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African Journal of Herpetology
ISSN: 2156-4574 (Print) 2153-3660 (Online) Journal homepage: http://www.tandfonline.com/loi/ther20
Genetic diversity and differentiation of the
Western Leopard Toad (Sclerophrys pantherina)
based on mitochondrial and microsatellite
Jessica M. da Silva , Kevin A. Feldheim, G. John Measey , Stephen Doucette-
Riise, Ryan J. Daniels, Lucas F. Chauke & Krystal A. Tolley
To cite this article: Jessica M. da Silva , Kevin A. Feldheim, G. John Measey , Stephen
Doucette-Riise, Ryan J. Daniels, Lucas F. Chauke & Krystal A. Tolley (2017) Genetic
diversity and differentiation of the Western Leopard Toad (Sclerophrys pantherina) based on
mitochondrial and microsatellite markers, African Journal of Herpetology, 66:1, 25-38, DOI:
To link to this article: http://dx.doi.org/10.1080/21564574.2017.1294115
View supplementary material Published online: 23 Jun 2017.
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Genetic diversity and differentiation of the Western
Leopard Toad (Sclerophrys pantherina) based on
mitochondrial and microsatellite markers
JESSICA M. DA SILVA
*,KEVIN A. FELDHEIM
G. JOHN MEASEY
,LUCAS F. CHAUKE
&KRYSTAL A. TOLLEY
Kirstenbosch Research Centre, South African National Biodiversity Institute, Private Bag X7, Claremont, Cape
Town, South Africa;
Department of Botany & Zoology, University of Stellenbosch, Private Bag X1, Matieland
7602, Stellenbosch, South Africa;
Pritzker Laboratory for Molecular Systematics and Evolution, The Field
Museum, 1400 S. Lake Shore Drive, Chicago, IL 60605, USA;
Centre for Invasion Biology, Department of
Botany and Zoology, Stellenbosch University, Natural Sciences Building, Matieland, Stellenbosch, South Africa;
Department of Biological Sciences, University of Cape Town, Private Bag X3, Rondebosch 770, Cape Town,
Abstract.—Intraspeciﬁc genetic diversity provides the basis for evolutionary change and is
therefore considered the most fundamental level of biodiversity. Mitochondrial DNA (mtDNA)
and microsatellite loci are the markers most typically used in population-level studies; however,
their patterns of genetic variation are not always congruent. This can result in different
interpretations of the data, which can impact on management decisions, especially for
threatened species. Consequently, in this study, we developed and analysed novel microsatellite
markers for the Endangered Western Leopard Toad (WLT), Sclerophrys pantherina, and
compared the results to previously published mtDNA data to compare the level of genetic
diversity between the two molecular markers. The microsatellite evidence showed signs of a
past bottleneck, yet relatively high levels of genetic diversity and low genetic differentiation
between two sampling sites. In contrast, the mtDNA revealed moderate to low levels of
diversity between sampling sites, and strong genetic differentiation. An explanation for the
conﬂicting patterns may be that the current genetic signature, as depicted by the microsatellite
data, is not yet reﬂected in the mitochondrial dataset; and, as such the data are depicting a
timeline for genetic variation within the WLT. Both markers revealed important information
about the two sampling sites, which can help inform conservation management of the species.
Key words.—Africa; Amietophrynus; Bufonidae; conservation; endangered; genetic
Intraspeciﬁc genetic diversity provides the basis for evolutionary change and is therefore
considered the most fundamental level of biodiversity (May 1994). This diversity is gov-
erned by the loss and gain of alleles, which can be the result of mutations, random genetic
drift, natural and sexual selection or migration. In our human-mediated world, ecological
*Corresponding author. Email: email@example.com
African Journal of Herpetology,
Vol. 66, No. 1, 2017, 25–38
ISSN 2156-4574 print/ISSN 2153-3660 online
© 2017 Herpetological Association of Africa
disturbance is proving to be a key driver of changes to the effective population size (N
a species (for an in-depth review, see Banks et al. 2013); and, over time, these changes can
greatly affect a species’genetic diversity (Alcala & Vuilleumier 2014; Ellegren & Galtier
2016). It is well established that a reduction in genetic diversity compromises the adaptive
potential of a species and, therefore, decreases its long-term survival (Lacy 1997; Markert
et al. 2010). This is especially true for threatened species, which can sometimes be charac-
terised by low levels of genetic variation, often due to inbreeding, population bottlenecks,
or the disproportionate effects of genetic drift on small populations (e.g. England et al.
2003; Spielman et al. 2004; Allendorf 2005; Willoughby et al. 2015).
Molecular markers, such as mitochondrial DNA (mtDNA) and microsatellite loci,
are powerful tools for estimating the diversity among individuals and populations
(e.g. Avise et al. 1987; Schwartz et al. 2007). However, patterns of genetic variation
are not always congruent between the two. At times, greater population differentiation
has been found using microsatellite markers (e.g. Lu et al. 2001; Johnson et al. 2003;
De Oliveira Francisco et al. 2013; Kolleck et al.2013; da Silva et al. 2016); while the
reverse has also been documented (e.g. Castella et al. 2001; Yang & Kenagy 2009).
