Download The tales of two geckos: does dispersal prevent extinction in recently

Molecular Ecology (2007) 16, 3299–3312
doi: 10.1111/j.1365-294X.2007.03352.x
The tales of two geckos: does dispersal prevent extinction in
recently fragmented populations?
Blackwell Publishing Ltd
M . H O E H N ,*† S . D . S A R R E † and K . H E N L E *
*Helmholtz Centre for Environmental Research — UFZ, Department of Conservation Biology, Permoserstrasse 15, 04318 Leipzig,
Germany, †Applied Ecology Research Group, University of Canberra, ACT 2601, Australia
Abstract
Although habitat loss and fragmentation threaten species throughout the world and are a
major threat to biodiversity, it is apparent that some species are at greater risk of extinction
in fragmented landscapes than others. Identification of these species and the characteristics
that make them sensitive to habitat fragmentation has important implications for conservation management. Here, we present a comparative study of the population genetic structure
of two arboreal gecko species (Oedura reticulata and Gehyra variegata) in fragmented and
continuous woodlands. The species differ in their level of persistence in remnant vegetation
patches (the former exhibiting a higher extinction rate than the latter). Previous demographic
and modelling studies of these two species have suggested that their difference in persistence
levels may be due, in part, to differences in dispersal abilities with G. variegata expected
to have higher dispersal rates than O. reticulata. We tested this hypothesis and genotyped
a total of 345 O. reticulata from 12 sites and 353 G. variegata from 13 sites at nine microsatellite loci. We showed that O. reticulata exhibits elevated levels of structure (FST = 0.102
vs. 0.044), lower levels of genetic diversity (HE = 0.79 vs. 0.88), and fewer misassignments
(20% vs. 30%) than similarly fragmented populations of G. variegata, while all these parameters
were fairly similar for the two species in the continuous forest populations (FST = 0.003 vs.
0.004, HE = 0.89 vs. 0.89, misassignments: 58% vs. 53%, respectively). For both species, genetic
structure was higher and genetic diversity was lower among fragmented populations than
among those in the nature reserves. In addition, assignment tests and spatial autocorrelation
revealed that small distances of about 500 m through fragmented landscapes are a barrier
to O. reticulata but not for G. variegata. These data support our hypothesis that G. variegata
disperse more readily and more frequently than O. reticulata and that dispersal and habitat
specialization are critical factors in the persistence of species in habitat remnants.
Keywords: continuous, dispersal, effective population size, geckos, generalist, habitat fragmentation,
microsatellites, population genetics, specialization
Received 9 December 2006; revision accepted 20 March 2007
Introduction
Habitat loss and fragmentation threaten species throughout the world and are a major threat to biodiversity
(Groombridge 1992; WCMC 1992). When formerly
contiguous vegetation becomes fragmented through land
clearing, small isolated populations will result for many
species. Emerging empirical evidence suggests that some
species are at greater risk of extinction in fragmented
Correspondence: Marion Hoehn, Fax: +49 341 235 3191; E-mail:
[email protected]
© 2007 The Authors
Journal compilation © 2007 Blackwell Publishing Ltd
landscapes than others. Identification of these species and
the characteristics that make them sensitive to habitat
fragmentation has important implications for the management of species as well as contributing to our understanding
of ecological and evolutionary theory (Sarre et al. 1995;
MacNally et al. 2000; Davies et al. 2000, 2001, 2004; Henle
et al. 2004a, b; Schmuki et al. 2006).
Dispersal between an individual’s birthplace and that of
its offspring is one of the most important life-history traits
in species persistence (Koenig et al. 1996; Clobert et al. 2001;
Sumner et al. 2001). Dispersal will usually result in gene
flow, the movement and integration of alleles from one
3300 M . H O E H N , S . D . S A R R E and K . H E N L E
population into another. Gene flow protects small populations from processes such as reduction in the number of
alleles, reduction in heterozygosity, and reduction in
genetic diversity through genetic drift and inbreeding
(Frankham et al. 2002; Keller & Waller 2002). Although
dispersal used to be difficult to study, genetic markers
provide a powerful tool for obtaining indirect estimates of
dispersal and gene flow in natural populations. Recent
advances in statistical techniques and the analysis of microsatellite data promise to simplify studies of dispersal and
colonization. For instance, assignment testing can improve
the resolution of dispersal patterns by assigning individuals to their most likely population of origin (Paetkau et al.
1995; Rannala & Mountain 1997; Cornuet et al. 1999; Pritchard
et al. 2000). Like F-statistics, the basis of assignment protocols
is an estimate of the allele frequency within populations
and therefore requires an a priori identification of population boundaries. In contrast, analytical methods that do not
require information about population boundaries, such as
isolation-by-distance analysis and spatial autocorrelation,
can use genetic and geographical information to describe
dispersal and genetic spatial structure in a two-dimensional
landscape (Rousset 1997; Peakall & Smouse 2001; Double
et al. 2005).
Here, we present a comparative study of the genetic
structure of two gecko species in fragmented and continuous
woodlands. The species occur sympatrically, but have
responded differently to the fragmentation of their woodland habitat. Gehyra variegata (tree dtella) is abundant and
widespread throughout the southern half of Australia. It is
a habitat generalist, and can be found on trees, logs, fallen
timber, shrubs, rocks, and in highly disturbed habitat.
Oedura reticulata (reticulated velvet gecko) is endemic to
the southwest of Western Australia and is a habitat specialist.
It is limited in its habitat range, being exclusively arboreal
and restricted to smooth-barked Eucalyptus woodlands.
