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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.
Ecology and Evolution. 2020;00:1–15.
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 (20052008),
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 and rever se pr imers ,
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) . Wh i l e th e pr o p o r tion 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 male–male (R2 = −.12; p = .025) dyad pairs in PMC
(Figure 4; Table S3). Autocorrelograms revealed fine-scale spa-
tio-genetic structure by pairwise proximity between individuals in
each dyad. Female kin-clustering was observed in both populations
(Figure 4a), where significantly positive autocorrelation occurred
over three female home-range radii in SSGR (0–6 km) and four in PMC
(0–8 km). This female kin-clustering effect was stronger over these
distances in the PMC. Significant clustering of females that were less
related than expected at random occurred for a range beyond this
distance in both populations (SSGR = 16–24 km; PMC = 14–24 km),
while all spatio-genetic structure showed no significant autocorrela-
tion (spatio-genetic independence) beyond 24 km in both reserves.
   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 9.13 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.
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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;00:1–15.
https ://
... Large carnivores are particularly sensitive to the negative effects of hunting due to their inherently low genetic diversity (Ausband & Waits, 2020;Milner et al., 2007), resulting from lower population sizes (Frankham, 1996). Although leopards are believed to be genetically diverse compared with other large carnivores (Spong et al., 2000;Uphyrkina et al., 2001), local effective population sizes (i.e. the number of individuals reproducing and thus contributing to the gene pool) may have been drastically reduced as a result of high local off-take in many populations (Balme et al., 2010;Naude et al., 2020;Pitman et al., 2015;Spong et al., 2000;Swanepoel et al., 2014). For instance, adult leopard mortality rates of over 50 per cent have been recorded outside protected areas in South Africa . ...
... For instance, adult leopard mortality rates of over 50 per cent have been recorded outside protected areas in South Africa . Intensive selective hunting also tends to remove large resident male leopards from the breeding population, which disrupts spatial population dynamics (Naude et al., 2020). ...
... We selected 20 nuclear microsatellite loci that are known to be polymorphic in South African leopards (Naude et al., 2020;Uphyrkina et al., 2001; see Table S3 ...
Full-text available
The red leopard (Panthera pardus) colour morph is a colour variant that occurs only in South Africa, where it is confined to the Central Bushveld bioregion. Red leopards have been spreading over the past 40 years, which raises the speculation that the prevalence of this phenotype is related to low dispersal of young individuals owing to high off‐take in the region. Intensive selective hunting tends to remove large resident males from the breeding population, which gives young males the chance to mate with resident females that are more likely to be their relatives, eventually increasing the frequency of rare genetic variants. To investigate the genetic mechanisms underlying the red coat colour morph in leopards, and whether its prevalence in South Africa relates to an increase in genetic relatedness in the population, we sequenced exons of six coat colour associated genes and 20 microsatellite loci in twenty Wildtype and four red leopards. The results were combined with demographic data available from our study sites. We found that red leopards own a haplotype in homozygosity identified by two SNPs and a 1 bp deletion that causes a frameshift in the tyrosinase related protein 1 (TYRP1), a gene known to be involved in the biosynthesis of melanin. Microsatellite analyses indicate clear signs of a population bottleneck and a relatedness of 0.11 among all pairwise relationships, eventually supporting our hypothesis that a rare colour morph in the wild has increased its local frequency due to low natal dispersal, while subject to high human‐induced mortality rate.
... Felids are elusive species that occur in low densities and move over long distances, which imposes an additional challenge regardless of the method employed. The result is that only in exceptionally well-monitored populations inhabiting restricted areas can all individuals be sampled (Naude et al. 2020, for example). Nonetheless, our database showed that dispersal and philopatry are examined via molecular tools through genetic associations between individuals, or via direct observation of individuals obtained by telemetry or camera trapping (Appendix S1). ...
