & Hornocker, M. G. (2010). Spatial structure of Amur
(Siberian) tigers (Panthera tigris alta ica) on Sikhote-Alin Biosphere
Zapovednik, Russia. Journal of Mammalogy, 91(3), 737–748. https :// 44/09-MAMM-A-293.1
Goslee, S. C., & Urban, D. L. (20 07). The ecodist package for dissimilar-
ity-based analysis of ecological data. Journ al of Statistical Sof tware,
22(7), 1–19. https :// jss.v022.i07
Gosselin, J., Zedrosser, A ., Swenson, J. E. , & Pelletier, F. (2015). The rel at iv e
importance of direct and indirect effects of hunting mortalit y on the
population dynamics of brown bears. Proceedi ngs of the Royal Societ y B,
282(1798), e20141840. https ://
Goudet, J. (2002). FSTAT, a program to estimate and test gene diversities
and fixation indices, version Retrieved from http://www2.unil.
ch/izea/softw ares/fstat.html
Gour, D. S., Bhagavatula, J., Bhavanishankar, M., Reddy, P. A., Gupta, J.
A., Sarkar, S. S., Shivaji, S. (2013). Philopatry and dispersal pat-
terns in tiger (Panthera tigris). PLoS ONE, 8(7), e66956. https ://doi.
org/10.1371/journ al.pone.0066956
Greenwood, P. J. (1980). Mating systems, philopatr y and dispersal in
birds and mammals. Animal Behaviour, 28(4), 1140–1162. h tt ps ://doi.
org/10.1016/S 0003-3472(8 0)80103-5
Gundersen, G., Johannesen, E., Andreassen, H. P., & Ims, R. A. (2001). Source-
sink dynamics: How sinks affect demography of sources. Ecology Letters,
4(1), 14–21. https ://
Hanski, I., & Simberloff, D. (1997). The metapopulation approach, its
history, conceptual domain, and application to conservation. In I.
Hanski, & M. Gilpin (Eds.), Metapopulation Biology: Ecology, Genetics,
and Evolution (pp. 5–26). San Diego, CA: Academic Press.
Hardy, O. J., & Vekem ans, X. (2002). SPAGe Di: A versatile computer pro-
gram to analyse spatial genetic structure at the individual or popu-
lation levels. Molecular Ecology Resources, 2(4), 618–620. https ://doi.
Harries, P. (1993). Imagery, symbolism and tradition in a South African
Bantustan: Mangosuthu Buthelezi, Inkatha, and Zulu History. Histor y
and Theory, 32(4), 105–125. https ://
Hauenstein, S., Fattebert, J., Gruebler, M., Naef-Daenzer, B.,Pe'er, G., &
Hartig, F. (2019). Calibrating an individual-based movement model to
predict functional connectivity for little owls. Ecological Applications,
29(4), e01873. https ://
Jo hnson , S. A. , Walk er, H. D., & Hu d s on, C . M. (201 0 ). Dis persal ch aracter-
istics of juvenile bobc ats in south-central Indiana. Journal of Wildlife
Management, 74(3), 379–385. https ://
Jombar t, T. (20 08). adegenet: A R package for the multivariate analysis
of genetic markers. Bioinformatics, 24(11), 1403–1405. https ://doi.
org/10.1093/bioin forma tics/btn129
Kaiser, J. (2001). Building a case for biological corridors. Science,
293(5538), 2199. https :// ce.293.5538.2199
Kalinowski, S. T., Taper, M. L., & Marshall, T. C. (2007). Revising how
the computer program CERVUS accommodates genotyping error
increases success in paternity assignment. Molecular Ecology, 16 (5),
1099–1106 . https ://doi.o rg /10.1111/j.1365-294X. 20 07. 03 089.x
Kendall, K . C., Stetz, J. B., Boulanger, J., Macleod, A. C., Paetkau, D., &
White, G. C. (2 00 9). Demography and gen etic struc ture of a re cove r-
ing grizzly bear population. The Journal of Wildlife Management, 73(1),
3–17. https :// /10. 2193/20 08-33 0
Kumalo, S., & Mujinga, M. (2017). 'Now we know that the enemy is from
within': Shembeites and the struggle for control of Isaiah Shembe's
legacy and the Church. Journal for the Study of Religion, 30(2), 122–
153. ht tps :// /10.17159/ 2413-3027/2017/v30 n2a6
Lambin, X., Aars, J., & Piertney, S. B. (2001). Dispersal, intraspecific
competition, kin competition and kin facilitation: A review of the
empirical evidence. In E. D. Clobert, A. A. Dhondt, & J. D. Nichols
(Eds.), Dispersal: Individual, population, and community (pp. 110–122).
Oxford: Oxford University Press.
   NAUDE Et Al.
Logan, K. A., & Sweanor, L. L. (2000). Puma. In S. Demarais, & P. Krausman
(Eds.), Ecolog y and management of l arge mammals in No rth America (p p.
347–377). New Jersey: Prentice-Hall.
Loveridge, A. J., Searle, A. W., Murindagomo, F., & Macdonald, D. W.
(2007). The impact of sport-hunting on the population dynam-
ics of an African lion population in a protected area. Biological
Conservation, 134 (4), 548–558. https ://
Loveridge, A. J., Wang, S. W., Frank, L. G., & Seidensticker, J. (2010).
People and wild felids : conserv ation of cats and ma nagem ent of con-
flicts. In D. W. Macdonald, & A. J. Loveridge (Eds.), Biology and conser-
vation of wild felids (pp. 161–197). Oxford, United Kingdom: Oxford
University Press.