Both species have been subjected to severe population
fragmentation in the Western Australian wheatbelt, where
numerous very small populations persist in patches of
woodland surrounded by land used for wheat crops. In
summer, the intervening matrix is often reduced to sparse
stubble or even predominantly bare earth (How & Kitchener
1983; Kitchener et al. 1988; Sarre 1995a, b, 1998; Sarre et al.
1995).
In a previous empirical study, Sarre et al. (1995) demonstrated that G. variegata showed a markedly higher persistence
than O. reticulata in habitat remnants (97% remnant occupancy vs. 72%). In addition, studies 10 years apart (1991
and 2001) documented extinction of O. reticulata in three of
33 habitat remnants, whereas G. variegata has persisted at
comparable population sizes in all 33 remnants over the
same time period (M. Hoehn, unpublished data). Individualbased population viability models developed for both
species and including demographic and environmental
stochasticity also show contrasting expectations for the
two species (Sarre et al. 1996; Wiegand et al. 2001, 2002).
Whereas rates of persistence observed were lower for O.
reticulata than those predicted by the population viability
model, the opposite was the case for G. variegata. One of
the reasons for the discrepancies between predicted and
observed distributions may be that the models did not
include an expectation of dispersal between remnants for
either species. While the dispersal capability of O. reticulata
is believed to be low, pitfall trapping and anecdotal evidence
suggest that movement by G. variegata is also infrequent
(Sarre et al. 1995; R. How, personal communication).
Nevertheless, occasional longer-distance dispersal is
known for G. variegata (Moritz 1987; K. Henle & B. Gruber,
unpublished data.). Sarre et al. (1996) suggested that the
discrepancy between the observed and modelled persistence
rates could be best explained by greater rates of movement
for G. variegata than for O. reticulata.
To investigate further this dichotomy of responses to
fragmentation, we used microsatellite DNA markers to
compare the genetic structure and rates of dispersal in the
two gecko species. Our expectation is that the high level of
persistence exhibited by G. variegata is a result of a higher
dispersal rate compared with O. reticulata. Thus, we predicted higher levels of genetic differentiation and lower rates
of dispersal among O. reticulata than among G. variegata
populations. We surveyed the genetic structure in fragmented
and continuous populations of these two species to test our
hypotheses. In addition, we expected a loss of genetic
diversity and a higher level of genetic structure for both
species in habitat fragments.
Materials and methods
Study area and sampling
The study area was located between Kellerberrin and
Trayning in the Western Australian wheatbelt (Fig. 1).
Large areas of native vegetation have been removed from
the region and replaced by agricultural crops, pastures,
and livestock. Since 1900, approximately 93% of the original
vegetation has been cleared and the remnant vegetation is
distributed over thousands of patches of varying size
(Saunders & Hobbs 1991; Hobbs 1993; Saunders et al. 1993).
Tissue samples were collected from the two gecko species during the summer months, from November 2000
until March 2001, in December 2003, and in November
2005. A total of 345 Oedura reticulata individuals from six
habitat fragments and six locations in two nature reserves
and a total of 353 Gehyra variegata individuals from seven
habitat fragments and six locations in two nature reserves
were sampled for genetic analysis. The majority of continuous habitat has been cleared in the area, but three sites per
species were located within 1 km in the North Bandee
© 2007 The Authors
Journal compilation © 2007 Blackwell Publishing Ltd
D I S P E R S A L A N D E X T I N C T I O N I N F R A G M E N T E D G E C K O P O P U L A T I O N S 3301
Fig. 1 Map of the study area and location
of sites in the Western Australian wheatbelt. Oedura reticulata populations inhabit
fragments labelled ORF1-6 and continuous
sites labelled ORC1-3 (North Bandee Nature
Reserve), Gehyra variegata fragments are
labelled GVF1-7 and continuous sites
GVC1-3 (North Bandee Nature Reserve).
Korrelocking Nature Reserve with sites
labelled ORC4-6 and GVC4-6 are not shown.
Nature Reserve, which comprises 174 ha of continuous
woodland habitat. Another three populations of each
species where located within 1.2 km in the Korrelocking
Nature Reserve (259 ha).
Lizards were located at night using head-torches and
captured by hand. The tip of the tail of each individual was
removed and stored in liquid nitrogen. In all fragments,
25–30 samples were collected with the exception of fragment population 7 (N = 13) for G. variegata, where no further individuals could be located. The sample populations
were labelled independently for each species, with one
habitat fragment (or ‘patch’ or ‘remnant’) equivalent to one
sample population. The O. reticulata populations were
labelled ORF1-6, and the G. variegata populations GVF1-7
(Table 1). In two cases, a habitat remnant was used as a
sample population for both species, that is G. variegata population GVF3 inhabited the same fragment as O. reticulata
population ORF5, and similarly, G. variegata population
GVF6 occupied the same habitat patch as O. reticulata
population ORF4. All other sample populations were from
separate fragments, with no overlap between the two
species. Continuous woodland sites were used as sample
populations for both species and were numbered ORC1-3
and GVC1-3 in the North Bandee Nature Reserve and
ORC4-6 and GVC4-6 in the Korrelocking Nature Reserve.
The presence and absence of O. reticulata and G. variegata
populations in the study area was determined in previous
surveys (Sarre et al. 1995; M. Hoehn, unpublished data).
