Canadian Journal of Zoology
Spatial genetic structure in the rock hyrax (Procavia capensis) across the Namaqualand and western Fynbos areas of South Africa – a mitochondrial and microsatellite perspective
Journal: Canadian Journal of Zoology
Manuscript ID cjz-2019-0154.R3
Manuscript Type: Article
Date Submitted by the 19-Mar-2020 Author:
Complete List of Authors: Visser, Jacobus; Stellenbosch University, Department of Botany and Zoology; Cape Peninsula University of Technology, Department of ConservationDraft and Marine Sciences Robinson, Terence; Stellenbosch University, Department of Botany and Zoology Jansen van Vuuren, Bettine; University of Johannesburg, Department of Zoology
Is your manuscript invited for consideration in a Special Not applicable (regular submission) Issue?:
Afrotheria, Cape Flats, Knersvlakte, landscape connectivity, landscape Keyword: genetics, Procavia capensis, rock hyrax
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Spatial genetic structure in the rock hyrax (Procavia capensis) across the
Namaqualand and western Fynbos areas of South Africa – a mitochondrial
and microsatellite perspective
J.H. Visser a*†, T.J. Robinson a and B. Jansen van Vuuren b
a Department of Botany and Zoology, University of Stellenbosch, Private Bag XI, Matieland
7602, South Africa
b Centre for Ecological Genomics and Wildlife Conservation, Department of Zoology,
University of Johannesburg, P.O. Box 524, Auckland Park 2000, South Africa * Current address: Department of Draft Conservation and Marine Sciences, Cape Peninsula University of Technology, P.O. Box 652, Cape Town 8000, South Africa
J.H. Visser: [email protected]
T.J. Robinson: [email protected]
B. Jansen van Vuuren: [email protected]
†Corresponding author: J.H. Visser
Address: Department of Conservation and Marine Sciences, Cape Peninsula University of
Technology, P.O. Box 652, Cape Town 8000, South Africa
e-mail: [email protected]
Tel: +2721 903 1802
Spatial genetic structure in the rock hyrax across the Namaqualand and western
Fynbos areas of South Africa – a mitochondrial and microsatellite perspective
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Jacobus H. Visser, Terence J. Robinson and Bettine Jansen van Vuuren
Abstract
The interplay between the biotic and abiotic environments is increasingly recognized as a major determinant of spatial genetic patterns. Among spatial genetic studies, saxicolous/rock- dwelling species remain underrepresented in spite of their strict dependence on landscape structure. Here we investigated patterns and processes operating at different spatial- (fine and regional scale) and time-scales (using mitochondrial and microsatellite markers) in the rock hyrax (Procavia capensis Pallas, 1766). Our focus was on the western seaboard of South Africa, and included two recognized biodiversityDraft hotspots (Cape Floristic Region and Succulent Karoo). At fine spatial scale, significant genetic structure was present between four rocky outcrops in an isolated population, likely driven by this species’ social system. At a broader spatial scale, ecological dependence on rocky habitat and population-level processes, in conjunction with landscape structure, appeared as the main drivers of genetic diversity and structure. Large areas devoid of suitable rocky habitat (e.g., the Knersvlakte, Sandveld and
Cape Flats, South Africa) represent barriers to gene-flow in the species, although genetic clusters closely follow climatic/geological/phytogeographic regions, possibly indicating ecological specialisation or adaptation as contributing factors enforcing isolation. Taken together, our study highlights the need to consider both intrinsic and extrinsic factors when investigating spatial genetic structures within species.
Key words: Afrotheria; Cape Flats; Knersvlakte; landscape connectivity; landscape genetics; phylogeography; Procavia capensis; rock hyrax
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Draft
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Introduction
Fundamental drivers of biodiversity include the patterns of species distributions in conjunction with their genetic structures (Silvertown and Antonovics 2001). These aspects are both biotically (e..g, dispersal ability, life-history, social/mating system, ecological specificity, distribution) and abiotically (landscape structure, historical climatic- and geological changes) influenced (Avise et al. 1987; Hewitt 2001; Avise 2009; Papadopoulou and Knowles 2016). Among scientific disciplines, landscape genetics (sensu lato) offers a powerful framework to evaluate the intrinsic and extrinsic drivers of spatiotemporal genetic variation, drawing on landscape ecology, population genetics and spatial statistics to understand how landscape characteristicsDraft structure genetic variation across populations and individuals (Manel et al. 2003; Holderegger and Wagner 2006; Storfer et al. 2007). Most importantly, this approach analyzes spatial genetic data without a priori population assignment and therefore resolves patterns of micro-evolutionary processes at a finer scale compared to phylogeography or biogeography (Manel et al. 2003). Landscape genetic studies may therefore be successfully employed to evaluate species-specific ecological hypotheses, quantify the impact of landscape variables on genetic variation, identify (often cryptic) genetic breaks/barriers, secondary contact, source-sink dynamics as well as dispersal corridors. In sum, it is a powerful adjunct in conservation initiatives that seek to determine/assign conservation and management units in taxa (Manel et al. 2003; Storfer et al.
2007; Segelbacher et al. 2010; Storfer et al. 2010; Keller et al. 2015).
Genetic studies are increasingly recognizing the biology/life-history and ecology of species as a major determinant of spatial genetic patterns (e.g., Papadopoulou and Knowles 2016;
Zamudio et al. 2016). An ecological group which remains interesting in a landscape genetic
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context are taxa adapted to a saxicolous (rock-dwelling) existence. In South Africa,
saxicolous species have received attention in phylogeographic investigations, especially
along the western regions of the country. Among these studies, two geographic features
namely the Knersvlakte (Matthee and Robinson 1996; Lamb and Bauer 2000; Matthee and
Flemming 2002; Smit et al. 2007; Daniels et al. 2010; Portik et al. 2011; see Fig. 1b) and
Cape Flats (Daniels et al. 2001; Wishart and Hughes 2001, 2003; Gouws et al. 2004, 2010;
Swart et al. 2009; McDonald and Daniels 2012; see Fig. 1b), have been consistently
identified as phylogeographic disruptors (barriers to gene-flow) in saxicolous vertebrate and
invertebrate taxa respectively. These studies have, however, been based on coarse sampling
methods and often suffered from small sample sizes and restricted number and variety of
markers (especially hypervariable markers such as microsatellites, but see Portik et al. 2011)
thus precluding landscape genetic inference.Draft
Previous work suggest that landscape matrix and connectivity strongly influence genetic
patterns in species restricted to saxicolous habitat given that rocky outcrops is often spatially
heterogeneous (see e.g. Smit et al. 2010). The rock hyrax, Procavia capensis (Pallas 1766),
which is tightly bound to this particular habitat type (especially granitic/metamorphic rock)
and vegetation (Sale 1966; Hoeck 1975, 1989; Fourie 1983; Smithers 1983), would be
particularly prone to its influence. The intervening areas, considered hostile due to predation
and few refuge sites (Turner and Watson 1965; Fairall et al. 1986; Fairall and Hanekom
1987; Kotler et al. 1999; Druce et al. 2006), place a cost on dispersal which should, in turn,
impact on rock hyrax genetic structure.