Matocq, M. D., & Lacey, E. A. (200 4). Philopatry, kin clusters, and ge-
netic relatedness in a population of woodrats (Neotoma macrotis).
Behavioural Ecology, 15(4), 647–653. https ://
o/ar h0 56
McManus, J. S., Dalton, D. L., Kotzé, A., Smut s, B., Dickman, A., Marshal,
J., & Keith, M. (2014). Gene flow and population structure of a sol-
itary top carnivore in a human-dominated landscape. Ecology a nd
Evolution, 5(2), 335–344. https ://
Menotti-Raymond, M., David, V. A., Lyons, L. A., Schäffer, A. A., Tomlin,
J. F., Hutton, M. K ., & O'Brien, S. J. (1999). A genetic linkage map of
micro sate ll it es in th e do mestic ca t (Felis catus). Genomics, 57(1), 9–23.
https ://
Milner, J. M., Nilsen, E. B., & Andreassen, H. P. (2007).
Demographic side effects of selective hunting in ungulates
and carnivores. Conservation Biology, 21(1), 36–47. https ://doi.
org /10.1111/j .1523-1739.2006.0 0591.x
Miththapala, S., Seidensticker, J., Phillips, L. G., Fernando, S. B. U., &
Sm allw ood, J. A . (198 9). Id e ntif ic atio n of in divid ual le opards ( Panthera
pardus kotiya) using spot pattern variation. Journal of Zoolog y, 218(4),
527–536. ht tp s ://doi.or g/10.1111/ j.14 69-7998.1989.tb 049 96. x
Moore, J. A ., Draheim, H. M., Etter, D., Winterstein, S., & Scribner, K . T.
(2014). Application of large-scale parentage analysis for investigating
natal dispersal in highly vagile vertebrates: A case study of American
black bears (Ursus americanus). PLoS ONE, 9(3), e91168. https ://doi.
org/10.1371/journ al.pone.0091168
Munson, L., Brown, J. L., Bush, M., Packer, C., Janssen, D., Reiziss, S.
M., & Wildt, D. E. (1996). Genetic diversity affects testicular mor-
pholog y in free-ranging lions (Panthera leo) of the Serengeti Plains
and Ngorongoro Crater. Journal of Reproduction and Fertility, 108(1),
11–15. https ://
Newby, J. R ., Mills, S. L., Ruth, T. K., Pletscher, D. H., Mitchell, M. S.,
Quigley, H. B., DeSimone, R. (2013). Human-caused mor tality
influences spatial population dynamics: Pumas in landscapes with
varying mortality risks. Biological Conservation, 159 (2 013) , 230 –239.
https ://
Onorato, D., Desimone, R., White, C., & Waits, L. P. (2011). Genetic as-
sessment of paternity and relatedness in a managed population of
cougars. Journal of Wildlife Management, 75(2), 378–384. https ://doi.
Paradis, E., & Schliep, K. (2018). ape 5.0: An environment for modern
phylogenetics and evolutionary analyses in R. Bioinformatics, 35(3),
526–528. htt ps :// /10.1093/bioin forma tic s/bty6 33
Peakall, R., Ruibal, M., & Lindenmayer, D. B. (2007). Spatial autocorrela-
tion analysis offers new insights into gene flow in the Australian
bush rat, rat tus fuscipes. Evolution, 57(5), 1182–1195. ht tps ://doi.
org/10.1111/j.0014-3820.2003.tb003 27.x
Perrin, N., & Mazalov, V. (200 0). Local competition, inbreeding, and the
evolution of sex-biased dispersal. The American Naturalist, 155(1),
116–127. https ://
Pilgrim, K. L., McKelvey, K. S., Riddle, A. E., & Schwartz, M. K. (2004). Felid sex
identification based on non-invasive genetic samples. Molecular Ecology,
5(1), 60–61. https ://
QGIS Development Team. (2018). QGIS Geographic Information System.
Beaverton, OR: Open Source Geospatial Foundation. Retrieved
R Core Team. (2018). R: A language and environm ent for statistical comput-
ing. Vienna, Austria: R Foundation for Statistical Computing. Retrieve
from https ://www.r-proje
Rice, W. R. (1989). Analysing tables of statistical test s. Evolution, 43(1),
223–225. https :// 20.x
Riley, S. P. D., Serieys, L. E. K., Pollinger, J. P., Sikich, J. A., Dalbeck, L., Wayne,
R. K., & Ernest , H. B. (2014). Individual behaviors dominate the dynamics
of an urban mountain lion population isolated by roads. Current Biology,
24(17), 1989–1994. https ://
Rogan, M. S., Balme, G. A., Distiller, G., Pitman, R. T., Broadfield, J., Mann,
G. K . H., … O'Riain, M. J. (2019). The influence of movement on the
occupancy-density relationship at small spatial scales. Ecosphere,
10(8), e 02807. https ://
Ropiquet, A., Knight, A. T., Born, C., Martins, Q., Balme, G., Kirkendall,
L., Matthee, C. A. (2015). Implications of spatial genetic patterns
for conserving African leopards. Comptes Rendus Biologies, 338(11),
728–737. https ://
Rutledge, L. Y., Patterson, B. R., Mills, K . J., Loveless, K. M., Murray, D., &
White, B. N. (2010). Protection from harvesting restores the natural
social structure of eastern wolf packs. Biological Conservation, 143(2),
332–339. https ://
Sharma, S., Dutt a, T., Maldonado, J. E., Wood, T. C., Panwar, H. S., &
Seidensticker, J. (2013). Forest corridors maintain historical gene
flow in a tiger metapopulation in the highlands of central India.