Some possible explanations for these discrepancies include: (i) differences in the selec-
tion intensities acting on each marker; (ii) the different mutation rates between markers;
and, (iii) the N
for maternally-inherited markers, such as mtDNA, differ from those of
microsatellites which are biparentally inherited markers (Johnson et al. 2003). Genetic
diversity is also inﬂuenced by patterns of mating, sex-biased dispersal and other demo-
graphic parameters (Chesser & Baker 1996; Johnson et al. 2003; Yang & Kenagy 2009;
De Oliveira Francisco et al. 2013; Kolleck et al. 2013), which can further contribute to
the discrepancies in genetic diversity estimated between mtDNA and microsatellite
markers. In general, mtDNA is most often used to analyse phylogeographic events,
while microsatellites typically provide more ﬁne-scale resolution of more recent demo-
graphic events (e.g. Avise et al. 1987). Consequently, researchers must carefully deter-
mine what questions they want to address, so that the appropriate markers can be
utilised (Hoban et al. 2013). This is especially critical for research on threatened
species that have small and/or declining populations, such as the Western Leopard
Toad (WLT: Sclerophrys pantherina; previously Bufo pantherina and Amietophrynus
The WLT is endemic to the winter rainfall region of the southwestern Cape of South
Africa where it falls into two historically disjunct areas: (1) to the west of False Bay,
including the Cape metropolitan area, and (2) to the east of False Bay (De Villiers
2004; Measey & Tolley 2011;Fig. 1). Its Endangered status is based on its small distri-
bution (area of occupancy [AOO] 440 km
) and the ongoing reduction in quantity and
quality of habitat associated with urbanisation and agricultural expansion throughout its
range (de Villiers 2004; SA-FRoG 2016; Measey 2011). Adults are explosive breeders,
moving to breeding sites during the antipodean winter (late July to early September)
where they lay large clutches of spawn in strings (Cherry 1992b; de Villiers 2004). Tad-
poles take around three months to reach metamorphosis, and males are known to breed
after one year and females after two (Cherry 1992a).
In order to investigate the structure and distribution of S. pantherina across its range, a
recent study utilised the mitochondrial marker, ND2 (Measey & Tolley 2011), as it was
thought to have sufﬁcient population-level variation (Cunningham & Cherry 2004).
Two genetically distinct populations were recognised that corresponded to the distribution
of the toads (refer to Fig. 1): population 1 –toads within the Cape Metropolitan area
26 DA SILVA ET AL.—Genetic diversity in the Western Leopard Toad
(CMA) –and population 2 –toads east of False Bay (EFB). Moreover,
haplotype and nucleotide diversity were greater for the CMA population compared to
the EFB population, with no shared haplotypes across the two populations (Measey &
The relatively low genetic diversity found among EFB individuals, coupled with local
extinctions and a small number of remaining breeding sites could be an indication that this
population is more vulnerable than the CMA population (Measey & Tolley 2011). With
ongoing development in the EFB lowlands due to agriculture and tourism, the vulnerability
of this population is likely to worsen. Consequently, it has been recommended that this
population be treated as a separate management unit to safeguard the future of the remain-
ing breeding sites (Measey & Tolley 2011).
Given that this previous study only utilised a single mtDNA marker and the two popu-
lations were estimated to have diverged as recently as 1.2 thousand years ago (Measey &
Tolley 2011), the results may not adequately reﬂect the current genetic diversity within the
species. Consequently, in this study, we developed and analysed novel microsatellite
markers for S. pantherina and compared the results to the previously published ND2
sequence data, speciﬁcally for the EFB population, to compare the level of genetic diver-
sity between the two molecular markers.
Figure 1. Map depicting the distribution of Sclerophrys pantherina (inset) in the southwestern Cape,
South Africa. Shaded blue areas show the disjunct distribution of the species. Circles denote known
sampling sites: black-ﬁlled circles denote the two sampling sites included in this study; while the
white ﬁlled circles are sampling sites within the Cape Metropolitan Area (refer to Measey &
AFRICAN JOURNAL OF HERPETOLOGY 2017 27
MATERIALS AND METHODS
Sampling was conducted during the 2010 breeding season in July and August at night
when the toads are most active. Two sampling sites within the EFB were targeted: Uilenk-
raal (S 34° 34′6″E 19° 27′57″) and Klein Paradijs (S 34° 39′10″E 19° 32′1″:Fig. 1)
which are 11.25 km apart in a straight line. Both sites are artiﬁcial impoundments which
S. pantherina have used for oviposition for many years. A single toe clip (the tip to the
ﬁrst articulation on the inside toe on the left foot) was collected for each toad using ster-
ilised surgical scissors. In addition to providing tissue for DNA analysis, the toe clip
was used as an identiﬁcation of previously captured individuals. Each toe clip was
stored in 99% ethanol and stored at -40 °C until DNA was extracted.
Microsatellite Development and Optimisation
Twelve microsatellite markers were developed for S. pantherina (online supplemental
material, Table S1), using an enrichment protocol (Glenn & Schable 2005). Genomic
DNA (gDNA) from one individual was digested with RsaI and XmnI, and SuperSNX24
linkers were ligated onto the ends of gDNA fragments. Linkers act as priming sites for
polymerase chain reactions (PCR) in subsequent steps. Five tetranucleotide [(AAAT)
] and six trinucleotide [(AAT)
] biotinylated probes were hybridised to gDNA in two
separate reactions. Streptavidin-coated magnetic beads (Dynabeads® M-280 Invitrogen,
Carlsbad, CA) were added to the probe-gDNA complexes, and this mixture was washed
twice with 2× SSC, 0.1% SDS and four times with 2× SSC, 0.1% SDS at 52
Between washes, a magnetic particle collecting unit was used to capture the magnetic
beads which are bound to the biotin-gDNA complex. This allows us to capture gDNA con-
taining repeats while other fragments are washed away. Enriched fragments were removed
from the probes by denaturing at 95
C and precipitated with 3 Msodium acetate and 95%
ethanol. To increase the amount of fragments, a “recovery”PCR was performed in a 25 µl
reaction containing 1× PCR buffer (10 mMTris-HCl, 50 mMKCl, pH 8.3), 1.5 mMMgCl
0.16 mMof each dNTP, 0.52 µMof the SuperSNX24 forward primer, 10× BSA, 1U Ta q
DNA polymerase, and approximately 25 ng enriched gDNA fragments. Thermal
cycling, performed in a BIO-RAD DYAD, was as follows: 95 °C for 2 min followed
by 25 cycles of 95 °C for 20 s, 60 °C for 20 s and 72 °C for 90 s, and a ﬁnal elongation
step of 72 °C for 30 min. PCR fragments were then cloned using the TOPO-TA Cloning®
kit following the manufacturer’s protocol (Invitrogen). Bacterial colonies containing a
vector with gDNA (i.e. white colonies) were used as a template for subsequent PCR.