This distribution data was used to design the following
© 2007 The Authors
Journal compilation © 2007 Blackwell Publishing Ltd
sampling strategies (semi-experimental approach) for
studying assignment and dispersal between pairs of habitat patches on a fine geographical scale. We selected three
pairs of O. reticulata populations, separated by 150, 550,
and 580 m, and three pairs of G. variegata populations, separated by 150, 300, and 1000 m (Table 1). In most cases the
distance to other extant populations was large and we consider that dispersal from individuals of other populations
into the sample populations was likely to be very low.
However, two neighbouring O. reticulata populations (ORF4
and ORF1) were close to the same roadside vegetation
(150 m and 230 m distant), which could harbour a potential
source population of O. reticulata or function as a corridor.
Two neighbouring G. variegata populations were also close
to roadside vegetation (0 m and 350 m distant), but in this
case the roadside vegetation did not create a connection
between the neighbouring habitat fragments. All other
sample populations, for both species, were separated from
roadside vegetation by distances exceeding 400 m. In the
North Bandee Nature Reserve, we selected two pairs of
populations separated by 400 m and 650 m and in the
Korrelocking Nature Reserve, two pairs of populations
separated by 700 m and 500 m for each species.
The census population size was determined with the
program capture (Otis et al. 1978) for another study (Hoehn
2006). The values ranged from 33 to 197 individuals for
O. reticulata and from 18 to 266 for G. variegata and we selected
populations so that the mean population size (124 and 95,
respectively) would be similar between species.
3302 M . H O E H N , S . D . S A R R E and K . H E N L E
Table 1 Habitat fragments and continuous forest sites sampled (C1-3 North Bandee Nature Reserve, C4-6 Korrelocking Nature Reserve)
and reference number (ref) referring to the study of Sarre et al. (1995). The number of individuals genotyped (samples), fragment size (size
in ha), population size (pop size) calculated by the program capture (Hoehn 2006), distances to neighbouring fragments (distance NF) and
the road side vegetation (distance RSV) and, approximate time (years) since isolation from Sarre (1995a)
Fragment
Ref
Species
Samples
Size
Pop size
Distance NF
Distance RSV
Isolation
ORF1
ORF4
ORF2
ORF3
ORF5
ORF6
ORC1
ORC2
ORC3
ORC4
ORC5
ORC6
GVF1
GVF2
GVF3
GVF4
GVF5
GVF6
GVF7
GVC1
GVC2
GVC3
GVC4
GVC5
GVC6
170
—
s85
s84
442
—
—
—
—
—
—
—
171
—
442
—
169
s143
168
—
—
—
—
—
—
Oedura
Oedura
Oedura
Oedura
Oedura
Oedura
Oedura
Oedura
Oedura
Oedura
Oedura
Oedura
Gehyra
Gehyra
Gehyra
Gehyra
Gehyra
Gehyra
Gehyra
Gehyra
Gehyra
Gehyra
Gehyra
Gehyra
Gehyra
30
27
30
27
30
30
30
17
30
31
32
31
30
30
30
30
30
25
13
30
24
29
33
23
27
0.5
0.4
0.8
0.4
5.4
2
—
—
—
—
—
—
0.3
1.4
5.4
2
0.3
0.5
0.6
—
—
—
—
—
—
197
35
168
33
167
141
—
—
—
—
—
—
—
76
266
127
50
30
18
—
—
—
—
—
—
580 m
580 m
550 m
550 m
150 m
150 m
400 m
400 m/650 m
650 m
700 m
700 m/500 m
500 m
300 m
300 m
150 m
150 m
1000 m
1000 m
400 m
400 m/650 m
650 m
700 m
700 m/500 m
500 m
400 m
230 m
150 m
1000 m
500 m
450 m
450 m
—
—
—
—
—
—
350 m
0m
450 m
450 m
400 m
1000 m
250 m
—
—
—
—
—
—
90
90
60
60
90
90
—
—
—
—
—
—
90
90
90
90
80
80
80
—
—
—
—
—
—
Laboratory methods
DNA was extracted from the tip of the tail of each individual using the Chelex extraction method (Walsh et al.
1991). We genotyped individuals of O. reticulata using
nine tetranucleotide microsatellite loci developed from an
enriched library for this species (OR205, OR220, OR266,
OR6F4, OR10H7, OR11G3, OR12D7, OR12D9, OR14A7)
(Hoehn & Sarre 2005). For G. variegata, we genotyped
individuals using nine tetranucleotide microsatellite markers
cloned from an enriched library for this species (GV1C5,
GV1C10, GV1F1, GV3B5, GV3C6, GV3E10, GV4B6, GV4G6,
GV4C9) (Hoehn & Sarre 2006). Polymerase chain reaction
(PCR) amplification and genotyping using the Beckman
Coulter CEQ 8000 were performed according to conditions
described in Hoehn & Sarre (2005) and Hoehn & Sarre
(2006).
locus — were calculated using fstat 2.9.3 (Goudet 1995,
2001). Global tests for deviation from Hardy–Weinberg
were performed. In addition, we tested heterozygote deficits
per locus and habitat fragment employing a sequential
Bonferroni correction (fstat 2.9.3).
The population genetic structure was investigated by
estimating FST using the method of Weir & Cockerham
(1984) in the program fstat 2.9.3. The significance of mean
F-statistics was assessed by constructing 95% confidence
intervals (CI) by jackknifing across loci using genetix 4.01
(Belkhir et al. 2001). For the comparison of mean FST
between species and between landscapes, t-tests for statistical significance were performed. Interpretation of genetic
differentiation values between species is often problematic
because of their dependence on the level of genetic variation.