Proceedi ngs of the Royal Society B: Biological Sciences, 280(1767),
20131506. https ://
Smith, J. L . D. (1993). The role of dispersal in structuring the Chitwan
tiger population. Behaviour, 124(3/4), 165–195. https ://doi.
org/10.1163/15685 3993X 0 0560
Smouse, P. E., & Peakall, R. (2001). Spatial autocorrelation analysis of in-
dividual multiallele and multilocus genetic structure. Heredit y, 82(5),
561–573. https :// 0518.x
Støen, O.-G ., Bellemain, E., bø, S., & Swenson, J. E. (2005). Kin-
related spatial structure in brown bears Ursus arctos. Behavioural
Ecology and Sociobiology, 59(2), 191–197. https ://doi.or g/10.10 07/
Swanepoel, L. H., Lindsey, P., Somers, M. J., van Hoven, W., & Dalerum,
F. (2011). The relative importance of trophy harvest and retaliatory
killing of large carnivores: South African leopards as a case study.
South African Journal of Wildlife Research, 44(2), 115–134. https ://doi.
Sweanor, L. L., Logan, K. A., & Hornocker, M. G. (2001). Cougar dispersal pat-
terns, metapopulation dynamics, and conservation. Conservation Biology,
14(3), 798–808. https ://
Thorn, M., Green, M., Dalerum, F., Bateman, P. W., & Scott, D. M. (2012).
What drives human–carnivore conflic t in the North West Province
of South Africa? Biological Conservation, 150(1), 23–32. https ://doi.
Tucker, M. A., Böhning-Gaese, K., Fagan, W. F., Fryxell, J. M., van
Moorter, B., Alberts, S. C., Mueller, T. (2018). Moving in the
Anthropocene: Global reductions in terrestrial mammalian move-
ments. Science, 359(6374), 466–469. https ://
scien ce.aam9712
Uphyrkina, O., Johnson, W. E., Quigley, H., Miquelle, D., Marker, L., Bush,
M., & O’Brien, S. J. (2001). Phylogenetics, genome diversity and or-
igin of modern leopard. Panthera Pardus. Molecu lar Ecolog y, 10(11),
2617–2633. https ://
Vangestel, C., Mergeay, J., Dawson, D. A., Vandomme, V., & Lens, L.
(2011). Spatial heterogeneity in genetic relatedness among house
sparrows along an urban–rural gradient as revealed by individu-
al-based analysis. Molecular Ecology, 20(22), 4643–4653. https ://doi.
org /10.1111/j .1365-294x. 2011.05316.x
Wang, J. (2002). An estimator for pairwise relatedness using molecular
markers. Genetics, 160 (3), 1203–1215.
Weilenmann, M., Gusset, M., Mills, D. R., Gabanapelo, T., & Schiess-Meier,
M. (2010). Is translocation of stock-raiding leopards into a protected
area with resident conspecifics an effective management tool? Wildlife
Research, 37(8), 702–707. https ://
Weise, F. J., Stratford, K. J., & van Vuuren, R. J. (2014). Financial
costs of large carnivore translocations accounting for conser-
vation. PLoS ONE, 9(8), e105042. https ://
Whitman, K., Star field, A. M., Quadling, H. S., & Packer, C. (2004).
Sustainable trophy hunting of African lions. Nature, 428(6979), 175–
178. https :// e02395
Williams, V. L., Loveridge, A. J., Newton, D. J., & MacDonald, D. W.
(2017). A roaring trade? The legal trade in Panthera leo bones from
Africa to East-Southeast Asia. PLoS ONE, 12(10), 1–22. https ://doi.
org/10.1371/journ al.pone.0185996
Winner, K., Noonan, M. J., Fleming, C. H., Olson, K. A., Mueller, T.,
Sheldon, D., & Calabrese, J. M. (2018). Statistical inference for home
range overlap. Methods in Ecology and Evolution, 9(7), 1679–1691.
https : // x.13027
Wolff, J. O. (1994). More on juvenile dispersal in mammals. Oikos, 71(2),
349–352. https ://
Woodroffe, R., & Ginsberg, J. R. (1998). Edge effects and the extinction
of populations inside protected areas. Science, 280(5372), 2126–
2128. https :// ce.280.5372.2126
Wultsch, C., Caragiulo, A., Dias-Freedman, I., Quigley, H., Rabinowitz,
S., & Amato, G. (2016). Genetic diversity and population structure
of mesoamerican jaguars (Panthera onca): Implications for conser-
vation and management. PLoS ONE, 11(10), e 0162377. https ://doi.
org/10.1371/journ al.pone.0162377
Additional supporting information may be found online in the
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 ://
... Leopard home range overlap varies throughout Africa, ranging between 25% and 60% for neighbouring males (Jenny 1996, Steyn & Funston 2009), although instances of zero home range overlap have also been recorded (Mizutani & Jewell 1998). Nevertheless, mother-daughter associations appear to have the highest levels of home range overlap (Naude et al. 2020), presumably because of their relatedness. Leopard spatial patterns are also affected by human-induced mortality. ...
... Dispersal strategies lead to smaller overlaps at low densities, and dispersal strategies differ between males and females. Male dispersal is driven mostly by mate competition (Fattebert et al. 2015, Naude et al. 2020, and thus young males tend to emigrate. Female dispersal is affected mostly by philopatry, where, in favourable conditions, the benefit of daughters staying outweighs the cost to the mothers (i.e. the resident fitness hypothesis; Naude et al. 2020). ...