... Some studies showed higher reproductive success in philopatric females, who tend to produce more cubs and are more likely to share their home ranges with at least some of those cubs (Pusey & Packer 1987, Goodrich et al. 2010. The effects of inclusive fitness (Fattebert et al. 2015, Krojerová-Prokešová et al. 2018, Naude et al. 2020 as intrinsic advantages of philopatry would also promote this behaviour. In addition, demographic aspects of the populations were shown to endorse female philopatry. ...
... In addition, demographic aspects of the populations were shown to endorse female philopatry. Low population density allows the establishment of the home ranges of daughters in vacant areas near the natal area (Newby et al. 2013, Fattebert et al. 2015, Krojerová-Prokešová et al. 2018, Naude et al. 2020. In contrast, high population density, as occurring in crowded areas in response to habitat fragmentation (Tscharntke et al. 2012, Arce-Penã et al. 2019, would promote the spatial rearrangement of the home range of the mother in order to guarantee the survival of both mother and daughter (Croteau et al. 2010). ...
Typically, males of polygamous mammals are responsible for population connectivity and gene flow via dispersal, whereas females, showing stronger philopatry, strengthen local population stability and growth. These expectations can be disrupted by human disturbances; however, this possibility has been poorly examined in wide-ranging mammals that are important targets for conservation. By reviewing philopatry and dispersal in felids, we aimed to evaluate: 1) whether the sex-related patterns of philopatry and dispersal predicted for polygamous mammals are prevalent in felids, 2) possible major causes underlying each of these behaviours, and 3) if, and to what extent, anthropogenic disturbances can alter patterns of philopatry and dispersal in this animal lineage. We synthesised the available literature (n = 55 papers) comprising 12 species. Puma concolor was the most-studied species, followed by other large species. Both philopatry and dispersal were heterogeneously defined, depending on the study aim and the method employed (telemetry, camera trapping or molecular tools). Most species followed the predicted philopatric and dispersal patterns, and most study areas (76%) were under some type of anthropogenic disturbance drivers. Philopatry was linked to females’ higher dependency on the quality and availability of resources, and to their social dynamics, higher reproductive success, inclusive fitness and demographic aspects of population. Dispersal was frequently linked to competition for mates and resources, and inbreeding avoidance. However, some plasticity was observed in both philopatry and dispersal, especially under the presence of anthropogenic drivers. For example, hunting can create open territories, increasing the number of philopatric females and opportunistic philopatric males. Habitat fragmentation can increase population isolation and male dispersal distance, and the presence of anthropogenic or natural barriers can result in unsuccessful dispersal attempts. We postulate that human activities affect long-term population persistence in the Felidae, via disruption of sex-related patterns of spatial dynamics.
... Dispersing subadult male leopards can move up to 353 km from their natal sites or 195 km when measured in a straight line from the natal origin (Fattebert et al., 2013), so such long distances (˜200-400 km) can be contemplated for translocating subadult animals, and also for genetic reasons, as has been recently discovered in human-influenced landscapes in South Africa where unde-sirable natal philopatry of males readily occurs (Naude et al., 2020). ...
... and they were found to be unrelated, which would be unusual, given female leopard philopatry Fattebert et al., 2015;Naude et al., 2020). ...
... Vacant leopard territories are re-colonized by male dispersers within 3 months of a predecessor's death (Balme et al., 2009), and in our example with the death of LM08 (Table 3), this took place after about 6 months when we could speculate of a territory take-over, which was possibly a male leopard that had been residing within its territory (or margins) for a while (cf. Naude et al., 2020). ...