PCR products were then cleaned using Shrimp Alkaline Phosphatase and Exonuclease I
according to the manufacturer’s protocol (Affymetrix, Santa Clara, CA). DNA sequencing
was performed using the BigDye® Terminator v3.1 Cycle Sequencing Kit (Applied Bio-
systems, Foster City, CA). Sequencing reactions were precipitated with 125 mM EDTA
and 100% ethanol and run on an ABI 3730 DNA Analyzer. Primer3 (http://frodo.wi.
mit.edu/cgi-bin/primer3/primer3_www.cgi) was used to develop microsatellite PCR
Optimisation of the 12 markers was carried out in a 10 µl reaction volume containing
approximately 5–50 ng of DNA template, 80 µMof dNTPs, and 0.2 µMof each primer.
28 DA SILVA ET AL.—Genetic diversity in the Western Leopard Toad
The PCR buffer concentrations, MgCl
concentrations and annealing temperatures (T
varied with the Taq used and the loci targeted (Table S1). Thermal cycling parameters
were as follows: 95 °C for 4 min, followed by 40 cycles at 95 °C for 30 s, T
for 30 s,
72 °C for 45 s and a ﬁnal extension at 72 °C for 5–10 min. Successful products were
then combined in a poolplex format and proﬁled at the Central Analytical Facility at Stel-
lenbosch University using an ABI 3100xl Prism (Applied Biosystems, Foster City, CA),
ROX 500 or LIZ 500 as the internal size standard, and POP-7 as the polymer, as per man-
ufacturer’s recommendation. Alleles were scored and binned using the microsatellite
plugin in Geneious version 9.1.5 (Kearse et al. 2012).
Microsatellite genotypes.—Microsatellites were screened for genotyping errors and null
alleles using Micro-Checker version 2.2.3 (Van Oosterhout et al. 2004) as loci possessing
either of these would not typically be considered useful in population-level studies as they
may affect the estimation of population differentiation, for example, by reducing the
genetic diversity within populations (e.g. Paetkau & Strobeck 1995). As such, some
studies have attempted to correct for null alleles in population genetic studies by statisti-
cally adjusting the visible allele and genotype frequencies (e.g. Roques et al. 1999;
Chapuis & Estoup 2007). However, the estimation of null alleles can be biased upwards
in populations that are either inbred or consist of closely related individuals (Chybicki
& Burczyk 2009; Campagne et al. 2012), and, conversely, that estimations of inbreeding
can be biased upwards when null alleles or genotyping errors occur (e.g. Björklund 2005).
Consequently, automatically correcting for null alleles might be unnecessary and inap-
propriate. To better assess the likely presence of null alleles, we conducted a simultaneous
estimation of the inbreeding coefﬁcient (f), null allele frequencies (n), and random geno-
typing failure (b) using the software INEst v2 (Chybicki et al. 2011). This method uses a
Bayesian approach to simultaneously estimate the three parameters, which should provide
a more accurate estimation of each parameter because it allows for the relative contribution
of each through selection of the best ﬁtting model (i.e. model with the lowest Deviance
Information Criterion [DIC]).
The Markov Chain Monte Carlo (MCMC) was run with 500 000 cycles and 10% burn-in,
saving parameters every 100 cycles. The DICs from each model were then compared, with
the better (lowest) scoring model considered a better ﬁt. Given that the effects of null
alleles are locus-speciﬁc (Dakin & Avise 2004), their presence should be reﬂected in the
best ﬁtting models of each population, or in this case, sampling site. An examination of
the p[j,k] parameter from the INEst results should provide conﬁrmation of the locus/
loci containing null alleles This parameter examines the frequency of the kth allele at
the jth locus, where p[j, 0] denotes the null allele frequency at the jth locus
(Chybicki et al. 2011), For these loci, we manually adjusted allele frequencies using the
Brookﬁeld 1 algorithm implemented in Micro-Checker. This method discounts non-
amplifying individuals when calculating null allele frequencies, as opposed to the Brook-
ﬁeld 2 algorithm, which treats non-ampliﬁcations as data and regards them as null homo-
zygotes when calculating null allele frequencies (Brookﬁeld 1996). By correcting for null
alleles in this way, we were able to maximise sample size and retain loci that may have
sufﬁcient variability to detect patterns. All subsequent analyses were conducted on this cor-
AFRICAN JOURNAL OF HERPETOLOGY 2017 29
Genepop on the Web (Raymond & Rousset 1995; Rousset 2008) was used to test for
linkage disequilibrium (LD) between loci using the log-likelihood ratio test (Cockerham &
Weir 1977). Analyses for LD were run with a MCMC dememorisation of 10 000 for 100
batch runs of 1 000 iterations. For each locus, the number of alleles (allelic richness: A
allelic size range, observed (H
) and expected (H
) heterozygosities (Nei 1987), and devi-
ations from Hardy–Weinberg equilibrium (HWE) were also estimated for the two sampling
sites using ARLEQUIN version 184.108.40.206 (Excofﬁer & Lischer 2010). Deviations from HWE
were tested using 1.0 ×10
Markov chains and 100 000 dememorisation steps. To minimise
the possibility of Type I errors, tests for linkage and Hardy–Weinberg disequilibria were
corrected for multiple comparisons by applying Holm’s sequential Bonferroni correction
(Holm 1979; Rice 1989).
ARLEQUIN was used to test for signatures of genetic bottlenecks in the two sampling
sites using the Garza–Williamson M-ratio (G-W: Garza & Williamson 2001) and to deter-
mine the level of gene ﬂow between sites using R
(Slatkin 1995). The M-ratio is esti-
mated according to the equation M= k/ R+1, where k represents the number of alleles at
a locus and R is the associated allelic range (Garza & Williamson 2001; Excofﬁer &
Lischer 2010). Populations that have experienced a reduction in their effective population
size exhibit a larger reduction in allele numbers than range (Excofﬁer & Lischer 2010).