Ecological preference aside, the rock hyrax exhibits several life-history attributes which
should further impact genetic structure within and among populations. Rock hyrax population
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sizes fluctuate, sometimes leading to local extinctions (van der Horst 1941; Lensing 1979;
Fairall et al. 1986; Fairall and Hanekom 1987; Hoeck 1989; Barry and Mundy 1998;
Chiweshe 2007). It is conceivable that population declines result in bottlenecks and a loss of genetic diversity (Gerlach and Hoeck 2001). Conversely, in instances of population expansion, when marginal habitats are colonized (van der Horst 1941; Lensing 1979; Barocas et al. 2011) and there is exchange among previously isolated demes, increased genetic diversity may result. Moreover, the rock hyrax exhibits a polygynous social system, with social units comprised of a dominant territorial male which monopolizes several adult breeding females, and subadult and juvenile animals of both sexes (Fourie 1983; Gerlach and
Hoeck 2001). Several peripheral males may be associated with colonies, but they are aggressively excluded by the dominant male (Fourie 1983). Peripheral males replace dominant males when the latter becomesDraft too old or weak to defend territories. In this social system, males disperse from their natal colonies either voluntarily (early) or through antagonistic interactions from the dominant male (later); females remain largely phylopatric
(Coe 1962; Fourie and Perrin 1987; Hoeck 1982, 1989; Hoeck et al. 1982; Fourie 1983). Like other polygynous mammals (reviewed in Storz 1999), the rock hyrax mating system should partition populations into breeding groups that are maintained both by the phylopatry of females and the aggressive exclusion of immigrant males (Greenwood 1980; Dobson 1982;
Handley and Perrin 2007).
Previous investigations by Prinsloo and Robinson (1992) and Maswanganye et al. (2017) uncovered significant genetic structure at a macro-scale across the northern and central South
African range of the rock hyrax. Both studies retrieved two major clades (a south-western and north-eastern clade) which either track different historical dispersal routes (Prinsloo and
Robinson 1992), or follow isolation in separate mountain refugia during historical climatic
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changes (Maswanganye et al. 2017). These studies included similar and coarse sampling
methods combined with limited sample sizes. Consequently, clarifying the biotic and abiotic
factors responsible for genetic structure and diversity among hyrax populations remains
unresolved, begging resolution by a thorough landscape genetic investigation.
Here, we investigate the influences of ecological specialisation, social system characteristics
as well as landscape connectivity as potential drivers of genetic patterns in the saxicolous
rock hyrax. Considering that genetic and ecological patterns and processes may vary with
spatial and temporal scale (Storfer et al. 2007; Cushman and Landguth 2010; Segelbacher et
al. 2010; Manel and Holderegger 2013), we base our results on large sample sizes, intensive
geographic sampling on two spatial scales and examine temporal differences in genetic
patterns by employing markers with markedlyDraft different mutation rates (mitochondrial DNA
and microsatellites, see Ashley and Dow 1994; Schlötterer and Pemberton 1994). At a fine
intrapopulation spatial scale, we investigate genetic structure between four different rock
outcrops comprising a single isolated population of rock hyrax. The genetic structure here
should be driven by intrinsic factors such as population dynamics and social system
characteristics rather than landscape composition. At a broader spatial scale, we include rock
hyrax sampled across the understudied western Fynbos region (which includes the Olifants
River, West Coast, Cape Peninsula, and Overberg regions) and Namaqualand region (see Fig.
1). In doing so, we aimed to elucidate the impacts of landscape connectivity (extrinsic
factors) in conjunction with population dynamics and ecological preference (intrinsic factors)
in driving spatial genetic variation. The sampled regions are comprised of markedly different
landscape compositions, with the Namaqualand region harbouring abundant granitic
outcrops, whereas the western Fynbos (Cape Floristic) region contains fewer and more
isolated rocky outcrops suitable for hyrax utilization. In addition, our sampling protocol was
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designed to shed light on the two features previously identified as important geographic obstacles to gene-flow and hence genetic variation in saxicolous species: the Knersvlakte and
Cape Flats.
Materials and Methods
Sample collection
Analyses of regional genetic structure was based on 391 specimens from 16 localities (see
Table 1 for sample sizes; localities hereafter referred to as populations) across the Namaqualand/western Fynbos regionsDraft (Fig. 1). Assessment of genetic patterns at a fine spatial scale included specimens from four separate rocky outcrops in the isolated
Vredenburg population (locality 11 on Fig. 1, position of the rocky outcrops on Fig. 2a).
Animals were sampled using cat containment traps (Pet Creations, Unit Number
SKU_10262) baited with banana. Ear-clippings were taken (ethics clearance from the
Stellenbosch University Ethics Committee: 11NP_JAN01) and stored at room temperature in a saturated salt solution supplemented with 20% dimethyl sulfoxide (DMSO). Animals were released at the site of capture.
Experimental procedures
Total genomic DNA was extracted from ear clippings using a commercial DNA extraction kit
(DNeasy Blood and Tissue kit; Qiagen) following the manufacturer's protocols. The entire mitochondrial cytochrome b gene was amplified and sequenced using universal primers
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(L14724 and H15915, Kocher et al. 1989; Irwin et al. 1991; see Jansen van Vuuren et al.
2017 for additional details).
Amplification of the cytochrome b region was performed for 10 adult animals per sampling
locality. Because a proportion of the specimens consisted of juvenile individuals, it was
expected that juveniles would bias results through over-representation of single matrilines
(given the uniparental inheritance of mitochondrial DNA). Amplification of the cytochrome b
region was therefore performed for five adult males and five adult females selected randomly
from each sampling locality, as it was expected that these individuals would, at least to some
extent, be unrelated and therefore better reflect the genetic diversity of the population. PCR
amplification followed standard protocols (see Smit et al. 2008 for details). Successful
amplifications were visualized on a 1%Draft agarose gel with subsequent sequencing reactions
using Big Dye chemistry. Electropherograms of the raw data were aligned and checked
manually (Geneious Pro™ 7.0 software; Biomatters Ltd, New Zealand).
Of the 15 microsatellite loci genotyped in this study (sourced from Gerlach et al. 2000; Koren
and Geffen 2011), only four loci were polymorphic in the various P. capensis populations
(Hy-D49, Hy-T12, Hy-T17, Gerlach et al. 2000; HCA18, Koren and Geffen 2011); all other
were monomorphic. Following primer optimization, all four polymorphic loci were amplified
at 48ºC; subsequent amplifications were performed in a multiplex at this annealing
temperature - loci showed non-overlapping ranges in allele size, with loci labelled using
separate fluorescent dies. A Multiplex PCR Kit (Qiagen) was used for the amplification. For
genotyping, 1 µl of diluted (1/80) PCR product was combined with 15 µl of deionized
formamide and 0.2 µl of the GS500LIZ size standard (Applied Biosystems). Genotyping and
allele scoring was performed on all available specimens from each sampling locality
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following the protocols outlined in Karsten et al. (2011). Genotyping error rate was estimated
through the repeat genotyping of a subsample of 5% of specimens, followed by direct-count
error estimation outlined in Hess et al. (2012).
Data analyses
Summary statistics and population analyses
The presence of null alleles introduces a potential bias to analyses. Genepop version 4.0.10
(Raymond and Rousset 1995; Rousset 2008) was used to assess whether null alleles were
present in the microsatellites data; we followed Okello et al. (2005) in viewing a null allele
frequency of >0.2 as indicative of theirDraft presence. Linkage disequilibrium was investigated
using Genepop version 4.0.1 running Markov chains for 10 000 iterations. Conformation of
populations to Hardy-Weinberg equilibrium (HWE) was assessed in Genalex version 6.4
(Peakall and Smouse 2006).
At the fine scale, relatedness within and among rocky outcrops was assessed through the
number of full-sib and half-sib dyads (a pair of individuals) identified using a full Maximum
Likelihood (ML) search in Colony version 2.0.6.4 (Jones and Wang 2010). The analysis was
performed without specifying sex (including all individuals as “offspring”) with the program
set to allow for inbreeding. In addition, the inbreeding coefficient FIS for each outcrop was calculated in FSTAT version 2.9.3.2 (Goudet 2001).