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1. Leopards are often translocated away from where they are caught as non-lethal human-wildlife conflict mitigation. It is alleged that leopards fail to settle where they are translocated to, owing to territoriality. We address the need to publish more accounts of successful repatriation of leopards, but also include novel applications aimed at orphans and confiscated leopards. 2. We satellite collared 16 leopards which included a mixture of relocated and translo-cated leopards, of which the latter included conventional damage causing animals (DCAs, viz 'problem animals'), orphans and confiscations. We determined standard home-range metrics and assessed home-range stabilization as a means of determining site fidelity. Premature mortality and site infidelity, that is homing back to origins, were considered failures. We looked at range stabilization by examining successive monthly ranges against that of the preceding month, that is utilization distribution overlap indices (UDOIs). 3. Relocations turned out to be residents (˜3 km, n = 3), while they were immune to intervention, while translocations resulted in 50% success (n = 12), which were invariably confiscated adults of unknown origin, and simulations of natal dispersals of orphans (˜25 km, n = 3). DCAs never settled where released (˜90 km, n = 5). Resident leopards showed high monthly UDOIs, and for those translocated a minimum of 0.15 was benchmarked to suggest range stability, which also reflected large spatial ranging. 4. Success in home-range establishment was associated with landscapes which were unsaturated by other leopards, but anthropogenic threats still persisted, such that survival after a year was˜45%, but was not different to the normal background mortality of areas outside protected areas in the country. Operations are costly, particularly that to do with veterinary treatment, immobilization, collars and temporary keeping, but such costs can be carried by public interest groups. 5. All adults (>3 years) of known origin should be relocated (transported distance < home-range diameter), while subadults (1-3 years) can be considered for translocations (transported distance > home-range diameter), while heeding This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
... Leopards, like many solitary polygynous mammals, generally exhibit female philopatry and male-biased natal dispersal (Balme et al., 2017;Fattebert et al., 2015Fattebert et al., , 2016Greenwood, 1980;Naude et al., 2020). Populations could be regulated by density-dependent controls such as infanticide, social strife and territoriality that regulates population growth in carnivores (Cariappa et al., 2011;Kissui and Packer, 2004). ...
... Some were kinked near the base of the tail, whereas most were kinked towards the end of the tail (Fig. S2). To ascertain reasons for this, we recommend a study to assess more closely the integrity of this population in the face of potential genetic bottlenecks (Naude et al., 2020) and provision for appropriate conservation strategies (for example, see Wikramanayake et al., 2010;Wikramanayake et al., 2011). ...
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The decline and extirpation of large carnivore populations can lead to cascading effects in natural ecosystems. An understanding of large carnivore population densities, distribution and dynamics is therefore critical for developing effective conservation strategies across landscapes. This is particularly important in island environments where species face increased extinction risk due to genetic isolation coupled with local losses of finite habitat. The Sri Lankan leopard Panthera pardus kotiya is one of two remaining island-living leopards on Earth and the only apex predator in Sri Lanka. Despite its iconic status in Sri Lanka, robust research on the species has been limited to only a handful of scientific studies, limiting meaningful scientific recommendations for the species’ conservation and management. In this study, we conducted a single season camera trap survey in Sri Lanka’s largest protected area, Wilpattu National Park (1,317 km²), located in the country’s northwest. Our objective was to estimate key ecological state variables of interest (density, abundance, sex-specific movement and spatial distribution) of this leopard subspecies. Our results indicate that Wilpattu National Park supports a density of 18 individuals/100 km² (posterior SD=1.5; 95% HPD interval=16–21) with a mean abundance of 144 (posterior SD=15) individual leopards and a healthy sex ratio (f:m=2.03:1). The estimated activity range for male leopards >2 years old was 49.53 km² (Posterior SD=3.43; HPD interval=43.09–56.41) and for female leopards >2 years old was 22.04 km² (Posterior SD=1.82; HPD interval=18.34–25.65). This density falls at the higher end of published estimates for the species anywhere in its global range, based on similar methods. Given Sri Lanka’s limited size, this national park system should be considered as a critical stronghold that maintains a source population of leopards, contributing to the long-term population viability of leopards in the larger landscape.
... Most of these leopards were released in the previous year, suggesting that these translocations were critical to recolonisation, and it was encouraging that these reintroductions have resulted in the successful residency of some leopards (Power et al. 2021). This relatively sparse population could, however, be construed as functionally extinct when contrasted with ecologically comparable areas (Rogan et al. 2019), and it seems leopards respond by increasing individual home-range sizes (Marker and Dickman 2005), which may have consequences for various population related status surveys (Rogan et al. 2019;Naude et al. 2020b). ...