... Male dispersal is driven mostly by mate competition (Fattebert et al. 2015, Naude et al. 2020, and thus young males tend to emigrate. Female dispersal is affected mostly by philopatry, where, in favourable conditions, the benefit of daughters staying outweighs the cost to the mothers (i.e. the resident fitness hypothesis; Naude et al. 2020). For example, at high densities, mean overlap between all individuals (both sexes) was between 18% and 20%, but within kin-groups it was as high as 60% (Naude et al. 2020). ...
... Changes in landscape and habitat quality induced by anthropogenic disturbances can indeed impact populations' genetic structure and gene flow, leading to or exacerbating source-sink dynamics as often observed among large carnivores (Riley et al. 2006;Crooks et al. 2011;Andreasen et al. 2012;Dolrenry et al. 2014;Warren et al. 2016;Zemanova et al. 2017;Lorenzana et al. 2020). In addition, many studies have shown that human-related mortality also affects connectivity of populations not only through direct density control, but also influencing movement patterns and population dynamics of many species (Delibes et al. 2001;Robinson et al. 2008;Packer et al. 2009;Creel and Rotella 2010;Andreasen et al. 2012;Trumbo et al. 2019;Naude et al. 2020). Understanding how these landscape modifications and mortality affect gene flow is particularly important for conservation of geographically wide-distributed species, such as large carnivores (Loxterman 2011;Balkenhol et al. 2014;Reddy et al. 2019;Lorenzana et al. 2020;Naude et al. 2020), which are susceptible to landscape changes owing to their vast home ranges, general dependence on continuous undisturbed habitat for dispersal, and high sensitivity to human activities (Jackson et al. 2005;Roques et al. 2015;Zemanova et al. 2017). ...
... In addition, many studies have shown that human-related mortality also affects connectivity of populations not only through direct density control, but also influencing movement patterns and population dynamics of many species (Delibes et al. 2001;Robinson et al. 2008;Packer et al. 2009;Creel and Rotella 2010;Andreasen et al. 2012;Trumbo et al. 2019;Naude et al. 2020). Understanding how these landscape modifications and mortality affect gene flow is particularly important for conservation of geographically wide-distributed species, such as large carnivores (Loxterman 2011;Balkenhol et al. 2014;Reddy et al. 2019;Lorenzana et al. 2020;Naude et al. 2020), which are susceptible to landscape changes owing to their vast home ranges, general dependence on continuous undisturbed habitat for dispersal, and high sensitivity to human activities (Jackson et al. 2005;Roques et al. 2015;Zemanova et al. 2017). Under metapopulation dynamics, the persistence of populations crucially relies on landscape connectivity, which represents the degree to which the landscape facilitates or impedes individual movements between habitat patches (Taylor et al. 1993;Fahrig and Merriam 1994). ...
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ContextIdentification of areas with high connectivity is crucial for large carnivores’ management and conservation, especially where landscape has been modified by human activities. Partially under legal hunting control, south-central Argentine pumas (Puma concolor) have been described to be structured into two distinct groups with an inverse correlation between gene flow and hunting pressure.Objectives To further assess puma genetic structure and test whether isolation-by-distance and/or isolation-by-resistance could explain the previously reported putative correlation between gene flow and hunting pressure.Methods We explored spatial segregation of pumas by testing for hierarchical structure within previously identified clusters, genetic differentiation among sampling regions, and isolation-by-distance among individuals. Using a land cover resistance-based approach, we assessed landscape influence on puma connectivity to analyze landscape permeability between sampling sites.ResultsOur study added a third genetic group to the previously identified clusters, reporting significant genetic differentiation among sampling regions. We also observed a significant correlation among geographic and genetic distances, supporting genetic structure and gene flow pattern of connectivity. We identified a continuous high current flow across the landscape where shrublands are the primary habitat, whereas landscape permeability declined as grassland cover increases.Conclusions Genetic structure and gene flow among south-central Argentine pumas can be partially related to the landscape connectivity pattern observed in the area. These results are extremely important for puma conservation in the area because the identification of high-permeability linkage zones can now be used to gather ecological fine-scale data to support more appropriate conservation strategies, aiming to preserve important dispersal areas for this apex predator.
... Historically, South African leopards are considered to be a single population that consists of several geographically distinct clades, which were likely shaped by isolation-by-distance [14], habitat fragmentation [20], and anthropogenic mortality [21]. It has previously been suggested that a cryptic subspecies may occur within South Africa, based on some unique phenotypic traits in this population, such as the small-sized Cape leopard [22], although no genetic data has yet supported this. ...
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Revealing phylogeographic structure is important for accurate subspecies delineation and understanding a species’ evolutionary history. In leopards ( Panthera pardus ), there are currently nine subspecies recognized. On the African continent, only one subspecies occurs ( P. p. pardus ), although historic mitochondrial DNA suggests the presence of three putative continental lineages: (1) West Africa (WA), (2) Central Africa (CA), and (3) Southern Africa (SA). So far, genome-wide data did not recover this phylogeographic structure, although leopards in the southern periphery of their distribution range in Africa have not yet been investigated in detail. The Mpumalanga province of South Africa is of particular interest, as here the CA and the SA clade possibly meet. The aim of this study was to characterize the first mitogenomes of African leopards from Mpumalanga, to help clarifying how South African leopards fit into continental patterns of genetic differentiation. Complete mitogenomes from six leopards were assembled de novo and included in phylogenetic analysis, in combination with other publicly available mitogenomes. Bayesian inference and Maximum Likelihood analyses identified two deeply diverged putative lineages within South Africa, which are more genetically distinct than two subspecies in Asia. The lineages dated back to 0.73–0.87 million years ago, indicating that they originated during the climatically unstable Mid-Pleistocene, as seen in other large mammals. The Pleistocene refuge theory states that the maintenance of savanna refugia in South Africa promoted the divergence between populations. As such, leopards may reflect the unique climatic history of South Africa, which has resulted in eminent and endemic genetic diversity.