To address this, we applied a standardized measure of
genetic differentiation (Hedrick 1999, 2005). The standardized
GST, GST
′ is defined as:
Statistical analysis
Descriptive statistics — the allelic richness (An) (number of
alleles corrected by sample size, based on a minimum
sample size of 13 individuals for both species) and the
observed (HO) and expected (HE) heterozygosities per
GST
′ =
GST
GST(max)
(1)
where GST is an estimate of FST for a locus with multiple
alleles and
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GST(max) =
(k − 1)(1 − H S )
k − 1 + HS
(2)
where k is the number of equal-sized subpopulations
and HS is the average subpopulation Hardy–Weinberg
heterozygosity. Pairwise GST
′ values were calculated for
each species. The significance of GST
′ between species and
between landscapes was assessed by calculating the mean
GST
′ and performing a t-test.
Isolation by distance was tested through linear regression of FST/(1 – FST) against the geographical distance
between pairs of habitat fragments (Rousset 1997). The
Mantel test option in fstat 2.9.3 was used to assess the
significance of correlation between matrices of genetic differentiation and geographical distance between sampled
populations. Geographical straight-line distance between
the edges of the fragments within the study area was determined using the map generated by M. G. Brooker, CSIRO,
Division of Wildlife and Ecology, Perth and Department of
Agriculture, Western Australia.
We applied spatial autocorrelation (SA) analysis,
implemented in genalex 6.0 (Peakall & Smouse 2001),
as an additional method to estimate if dispersal and gene
flow are limited. Unlike traditional SA (Sokal & Oden 1978),
this technique employs a multivariate approach that
strengthens the spatial signal and reduces the noise. Using
pairwise geographical and pairwise squared genetic distance
matrices, it generates an autocorrelation coefficient, r,
which is closely related to Moran’s I (Sokal & Oden 1978).
The autocorrelation coefficient provides a measure of the
genetic similarity between pairs of individuals whose geographical separation falls within the specified distance class.
Significant positive autocorrelation implies that individuals
within a particular distance class are more genetically similar
than expected by random. Tests for statistical significance
were performed by random permutation with a Monte
Carlo simulation performed with 1000 permutations and
1000 bootstraps for estimation of 95% confidence intervals.
We performed this analysis separately for habitat fragments
and nature reserves using the even-distance class option
(using 25 distance classes, 100 m each).
To provide an index of dispersal rates between populations, assignments were conducted using the programs
structure 2.1 (Pritchard et al. 2000) and geneclass 1.0.02
(Cornuet et al. 1999). Using the Bayesian clustering method
implemented in structure 2.1, six populations (three
population pairs) in the fragments and six populations (four
population pairs) in the continuous forest were analysed
for each species. Population GVF7 was omitted from this
analysis because of the small sample size (N = 13) and
because dispersal was assessed between pairs of populations. For every population pair, five independent runs of
K = 2 were performed at 200 000 Markov chain Monte
Carlo repetitions and 100 000 burn-in periods. No prior
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Journal compilation © 2007 Blackwell Publishing Ltd
information about the potential source populations was
incorporated, admixture was assumed and the model with
correlated allele frequency was selected. For some population pairs, structure 2.1 failed to confidently assign any
individual to a single genetic population. Thus, in a second
step of the analysis, runs were performed at K = 1–5 to
assess if some of the paired sites actually constitute a single
panmictic population.
The Bayesian method of likelihood-based assignment
tests were implemented using geneclass 1.0.02 (Rannala
& Mountain 1997; Cornuet et al. 1999). In a first step, individuals were directly assigned to the population for which
their probability of belonging is highest. In a second step,
a probability that the individual belongs to each population
was simulated and individuals were assigned to the population for which their probability of belonging is highest,
and where the arbitrary threshold value is above P = 0.05.
Results
A total of 345 Oedura reticulata from 12 sites (six fragments
and six continuous forest sites) and 353 Gehyra variegata
from 13 sites (seven fragments and six continuous forest
sites) were genotyped at nine microsatellite loci. Four of
the 108 tests for Hardy–Weinberg equilibrium in O. reticulata
and six of the 117 tests in G. variegata showed significant
deviation from expected genotype frequencies after
Bonferroni correction; all were due to a deficiency of
heterozygotes. The heterozygote deficiency may be due to
null alleles; although there does not appear to be any
consistent pattern of Hardy–Weinberg deviation among
loci, or populations. No linkage disequilibrium was detected
between loci within populations at any locality and therefore independence among loci has been assumed in the
subsequent analyses.
Genetic diversity
In O. reticulata, allelic richness (An) per site and locus ranged
from 4.00 to 19.26 in fragments and from 6.00 to 17.53 in
continuous forest sites. We detected a highly significant
difference in the mean allelic richness between fragmented
and continuous forest sites (mean 7.97 and 11.36, respectively,
Mann–Whitney U-tests: P < 0.001) (Fig. 2a). In G. variegata,
allelic richness (An) per site and locus ranged from 4.74 to
16.00 in fragments and from 7.44 to 16.42 in continuous
forest sites. The difference in the allelic richness between
fragments and continuous forest sites was significant (mean
9.95 and 10.77, respectively, P < 0.05). Our analysis showed
that fragmented populations of O. reticulata have significantly
lower allelic richness than fragmented populations of G.
variegata (P < 0.001), while there is no significant difference
in allelic richness between populations of the two species
in the nature reserves (P = 0.48) (Fig. 2b).
3304 M . H O E H N , S . D . S A R R E and K . H E N L E
Fig. 2 Comparison of allelic richness (An),
expected (HE) heterozygosity, FST and GST
′
estimates (a) between fragments and
continuous forest site for Oedura reticulata
(above) and Gehyra variegata (below) and
(b) between Oedura reticulata and Gehyra
variegata in fragments (above) and
continuous forest site (below).