Genetic diversity estimates for the population based on the mitochondrial DNA data included number of haplotypes, number of polymorphic sites and haplotype diversity (calculated in
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Arlequin version 3.5; Excoffier and Lischer 2010). Genetic diversity was calculated for the
microsatellite dataset; these included allelic diversity indices (mean number of alleles and He;
Genalex version 6.4; Peakall and Smouse 2006), as well as the inbreeding coefficient FIS
(calculated in FSTAT version 2.9.3.2; Goudet 2001). Effective population sizes (Ne) were
estimated for both the mitochondrial and microsatellite data based on Θ values (a statistic
related to Ne calculated in Migrate version 3.6.3; Beerli 2009) and estimated Ne values
(Colony version 2.0.6.4; Jones and Wang 2010). Values of Θ were derived from FST
calculation, based on runs of 1 long chain (10 000 000 iterations) and 2 short chains (1000
000 iterations). Adequate convergence was assessed based on the acceptance ratios listed at
the end of the output file (Beerli 2009). Draft To determine whether genetic variation was significantly structured, pair-wise ΦST
(mitochondrial data) and FST (microsatellites) values were calculated between populations at
a regional scale, as well as between the populated rocky outcrops for our fine scale data.
Significance was assessed through 9 999 permutations of the mitochondrial data in Arlequin
version 3.5 (Excoffier and Lischer 2010) and Spagedi version 1.3 (Hardy and Vekemans
2009) for the nuclear data.
To investigate the effect of the landscape on population genetic variables (genetic diversity
and genetic structure), these were partitioned into three areas of geological and
phytogeographical similarity (see Table S7): the Namaqualand region (Springbok, Garies,
Brand-se-Baai, Nuwerus and Kliprand; localities 1 - 5 on Fig. 1a), an area west of the
Knersvlakte (localities Nieuwoudt-ville and Loeriesfontein; localities 6 - 7 on Fig. 1a), and
the south-western Cape Floristic Region (localities Klawer, Donkiesbaai, Elands Bay, Ceres,
Vredenburg, Paardeberg, Table Mountain, Boulders and Bettysbaai; localities 8 - 16 on Fig.
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1a). Mixing of divergent haplotypes was evident at some localities (see Results; these results largely stem from single individuals characterized by very divergent DNA haplotypes), and a second set of similar comparisons was performed where these individuals with highly divergent haplotypes were excluded. This was done to remove bias in genetic diversity resulting from secondary contact. This approach allows for the comparison of long-standing population genetic patterns within respective demes sharing common evolutionary histories.
In addition, three population compositions showed extensive mixing of haplotypes/genotypes
(Klawer, Donkiesbaai and Elands Bay). Consequently, these localities were removed from the second set of comparisons to obtain a clearer picture on the influence of landscape structure on patterns of genetic diversity/structure. All statistical comparisons were performed in the IBM SPSS Statistics package using a Chi-square test. Draft
Clustering analyses
To investigate the spatial location of genetic clusters across the sampled distribution at a regional and fine scale, clustering analyses were performed separately on the mitochondrial sequence data and nuclear microsatellite data. Clustering analyses of the mitochondrial data
(regional scale) were performed in BAPS version 6.0 (Corander and Tang 2007; Corander et al. 2008a,b) under both a non-spatial approach (“Clustering of individuals”) as well as a spatial approach (“Spatial clustering of individuals”) to visualize the location of clusters across the landscape. For the microsatellite data, clustering analyses were performed in TESS version 3.1 (Durand et al. 2009) using both “admixture” and “no admixture” models. Results were based on 40 000 sweeps of the data with a burnin of 10 000 sweeps. For each value of the number of clusters “K” (K = 1 – 16), 10 runs were performed with subsequent averaging over runs using CLUMPP version 1.1.2 (Jakobsson and Rosenberg 2007) and selection of the
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lowest DIC (Deviance Information Criterion). A curve of the DIC values was constructed and
Kmax selected where this curve exhibits a plateau (see Durand et al. 2009).
Distinct genetic clusters often result from barriers to gene-flow and reflect limited genetic
exchange across the landscape. To further investigate the existence of these barriers, an
interpolation-based graphic approach was used in the programme Alleles In Space (AIS)
version 1.0 (Miller 2005). AIS uses Monmonier’s algorithm to detect barriers by searching
for the greatest genetic distance between any two locations in a triangle. The default settings
of “midpoint derived from Delaunay triangulation” and “residual genetic distances” were
used; the “distance weight value” was set to 1.5 when the visual spatial approach was
adopted. Draft
Genealogical and molecular dating analyses
Genealogical analyses were performed (based on the mitochondrial dataset) to detect
divergent clades and to date divergence events. An additional four P. capensis sequences
were available on public databases (see Table S1 for accession numbers); these were
downloaded and aligned to the data generated in the present study. Outgroup taxa for the
genealogical analyses included members of all the Afrotherian families (see Table S1 for
accession numbers).
Prior to phylogenetic analyses, the best-fit substitution model was selected through Modeltest
version 2.1 (Guindon and Gascuel 2003; Darriba et al. 2012) based on the Akaike
Information Criterion (AIC, Akaike 1973). Phylogenetic reconstructions followed Bayesian
Inference (BI) methods and ML with nodal support determined through posterior
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probabilities and bootstrapping respectively. Bayesian trees were constructed in MrBayes version 3.2.6 (Ronquist et al. 2011) through running 5 x 106 generations with sampling every
500 generations. Adequate convergence of the MCMC chain was assessed by viewing the
Effective Sample Size (ESS) of parameters in Tracer version 1.5 (Rambaut and Drummond
2003). After discarding the first 25% of the trees as burnin, a consensus tree with posterior probabilities was constructed and visualized in Figtree version 1.4.2 (Available at: http://tree.bio.ed.ac.uk/software/figtree/).
A ML analysis was performed in RAxML version 7.0.4 (Stamakis 2014), with nodal support determined through 10 000 bootstrap replicates. A majority rule consensus tree was constructed and visualized using Dendroscope version 3.5.8 (Huson and Scornavacca 2012). Draft
To obtain estimates of divergence times for the major clades, a relaxed molecular clock approach was adopted in BEAST version 1.6.1 (Drummond et al. 2007). Five fossil calibration points were included: the root of the Paenangulata (63.4 ± 7.8 Mya, Gheerbrant
2009), the divergence between Amblysomus and Chrysochloris (17.8 ± 16.1 Mya, Avery
2001; Seiffert et al. 2007), the divergence between Dugong and Trichechus (36.0 ± 4.6 Mya,
Domning 1994, 2001; Voß 2008), the divergence between Loxodonta and Elephas (14.9 ±
8.1 Mya, Vignaud et al. 2002; Tassy 2003; Shoshani and Tassy 2005; Lebatard et al. 2008) and the divergence between Heterohyrax and Procavia (14.6 ± 8.5 Mya, Ambrose et al.
2007; Pickford 2007). Runs continued for 30 x 106 generations with sampling every 1 000 generations (burnin = 7 500). Results were visualized in Tracer version 1.5 (Rambaut and
Drummond 2003) to assess adequate convergence of the MCMC chain and a consensus tree constructed using Figtree version 1.4.2 (Available at: http://tree.bio.ed.ac.uk/software/figtree/).
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Due to the lower sensitivity of phylogenetic trees for detecting intraspecific variation and
relationships (Posada and Crandall 2001) a haplotype network was constructed using TCS
1.21 (Clement et al. 2000) as an alternative approach. To determine the amount of genetic
divergence among hyrax groups identified in the phylogenetic analyses, as well as hyrax
populations sampled in geographically distant localities, uncorrected sequence divergences
were calculated in DnaSP version 5.10.01 (Librado and Rozas 2009).
Results
Summary statistics and population analyses Draft
Mitochondrial sequence data were generated for 160 specimens, characterized by 42
haplotypes (KM244950 – KM245022; Table S1); the microsatellite dataset included 391
individuals. Less than 5% of the microsatellite dataset comprised missing data (Table S3)
with a genotyping error of <0.01 incorrect alleles per genotype. No linkage was detected
between loci, and loci did not bear signatures of null alleles (Table S3). Several loci were not
in HWE over the landscape (Table S4), but these were not consistent within populations, or
across markers. Significant pairwise differentiation was evident between all localities across
the sampled distribution based on both the mitochondrial and microsatellite datasets
(mitochondrial: ɸST = 0.12-1.00; microsatellite FST = 0.02-0.42, Table 2).