... Our results reinforce the expectation that the human-dominated matrix can impact even generalist and highly mobile species, such as cougars. Furthermore, collision with vehicles is a clear threat to cougars and other mammal species, affecting both males and females, but impacting males predominantly(Schwab and Zandbergen 2011;Abra et al. 2021).The long-term effects of the actual landscape con guration warrant further research, however, the ndings reported here are of signi cance, as it is known that unsuccessful dispersal and male and female kin clustering can impact gene ow and increase inbreeding events(Naude et al. 2020;Westekemper et al. 2021).Altogether, the alterations of the landscapes that these populations currently face and may continue to face in the future threaten the long-term persistence of cougar populations in South America and elsewhere and represent an urgent conservation agenda.Declarations Funding: This work was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo (SISBIOTA -Top predators network, FAPESP 2010/52315-Parque Zoológico de São Paulo (FPZSP), Natural Environment Research Council (NERC, NE/S011811/1), Stanford's School of Humanities and Sciences, IDEA WILD (SARABRAZ1113) and Neotropical Grassland Conservancy (NGC). Financial interests: The authors declare they have no nancial interests. ...
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Habitat loss and fragmentation threaten population persistence because they affect the individuals’ ability to disperse between remaining habitat patches and reduce areas of refuge for populations. In cougars ( Puma concolor ), males are predominantly dispersers while females tend to be philopatric. To examine cougar philopatry and dispersal ability in a human-dominated landscape in Brazil, we performed relatedness and spatial autocorrelation analyses based on genetic samples of cougars inhabiting forest fragments within a human-modified matrix, a continuous forest, and a pool of road-killed individuals. Our expectation was that females would be more related to each other and show a more positive autocorrelation than males in areas with less human disturbance because male dispersal would not be constrained. We found similarly high relatedness and a positive spatial autocorrelation at the shortest spatial scale (0-100 km) for both males and females from the forest fragments within a human-modified matrix. We also detected higher male:female ratio from roadkills, likely due to males’ higher tendency to disperse. Our results confirm female philopatry in the forest fragments. However, high relatedness and positive autocorrelation also observed for males in these fragments suggest male kin clustering, which could be a result of unsuccessful dispersal. Cougar unsuccessful dispersal has already been reported in North America in response to human-altered landscapes, but here we present the first evidence of this process in a South American cougar population. Further research is warranted to assess the specific causes of male unsuccessful dispersal and how it can affect species persistence in human-dominated landscapes.
... Relatively few studies have examined the effects of human disturbance to biological metrics other than the population size or growth rate such as sex ratios, age structure, and social structure, yet several have reported notable impacts. For example, infanticide increased with male harvest in both African lions (Panthera leo) (Loveridge et al. 2007) and Scandinavian brown bears (Ursus arctos) (Leclerc et al. 2017), and humancaused mortality disrupted dispersal patterns in African leopards (Panthera pardus pardus), resulting in higher rates of inbreeding (Naude et al. 2020). ...
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Transboundary movement of wildlife results in some of the most complicated and unresolved wildlife management issues across the globe. Depending on the location and managing agency, gray wolf (Canis lupus) management in the US ranges from preservation to limited hunting to population reduction. Most wildlife studies focus on population size and growth rate to inform management, but relatively few examine species biological processes at scales aside from that of the population. This is especially important for group‐living species such as the gray wolf, for which the breeding unit is the social group. We analyzed data for gray wolf packs living primarily within several US National Park Service units (years of data): Denali National Park and Preserve (33 years), Grand Teton National Park (23 years), Voyageurs National Park (12 years), Yellowstone National Park (27 years), and Yukon‐Charley Rivers National Preserve (23 years). We identified two gray wolf biological processes that differed from population size – namely, pack persistence and reproduction – and determined that while human‐caused mortality had negative effects on both, pack size had a moderating effect on the impacts of mortality.
... McManus et al. (2015) also estimated low to moderate gene flow connecting subpopulations within the Western and Eastern Cape. Yet, despite their importance to the conservation and management of leopards (Naude et al. 2020), gene flow and connectivity between populations have not been extensively studied in South Africa. ...
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The leopard (Panthera pardus) is facing the threat of continued population decline across its range. In order to inform more effective conservation management programs, genetic information is needed from leopard populations that persist in previously unstudied, isolated and highly fragmented protected areas. The aim of this study was to explore the population structure and genetic diversity of leopard populations across the Mpumalanga province of South Africa. We collected a total of 33 leopard samples from four major locations along a west to east transect across the province. We analysed 17 polymorphic microsatellites and two regions of the mitochondrial genome (NADH-5 and Cytb) to determine the genetic structure of the leopard population in the province. We also calculated genetic diversity indices and explored gene flow in the region. We found that while there is gene flow occurring across the province, the population was genetically structured. We identified two major population units that we describe as ‘West Mpumalanga’ and ‘East Mpumalanga’. Gene flow was moderate between the two populations and we found very high genetic diversity levels compared to other leopard populations previously studied in South Africa. From a conservation perspective, our results show that gene flow is still occurring across seemingly isolated leopard populations that exist in fragmented landscapes, highlighting the importance of all leopard populations in South Africa. Management authorities need to focus conservation efforts on maintaining corridors between regions that are suitable for leopard occupancy and work closely with human settlements to minimise human-leopard conflicts.