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D I S P E R S A L A N D E X T I N C T I O N I N F R A G M E N T E D G E C K O P O P U L A T I O N S 3305
Average expected heterozygosity (HE) observed in O.
reticulata ranged from 0.69 to 0.87 in fragments (mean HE:
0.79) and from 0.85 to 0.92 in continuous forest sites (mean
HE: 0.89). In G. variegata, expected heterozygosity (HE)
averaged across loci ranged from 0.85 to 0.90 in fragments
(mean HE: 0.88) and from 0.88 to 0.91 in continuous forest
sites (mean HE: 0.89). Populations of both species showed
a significant difference in heterozygosity between fragments
and continuous forest sites (Fig. 2a). However, the level of
significance for O. reticulata (P < 0.001) was considerably
higher than that for G. variegata (P < 0.05). Furthermore,
tests revealed that fragment populations of O. reticulata
have significantly lower heterozygosity than fragment
populations of G. variegata (P < 0.001), while there is no significant difference between populations of either species in
the continuous forests (P = 0.42) (Fig. 2b).
Population differentiation
Both gecko species showed significant genotypic differentiation among fragment populations (P < 0.01). However,
FST measures revealed higher levels of subdivision among
fragmented populations of O. reticulata (FST = 0.102; 95% CI
0.086–0.119) than among fragmented populations of G.
variegata (FST = 0.044; 95% CI 0.037– 0.050, t-test: P < 0.001).
Conversely, no significant difference in subdivision was
observed between species in the continuous woodland
(O. reticulata: FST = 0.003; 95% CI 0.001– 0.005; G. variegata:
FST = 0.004; 95% CI 0.001– 0.007, t-test: P = 0.26) and there
was no significant genotypic differentiation among continuous populations (Fig. 2b).
In O. reticulata, pairwise FST estimates ranged from 0.041
to 0.163 among fragment populations and from 0.001 to
0.007 among populations in the continuous nature reserves.
The level of differentiation was significantly higher in fragmented (FST = 0.109; 95% CI 0.079 – 0.140) compared with
continuous populations (FST = 0.003; 95% CI 0.001– 0.005, ttest: P < 0.001) (Fig. 2a). The same trend was apparent in G.
variegata with pairwise FST estimates ranging from 0.007 to
0.081 among fragment populations and from 0.000 to 0.009
among populations in the continuous woodland. Differentiation among fragmented populations (FST = 0.025; 95%
CI 0.010–0.041) appeared to be significantly higher than
among continuous populations (FST = 0.004; 95% CI 0.001–
0.007, t-test: P < 0.01) (Fig. 2a). For the previous comparison,
we included pairwise FST estimates only from fragment
populations that were in close proximity to each other. The
mean geographical distance between these fragments was
670 m for O. reticulata and 650 m for G. variegata compared
with a mean geographical distance of 750 m among the
continuous forest sites for both species.
Using the standardized values of GST, the level of differentiation was also higher in O. reticulata (GST
′ = 0.45, 95% CIs:
0.40–0.49) than in G. variegata ( GST
′ = 0.34, 95% CIs: 0.29–
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Journal compilation © 2007 Blackwell Publishing Ltd
0.39). Tests showed that there was a significant difference
between species (t-test, P < 0.01). All other test followed
the trend shown by the FST values (Fig. 2b).
Most pairs of sites exhibited a fairly similar range of FST
values, although for O. reticulata population, ORF3 had
larger values on average than the other sites. In addition,
ORF3 seemed to have lower allelic richness and heterozygosity. Therefore, we performed all calculations without
the outlier population and found that differences between
the species in A n and H S and F ST were still significant
(P < 0.001) with the exception that the standardized
value of GST
′ was only approaching significance (t-test,
P = 0.06) compared with P < 0.05 when all populations are
included.
Isolation by distance
A positive association between genetic differentiation
(FST/1 – FST) and the ln of geographical distance separating
samples (P < 0.01) was observed for G. variegata. But no
such pattern was apparent for O. reticulata with or without
the outlier population of ORF3 when total geographical
distance was taken into account, suggesting that distance
between remnants had no influence on the genetic structure
in this species (Fig. 3).
Fig. 3 Relationship between the logarithms of geographical
distances and genetic differentiation estimated as FST/(1 – FST) for
Oedura reticulata (above) and Gehyra variegata (below).
3306 M . H O E H N , S . D . S A R R E and K . H E N L E
Fig. 4 (a) Spatial autocorrelation correlograms for fragmented Oedura reticulata
populations (above) and fragmented Gehyra
variegata populations (below). (b) Spatial
autocorrelation correlograms for continuous
Oedura reticulata populations (above) and
continuous Gehyra variegata populations
(below). Distances are in metres and only
populations from the Korrelocking Nature
Reserve are shown. The permuted 95%
confidence interval (dashed lines) and the
bootstrapped 95% confidence error bars are
also shown. The autocorrelation coefficient,
r, provides a measure of the genetic
similarity between pairs of individuals and
significant positive autocorrelation implies
that individuals within a particular distance
class are more genetically similar than
expected by random.