Accordingly, an M-ratio less than 0.68 (value derived from stable wild populations)
would indicate that the sampling site has been through a bottleneck at the locus under
examination, whereas a value closer to one is indicative of stationary/stable populations
(Garza & Williamson 2001; Peery et al. 2012). Gene ﬂow was measured using R
instead of F
because it relies on a stepwise mutation model which is better suited for
the high mutation rates and memory dependent allele mutations found within microsatellite
loci (Di Rienzo et al. 1994; Slatkin 1995). In contrast, F
relies upon the inﬁnite allele
model, which assumes low mutation rates and a mutation process independent of the
prior allelic state (Weber & Wong 1993; Slatkin 1995). The number of migrants (N
between populations was then calculated from the equation, N
= 0.25*(1- F
(Slatkin & Barton 1989), using R
in place of F
. Genetic differentiation can be cate-
gorised as great if R
> 0.15, moderate if R
= 0.05–0.15, and little if R
(Wright 1978). Similarly, the level of gene ﬂow between sampling sites is categorised as
high if N
>1, intermediate if N
= 0.25 to 0.99, and low if N
< 0.25 (Wright 1978).
mtDNA sequences.—The ND2 sequence data for Uilenkraal and Klein Paradijs data from
Measey & Tolley (2011) was used to estimate genetic diversity within S. pantherina.
Speciﬁcally, the number of haplotypes (h), haplotypic diversity (hd), nucleotide diversity
(π), the number of polymorphic sites (S), and the total number of mutations (Eta) were cal-
culated using DnaSP version 5.10.1 (Librado & Rozas, 2009). Haplotype diversity (or gene
diversity) is deﬁned as the probability that two randomly sampled alleles are different; and,
nucleotide diversity is deﬁned as the average number of nucleotide differences per site in
pairwise comparisons among DNA sequences (Nei 1987). DnaSP was also used to deter-
mine the level of genetic differentiation and gene ﬂow between sampling sites; and, Φ
which is analogous to F
, was calculated in ARLEQUIN. F
is an allelic calculation that
assumes all alleles are equidistant from each other; whereas Φ
(the nucleotide diversity
calculation) allows for different distances between different alleles. Lastly, a median-
joining network was created using NETWORK, version 5.0 (Bandelt et al. 1999) to visu-
alise the relationships among the mtDNA haplotypes between the two sampling sites.
30 DA SILVA ET AL.—Genetic diversity in the Western Leopard Toad
All 12 loci were polymorphic with the number of alleles ranging from 3 to18 (Table 1). There
was no evidence of large allele dropout or scoring errors due to stutter peaks; however, the
likely presence of null alleles due to homozygote excess was detected in both Klein Paradijs
and Uilenkraal sampling sites (Tables 1 and 2). Because null alleles are locus-speciﬁc,
WLT16 and WLT39 likely represent ‘true’null alleles. Although the best-ﬁtting INEst
models also indicated the inﬂuence of inbreeding, with average F
= 0.0247 for Uilenkraal
and 0.0588 for Klein Paradijs sampling sites, the effects are minimal. An examination of the
95% posterior probability intervals for F
included zero for Uilenkraal, indicating that there
is no signiﬁcant inbreeding in this population (Table 2).
No signiﬁcant linkage disequilibrium was detected for any of the loci; however, two
loci were found to deviate from HWE (WLT39 & WLT42: Table 1). Because locus
WLT39 was identiﬁed as possibly having null alleles across populations, this is the
most probable explanation for its deviation from HWE. Locus WLT42, on the other
hand, only showed deviations from HWE for Klein Paradijs, indicating that these devi-
ations are most likely attributed to some biological factor such as the Wahlund effect or
inbreeding (Chakraborty et al. 1992). Given the highly fragmented nature of the WLT
habitat, it is possible that small populations have become ﬁxed for diverse alleles.
Evidence of a genetic bottleneck was found across all loci for both sampling sites (M-ratio:
Table 1), which also show fairly similar levels of microsatellite genetic diversity for all indices
examined (Table 3) and exhibit little differentiation (R
: 0.082, P< 0.001; N
Unlike the microsatellite results, analysis of the mtDNA data revealed differing levels of
diversity between Uilenkraal and Klein Paradijs sampling sites (Table 3). In particular,
Uilenkraal was found to have moderate haplotype diversity (hd = 0.567 ± 0.051), yet
low nucleotide diversity (π= 0.00095 ± 0.00022); while Klein Paradijs has low haplotype
diversity (hd = 0.154 ± 0.126) and very low nucleotide diversity (π= 0.00019 ± 0.0016).
The two populations share one haplotype, possessed by the majority of individuals
sampled (Fig. 2), indicating a previously continuous population or past gene ﬂow
between populations; however, the two populations were found to be strongly differen-
= 0.41991, P=0.002).
Although the mtDNA and microsatellite evidence presented here reveal different patterns
with respect to the genetic diversity within the two sampling sites of S. pantherina, simi-
larities were also present with both markers showing signs of population bottlenecks and
the presence of gene ﬂow between Uilenkraal and Klein Paradijs. A possible explanation
for the conﬂicting patterns of genetic diversity may be that the current genetic signature (as
depicted by the microsatellite data) is not yet reﬂected in the mitochondrial dataset due to
the slower evolution of the mtDNA markers. The data may, therefore, be depicting a time-
line for genetic variation within the WLT, with the mtDNA data revealing the more
AFRICAN JOURNAL OF HERPETOLOGY 2017 31
Table 1. Descriptive statistics of genetic variability for 12 Sclerophrys pantherina microsatellite loci across two sampling sites in the southwestern Cape, South
Uilenkraal (n=39) Klein Paradijs (n=41)
HWE Null MN
HWE Null M
WLT1 13 134–194 0.78 0.89 0.0937 Maybe 0.230 11 138–182 0.70 0.76 0.2801 No 0.244
WLT2 13 119–179 0.81 0.88 0.2772 No 0.230 10 123–167 0.80 0.80 0.5150 No 0.222
WLT16 14 142–358 0.62 0.92 0.0006 Maybe 0.065 15 142–358 0.74 0.91 0.0411 Maybe 0.069
WLT25 11 167–219 0.92 0.90 0.8701 No 0.226 9 159–207 0.69 0.81 0.0443 No 0.204
WLT36 14 216–276 0.90 0.89 0.6666 No 0.230 9 216–280 0.75 0.82 0.1783 No 0.138
WLT38 16 275–387 0.89 0.92 0.1888 No 0.150 18 266–387 0.91 0.93 0.4804 No 0.148
WLT39 5 157–181 0.42 0.71 0.0031 Maybe 0.200 3 165–181 0.32 0.56 0.0087 Maybe 0.176
WLT40 10 152–200 0.61 0.85 0.0054 Maybe 0.204 9 148–200 0.71 0.85 0.0643 No 0.170
WLT42 6 179–211 0.39 0.43 0.1636 No 0.182 5 187–211 0.27 0.60 <0.001* Maybe 0.240
WLT44 15 195–347 0.74 0.87 0.0260 Maybe 0.118 8 195–343 0.75 0.75 0.4391 No 0.054
WLT76 4 174–183 0.57 0.54 0.8948 No 0.400 4 174–183 0.73 0.67 0.5540 No 0.400
WLT88 7 197–245 0.67 0.74 0.7256 No 0.163 6 197–245 0.63 0.78 0.0237 Maybe 0.142
, Number of observed alleles; S, allele size range; H
,observed and H
,expected heterozygosity; HWE, Hardy–Weinberg equilibrium Pvalue; Null, presence of null alleles; M,
Garza–Williamson index. Signiﬁcant deviations from HWE are denoted by *, based on a Bonferroni signiﬁcance value of 0.0042 (P< 0.05/12).