... Hayward (2009) suggested that in southern Africa, 'halos of defaunation' could form around poorly resourced PAs due to the demand for bushmeat. Such population sinks are likely to form around the PAs of the BMC and without sufficient reproduction within and immigration into these areas to counteract the high anthropogenically-driven mortality rate, there will be severe consequences for resident wildlife populations with an increased extinction risk due to stochastic events, such as the frequent and extensive fires common to the region (Bradshaw & Cowling, 2014;Naude et al., 2020a;Woodroffe & Ginsberg, 1998). Snares were also more likely to be found closer to rivers (i.e., between 0.5 and 3.2 km), which are known to attract wildlife. ...
Wire-snare poaching for bushmeat is increasingly recognised as a global threat to biodiversity and is directly linked to the reduction or extirpation of targeted species, threatened species bycatch and the loss of functional ecosystem processes. However, studies evaluating the extent and underlying dynamics of bushmeat poaching in southern Africa remain limited. Despite growing evidence of wire-snaring incidence in the Boland Mountain Complex of South Africa, formal research has been restricted to unverified reporting. Through systematic anti-poaching patrols on private agricultural properties bordering protected areas, this study characterised snaring, compared interview- and patrol-reported incidence to quantify the influence of socioeconomic and biophysical determinants of bushmeat poaching, and spatially predicted poaching risk throughout the region. In total, 671 snares were located during 96 (46%) bi-annual patrols (June 2019 to June 2020), covering a total distance of 1,332 km across 112 private properties. Of these snares, 537 (80%) were anchored and active. Furthermore, snares were primarily positioned along game trails (47%) and fence lines (39%) where they were predominantly anchored to trees (40%) or fence posts (39%). Snares were mainly made of wire (70%) or nylon (19%) and suspended at 0–60 cm (97%). Snaring incidence did not differ significantly (P = 0.186) between interviews (n = 307) and patrols (n = 180), reporting on average 2.32 ± 0.23 (SE) snares on the property in the month preceding each interview and 3.34 ± 0.55 snares removed on interviewed properties (n = 92). Interview-reported snaring positively correlated with the number of resident families per property and the use of lethal control measures but was negatively correlated with owners endorsing punitive measures and where orchards were the primary agricultural output. In contrast, patrol-reported snaring frequency increased with the number of resident families per property, farmer residency and a primary agricultural output of orchards. High risk areas for snaring were predicted between 2.2 and 5.8 km from the nearest public street, between 1.5 and 2.2 km from the nearest settlement, at elevations of 300–500 m, between 1.8 and 2.5 km from the nearest protected area and 0.5–3.2 km from the nearest river, thus identifying new poaching hotspots across the region. This study features a novel interdisciplinary approach to understanding the complex nature of bushmeat poaching and adds applied conservation value by optimising current monitoring and law enforcement efforts.
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Habitat loss and fragmentation threaten population persistence because they affect the ability of individuals to disperse between remaining patches of good-quality habitat and reduce refuge areas for populations. In cougars (Puma concolor), males are predominantly dispersers while females tend to be more philopatric. To examine the dispersal ability and philopatry of cougars in a human-dominated landscape in Brazil, we performed relatedness and spatial autocorrelation analyses based on genotyped cougars from different sampling groups: forest fragments within a human-modified matrix, continuous forest, and road-killed individuals. We found similarly high relatedness and a positive spatial autocorrelation at the shortest spatial scale (0-100 km) for both males and females from the forest fragments within a human-modified matrix. In the continuous forest and among cougars sampled as roadkills, we detected no spatial autocorrelation and observed low relatedness for both sexes. We also detected higher male:female sex ratio among road-killed individuals, likely due to the greater dispersal tendency of males. Our results confirm female philopatry in the forest fragments. However, the high relatedness and positive autocorrelation observed in the forest fragments suggest kin clustering also for males, which may be a result of unsuccessful dispersal. We reported the first evidence for a South American cougar population of unsuccessful dispersal in response to human-altered landscapes. Further research is needed to assess the specific causes of male unsuccessful dispersal and how it may affect species persistence in human-dominated landscapes.
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Conservation and management of wide-ranging carnivores like cougars (Puma concolor), which occur across human-altered landscapes can benefit from an in-depth understanding of their genetic status. Here, we apply the largest collection of multi-locus genotypes currently available for cougars (n = 1,903) to provide a comprehensive assessment of genetic diversity, gene flow, and source-sink dynamics for cougars occurring across Washington, United States and south-central British Columbia, Canada. We found that cougars in the Olympic, Cascade, Kettle, Selkirk, and Blue Mountains ecosystems are genetically differentiated into two clusters with varying degrees of admixture, indicating moderate levels of gene flow across the area with the exception of the Olympic Peninsula and the Blue Mountains which form more distinct genetic groups. We detected several first-generation migrants confirming long-distance movements within our study system, but also observed that migration rates between areas were asymmetrical, which is an indication of genetic source-sink dynamics. Genetic diversity and inbreeding followed a clinal east-to-west pattern with Olympic Peninsula cougars having the lowest genetic diversity and highest inbreeding coefficients among all sites. Spatial autocorrelation results for cougars did not follow sex-specific patterns suggesting that anthropogenic pressures such as habitat fragmentation and/or mortality sources may have an impact on their spatial dynamics. As cougar habitat in the northwestern United States continues to be affected by rising levels of urbanization and anthropogenic activities, long-term regional genetic monitoring represents a critical decision-support tool for formulating effective cougar conservation and management actions to prevent further genetic decline and promote long-term persistence of cougar populations.