... Leopard populations occur in highly permeable landscapes in northern South Africa (Pitman et al. 2017) and given known conservation corridors in NWP (READ 2015), interventions may need to continue, so that this population can be connected to more stable populations, such as that in the highly isolated PNP (Mann et al. 2018), although mindful also of genetic considerations (Naude et al. 2020b). ...
Although highly adaptable, leopards incur substantial mortality in human-modified landscapes and generally subsist at lower densities than in protected areas. Leopard populations are difficult to enumerate across any landscapes, though there have been strides to improve upon this, particularly in South Africa. This study aimed to determine the population density of leopards in the Magaliesberg mountain range of the North West province in 2015 and provided a longitudinal comparison of these camera-trapping sites. It appraises the efficacy of interventions aimed at improving the status quo of zero leopards found during a prior survey in 2011. Such interventions included a moratorium on sport hunting of the species, and the reintroduction of four individuals, two of each sex, into this area. Camera trapping over 10 months detected seven unique individuals, including one juvenile and six adults, consisting of four males and three females, half of which were previously reintroduced or progeny thereof. A Bayesian capture-recapture abundance model indicated a population of 5–7 individuals occurring within 1 480 km² of available habitat, yielding a density estimate of 0.34–0.47 adult leopards per 100 km², which is a relatively low estimate, likely due to population suppression from anthropogenic pressures surrounding the site (i.e., snaring). This study demonstrates that large carnivore populations can recolonise their former range via targeted interventions within topographical refugia.
... For example, Jędrzejewski et al. (2017) found that retaliatory killing of jaguars Panthera onca in South America was the main driver of their local extirpation. Deficiency in wild prey base and illegal killing of large carnivores have been described as limiting factors for population growth of carnivores (Naude et al., 2020) and wild prey recovery plans are unable to sustain carnivore populations if intensity of illegal killing is high (Bleyhl et al., 2021). ...
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Many large mammalian carnivores are facing population declines due to illegal killing (e.g., shooting) and habitat modification (e.g., livestock farming). Illegal killing occurs cryptically and hence is difficult to detect. However, reducing illegal killing requires a solid understanding of its magnitude and underlying drivers, while accounting for the imperfect detection of illegal killing events. Despite the importance of illegal killing of large carnivores in comparison with other causes of mortality, its relationship with potential drivers such as livestock density and wild prey abundance is rarely described. Using ranger‐collected data (2007‐2019) of leopard killing events and data on covariates (livestock density, wild prey abundance, road length, protected area size, elevation) across Iran, we applied a single‐visit N‐mixture model to jointly model variation in detection probability and expected annualized number of leopard killing events. Over the study period, we estimated 428 leopard mortalities (95% CI 184–1014), which was 45% larger than the observed number. Expected intensity of leopard killing was positively related to protected area size, livestock density and wild prey abundance. Detection of leopard killing was higher in areas with more developed road networks. Synthesis and applications Ranger based monitoring data on poaching of carnivores are cost effective, but traditional analysis does not take into account imperfect detection. We show that innovative statistics (single‐visit N‐mixture modeling) can reliably quantify poaching events and address their drivers, at large geographical scales. We used the example of the Persian leopard across Iran, but our approach is also applicable to understand killing dynamics of other species. Results suggest that a high frequency of leopard killing is likely to occur in areas with > 100 livestock per km2 and > 450 individuals of wild prey per km2. This highlights the need for improved management of livestock grazing and effective measures around high‐risk protected areas to mitigate human‐leopard conflict and reduce killing of leopards. Synthesis and applications Ranger based monitoring data on poaching of carnivores are cost effective, but traditional analysis does not take into account imperfect detection. We show that innovative statistics (single‐visit N‐mixture modeling) can reliably quantify poaching events and address their drivers, at large geographical scales. We used the example of the Persian leopard across Iran, but our approach is also applicable to understand killing dynamics of other species. Results suggest that a high frequency of leopard killing is likely to occur in areas with > 100 livestock per km2 and > 450 individuals of wild prey per km2. This highlights the need for improved management of livestock grazing and effective measures around high‐risk protected areas to mitigate human‐leopard conflict and reduce killing of leopards.