32 DA SILVA ET AL.—Genetic diversity in the Western Leopard Toad
Table 2. Results of Bayesian individual inbreeding model for Sclerophrys pantherina. Models were run using all 12 microsatellite loci. Model parameters include
inbreeding (f), null alleles (n) and random genotyping errors (b).
Klein Paradijs (n=41)
DIC ΔDIC Avg (F
) 95% HPD DIC ΔDIC Avg (F
) 95% HPD
nfb 3294.720 1.274 0.0247 0.0000–0.0619 nfb 3084.212 18.488 0.0588 0.0156-0.1069
nb 3295.994 19.807 NA NA nb 3102.700 3.919 NA NA
fb 3315.801 19.398 0.1053 0.0596–0.1504 fb 3106.619 14.333 0.1254 0.0785–0.1674
b 3335.199 50.288 NA NA nf 3120.952 22.009 0.0805 0.0325–0.1261
nf 3385.487 1.369 0.0372 0.0000–0.0819 n 3142.961 14.834 NA NA
n 3386.856 –NA NA b 3157.795 –NA NA
DIC, Deviance Information Criterion; ΔDIC, difference in DIC from the best model; Avg (F
), average inbreeding coefﬁcient for the population; HPD, highest probability density.
AFRICAN JOURNAL OF HERPETOLOGY 2017 33
ancestral relationships and the microsatellite data the most recent demographic structuring.
Both markers revealed important information about the two sampling sites, which can help
inform conservation management of the species.
The mtDNA data uncovered different levels of diversity between Uilenkraal and Klein
Paradijs, with the former showing moderate haplotype diversity and low nucleotide diversity,
and the latter showing low levels of both diversity measures. The low nucleotide diversity esti-
mates suggest that both populations underwent a bottleneck in the recent past. There may have
been a single WLT population in the EFB region, or possibly even across the entire range of
S. pantherina, during the last glacial maxima when areas of the continental shelf were exposed
by lowered sea levels (Schreiner et al.2013; Mokhatla et al. 2015). Then, during the Holo-
cene, the rising sea levels and a distinct drying period may have caused a signiﬁcant retraction
in the distribution of the species, creating a population bottleneck (Measey & Tolley, 2011),
and through time differentiation between the two sampling sites.
Evidence of a bottleneck is also reﬂected in the microsatellite results; however, the data
also indicate strong gene ﬂow between sites and moderate to high levels of overall genetic
variation as indicated by the allelic richness (the most robust indicator for monitoring
Table 3. Mean microsatellite and mitochondrial DNA genetic diversity indices for Sclerophrys
pantherina from two sampling sites.
Genetic diversity Uilenkraal Klein Paradijs
Sample size (n)3941
Allelic richness (A
) 10.667 8.911
Observed heterozygosity (H
) 0.693 0.667
Expected heterozygosity (H
) 0.795 0.770
Sample size (n)2213
Length (bp) 741 741
No. haplotypes (h)32
Haplotypic diversity (hd) 0.567 ± 0.051 0.154 ± 0.126
Nucleotide diversity (π) 0.00095 ± 0.00022 0.00019 ± 0.00016
No. polymorphic sites (S)3 1
Total no. mutations (Eta)3 1
Figure 2. A median-joining haplotype network based on the ND2 mitochondrial gene for Scler-
ophrys pantherina. Haplotypes are represented by circles and show the proportion of toads from
Uilenkraal (white; n=22) and Klein Paradijs (black; n=13). The area of the circle is proportional
to the number of individuals with that haplotype, and the length of the connecting lines is pro-
portional to the number of base changes between haplotypes (refer to Table 3).
34 DA SILVA ET AL.—Genetic diversity in the Western Leopard Toad
genetic diversity and decline: Hoban et al. 2014)ofeachsamplingsite(A
10.7 and 8.9 for
Uilenkraal and Klein Paradijs, respectively; Tab le 3), compared to other toad species (e.g. Wu
&Hu2010; Roth & Jehle 2016;daSilvaet al. 2016). These microsatellite data tend to depict
the more recent genetic diversity for the species; and, on the surface, it could be interpreted that
the two sampling sites are now healthy and thriving after the bottleneck. However, high levels
of genetic diversity do not always equate to viable populations (Habel & Schmitt 2012).