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Spatial patterns of and competition for resources by territorial carnivores are typically explained by two hypotheses: 1) the territorial defence hypothesis and 2) the searching efficiency hypothesis. According to the territorial defence hypothesis, when food resources are abundant, carnivore densities will be high and home ranges small. In addition, carnivores can maximise their necessary energy intake with minimal territorial defence. At medium resource levels, larger ranges will be needed, and it will become more economically beneficial to defend resources against a lower density of competitors. At low resource levels, carnivore densities will be low and home ranges large, but resources will be too scarce to make it beneficial to defend such large territories. Thus, home range overlap will be minimal at intermediate carnivore densities. According to the searching efficiency hypothesis, there is a cost to knowing a home range. Larger areas are harder to learn and easier to forget, so carnivores constantly need to keep their cognitive map updated by regularly revisiting parts of their home ranges. Consequently, when resources are scarce, carnivores require larger home ranges to acquire sufficient food. These larger home ranges lead to more overlap among individuals' ranges, so that overlap in home ranges is largest when food availability is the lowest. Since conspecific density is low when food availability is low, this hypothesis predicts that overlap is largest when densities are the lowest. We measured home range overlap and used a novel method to compare intraspecific home range overlaps for lions Panthera leo ( n = 149) and leopards Panthera pardus ( n = 111) in Africa. We estimated home range sizes from telemetry location data and gathered carnivore density data from the literature. Our results did not support the territorial defence hypothesis for either species. Lion prides increased their home range overlap at conspecific lower densities whereas leopards did not. Lion pride changes in overlap were primarily due to increases in group size at lower densities. By contrast, the unique dispersal strategies of leopards led to reduced overlap at lower densities. However, when human‐caused mortality was higher, leopards increased their home range overlap. Although lions and leopards are territorial, their territorial behaviour was less important than the acquisition of food in determining their space use. Such information is crucial for the future conservation of these two iconic African carnivores.
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The global decline of large carnivores demands effective and efficient methods to monitor population status, particularly using non‐invasive methods. Density is among the most useful metrics of population status because it is directly comparable across space and time. Unfortunately, density is difficult to measure reliably, especially for mobile, cryptic species. Recently, efforts have turned to approximating density based on its relationship to more readily estimable indices of occurrence. However, the relationship between density and such indices is contingent on several key assumptions that field studies often violate. Recent research has shown that these relationships are unreliable where sampling units are not independent, as is often the case when estimating density or occurrence of large carnivores. Here, we use the largest data set thus far collected for leopards (Panthera pardus)—88 camera‐trap surveys undertaken in 24 protected areas between 2013 and 2018—to explore how density and other population characteristics relate to parameter estimates in occupancy and Royle–Nichols abundance models. We show how home‐range size confounds underlying relationships, with larger home ranges inflating the proportion of area used (PAU) and resulting in double counting in abundance models. Relativizing estimates of occupancy and abundance by home‐range size improved their relationship with density, but the relationship remained weak and largely uninformative for management. Our findings illustrate the pitfalls of using the PAU or abundance as implicit proxies for density and highlight the challenges of assessing population status for wide‐ranging, cryptic species across fragmented landscapes.
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Dispersal is crucial for population viability and thus a popular target for conservation measures. However, the ability of individuals to move between habitat patches is notoriously difficult to estimate. One solution is to quantify functional connectivity via realistic individual‐based movement models. Such simulation models, however, are difficult to build and even more difficult to parameterize. Here, we use the example of natal little owl (Athene noctua) dispersal to develop a new analysis chain for the calibration of individual‐based dispersal models using a hybrid of statistical parameter estimation and Approximate Bayesian Computation (ABC). Specifically, we use locations of 126 radio‐tracked juveniles to first estimate habitat utilization by generalized additive models (GAMs) and the biased random bridges (BRB) method. We then include the estimated parameters in a spatially explicit individual‐based model (IBM) of little owl dispersal and calibrate further movement parameters using ABC. To derive efficient summary statistics, we use a new dimension reduction method based on random forest (RF) regression. Finally, we use the calibrated IBM to predict the dispersal potential of little owls from local populations in south‐western Germany to suitable habitat patches in northern Switzerland. We show that pre‐calibrating habitat preference parameters while inferring movement behavioral parameters via ABC is a computationally efficient solution to obtain a plausible IBM parameterization. We also find that dimension reduction via RF regression outperforms the widely used least squares regression, which we applied as a benchmark approach. Estimated movement parameters for the individuals reveal plausible inter‐individual and inter‐sexual differences in movement behavior during natal dispersal. In agreement with a sex‐biased dispersal distance in little owls, females show longer individual flights and higher directional persistence. Simulations from the fitted model indicate that a (re‐)colonization of northern Switzerland is generally possible, albeit restricted. We conclude that the presented analysis chain is a sensible work‐flow to assess dispersal connectivity across species and ecosystems. It embraces species‐ and individual‐specific behavioral responses to the landscape and allows likelihood‐based calibration, despite an irregular sampling design. Our study highlights existing, yet narrow dispersal corridors, which may require enhancements to facilitate a recolonization of little owl habitat patches in northern Switzerland. This article is protected by copyright. All rights reserved.