At a fine spatial scale, the ten specimens from the Vredenburg locality all had the same
mitochondrial DNA haplotype (Table 1), and therefore all analyses pertaining to the fine-
scale distribution were based on microsatellite data alone. Microsatellite data were generated
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for 75 specimens and indicated significant genetic structure among the four rocky outcrops
(Table S2). Three genetic clusters were retrieved at this spatial scale (Fig. 2b) pertaining to outcrops A, B/C and D. Although none off the clusters were totally genetically discrete (i.e.,
100% of individuals assign to the single cluster), outcrops A and B respectively contained only single individuals exhibiting genetic elements which assign to adjacent outcrops.
Conversely, outcrop D displayed a large number of individuals which assign to outcrops B/C, with outcrop C exhibiting extensive admixture, and elements from all three genetic clusters.
This clustering pattern was also supported by the relatedness analyses, where relatedness within rock outcrops were higher than between rock outcrops (Fig. 2c). Finally, inbreeding was evident within all four outcrops, albeit only to a significant degree in three outcrops
(outcrop A: F = 0.274; p = 0.02, outcrop B: F = 0.035; p = 0.12, outcrop C: F = 0.310; p IS DraftIS IS = 0.01, outcrop D: FIS = 0.313; p = 0.01).
With the data partitioned into the three geological/phytogeographic areas at a regional spatial scale, all comparisons had non-significant results with the exception of ΦST/FST values
(mitochondrial DNA and microsatellites respectively; ΦST: p = 0.01; FST: p = 0.000) and the number of alleles (p = 0.021, Table S5). Where significant differences were detected, values from Namaqualand were respectively significantly lower (ΦST Namaqualand: 0.38 ± 0.2; ΦST western Fynbos: 0.73 ± 0.22; FST Namaqualand: 0.04 ± 0.01; FST western Fynbos: 0.19 ±
0.10) and higher (number of alleles Namaqualand: 7.150 ± 0.576; (number of alleles western
Fynbos: 4.778 ± 1.670) compared to the western Fynbos region (Table S5).
In the second set of comparative analyses (with divergent haplotypes and extensively admixed localities removed), significant differences were evident for most comparisons, with the exception of He and Θ (mitochondrial DNA), and He and FIS (microsatellite, Table S6).
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Where significant differences exist, Namaqualand showed significantly higher diversity,
larger effective population sizes, and less structured populations when compared to the
western Fynbos region (Table S6).
Clustering analyses
Genetic diversity was partitioned into five geographically distinct genetic clusters based on
the mitochondrial and microsatellite datasets (Fig. 3; refer to Fig. 1a for a rendering of the
geographic areas where these clusters are evident). These clusters broadly pertain to the
Namaqualand, Olifants River, West Coast, Cape Peninsula and Overberg (Fig. S1) regions;
each of these is separated by one or more barrier to gene-flow (Fig. 3). Populations
comprising each cluster were mostly consistent,Draft irrespective of the marker type
(mitochondrial or microsatellite) although some slight variation in cluster composition was
evident in the Namaqualand and West Coast clusters.
Within clusters, common haplotypes and genotypes are generally fixed, with hybrid
individuals (individuals who share a genotype composition from two or more clusters) as
relatively rare (genotypes assigning as pure to clusters). Individuals which assign as pure
(>90% of their genotype assignment) to a cluster other than where they were sampled are also
relatively rare, and follow an equal sex ratio (also evident in the mitochondrial DNA).
Genealogical and molecular dating analyses
The mitochondrial DNA revealed two major clades corresponding to the Namaqualand (well
supported) and western Fynbos (poorly supported) areas (Fig. 4a). These previously
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unrecognized clades (separated by 1.9% uncorrected sequence divergence) were similarly retrieved in the haplotype network, and could not be connected at the 95% confidence level
(Fig. 4b). The geographic separation between these clades appears centred on the Knersvlakte region, and temporally spans a period during the late Miocene (8.3 – 5.8 million years ago).
Within each major clade, shallow genetic structure was evident with mostly single mutational steps separating haplotypes. The major geographic separation of these clades (defined by the presence of the Knersvlakte was also confirmed by AIS analysis; Fig. 3a) was calculated to have occurred during the late Miocene (Fig. S2).
Discussion
Fine scale genetic structure Draft
Rock hyrax colonies in the isolated Vredenburg population (Fig. 1), used for the analysis of fine scale genetic structure, are 210 m to 850 m apart, falling well within the proposed dispersal distance of rock hyrax (200 m to 500 m, Fourie 1983; also see Hoeck 1989; Gerlach and Hoeck 2001; Barocas et al. 2011). In addition, no predators (either birds or mammals) were observed in the area over several years (J.H. Visser, personal observation), and therefore, dispersal should incur minimal costs (see e.g., Turner and Watson 1965; Fairall et al. 1986; Kotler et al. 1999; Druce et al. 2006). Although this landscape matrix should intuitively result in panmixia, significant genetic structure (FST = 0.01 - 0.07, p<0.01; Table
S2) was found between rock hyrax colonies on this fine spatial scale. Furthermore, this network of rock outcrops harbour three genetic clusters (outcrop A, outcrops B/C and outcrop
D; Fig. 2b) which largely consist of related individuals (Fig. 2c), although a small proportion
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of unrelated individuals is also evident. Although indicative of these fine scale patterns, our
results should be considered with caution contingent upon a larger microsatellites dataset.
Although the landscape matrix here should not be a major determinant of the observed
genetic structure, given the dispersal distance, it appears that social system characteristics
(colony structure) drive genetic patterns at this fine spatial scale. Following such a social
system, genetic structure therefore appears to result from the fixation of different alleles
among the outcrops, thereby following the Wahlund effect. There is little agreement in the
literature surrounding rock hyrax colony structure. According to Fourie (1983), rock hyrax
colonies are matrilocal comprising multi-female kingroup, with recruitment into the breeding
group from the resident sub-adult females. Conversely, genetic evidence from Gerlach and
Hoeck (2001) indicate that adult animalsDraft are often non-relatives, but rather that a related kin-
group consists of family units of parents and their offspring. Following Fourie (1983), gene-
flow between colonies is either facilitated by dispersing adult females, natal dispersal by
juveniles before the mating season, or replacement of the dominant male by a peripheral
male. The role of the peripheral males in mating is also contentious, with Fourie (1983)
suggesting that peripheral males mate with subadult females and the dominant male mates
only with the adult females, whereas Koren and Geffen (2006) propose that females mate
with multiple partners (both dominant and peripheral males). Dominant males are replaced
annually, but longer tenure results in the subadults mating with the peripheral males,
preventing inbreeding (Fourie 1983). Consequently, interpreting results in the present study is
difficult, as genetic patterns produced by these colony structures are not mutually exclusive.
Even so, it appears that both adult and juvenile animals of both sexes constituting these
discrete clusters may be related. This adds some support to suggestions by Fourie (1983) that
colonies are matrilocal and that recruitment proceeds from the subadult group. Indeed, this
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subadult group may be part of a kin-group consisting of parents and offspring (Gerlach and
Hoeck 2001). Sampling in the present study was performed during the breeding season
(before natal dispersal), when juvenile animals were abundant. This would have increased kin-group detection through a propensity of full-sibling dyads. In addition, the number of markers used here may have precluded the robust recovery of some genetic patterns (e.g., in the relatedness analyses) and the results here should be considered as preliminary, contingent upon the addition of further genetic information from Next Generation Sequencing (NGS) technologies.