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In an effort to restore parts of their historical geographical range, and in recognition of their ability to restore ecosystem functioning and of the financial benefits they can provide through ecotourism, large carnivores have been reintroduced in many protected areas from which they were previously extirpated. Similar to dispersing animals, translocated individuals often undertake long-distance exploratory movements before establishing home ranges. Post-release monitoring of reintroduced carnivores is common, but the mechanisms of population establishment are rarely examined, limiting our understanding of reintroduction success. We monitored survival and post-release movements of seven cheetahs Acinonyx jubatus reintroduced to Liwonde National Park, Malawi, to evaluate early population establishment. Exploratory phases post-release lasted 29–174 days. Duration of pre-release holding periods in the boma had no significant effect on daily distance moved. Males travelled significantly farther and established home ranges later than females. All cheetahs showed release site fidelity and all females birthed their first litter within 4 months of release. Within 2 years of reintroduction, the newly established population consisted of 14 cheetahs, with demographic attributes similar to those recorded in the source populations. Based on individual settlement, survival and reproduction rates, we deemed this reintroduction successful in re-establishing a breeding population of cheetahs in Liwonde. Our findings suggest the drivers of settlement and population establishment for reintroduced cheetahs are complex, highlighting the importance of assessing and reporting post-release monitoring data.
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Effective conservation requires understanding the processes that determine population outcomes. Too often, we assume that protected areas conserve wild populations despite evidence that they frequently fail to do so. Without large‐scale studies, however, we cannot determine what relationships are the product of localized conditions versus general patterns that inform conservation more broadly. Leopards' (Panthera pardus) basic ecology is well‐studied but little research has investigated anthropogenic effects on leopard density at broad scales. We investigated the drivers of leopard density among 27 diverse protected areas in northeastern South Africa to understand what conditions facilitate abundant populations. We formulated ten working hypotheses that considered the relative influence of bottom‐up biological factors and top‐down anthropogenic factors on leopard density. Using camera‐trap survey data, we fit a multi‐session spatial capture‐recapture model with inhomogenous density for each hypothesis and evaluated support using an information theoretic approach. The four supported hypotheses indicated that leopard density is primarily limited by human impacts, but that habitat suitability and management conditions also matter. The proportion of camera stations that recorded domestic animals, a proxy for the extent of human impacts and protected area effectiveness, was the only predictor variable present in all four supported models. Protected areas are the cornerstone of large felid conservation, but only when the human‐wildlife interface is well‐managed and protected areas shelter wildlife populations from anthropogenic impacts. To ensure the long‐term abundance of large carnivore populations, reserve managers should recognize the ineffectiveness of “paper parks” and promote contiguous networks of protected areas that offer leopards and other large mammal populations greater space and reduced human impacts.
