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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 © 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 3303 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 © 2007 The Authors 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). © 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 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– © 2007 The Authors 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. 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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