Levels of genetic diversity differ greatly among species and especially between eco-
logical generalists and specialists. Generalist species tend to have high genetic diversity
within their populations and low genetic differentiation among them due to the absence
of bottlenecks, strong gene ﬂow, and little effects of genetic drift. Specialist species tend
towards low genetic diversity and strong genetic differentiation due to small or ﬂuctuating
populations that experience bottlenecks (e.g. Hughes et al. 1999; Schmitt et al. 2005;
Habel & Schmitt 2009,2012). Based on these distinctions, S. pantherina is probably
not a true habitat specialist nor generalist, but might be an ecologically intermediate
species. Such species must maintain high levels of diversity within their populations,
which is facilitated by gene ﬂow, thereby forcing them to exist in metapopulations
(Habel & Schmitt 2012). Without a network of populations, the species might suffer
greatly from the consequences of population bottlenecks, which result in shifts in allele fre-
quencies, often resulting in the loss of genetic adaptations (Luikart et al. 1998).
Unlike many of the Cape lowland endemics (e.g. Xenopus gilli,Microbatrachella
capensis), the WLT appears to have a high tolerance for disturbance with most, if not
all, of its populations currently breeding in anthropogenically altered habitats (Mokhatla
et al. 2015). This adaptability will be essential to the long-term survival of this species,
which will need to counter predicted fragmentation due to newly created impoundments
in both urban and rural areas (Mokhatla et al. 2015). In addition, this species faces the
threat of an invasive congener currently spreading in the CMA range (Measey et al.
2017). However, there might be a threshold of disturbance beyond which populations
are not able to recover. This might very well explain the loss of other sampling sites in
the EFB region, which were known prior to the 1990s (i.e. Pringle Bay, Betty’s Bay and
Kleinmond: Measey & Tolley 2011;Fig. 1).
Given that the EFB is subject to ongoing development, the two sampling sites exam-
ined here, especially Klein Paradijs, may also be at risk. It is, therefore, imperative that the
current genetic diversity within these two sampling sites be maintained over time, with
conservation efforts focused on preserving connectivity between sites to ensure adequate
gene ﬂow between sampling sites. Future studies using these novel microsatellites could
then be used to monitor the frequency of dispersal between sampling sites and, ultimately,
the extent of genetic erosion/preservation within the EFB.
We would like to thank the South African National Biodiversity Institute and the Field
Museum’s Pritzker Laboratory for Molecular Systematics and Evolution for providing
logistical support and funding for this study. Additional funding was provided by the
SANBI-NORAD Threatened Species Programme. Many thanks also to the landowners,
who gave permission to access their property, Susanna Fuchs and Aletta Groenwald.
Sampling was conducted under permit from the Western Cape provincial conservation
authority, CapeNature (#AAA004-00090-0035) and an ethical clearance certiﬁcate
(# 0001/08 from SANBI Ethics Committee).
AFRICAN JOURNAL OF HERPETOLOGY 2017 35
Supplemental data for this article can be accessed at http://dx.doi.org10.1080/21564574.
ALCALA, N. & S. VUILLEUMIER.2014. Turnover and accumulation of genetic diversity across large
time-scale cycles of isolation and connection of populations. Proc. R. Soc. B. 281: 20141369.
ALLENDORF,F.2005. Genetic drift and the loss of alleles versus heterozygosity. Zoo Biol. 5: 181–190.
AVISE, J.C., J. ARNOLD, R.M. BALL,JR,E.BERMINGHAM,T.LAMB, J.E. NEIGEL, C.A. REEB & N.C.
SAUNDERS.1987. Intraspeciﬁc phylogeography: the mitochondrial DNA bridge between
population genetics and systematics. An. Rev. Ecol. Syst. 18: 489–522.
BANDELT, H.-J., P. FORSTER &A.RÖHL.1999. Median-joining networks for inferring intraspeciﬁc
phylogenies. Mol. Biol. Evol. 16: 37–48.
BANKS, S.C., G. J. CARY, A.L. SMITH, I.D. DAVIES, D.A. DRISCOLL, A.M. GILL,D.B.LINDENMAYER &R.
PEAKALL.2013. How does ecological disturbance inﬂuence genetic diversity? Trends Ecol. Evol.
BJÖRKLUND,M.2005. A method for adjusting allele frequencies in the case of microsatellite allele
drop-out. Mol. Ecol. Notes 5: 676–679.
BROOKFIELD, J.K.Y. 1996. A simple new method for estimating null allele frequency from heterozy-
gote deﬁciency. Mol. Ecol. 5: 453–455.
CAMPAGNE, P., P.E. SMOUSE,G.VAROUCHAS, J.F. SILVAIN &B.LERU.2012. Comparing the van
Oosterhout and Chybicki–Burczyk methods of estimating null allele frequencies for inbred
populations. Mol. Ecol. Resour. 12: 975–982.
CASTELLA, V., M. RUEDI &L.EXCOFFIER.2001. Contrasted patterns of mitochondrial and nuclear
structure among nursery colonies of the Myotis myotis. J. Evol. Biol. 14: 708–720.
CHAKRABORTY, R., M. DEANDRADE,S.P.DAIGER &B.BUDOWLE.1992. Apparent heterozygote
deﬁciencies observed in DNA typing data and their implications in forensic applications. Ann.
Hum. Genet. 56: 45–57.
CHAPUIS, M.-P. & A. ESTOUP.2007. Microsatellite null alleles and estimation of population differen-
tiation. Mol. Biol. Evol. 24: 621–631.
CHERRY, M.I. 1992a. Body size, age and reproduction in the leopard toad, Bufo pardalis. J. Zool.
Lond. 228: 41–50.
CHERRY, M.I. 1992b. Sexual selection in the leopard toad, Bufo pardalis. Behaviour 120: 164–176.
CHESSER, R.K. & R.J. BAKER.1996. Effective sizes and dynamics of uniparentally and diparentally
inherited genes. Genetics 144: 1225–1236.
CHYBICKI, I.J. & J. BURCZYK.2009. Simultaneous estimation of null alleles and inbreeding
coefﬁcients. J. Hered. 100: 106–113.
CHYBICKI, I.J., A. OLESKA &J.BURCZYK.2011. Increased inbreeding and strong kinship structure in
Taxus baccata estimated from both AFLP and SSR data. Heredity 107: 589–600.
COCKERHAM, C.C. & B.S. WEIR.1977. Digenic descent measures for ﬁnite populations. Genet. Res.