Variation in home range size exists among and within wildlife populations. Home range size variation may be driven by both intrinsic and extrinsic factors, including sex, food and reproductive resources, density and competition. In this study, we investigated the sex‐specific impacts of prey and reproductive resources, conspecific density and competition on leopard Panthera pardus home range size at two spatio‐temporal scales in the Sabi Sand Game Reserve, South Africa. Male leopard home ranges were more than twice the size of those of females, in line with expectations for a solitary, polygamous species. Both male and female leopard space‐use were primarily driven by short‐term changes in intra‐sexual conspecific density. Females were influenced by both short and long‐term drivers, with long‐term prey availability (home range and core) and refugia (core) further impacting size. Males were almost exclusively influenced by short‐term drivers; home range size was further impacted by short‐term changes in female leopard and prey density, and age. Long‐term prey availability contributed to male leopard core size. The difference in impact of short‐ and long‐term drivers between the sexes likely relates to tenure expectations; males may be forced out of their territories at any time and should therefore optimize their space‐use based on present conditions. Female leopards, however, must secure a home range that maximizes their reproductive success in the short‐ and long‐term in order to raise cubs to independence. Our findings challenge expectations that space‐use is primarily resource‐driven and demonstrate the critical role of social factors in saturated populations of solitary species. Furthermore, we illustrate the importance of considering temporally variable factors across different timescales to fully understand their impact on mammalian spatial organization. We investigated the sex‐specific impacts of prey and reproductive resources, conspecific density and competition on leopard Panthera pardus home range size at two spatio‐temporal scales. Male home ranges were double the size of female home ranges, and both male and female leopard home range size were primarily driven by short‐term changes in intra‐sexual conspecific density. Females were influenced by short‐ and long‐term patterns, while males were influenced almost exclusively by short‐term changes; this is likely linked to the disproportionate investment in reproduction between the sexes. Our findings challenge expectations that space‐use is primarily resource‐driven and demonstrate the complex interplay of ecological and social factors in natural populations of solitary species
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Leopards (Panthera pardus) are in range-wide decline, and many populations are highly threatened. Prey depletion is a major cause of global carnivore declines, but the response of leopard survival and density to this threat is unclear: by reducing the density of dominant competitors (lions) prey depletion could create both costs and benefits for subordinate competitors. We used capture-recapture models fit to data from a 7-year camera trapping study in Kafue National Park, Zambia to obtain baseline estimates of leopard population density and sex-specific apparent survival rates. Kafue is affected by prey depletion, and densities of large herbivores preferred by lions have declined more than the densities of smaller herbivores preferred by leopards. Lion density is consequently low. Estimates of leopard density were comparable to ecosystems with more intensive protection and favorable prey densities. However, our study site was located in an area with good ecological conditions and high levels of protection relative to other portions of the ecosystem, so extrapolating our estimates across the park or into adjacent Game Management Areas would not be valid. Our results show that leopard density and survival within north-central Kafue remain good despite prey depletion, perhaps because (1) prey depletion has had weaker effects on preferred leopard prey, when compared to larger prey preferred by lions, and (2) the density of dominant competitors (lions), is consequently low. Our results show that the effects of prey depletion can be more complex than uniform decline of all large carnivore species, and warrant further investigation.
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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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Animal movement is fundamental for ecosystem functioning and species survival, yet the effects of the anthropogenic footprint on animal movements have not been estimated across species. Using a unique GPS-tracking database of 803 individuals across 57 species, we found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in areas with a low human footprint. We attribute this reduction to behavioral changes of individual animals and to the exclusion of species with long-range movements from areas with higher human impact. Global loss of vagility alters a key ecological trait of animals that affects not only population persistence but also ecosystem processes such as predator-prey interactions, nutrient cycling, and disease transmission.
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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.
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There is a growing recognition of the importance of indirect effects from hunting on wildlife populations, e.g., social and behavioral changes due to harvest, which occur after the initial offtake. Nonetheless, little is known about how the removal of members of a population influences the spatial configuration of the survivors. 2.We studied how surviving brown bears (Ursus arctos) used former home ranges that had belonged to casualties of the annual bear hunting season in southcentral Sweden (2007-2015). We used resource selection functions to explore the effects of the casualty's and survivor's sex, age, and their pairwise genetic relatedness, population density, and hunting intensity on survivors’ spatial responses to vacated home ranges. 3.We tested the competitive release hypothesis, whereby survivors that increase their use of a killed bear's home range are presumed to have been released from intraspecific competition. We found strong support for this hypothesis, as survivors of the same sex as the casualty consistently increased their use of its vacant home range. Patterns were less pronounced or absent when the survivor and casualty were of opposite sex. 4.Genetic relatedness between the survivor and the casualty emerged as the most important factor explaining increased use of vacated male home ranges by males, with a stronger response from survivors of lower relatedness. Relatedness was also important for females, but it did not influence use following removal; female survivors used home ranges of higher related female casualties more, both before and after death. Spatial responses by survivors were further influenced by bear age, population density, and hunting intensity. 5.We have showed that survivors exhibit a spatial response to vacated home ranges caused by hunting casualties, even in non-territorial species such as the brown bear. This spatial reorganization can have unintended consequences for population dynamics and interfere with management goals. Altogether, our results underscore the need to better understand the short- and long-term indirect effects of hunting on animal social structure and their resulting distribution in space. This article is protected by copyright. All rights reserved.