Spatial autocorrelation
The outcome of the spatial autocorrelation analysis of the
two gecko species is presented in Fig. 4. The correlogram
produced for the fragment populations in G. variegata
indicates a significant positive correlation in the first
three distance classes, 100 m (r = 0.070, P = 0.01), 200 m
(r = 0.027, P = 0.01) and 400 m (r = 0.033, P = 0.01), with an
intercept of 951 m (Fig. 4a). An autocorrelation focusing on
the nature reserve populations revealed no significant spatial
structure in any of the distance classes. For convenience,
Fig. 4(b) only shows the results from the populations in
the Korrelocking Nature Reserve, but results from the
North Bandee Nature Reserve are comparable. The spatial
autocorrelation of fragment populations for O. reticulata
showed contrasting results to the isolation-by-distance
analysis. The analysis revealed a significant positive genetic
structure up to a distance of 518 m, with positive r in the
distance classes, 100 m (r = 0.15, P = 0.01) and 200 m (r = 0.03,
P = 0.01). Spatial autocorrelation seems to have a higher
potential than isolation-by-distance analysis for identifying
structure at a genetic fine-scale level. Nevertheless, the
analysis of the continuous forest populations showed no
sign of significant positive correlation.
Assignment
Both the Bayesian clustering method (structure 2.1) and
the direct Bayesian method (geneclass 1.0.02) produced
consistent results that could be used to estimate dispersal
probabilities. Figure 5 shows the results from the Bayesian
clustering method (structure 2.1). We observed a higher
percentage of misassignments in fragmented populations
of G. variegata (mean of 30% for both structure 2.1 and
geneclass 1.0.02 methods, 0.070 and 0.045 standard error
(SE), respectively, subsequent data always presented in
this order) than of O. reticulata (mean of 15% and 20%,
0.047 and 0.096 SE, respectively). This occurred even
though the populations of O. reticulata were in slightly
closer proximity to each other. For O. reticulata, the
© 2007 The Authors
Journal compilation © 2007 Blackwell Publishing Ltd
D I S P E R S A L A N D E X T I N C T I O N I N F R A G M E N T E D G E C K O P O P U L A T I O N S 3307
Fig. 5 Average probability for the individuals of (a) Oedura reticulata and (b) Gehyra variegata populations having originated in the source
population or a neighbouring population derived from data of the Bayesian clustering method (structure 2.1). Neighbouring population
pairs are ORF1/ORF4; ORF2/ORF3; ORF5/ORF6 for Oedura reticulate and GVF1/GVF2; GVF3/GVF4; GVF5/GVF6 for Gehyra variegata.
assignment tests revealed that the two closest populations
(ORF5 and ORF6: 150 m) had the highest percentage of
misassigned individuals (27% and 50%, respectively).
Populations ORF1 and ORF4 and populations ORF2 and
ORF3 are isolated by 580 m and 550 m, respectively, and
the assignment tests revealed that between 6% and 19%
were misassigned for the first population pair, and between
0 and 4% for the second. For G. variegata, the highest
percentage (between 40% and 50%) of misassigned individuals was between the two populations GVF3 and GVF4,
which were closest in distance (150 m). Populations GVF1
and GVF2 are isolated by 300 m and had a misassignment
© 2007 The Authors
Journal compilation © 2007 Blackwell Publishing Ltd
rate between 17% and 27%. Populations GVF5 and GVF6
were 1000 m apart and showed relatively high misassignment rates (between 8% and 40%). In the continuous forest
populations, we observed a higher percentage of misassignments for both species (e.g. Korrelocking Nature Reserve:
O. reticulata: mean of 50% and 58%, 0.00 and 0.038 SE,
respectively; G. variegata: mean of 50% and 53%, 0.00 and
0.063 SE, respectively) than in the fragmented populations.
Data for the North Bandee Nature Reserve is corresponding.
Using the Bayesian approach with a threshold level of
P ≤ 0.05 for the habitat fragments, only 77 of 174 individuals
of O. reticulata and even fewer individuals of G. variegata
3308 M . H O E H N , S . D . S A R R E and K . H E N L E
(25 of 171) could be assigned. Thus, this level of constraint
provided little information: we identified only nine of 77
assigned O. reticulata as dispersers (mean = 3.0 per population pair) and six of 25 assigned G. variegata (mean = 2.0
per population pair). The majority of the O. reticulata
dispersers (five) were identified between the two closest
populations (ORF5 and ORF6) whereas the six G. variegata
dispersers were more evenly distributed among the three
population pairs. In the Korrelocking Nature Reserve, only
seven of 12 O. reticulata were assigned as dispersers (mean
= 3.5 per population pair) and four of four G. variegata were
assigned (mean = 2.0 per population pair).
For population pairs ORF5/ORF6 and GVF3/GVF4,
and for all the population pairs in the two nature reserves,
structure 2.1 failed to confidently assign any individual
to a single population. Thus, we estimated the number of
clusters (K) for these populations. Log-likelihood values
were found to be similarly low for K = 1 and K = 2 or lower
for K = 1. For comparison, we also calculated the number
of K for other population pairs, where log-likelihood
values were low for K ≥ 2, but substantially higher for
K = 1. This result indicates that the fragments, which are
in close geographical distances, and the two nature reserves
constitute a single panmictic population for both species.
Discussion
Dispersal is a key trait of species that avoid extinction
following habitat fragmentation. When populations become
fragmented, dispersal between patches can provide a
‘top-up’ or ‘rescue effect’ for small, resident populations,
which reduces the probability of local extinction (Brown
& Kodric-Brown 1977; Hanski 1999). Understanding the
impacts of human-induced fragmentation upon dispersal
as opposed to the impacts of other environmental changes
is problematic because of the nearly universal lack of
prefragmentation data in naturally occurring populations
(Srikwan & Woodruff 2000; Sumner et al. 2004). In this
context, comparative molecular approaches that contrast
fragmented and nonfragmented populations, combined
with detailed field and modelling studies, provide one of
the few ways of exploring such an important topic (Young
& Clarke 2000; Stow et al. 2001; Caizergues et al. 2003;
Segelbacher et al. 2003; Williams et al. 2003; Stow & Sunnucks
2004a, b).