Instances of gene-flow between clusters appear limited (with the exception of outcrop D), and almost exclusively (with the exception of one male) represent adult female animals derived from other colonies (results not shown).Draft As such, this gives support to suggestions of Fourie
(1983) that inter-colony gene-flow is facilitated by adult females. Such sex-biased dispersal likely results from rock hyrax social structure in conjunction with a high population density.
At such high population densities the random immigration of animals is deterred (Barocas et al. 2011) and dispersing males would confront resistance through aggressive exclusion by territorial males and therefore remain in their natal group area (Hoeck 1982; Fourie 1983).
Conversely, the lack of social hierarchy among females allows for less resistance when joining other colonies (Fourie 1983). Such female biased dispersal is not frequently encountered although it has been detected in pikas (Ochotona princeps Richardson, 1828;
Smith 1974), African wild dogs (Lycaon pictus Temminck, 1820; Frame and Frame 1976), chimpanzees (Pan troglodytes Blumenbach, 1775; Sugiyama 1973), mountain gorillas
(Gorilla gorilla Savage, 1847; Harcourt et al. 1976), the hamadryas baboon (Papio hamadrys
Linnaeus, 1758; Hammond et al. 2006) and in the white-lined bat (Saccoteryx bilineata
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Temminck, 1838; Bradbury and Vehrencamp 1976). In these social systems males acquire
and defend resources (reviewed in Handley and Perrin 2007).
Like other polygynous mammals (reviewed in Storz 1999), the rock hyrax social system
therefore partition populations into breeding groups that are maintained both by the
phylopatry of females and the aggressive exclusion of immigrant males (Greenwood 1980;
Dobson 1982; Handley and Perrin 2007). Similar fine scale patterns have been detected in
other polygynous mating systems e.g., the Soay sheep (Ovis aries Linnaeus, 1758; > 50m;
Coltmann et al. 2003), and the common vole (Microtus arvalis Pallas, 1778; 330m - 2560m;
Schweizer et al. 2007). Genetic structure, as evidenced by the FST in these studies (FST = 0.00
- 0.01, Coltmann et al. 2003; F = 0.01 - 0.05, Schweizer et al. 2007) were, however, ST Draft comparatively lower than reported here for the rock hyrax (FST = 0.01 - 0.07).
Genetic diversity and landscape connectivity
After accounting for the effect of secondary contact of divergent clades, genetic diversity in
both the mitochondrial DNA and microsatellite data appears comparatively higher in animals
from the Namaqualand region (Table S6), especially so for the mitochondrial DNA. Patterns
of genetic diversity are mirrored by estimates of effective population size, with Ne being
significantly higher for rock hyrax in Namaqualand. Furthermore, although genetic variation
was significantly structured across the entire sampled distribution (Table 2), genetic structure
was significantly higher among populations in the western Fynbos region compared to those
in Namaqualand.
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These patterns indicate the possible effect of ecological specificity and landscape
connectivity in the degree of population isolation. Rock hyrax show a strong ecological
dependence on suitable rock crevices, fissures and holes with adequate vegetation cover, and
therefore a strong association with metamorphic rocks in granite and gneiss outcrops (Sale
1966; Hoeck 1975, 1989; Fourie 1983). Across the distribution sampled here, rock hyraxes
almost exclusively occur in metamorphic rocks, only in single instances inhabiting other
geological types (e.g., Klawer, Elands Bay and Ceres). The Namaqualand region harbours an
abundance of this habitat type, whereas metamorphic rock outcrops in the western Fynbos
Region are sparsely distributed and are separated by large geographic distances.
Based on this habitat type, rock hyrax populations in the Namaqualand region are comparatively aggregated (a geographicallyDraft grouped distribution <1km apart) in contrast to those occurring in the western Fynbos Region (>50km apart). As such, the exchange of individuals should occur more frequently in the Namaqualand region, thereby leading to larger effective population sizes, higher genetic diversity and decreased differentiation.
Conversely, the fragmented habitat patches in the western Fynbos Region enforces the isolation of populations. Under these circumstances, genetic exchange is limited leading to reduced effective population sizes, inbreeding (also see Gerlach and Hoeck 2001) and the fixation of haplotypes/genotypes (Fig. 4), and more pronounced genetic differentiation.
Even though landscape connectivity drives differences in the genetic diversity and structure among rock hyrax populations, genetic diversity still appears relatively low and with inbreeding detected in all sampled populations (Table 1). This situation is in line with previous investigations on P. johnstoni (a species which inhabits granitic habitat “islands” on the grass plains of the Serengeti National Park, see Gerlach and Hoeck 2001). Along with
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evidence of inbreeding in individual rocky outcrops and at the population level, the authors
suggested that rock hyrax metapopulation dynamics are characterized by founder events by a
small number of individuals from source populations, followed by inbreeding and/or
bottlenecks. We agree with these assumptions to some extent, and this situation is especially
applicable in the case of the isolated populations in the western Fynbos Region. However,
given the genetic patterns operating at a fine spatial scale, the social system (e.g., social
aggregation and female phylopatry) of the rock hyrax may be a further determinant of
inbreeding. Genetic diversity in the rock hyrax is therefore not only affected by extrinsic
landscape structure, but also through intrinsic processes.
Genetic clustering Draft
Aside from landscape connectivity as an isolating agent to rock hyrax, genetic clustering of
demes closely follow geological patterns (Schifano et al. 1970), climatic patterns
(SolarGISv1.8, GeoModelSolar, 2012) and vegetation bioregions (Mucina and Rutherford
2006), further emphasising the intimate link between ecology (habitat composition) and
genetic exclusiveness (Fig. S1). As such, inter-regional barriers may represent abrupt changes
in habitat composition affecting the isolation of ancestral stocks and it is possible that
differentiation among clusters also has an adaptive component. For example, feeding
behaviour depends largely on rainfall and plant availability (Sale 1965, 1966; Turner and
Watson 1965; Hoeck 1975; Fourie 1983; Hoeck 1989). Indeed, intraspecific feeding
differences have been observed among populations of two extralimital species, P. johstoni
and H. bruceii (Hoeck 1975). Additionally, rainfall has a notable influence on mortality and
breeding season in the rock hyrax (Fourie 1983) and given their homeothermal body
temperature regulation, (Taylor and Sale 1969; Bartholomew and Rainey 1971; Brown and
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Downs 2006; Kingdon et al. 2013), rock hyrax activity patterns are also largely dependent on ambient temperature. Consequently, climatic and phytogeographic differences among the areas occupied by the different genetic clusters identified herein, may lead to adaptation/specialization to certain environments, which could further enforce isolation in such areas. The fixation of common haplotypes and genotypes within clusters further indicate the presence of inbreeding (see above). In addition, no sex-bias in dispersal across larger spatial scales in P. capensis, an observation that is shared with P. johnstoni across similar spatial scales (Gerlach and Hoeck 2001). Genetic clusters (refer to Fig. 1a for a rendering of the geographic areas where these clusters are evident) are discussed in more detail below.
Namaqualand cluster Draft
The Namaqualand cluster is phytogeographically associated with the Namaqualand
Hardeveld Bioregion (north of the Knersvlakte region) that tracks deposits of magnetite, gneiss and ultrametric rocks (Table S7). In the microsatellites, this cluster extends into the
Trans-Escarpment Succulent Karoo Bioregion (in deposits of conglomerate, greywacke and shale) to include the localities of Nieuwoudtville and Loeriesfontein. A different situation was observed in the mitochondrial DNA, where hyrax from both localities appear fixed for a haplotypic element shared among the adjacent Olifants River and West Coast clusters.
Secondary contact with the Namaqualand cluster is, however, evident among single individuals in the mitochondrial profile of the Nieuwoudtville locality (Fig. 2a; Fig. S1).