CUNNINGHAM, M. & M.I. CHERRY.2004. Molecular systematics of African 20-chromosome toads
(Anura: Bufonidae). Mol. Phylogenet. Evol. 320: 671–685.
DAKIN,E.E.&J.C.AVISE.2004. Microsatellite null alleles in parentage analysis. Heredity 93: 504–509.
DA SILVA, J.M., K.A. FELDHEIM, R.J. DANIELS,S.EDWARDS & K.A. TOLLEY.2016. Analysis of genetic
diversity in Rose’s mountain toadlet (Capensibufo rosei) using novel microsatellite markers.
Afr. J. Herp. 65: 69–82.
DEOLIVEIRA FRANCISCO, F., L.R. SANTIAGO & M.C. ARIAS.2013. Molecular genetic diversity in popu-
lations of the stingless bee Plebeia remota: A case study. Genet. Mol. Biol. 36: 118–123.
DEVILLIERS A. 2004. Species account: Bufo pantherinus A. Smith 1828. In L.R. MINTER,M.
BURGER, J.A. HARRISON,J.BISHOP &H.BRAACK (Eds) Atlas and Red Data Book of the Frogs
of South Africa, Lesotho and Swaziland. Smithsonian Institution Press, Washington DC,
36 DA SILVA ET AL.—Genetic diversity in the Western Leopard Toad
DIRIENZO, A., A.C. PETERSON, J.C. GARZA, A.M. VALDEZ,M.SLATKIN & N.B. FREIMER.1994.
Mutational processes of simple sequence repeat loci in human populations. Proc. Nat. Acad.
Sci. USA. 91: 3166–3170.
ELLEGREN, H. & N. GALTIER.2016. Determinants of genetic diversity. Nat. Rev. Genet. 17: 422–433.
ENGLAND, P.R., G.H.R. OSLER, L.M. WOODWORTH, M.E. MONTGOMERY, D.A. BRISCOE &R.FRANKHAM.
2003. Effects of intense versus diffuse population bottlenecks on microsatellite genetic diversity
and evolutionary potential. Conserv. Genet. 4: 595–604.
EXCOFFIER, L. & H. LISCHER.2010. Arlequin suite ver 3.5: A new series of programs to perform popu-
lation genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10: 564–567.
GARZA, J.C. & E.G. WILLIAMSON.2001. Detection of reduction in population size using data from
microsatellite loci. Mol. Ecol. 10: 305–318.
GLENN, T.C. & N.A. SCHABLE.2005. Isolating microsatellite DNA loci. In E.A. ZIMMER AND E.H.
ROALSON (Eds) Molecular Evolution: Producing the Biochemical Data, Part B. Vol. 395.
Methods in Enzymology. Academic Press, Amsterdam, pp. 202–222.
HABEL, J.C. & SCHMITT,T.2009. The genetic consequences of different dispersal behaviours in two
lycaenid butterﬂy species. Bull. Entomol. Res. 99: 513–523.
HABEL, J.C. & T. SCHMITT.2012. The burden of genetic diversity. Biol. Conserv. 147: 270–274.
HOBAN, S., P. ARNTZEN,G.BERTORELLE,J.BRYJA ,M.FERNANDES,K.FRITH,O.GAGGIOTTI,P.
GALBUSERA, J.A. GODOY, H.C. HAUFFE, A.R. HOELZEL,R.NICHOLS,S.PÉREZ-ESPONA,C.
PRIMMER, I.-R.M. RUSSO,G.SEGELBACHER, H.R. SIEGISMUND,M.SIHVONEN,P.SJÖGREN-GULVE,C.
VERNESI,C.VILÀ & M.W. BRUFORD.2013. Conservation Genetic Resources for Effective
Species Survival (ConGRESS): bridging the divide between conservation research and practice.
J. Nat. Conserv. 21: 433–437.
HOBAN, S., J.A. ARNTZEN, M.W. BRUFORD, J.A. GODOY, A.R. HOELZEL,G.SEGELBACHER,C.VILA &G.
BERTORELLE.2014. Comparative evaluation of potential indicators and temporal sampling proto-
cols for monitoring genetic erosion. Evol. App. 7: 984–998.
HOLM,S.1979. A simple sequential rejective multiple test procedure. Scand. J. Stat. 6: 65–70.
HUGHES, T.P., A.H. BAIRD, E.A. DINSDALE, N.A. MOLTSCHANIWSKYJ, M.S. PRATCHETT, J.E. TANNER &
B.L. WILLIS.1999. Patterns of recruitment and abundance of corals along the Great Barrier
Reef. Nature 397: 59–63.
JOHNSON, J.A., J.E. TOEPFER & P.O. DUNN.2003. Contrasting mitochondrial and microsatellite popu-
lation structure in fragmented populations of greater prairie-chickens. Mol. Ecol. 12: 3335–3347.
KEARSE, M., R. MOIR,A.WILSON,S.STONES-HAVA S ,M.CHEUNG,S.STURROCK,S.BUXTON,A.COOPER,
S. MARKOWITZ,C.DURAN,T.THIERER,B.ASHTON,P.MENTJIES &A.DRUMMOND.2012. Geneious
Basic: an integrated and extendable desktop software platform for the organization and analysis of
sequence data. Bioinformatics 28: 1647–1649. http://www.geneious.com
KOLLECK, J., M. YANG,D.ZINNER &C.ROOS.2013. Genetic diversity in Endangered Guizhou Snub-
nosed monkeys (Rhinopithecus brelichi): Contrasting results from microsatellite and mitochon-
drial DNA data. PLoS ONE 8: e73647.
LACY, R.C. 1997. Importance of genetic variation to the viability of mammalian populations.
J. Mammal. 78: 329–335.
LIBRADO, P. & J. ROZAS.2009. DnaSP v5: A software for comprehensive analysis of DNA poly-
morphism data. Bioinformatics 25: 1451–1452.
LU, G., D.J. BASLEY &L.BERNATCHEZ.2001. Contrasting patterns of mitochondrial DNA and micro-
satellite introgressive hybridization between lineages of lake whiteﬁsh (Coregonus clupeaformis):
relevance for speciation. Mol. Ecol. 10: 965–985.