Our fine-scale microsatellite DNA analysis of two species
of gecko (Oedura reticulata and Gehyra variegata) demonstrates the usefulness of such an approach. Clear hypotheses
emerged from long-term field and modelling studies
and we have demonstrated that the genetic structure of
fragmented O. reticulata populations is significantly higher
than in G. variegata. In addition, assignment tests revealed
that G. variegata has higher rates of interpatch dispersal
compared with O. reticulata suggesting that small distances
of about 500 m are a barrier to O. reticulata but not for G.
variegata. We also observed an isolation-by-distance effect
in FST values in G. variegata but not in O. reticulata, which
together with spatial autocorrelation analysis suggests that
G. variegata can disperse on a scale approaching a kilometre
rather then the few hundred metres achieved by O. reticulata.
Overall, O. reticulata exhibits lower levels of heterozygosity,
lower allelic richness, elevated levels of structure, fewer
misassignments than similarly fragmented populations of
G. variegata, while all these parameters were fairly similar
in the continuous forest populations. These findings are of
ecological significance to the distribution of populations
throughout the landscape of the Kellerberrin wheatbelt area.
From these results recommendations for possible management action to prevent extinctions can be derived. Here, we
discuss these patterns and their methodological limitations.
Structure in fragmented and nonfragmented populations
Our analyses, incorporating comparisons among fragmented
and nonfragmented populations and between species
with contrasting levels of persistence and speciality, reveal
several clear patterns concerning the impact of fragmentation upon these two species. First, in continuous and
relatively pristine habitat (nature reserves), the genetic
structure and the percentage of misassignments of the
two species are indistinguishable from each other on a
geographical scale of 1–1.2 km. On this scale, genetic
structure was very low for both species (FST = 0.003 OR;
0.004 GV). Spatial autocorrelation might be a better approach
for the detection of fine-scale genetic structure when other
methods fail to identify genetic patterns (Double et al. 2005).
However, there was no significant positive correlation
between any of the nature reserve populations. In addition,
the number of populations in the nature reserve was
estimated with the program structure 2.1 and the result
indicated that the populations in each of the two nature
reserves are approaching panmixis. Second, genetic structure
in both species was clearly influenced by fragmentation
through land clearance for agriculture. For both species,
levels of F ST were higher and the percentage of misassignments was lower among fragmented populations
than among those in the nature reserves. While GST
′ was
twice as high in O. reticulata than in G. variegata, the
impacts of fragmentation were still evident in G. variegata
with significantly higher levels of FST among fragmented
populations than among nature reserve populations.
Dispersal as a function of genetic and geographical
distance
Isolation by distance. The relationship between genetic differentiation and geographical distance contributes to our
understanding whether genetic differentiation is a result of
© 2007 The Authors
Journal compilation © 2007 Blackwell Publishing Ltd
D I S P E R S A L A N D E X T I N C T I O N I N F R A G M E N T E D G E C K O P O P U L A T I O N S 3309
limited dispersal or more complex demographic factors
(Leblois et al. 2000; Brouat et al. 2003). In general, it has been
argued that if dispersal is limited by distance, at equilibrium
between mutation, migration, and drift genetic and
geographical distance should be positively correlated. A
negative correlation or no isolation by distance is usually
interpreted as evidence that dispersal is not limited by
distance, such that the sampled region functions effectively
as one large population (Rousset 1997, 2001; Leblois et al.
2000; Brouat et al. 2003). However, there are at least two
other reasons why a lack of a significant pattern of isolation
by distance may occur. First, gene flow may be very low
over the distance sampled such that populations are
essentially isolated, and allele frequencies are determined
by drift. This was recently demonstrated in the alpine
butterfly, Parnassius smintheus, because of reduced connectivity of habitat (Keyghobadi et al. 2005). Second, the
Mantel test might not have enough power to detect genetic
structure at a fine scale.
On the basis of isolation-by-distance analysis alone, we
would interpret that the correlation between geographical
and genetic distance for G. variegata but not for O. reticulata
suggests that G. variegata moves between remnants and
O. reticulata does not. The alternative explanation, that
O. reticulata moves on a scale of 10 s of km, is contradicted
by the lack of high numbers of misassigned individuals
among closely located populations of O. reticulata. In
contrast, genetic differentiation is positively correlated
with total geographical distance in G. variegata, which
conforms to the theoretical expectations that the species is
dispersing, but that dispersal is limited by distance.
Spatial autocorrelation. The potential contribution of spatial
autocorrelation analysis in combination with hypervariable
DNA markers has been overlooked in animal studies
(Peakall et al. 2003; Double et al. 2005). While the isolationby-distance analysis failed to identify a landscape-related
genetic pattern in O. reticulata, the autocorrelation showed
that genetic structure is present at two distance classes,
100 m and 200 m. The intercept occurs at a relatively short
distance of 518 m, which indicates a low level of dispersal
and a small neighbourhood size (Vignieri 2005). Still, this
result is in rough agreement with the conclusions from
the isolation-by-distance and assignment analysis. The
performance of the isolation-by-distance analysis might
have been compromised by the clumping of the samples.
The observed spatial pattern in G. variegata implies that the
fragmented landscape is also a barrier to this species, but
that dispersal occurs at a larger scale than in O. reticulata.