Populations in the Namaqualand region appears fixed for a set of closely related haplotypes
(see Fig. 4b). Unique haplotypes follow a star-like pattern in the haplotype network, which likely indicate a recent population expansion and drift in small populations. Similarly,
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populations in the Namaqualand region cluster together genotypically. Gene-flow seems to
follow the Kamiesberg Mountains, while exchange along the Warm Bokkeveld Mountains is
also evident (see Fig. 1).
Olifants River cluster
This genetic cluster follows the Northwest Fynbos Bioregion in a geological area consisting
of deposits of quartzite, shale and tillite, although the Donkiesbaai population inhabits coastal
granite. Gene-flow is evident to a larger extent within this cluster, and also with the adjacent
West Coast cluster (with the exception of Elands Bay which appears genotypically fixed) and
possibly follows the Cederberg Mountains and Skurweberg Mountains. Secondary contact of
divergent clusters is especially evidentDraft in the Klawer/Donkiesbaai/Elands Bay localities and
which all share a largely similar habitat type (Table S7). Dispersal here is therefore likely
facilitated by a mosaic of rocky outcrops, mountainous terrain and coastal granite. It is also
worth noting that specimens in the Klawer and Donkiesbaai localities were sampled from two
geographical points in each. In the case of the Klawer locality, the first geographic point was
connected to the Warm Bokkeveld Mountains, while the second was on the open plains.
Similarly, specimens from Donkiesbaai were sampled along the coast, as well as at a point
further inland. The extensive secondary contact of divergent clades at these localities
therefore reflects an artefact of sampling different stocks (which are fixed for different
haplotypes/genotype, resulting in a Wahlund effect), or the retention of ancestral
polymorphism within these populations.
West Coast cluster
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This cluster includes single populations in isolated granite outcrops of the Southwest Fynbos
Bioregion. These populations appear separate from other clusters (haplotypically fixed) in their mitochondrial DNA, however the Paardeberg populations comprise genotypic elements from the adjacent Cape Peninsula and Overberg clusters. Conversely, the Vredenburg population appears spatiotemporally isolated relative to adjacent areas.
Cape Peninsula cluster
This cluster appears divergent in both haplotypes and genotypes and is found in geological extensions of Cape granite in the Southwest Fynbos Bioregion. The single haplotypes and genotypes fixed in these populations indicate extensive inbreeding in isolation. Limited gene- flow towards the Overberg cluster is evidentDraft in the microsatellites (Fig. 3b), likely following the coastal belt.
Overberg cluster
Similar to the Cape Peninsula, this cluster inhabits Cape granite deposits in the Southwest
Fynbos Bioregion. This population appears largely fixed for a common haplotype and genotype, although limited gene-flow is apparent with the adjacent West Coast and Cape
Peninsula clusters (Fig. 3).
Regional barriers
Given the influence of ecological specialisation and landscape connectivity in shaping genetic patterns in the rock hyrax, regional barriers invariably correspond to open areas
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devoid of suitable rocky habitat. A multitude of barriers shape genetic patterns within P.
capensis across the sampled distribution (Fig. 3). Although Hoeck (1989) postulated that
hyrax dispersal in excess of 15 kilometres was unlikely, rock hyraxes have been observed to
cross larger distances. For example, in the Steytlerville area a hyrax was found to occupy a
springhare burrow >40 kilometres from the nearest rocky habitat suggesting that they are able
to utilize a large variety of cover types (Kingdon 1971; Olds and Shoshani 1982; Rubsamen
et al. 1982). As a result, geographic barriers in P. capensis are not absolute, but rather simply
decrease the propensity for dispersal with increasing habitat fragmentation (also see Pither
and Taylor 1998; Keyghobadi et al. 2005; Broquet et al. 2006; Baguette and Van Dyck 2007).
Knersvlakte Draft
The Knersvlakte region formed ~ 18 Mya (Moon and Dardis 1988) although recent studies
suggest a much more ancient date (~ 90 Mya; Kounov et al. 2008). This arid plain (40 - 100
kilometres in width; Kounov et al. 2008) represents an abrupt change in both landscape
composition (geology and climate) and phytogeography relative to adjacent areas consisting
of quartzite, arkose, limestone, shale, phyllite, tillite, lava and tuff and harbouring the
Knersvlakte Bioregion. This open landscape offers little in the way of suitable rocky habitat
and hence cover for dispersing saxicolous species and it comes as no surprise that the
Knersvlakte has been identified as a phylogeographic disruptor in a broad diversity of
saxicolous vertebrate taxa (Pronolagus rupestris - Matthee and Robinson 1996; Agama atra -
Matthee and Flemming 2002; Swart et al. 2009; Elephantulus edwardii - Smit et al. 2007;
Homopus signatus - Daniels et al. 2010).
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As with other species, the Knersvlakte appears to be the main phylogeographic disruptor across the sampled distribution of P. capensis (Fig. 3), with two major matrilineal clades evident to the north (Namaqualand clade) and south (western Fynbos clade, Fig. 4) of the barrier. Separation of these clades date to the late Miocene and may follow three possible scenarios. Firstly, it may represent a colonization event of this distribution, followed by vicariance across the Knersvlakte. The direction of colonisation is not obvious from the current data and may have either proceeded from inland P. capensis populations (from the
Karoo area), reaching the western Fynbos and Namaqualand regions separately, or followed migration across Knersvlakte from either the south or north. Secondly, the divergence time between these clades also coincides with a major marine transgression (up to 300 meters above current; Siesser and Dingle 1981) which would have flooded the low-lying
Knersvlakte, thereby enforcing isolationDraft across this region. Thirdly, stable Pleistocene refugia on either side of the Knersvlakte have been retrieved by Maswanganye et al. (2017) and these may have been used by ancestral P. capensis. Isolation and divergence in these refugia, followed by secondary expansion may therefore be a possibility. However, although formidable, this barrier does not appear absolute with shared haplotypes/genotypes on either side indicating single gene-flow events. Importantly, given that these clades are well- differentiated, this would suggest that gene-flow has occurred subsequent to the divergence of the clades. Taken together, additional sampling from inland South Africa and Namibia may be required to determine the main agents of differentiation among P. capensis spanning the Knersvlakte.
Sandveld
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The Sandveld region constitutes a wide lowland area between the Atlantic Ocean and Cape
Fold Mountains (Baxter 1997) within the Olifants River and West Coast regions. The area
consists of unconsolidated superficial deposits of conglomerate, limestone and sandstone,
which create undulating sandy plains (Baxter 1997). On these sandy plains, the
predominating vegetation consists of the West Strandveld Bioregion and West Coast
Renosterveld Bioregion.
Due to a scarcity of suitable rocky habitat in the Sandveld, populations sampled in this part of
the distribution occur on islands of rocky habitat (with limited connectivity to similar habitats
e.g., the Vredenburg Peninsula), or in geological extensions of the main geological group.
Consequently, P. capensis populations across the Olifants River and West Coast regions are
spatiotemporally isolated, with gene-flowDraft tracking the mountain ranges.
Cape Flats
The Cape Flats are of similar geological and phytogeographical composition as the Sandveld
area (see above) and therefore structures P. capensis populations across the Cape Peninsula
and Overberg regions. The lack of suitable rocky habitat in this predominantly sandy area
(Schalke 1973; Adelana et al. 2010) has influenced genetic structures in a variety of
saxicolous (albeit invertebrate) taxa (Potamonautes brincki – Daniels et al. 2001; Elporia
barnardi - Wishart and Hughes 2001, 2003; Mesaphisopus capensis - Gouws et al. 2004,
2010; Peripatopsis capensis - McDonald and Daniels 2012). Divergences across this region
are, however, not as pronounced as those detected among populations spanning the
Knersvlakte, reflecting the more recent formation of the Cape Flats (ca. 50 000 - 20 000 B.P.;
Schalke 1973).