LUIKART, G., F.W. ALLENDORF, J.-M. CORNUET & W.B. SHERWIN.1998. Distortion of allele frequency
distributions provides a test for recent population bottlenecks. J. Hered. 89: 238–247.
MARKERT, J.A., D.M. CHAMPLIN,R.GUTJAHR-GOBELL, J.S. GREAR,A.KUHN & T.J. MCGREEVY.2010.
Population genetic diversity and ﬁtness in multiple environments. BMC Evol. Biol. 10: 205.
MAY, R.M. 1994. Biological diversity: differences between land and sea. Phil. Trans. R. Soc Lond. B.
MEASEY, G.J. 2011. Ensuring a future for South Africa’s frogs: a strategy for conservation research.
SANBI Biodiversity Series 19. South African National Biodiversity Institute, Pretoria.
MEASEY, G.J. & K.A. TOLLEY.2011. Investigating the cause of the disjunct distribution of
Amietophrynus pantherinus, the Endangered South African western leopard toad. Conserv.
Genet. 12: 61–70.
AFRICAN JOURNAL OF HERPETOLOGY 2017 37
MEASEY, J., DAVIES, S., VIMERCATI, G., REBELO, A., SCHMIDT,W.&TURNER, A.A. 2017. Invasive
amphibians in southern Africa: a review of invasion pathways. Bothalia 47(2), a2117. https://
MOKHATLA, M.M., D. RÖDDER & G.J. MEASEY.2015. Assessing the effects of climate change on dis-
tributions of Cape Floristic Region amphibians. S. Afr. J. Sci. 111: 1–7.
NEI,M.1987. Molecular Evolutionary Genetics. Columbia University Press, New York.
PAETKAU, D. & C. STROBECK.1995. The molecular basis and evolutionary history of a microsatellite
null allele in bears. Mol. Ecol. 4: 519–520.
PEERY, M.Z., R. KIRBY, B.N. REID,R.STOELTING,E.DOUCET-BËER,S.ROBINSON,C.VÁSQUEZ-CARRILLO,
J.N. PAULI & P.J. PALSBØLL.2012. Reliability of genetic bottleneck tests for detecting recent popu-
lation declines. Mol. Ecol. 21: 3403–3418.
RAYMOND,M.&F.ROUSSET.1995. GENEPOP (version 1.2): population genetics software for exact
tests and ecumenicism. J. Heredity 86: 248–249.
RICE, W.R. 1989. Analyzing tables of statistical tests. Evolution 43: 223–225.
ROQUES, S., P. DUCHESNE &L.BERNATCHEZ.1999. Potential of microsatellites for individual assign-
ment: the North Atlantic redﬁsh (genus Sebastes) species complex as a case study. Mol. Ecol.
ROTH, S. & R. JEHLE.2016. High genetic diversity of common toad (Bufo bufo) populations under
strong natural fragmentation on a Northern archipelago. Ecol. Evol. 6: 1626–1636.
ROUSSET,F.2008. Genepop’007: a complete reimplementation of the Genepop software for Windows
and Linux. Mol. Ecol. Res. 8: 103–106.
SA-FROG (IUCN SSC AMPHIBIAN SPECIALIST GROUP &SOUTH AFRICAN FROG RE-ASSESSMENT GROUP).
2016.Sclerophrys pantherina. The IUCN Red List of Threatened Species 2016:
Downloaded on 10 January 2017.
SCHMITT, T., RÖBER, S., SEITZ, A.. 2005. Is the last glaciation the only relevant event for the present
genetic population structure of the Meadow Brown butterﬂyManiola jurtina (Lepidoptera:
Nymphalidae)? Biol. J. Linn. Soc. 85: 419–431.
SCHREINER, C., D. RÖDDER & G.J. MEASEY.2013. Using models to test Poynton’s predictions.
Afr. J. Herpetol. 62: 49–62.
SCHWARTZ, M.K., G. LUIKART & R.S. WAPLES.2007 Genetic monitoring as a promising tool for con-
servation and management. Trends Ecol. Evol. 22: 25–33.
SLATKIN,M.1995. A measure of population subdivision based on microsatellite allele frequencies.
Genetics 139: 457–462.
SLATKIN, P.E. & N.H. BARTON.1989. A comparison of three indirect methods for estimating average
levels of gene ﬂow. Evolution 43: 1349–1368.
SPIELMAN, D., B.W. BROOK &R.FRANKHAM.2004. Most species are not driven to extinction before
genetic factors impact them. Proc. Natl. Acad. Sci. 101: 15261–15264.
VAN OOSTERHOUT, C., W.F. HUTCHINSON, D.P.M. WILLS &P.SHIPLEY.2004. Micro-Checker:
software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol.
Notes 4: 535–538.
WEBER, J.L. & C. WONG.1993. Mutation of human short tandem repeats. Hum. Mol. Genet. 2:
WILLOUGHBY, J.R., M. SUNDARAM, B.K. WIJAYAWARDENA, S.J.A. KIMBLE,Y.JI, N.B. FERNANDEZ, J.D.
ANTONIDES, M.C. LAMB, N.J. MARRA & J.A. DEWOODY.2015. The reduction of genetic diversity in
threatened vertebrates and new recommendations regarding IUCN conservation rankings. Biol.
Conserv. 191: 495–503.
WRIGHT,S.1978. Evolution and the Genetics of Populations. Variability Within and Among Natural
Populations, Vol. IV. University of Chicago Press, Chicago, IL.
WU, X.-B., HU, Y.-L.. 2010. Genetic diversity and molecular differentiation of Chinese toad based on
microsatellite markers. Mol. Biol. Rep. 37: 2379–2386.
YANG, D.-S. & G.J. KENAGY.2009. Nuclear and mitochondrial DNA reveal contrasting evolutionary
processes in populations of deer mice (Peromyscus maniculatus). Mol. Ecol. 18: 5115–5125.
Received: 29 September 2016; Final acceptance: 2 February 2017
38 DA SILVA ET AL.—Genetic diversity in the Western Leopard Toad