The spatial autocorrelation remains positive up to 400 m
and then begins to decline with an intercept of 951 m. In
both species, no significant genetic structure has been
identified in the continuous woodlands suggesting that the
populations are panmictic.
© 2007 The Authors
Journal compilation © 2007 Blackwell Publishing Ltd
Assignment. The benefit of the first analysis in our study,
the most-likely option, is that gecko individuals do not
necessarily need to be assigned with a high accuracy to
obtain an overall estimate of the dispersal probability. In
a second approach for identifying an exact number of
dispersers, the assignment test was then performed at a
threshold of P ≤ 0.05. We were able to estimate the percentage
of misassignments, which was on average lower for O.
reticulata than for G. variegata, suggesting that the number
of dispersers in G. variegata is nearly twice that in O. reticulata.
For both species, the percentage of misassignments was on
average higher between the continuous forest populations
than between the fragments.
With the semi-experimental approach, we were also able
to determine a rough distance of separation. For O. reticulata
a distance of 150 m through cleared habitat did not appear
to represent a barrier. Movement between the two pairs of
distant populations (ORF1/ORF4 and ORF2/ORF3) was
substantially lower than for those separated by only 150 m
indicating that 500–600 m is sufficient to prevent even
modest dispersal. Three of four individuals detected as
immigrants between these distant pairs were detected as
having moved between population pair ORF1/ORF4. These
two patches are adjacent to woodland roadside vegetation
that may act as a corridor for O. reticulata movement. This
possibility deserves future research as it suggests a potential
role for corridors in the conservation of species.
For G. variegata, the isolation of remnants by 150–300 m of
cleared matrix did not stop migrants from moving through
the modified landscape. At least one individual covered
distances of up to 1000 m between habitat patches. Data
from the isolation-by-distance and spatial autocorrelation
analyses suggest that movement over 1 km still occurs. Dispersal requires individuals to pass through rural landscapes
in which the native vegetation has been removed and replaced
by crops or stubble after harvest. The environment contains
a high density of introduced predators, and a scarcity of
suitable hiding places (even rocks and fences) that may be
used as temporary shelter for the geckos. Hence, successful
dispersal under these circumstances is likely to be a rare event.
We discovered that the number of identified dispersers
by assignment test where a threshold was established was
similar for the two gecko species or even slightly higher in
O. reticulata similar in fragmented and continuous landscapes. Theoretical and empirical research imply that there
is a positive relationship between the level of differentiation (FST) and the ability to correctly assign individuals
to their natal population (Eldridge et al. 2001; Berry et al.
2004). We suggest that the assignment test was more likely
to correctly identify the dispersers for O. reticulata, which
had an FST of 0.10, than for G. variegata with an FST of 0.04.
Therefore, we assume that in the species G. variegata, the actual
number of dispersers is likely to be higher than estimated.
The same might be true for the estimated number of
3310 M . H O E H N , S . D . S A R R E and K . H E N L E
dispersers between population pairs ORF5/6 and GVF3/4,
which are in close proximity and between the population
pairs in the continuous forest. The differentiation between
these populations is lower and consequently the number of
dispersers could be underestimated. In addition, further
structure 2.1 analysis showed that these populations
consist of one (K = 1) panmictic population.
Conclusion and conservation implications
In the study area, five other terrestrial gecko species
(Crenadactylus ocellatus, Diplodactylus granariensis, D. maini,
D. pulcher, and D. spinigerus) have already become extinct
in most remnant woodlands, presumably as a result of habitat
clearing and agriculture. We suggest that the differences
in the persistence of the two remaining gecko species are
mainly attributable to their different dispersal ability through
a matrix of habitat more suitable for G. variegata than for O.
reticulata. Similarly, in western Japan, it was demonstrated
that the house-dwelling gecko Gekko japonicus had higher rates
of gene flow between habitat fragments than its sympatric
counterpart, Gekko tawaensis, a species that is not present
in human constructions. It was argued that this was promoted by human-mediated transport ( Toda et al. 2003). The
few available comparative genetic studies of two or more
sympatric species support our conclusions (lizards: Branch
et al. 2003; small mammals: Matocq et al. 2000; Ehrich et al.
2001a, b; insects: Monaghan et al. 2002; Brouat et al. 2003).
From a conservation perspective, the specialist species
O. reticulata might be a good genetic indicator species for
monitoring the impact of anthropogenic perturbations in
the Western Australian wheatbelt. Although single species
cannot fully represent the fauna of fragmented habitats, it
might be a pragmatic approach to genetically monitor
a species which is especially sensitive to habitat fragmentation. Typically, conservation managers are required to
minimize management costs and time, and genetic monitoring
is still relatively expensive and time-consuming.
Acknowledgements
We thank Lachlan Farrington, Alex Quinn, Oliver Berry, and Niccy
Aitken for laboratory assistance and advice on laboratory techniques. Thanks to Bernd Gruber for comments on earlier drafts of
this manuscript. Thanks to Alex Quinn and Niccy Aitken for
proofreading. This work was funded by the DAAD, Universität
Erlangen-Nürnberg and a Collaborative Industry Grant from the
University of Canberra, Australia and the UFZ Centre for Environmental Research, Leipzig-Halle, Germany.
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The study presented here is part of Marion Hoehn’s PhD research.
She is interested in the application of molecular approaches to
questions in conservation and evolution, especially with the focus
on reptiles.
© 2007 The Authors
Journal compilation © 2007 Blackwell Publishing Ltd