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Conclusion
This study demonstrates an interplay between intrinsic (social system characteristics and ecological specificity) and extrinsic (landscape structure) factors in determining genetic patterns within a saxicolous species, the rock hyrax, P. capensis. This interplay is increasingly recognized in biodiversity research (e.g., Papadopoulou and Knowles 2016;
Zamudio et al. 2016), and is especially important to determine and predict patterns of diversity in habitat specialist taxa which are increasingly threatened by environmental changes. Although not always feasible, biodiversity research should aim to describe patterns and processes at multiple spatial and temporalDraft scales, and aim to include biological and ecological data in evaluating hypotheses. Landscape genetics therefore offers a framework to investigate finer grained hypotheses, in contrast to the coarse frameworks of phylogeography and biogeography where specific factors may be overlooked. The rock hyrax is a case in point, where the importance of landscape connectivity, ecological specialisation and social system characteristics have remained largely neglected in previous investigations (but see
Gerlach and Hoeck 2001). Among South African saxicolous species, this species is far more vagile and with a broader geographic range compared to other ecologically similar taxa.
Conservation initiatives should therefore take cognizance of the fact that tracts of unsuitable habitat may drive the isolation of further narrow-endemic lineages/taxa, which may possibly be under threat of environmental changes.
Acknowledgements
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This research was performed under permits issued by the relevant regional conservation
authorities (CapeNature permit number: AAA-004-00714-0035; SANParks permit: Issued 18
April 2011). Special thanks to all the land-owners (especially S. Rossouw, W. Basson, A.
Visser and W. Mostert) who allowed collection of animals on their property as well the staff
of SANParks (J. du Plessis), the Bettysbaai Penguin Colony (C. McGeorge) and the Table
Mountain Arial Cableway (M. Williams) for their assistance during sampling.
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Tables
Table 1 Genetic diversity indices of the 16 sampled rock hyrax (Procavia capensis) populations
Genetic diversity indices (for the separate mitochondrial and microsatellite datasets) of the 16 sampled rock hyrax (Procavia capensis) populations. The mitochondrial dataset presents the number of specimens (N), number of haplotypes, number of polymorphic sites and haplotype diversity for each sample locality as well as Θ, a statistic of effective population size. The microsatellite dataset presents the number of specimens (N), number of alleles, expected heterozygosity andDraft inbreeding coefficient (FIS), as well as the effective population size (Ne).
Mitochondrial Microsatellites
Locality N Haplotypes Polymorphic sites Haplotype Diversity Θ N # Alleles He FIS Ne Springbok (1) 10 3 3 0.511 ± 0.164 0.001 21 7.000 ± 2.121 0.703 ± 0.126 0.144 40 Garies (2) 10 3 3 0.711 ± 0.086 0.001 28 7.750 ± 1.750 0.677 ± 0.129 0.138 55 Brand-se-Baai (3) 10 1 0 0.000 ± 0.000 0.033 29 6.500 ± 1.443 0.669 ± 0.117 0.036 31 Nuwerus (4) 10 4 5 0.778 ± 0.091 0.001 26 7.750 ± 1.377 0.663 ± 0.140 0.157 37 Kliprand (5) 10 5 23 0.844 ± 0.080 0.003 20 6.750 ± 1.436 0.680 ± 0.103 0.054 72 Loeriesfontein (6) 10 2 1 0.467 ± 0.132 0.000 11 5.250 ± 0.854 0.740 ± 0.041 0.125 30 Nieuwoudt-ville (7) 10 2 21 0.600 ± 0.131 0.005 21 6.500 ± 1.708 0.661 ± 0.140 0.123 32 Klawer (8) 10 4 26 0.733 ± 0.101 0.044 15 6.500 ± 1.848 0.676 ± 0.089 0.186 42 Donkiesbaai (9) 10 4 15 0.778 ± 0.091 0.036 21 7.250 ± 1.702 0.743 ± 0.075 0.121 38 Elands Bay (10) 10 5 15 0.800 ± 0.100 0.001 25 4.750 ± 1.377 0.545 ± 0.096 0.098 23 Vredenburg (11) 10 1 0 0.000 ± 0.000 0.000 75 4.000 ± 0.816 0.617 ± 0.057 0.250 32 Ceres (12) 10 3 3 0.511 ± 0.164 0.011 22 6.250 ± 1.843 0.715 ± 0.079 0.258 25 Paardeberg (13) 10 1 0 0.000 ± 0.000 0.019 18 4.000 ± 0.816 0.574 ± 0.043 0.110 25
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Table Mountain (14) 10 1 0 0.000 ± 0.000 0.003 22 4.000 ± 0.707 0.591 ± 0.108 0.100 24 Boulders (15) 10 1 0 0.000 ± 0.000 0.039 12 1.750 ± 0.479 0.233 ± 0.145 -0.122 25 Bettysbaai (16) 10 2 12 0.356 ± 0.159 0.063 25 4.500 ± 0.645 0.657 ± 0.044 0.202 21 Total 160 42 44 0.947 ± 0.006 - 391 5.656 ± 0.368 0.634 ± 0.027 0.280 120
Table 2 Pairwise ɸST/FST values between the 16 sampled rock hyrax (Procavia capensis) populations
Pairwise ɸST/FST values between the 16 rock hyrax (Procavia capensis) populations sampled across the Namaqualand and western Fynbos
regions. Values above the diagonal are based on the microsatellite (FST) data (values in normal font are significant after Bonferroni correction of
the p-values) and those below the diagonal represent the (ɸST) mitochondrial cytochrome b sequence data (* = p<0.05, ** = p<0.01, and *** =
p<0.001). Values in bold are non-significant. Draft
Brand- Table Springbok Garies Nuwerus Kliprand Loeriesfontein Nieuwoudt- Klawer Donkiesbaai Elands Bay Vredenburg Ceres Paardeberg Boulders Bettysbaai Locality se-Baai Mountain (1) (2) (4) (5) (6) ville (7) (8) (9) (10) (11) (12) (13) (15) (16) (3) (14) Springbok (1) - 0.03 0.04 0.03 0.03 0.07 0.03 0.14 0.07 0.14 0.2 0.07 0.21 0.15 0.35 0.22 Garies (2) 0.42*** - 0.06 0.05 0.03 0.09 0.03 0.14 0.07 0.13 0.16 0.08 0.19 0.14 0.31 0.21 Brand-se-Baai (3) 0.59*** 0.67*** - 0.04 0.03 0.06 0.02 0.12 0.06 0.11 0.19 0.08 0.20 0.18 0.36 0.24 Nuwerus (4) 0.12* 0.48*** 0.61*** - 0.05 0.08 0.04 0.14 0.06 0.13 0.18 0.06 0.20 0.11 0.32 0.22 Kliprand (5) 0.13** -0.01 0.27*** 0.19*** - 0.07 0.02 0.15 0.05 0.12 0.20 0.06 0.21 0.14 0.36 0.22 Loeriesfontein (6) 0.97*** 0.95*** 0.99*** 0.95*** 0.83*** - 0.07 0.10 0.07 0.19 0.17 0.08 0.20 0.20 0.41 0.18 Nieuwoudt-ville (7) 0.63** 0.64*** 0.64** 0.62** 0.47*** 0.35*** - 0.13 0.06 0.11 0.19 0.09 0.21 0.16 0.35 0.22 Klawer (8) 0.70*** 0.70*** 0.71*** 0.69*** 0.56*** 0.46*** 0.14 - 0.05 0.22 0.18 0.09 0.11 0.19 0.37 0.11 Donkiesbaai (9) 0.78*** 0.77*** 0.79*** 0.77*** 0.64*** 0.53*** 0.23** -0.04 - 0.09 0.16 0.03 0.09 0.12 0.31 0.12 Elands Bay (10) 0.86*** 0.85*** 0.88*** 0.85*** 0.73*** 0.80*** 0.48*** 0.12 0.12 - 0.24 0.12 0.21 0.20 0.42 0.28 Vredenburg (11) 0.98*** 0.96*** 1.00*** 0.96*** 0.84*** 0.82*** 0.25** 0.39* 0.47** 0.79*** - 0.15 0.23 0.20 0.37 0.21
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Ceres (12) 0.95*** 0.94*** 0.97*** 0.94*** 0.81*** 0.95*** 0.61*** 0.27* 0.38*** 0.1 0.96*** - 0.15 0.08 0.34 0.13 Paardeberg (13) 0.98*** 0.96*** 1.00*** 0.96*** 0.84*** 0.90*** 0.35*** 0.47*** 0.54*** 0.81*** 1.00*** 0.96*** - 0.15 0.25 0.11 Table Mountain (14) 0.98*** 0.96*** 1.00*** 0.96*** 0.84*** 0.98*** 0.64*** 0.52*** 0.57*** 0.72*** 1.00*** 0.94*** 1.00*** - 0.26 0.19 Boulders (15) 0.98*** 0.96*** 1.00*** 0.96*** 0.85*** 0.98*** 0.66*** 0.57*** 0.62*** 0.75*** 1.00*** 0.95*** 1.00*** 1.00*** - 0.28 Bettysbaai (16) 0.88*** 0.88*** 0.90*** 0.87*** 0.76*** 0.80*** 0.49*** 0.46*** 0.50*** 0.64*** 0.80*** 0.78*** 0.82*** 0.80*** 0.82*** -
Draft
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Figure captions
Fig. 1. (a) Map showing the 16 sampling localities for the rock hyrax (Procavia capensis)
with an indication of the four geographic regions in which sampling was undertaken. (b) The
extent of the Namaqualand and western Fynbos regions are shown, with the white dots
representing the sampling localities. Here, the three major low-lying areas devoid of any
substantial rock hyrax habitat are demarcated by the red, yellow, and blue areas, respectively.