Human impact is near pervasive across the planet and studies of wildlife populations free of anthropogenic mortality are increasingly scarce. This is particularly true for large carnivores that often compete with and, in turn, are killed by humans. Accordingly, the densities at which carnivore populations occur naturally, and their role in shaping and/or being shaped by natural processes, are frequently unknown. We undertook a camera‐trap survey in the Sabi Sand Game Reserve (SSGR), South Africa, to examine the density, structure and spatio‐temporal patterns of a leopard Panthera pardus population largely unaffected by anthropogenic mortality. Estimated population density based on spatial capture–recapture models was 11.8 ± 2.6 leopards/100 km². This is likely close to the upper density limit attainable by leopards, and can be attributed to high levels of protection (particularly, an absence of detrimental edge effects) and optimal habitat (in terms of prey availability and cover for hunting) within the SSGR. Although our spatio‐temporal analyses indicated that leopard space use was modulated primarily by “bottom‐up” forces, the population appeared to be self‐regulating and at a threshold that is unlikely to change, irrespective of increases in prey abundance. Our study provides unique insight into a naturally‐functioning carnivore population at its ecological carrying capacity. Such insight can potentially be used to assess the health of other leopard populations, inform conservation targets, and anticipate the outcomes of population recovery attempts.
After more than fifteen years of existence, the R package ape has continuously grown its contents, and has been used by a growing community of users. The release of version 5.0 has marked a leap towards a modern software for evolutionary analyses. Efforts have been put to improve efficiency, flexibility, support for 'big data' (R's long vectors), ease of use, and quality check before a new release. These changes will hopefully make ape a useful software for the study of biodiversity and evolution in a context of increasing data quantity. Availability: ape is distributed through the Comprehensive R Archive Network: information may be found at
1.Despite the routine nature of estimating overlapping space use in ecological research, to date no formal inferential framework for home range overlap has been available to ecologists. Part of this issue is due to the inherent difficulty of comparing the estimated home ranges that underpin overlap across individuals, studies, sites, species, and times. Because overlap is calculated conditionally on a pair of home range estimates, biases in these estimates will propagate into biases in overlap estimates. Further compounding the issue of comparability in home range estimators is the historical lack of confidence intervals on overlap estimates. This means that it is not currently possible to determine if a set of overlap values are statistically different from one another. 2.As a solution, we develop the first rigorous inferential framework for home range overlap. Our framework is based on the AKDE family of home range estimators, which correct for biases due to autocorrelation, small effective sample size, and irregular sampling in time. Collectively, these advances allow AKDE estimates to validly be compared even when sampling strategies differ. We then couple the AKDE estimates with a novel bias‐corrected Bhattacharyya Coeffcient (BC) to quantify overlap. Finally, we propagate uncertainty in the AKDE estimates through to overlap, and thus are able to put confidence intervals on the BC point estimate. 3.Using simulated data, we demonstrate how our inferential framework provides accurate overlap estimates, and reasonable coverage of the true overlap, even at small sample sizes. When applied to empirical data, we found that building an interaction network for Mongolian gazelles (Procapra gutturosa) based on all possible ties, versus only those ties with statistical support, substantially inuenced the network's properties and any potential biological inferences derived from it. 4.Our inferential framework permits researchers to calculate overlap estimates that can validly be compared across studies, sites, species, and times, and test whether observed differences are statistically meaningful. This method is available via the R package ctmm. This article is protected by copyright. All rights reserved.