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In 1911, Isaiah Shembe (1865-1935) founded the Nazareth Baptist Church popularly known as KwaShembe (Dube 1936: 29). The church became the first amongst the Zulus to be founded 'with the quest to restore the Zulu to their glorious past' (Masondo 2004: 69-79). Today it is the oldest and most respected church founded with the intention of bringing Christianity and the quest for Zulu nationalism and culture together in South Africa. In its early days, the church was faced with much opposition from the missionaries who accused it of misleading people, polluting the gospel and sheep-stealing. Shembe had to continuously defend himself and his church against the external forces that sought to destroy him and his church. As a result, the church has had to walk a fine line, between belligerence and servility throughout the colonial and apartheid periods. However, its history has also been marked by forces from within that have divided the church into what has become seven splinter groups, or factions, that are at war with one another. The power-struggles and fights amongst family members have directly taken a toll on the once great church as each scrambles for a piece of the legacy, prestige, and resources, of the church and its founder. This article mainly examines the factors that lead to the conflicts that have divided the church into the seven groups that are at loggerheads with each other and threaten to destroy its legacy.
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Restrictions on roaming Until the past century or so, the movement of wild animals was relatively unrestricted, and their travels contributed substantially to ecological processes. As humans have increasingly altered natural habitats, natural animal movements have been restricted. Tucker et al. examined GPS locations for more than 50 species. In general, animal movements were shorter in areas with high human impact, likely owing to changed behaviors and physical limitations. Besides affecting the species themselves, such changes could have wider effects by limiting the movement of nutrients and altering ecological interactions. Science , this issue p. 466
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Harvest by means of hunting is a commonly used tool in large carnivore management. To evaluate the effects of harvest on populations, managers usually focus on numerical or immediate direct demographic effects of harvest mortality on a population's size and growth. However, we suggest that managers should also give consideration to indirect and potential evolutionary effects of hunting (e.g., the consequences of a change in the age, sex, and social structure), and their effects on population growth rate. We define “indirect effects” as hunting-induced changes in a population, including human-induced selection, that result in an additive change to the population growth rate “lambda” beyond that due to the initial offtake from direct mortality. We considered 4 major sources of possible indirect effects from hunting of bears: (1) changes to a population's age and sex structure, (2) changes to a population's social structure, (3) changes in individual behavior, and (4) human-induced selection. We identified empirically supported, as well as expected, indirect effects of hunting based primarily on >30 years of research on the Scandinavian brown bear (Ursus arctos) population. We stress that some indirect effects have been documented (e.g., habitat use and daily activity patterns of bears change when hunting seasons start, and changes in male social structure induce sexually selected infanticide and reduce population growth). Other effects may be more difficult to document and quantify in wild bear populations (e.g., how a younger age structure in males may lead to decreased offspring survival). We suggest that managers of bear and other large carnivore populations adopt a precautionary approach and assume that indirect effects do exist, have a potential impact on population structure, and, ultimately, may have an effect on population growth that differs from that predicted by harvest models based on direct effects alone.
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The African lion is the only big cat listed on CITES Appendix II, and the only one for which international commercial trade is legal under CITES. The trade in lion body parts, and especially the contentious trade in bones from South Africa to Asia, has raised concerns spanning continents and cultures. Debates were amplified at the 2016 CITES Conference of the Parties (CoP17) when a proposal to up-list lions to Appendix I was not supported and a compromise to keep them on Appendix II, with a bone trade quota for South Africa, was reached instead. CoP17 underscored a need for further information on the lion bone trade and the consequences for lions across the continent. Legal international trade in bones to Asia, allegedly to supply the substitute ‘tiger bone’ market, began in South Africa in February 2008 when the first CITES permits were issued. It was initially unclear the degree to which bones were sourced from captive-origin lions, and whether trade was a threat to wild lion populations. Our original assessment of the legal CITES-permitted lion bone trade from South Africa to East-Southeast Asia was for the period 2008–2011 (published 2015). In this paper, we consolidate new information that has become available for 2012–2016, including CITES reports from other African countries, and data on actual exports for three years to 2016 supplied by a freight forwarding company. Thus, we update the figures on the legal trade in lion bones from Africa to East-Southeast Asia in the period 2008–2016. We also contextualise the basis for global concerns by reviewing the history of the trade and its relation to tigers, poaching and wildlife trafficking. CITES permits issued to export bones escalated from ±314y⁻¹ skeletons from 2008–2011, to ±1312y⁻¹ skeletons from 2013–2015. South Africa was the only legal exporter of bones to Asia until 2013 when Namibia issued permits to export skeletons to Vietnam. While CITES permits to export ±5363 skeletons from Africa to Asia from 2008–2015 were issued (99.1% from South Africa; 0.7% from Namibia) (51% for Laos), actual exports were less than stated on the permits. However, information on actual exports from 2014–2016 indicated that >3400 skeletons were exported in that period. In total, >6000 skeletons weighing no less than 70 tonnes have been shipped to East-Southeast Asia since 2008. Since few wild lions are hunted and poached within South African protected areas, skeletons for the legal trade appear to be derived from captive bred lions. However, confirmation of a 116kg shipment from Uganda to Laos, and reports of lion poaching in neighbouring countries, indicate that urgent proactive monitoring and evaluation of the legal and illegal trade is necessary in African lion range states where vulnerable wild lion populations are likely to be adversely affected.