In addition, the location of the major mountain ranges (brown triangles) across the sampled
range are indicated as follows: (1) Kamiesberg Mountains, (2) Warm Bokkeveld Mountains,
(3) Cederberg Mountains, (4) Skurweberg Mountains, (5) Boland Mountains, (6) Hottentots Holland Mountains, and (7) Cape Peninsula.Draft The base map was created using StepMap version (StepMap GmbH, Berlin, Germany; available from https://www.stepmap.com/,
accessed 23 April 2018). The information portrayed on the figure was generated manually
and added to the base map using Microsoft PowerPoint version 2013 (Microsoft Corporation,
Redmond, Washington, USA). Colour version online.
Fig. 2. (a) Map of the fine-scale sampling of rock hyrax (Procavia capensis) colonies within
the Vredenburg population showing the relative shape, size, and position of the four sampled
rocky habitats. (b) Genetic clusters (based on the microsatellite data set) across this
distribution. (c) Percentage of full-sibling dyads (value to the left of the solidus and above a
line) and half-sibling dyads (value to the right of the solidus and below a line) within and
among rocky outcrops (in boldface type and normal type, respectively). Colour version
online.
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Fig. 3. Results from the graphical interpolation-based representation of the genetic landscape
(as performed in the program Alleles In Space) indicating barriers to gene flow (broken lines) over the sampled distribution of the rock hyrax (Procavia capensis). The genetic landscape is based on (a) the mitochondrial data set and (b) the microsatellite data set, with (1) a side view and (2) a top view of the genetic landscape. Peaks represent differentiation between sampled populations that are higher than the mean differentiation (flat landscape). (3) Location of the above barriers are indicated on the physical landscape, as well as the genetic composition of each population based on (4) the genetic clusters retrieved by the clustering analyses. The base map was created using StepMap version (StepMap GmbH, Berlin, Germany; available from https://www.stepmap.com/, accessed 23 April 2018). The information portrayed on the figure was generated manually and added to the base map using Microsoft PowerPoint version 2013 (Microsoft Corporation, DraftRedmond, Washington, USA). Colour version online.
Fig. 4. (a) Phylogeny of the sampled rock hyrax (Procavia capensis) populations based on the mitochondrial cytochrome b data. A “●” placed at nodes indicates strong nodal support
(PP ≥ 0.9, bootstrap value ≥ 70), whereas “–” indicates that a particular node received poor support or was not recovered through the particular approach. Indicators above nodes represent Bayesian posterior probabilities (PP) derived from BEAST and MrBayes, respectively, whereas the indicator below the nodes indicate maximum likelihood bootstrap values. In addition, (b) the haplotype network (based on the mitochondrial cytochrome b data) of the sampled rock hyrax populations is shown. The size of each circle and the numbers within circles reflect the number of specimens with a particular haplotype.
Numbers on branches represent the number of mutational steps between haplotypes. Colour version online.
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Draft
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Draft
Fig. 1. (a) Map showing the 16 sampling localities for the rock hyrax (Procavia capensis) with an indication of the four geographic regions in which sampling was undertaken. (b) The extent of the Namaqualand and western Fynbos regions are shown, with the white dots representing the sampling localities. Here, the three major low-lying areas devoid of any substantial rock hyrax habitat are demarcated by the red, yellow, and blue areas, respectively. In addition, the location of the major mountain ranges (brown triangles) across the sampled range are indicated as follows: (1) Kamiesberg Mountains, (2) Warm Bokkeveld Mountains, (3) Cederberg Mountains, (4) Skurweberg Mountains, (5) Boland Mountains, (6) Hottentots Holland Mountains, and (7) Cape Peninsula. The base map was created using StepMap version (StepMap GmbH, Berlin, Germany; available from https://www.stepmap.com/, accessed 23 April 2018). The information portrayed on the figure was generated manually and added to the base map using Microsoft PowerPoint version 2013 (Microsoft Corporation, Redmond, Washington, USA). Colour version online.
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Draft
Fig. 2. (a) Map of the fine-scale sampling of rock hyrax (Procavia capensis) colonies within the Vredenburg population showing the relative shape, size, and position of the four sampled rocky habitats. (b) Genetic clusters (based on the microsatellite data set) across this distribution. (c) Percentage of full-sibling dyads (value to the left of the solidus and above a line) and half-sibling dyads (value to the right of the solidus and below a line) within and among rocky outcrops (in boldface type and normal type, respectively). Colour version online.
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Draft
Fig. 3. Results from the graphical interpolation-based representation of the genetic landscape (as performed in the program Alleles In Space) indicating barriers to gene flow (broken lines) over the sampled distribution of the rock hyrax (Procavia capensis). The genetic landscape is based on (a) the mitochondrial data set and (b) the microsatellite data set, with (1) a side view and (2) a top view of the genetic landscape. Peaks represent differentiation between sampled populations that are higher than the mean differentiation (flat landscape). (3) Location of the above barriers are indicated on the physical landscape, as well as the genetic composition of each population based on (4) the genetic clusters retrieved by the clustering analyses. The base map was created using StepMap version (StepMap GmbH, Berlin, Germany; available from https://www.stepmap.com/, accessed 23 April 2018). The information portrayed on the figure was generated manually and added to the base map using Microsoft PowerPoint version 2013 (Microsoft Corporation, Redmond, Washington, USA). Colour version online.
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Fig. 4. (a) Phylogeny of the sampled rock hyraxDraft (Procavia capensis) populations based on the mitochondrial cytochrome b data. A “●” placed at nodes indicates strong nodal support (PP ≥ 0.9, bootstrap value ≥ 70), whereas “–” indicates that a particular node received poor support or was not recovered through the particular approach. Indicators above nodes represent Bayesian posterior probabilities (PP) derived from BEAST and MrBayes, respectively, whereas the indicator below the nodes indicate maximum likelihood bootstrap values. In addition, (b) the haplotype network (based on the mitochondrial cytochrome b data) of the sampled rock hyrax populations is shown. The size of each circle and the numbers within circles reflect the number of specimens with a particular haplotype. Numbers on branches represent the number of mutational steps between haplotypes. Colour version online.
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