Location and assignment to genetic clusters of ten subpopulations of <i>Phelsuma guimbeaui</i>.

<p><b>a</b> Subpopulations used for the microsatellite analyses. Colours in the pie charts indicate the proportion of genetic clusters identified using STRUCTURE 2.3.2. The three subpopulations only used for mtDNA analyses (L11, L12 and L13) are also shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093387#pone-0093387-g002" target="_blank">Figure 2a</a>; subpopulations marked with an asterisk are in the Black River mountains. <b>b</b> Bar plots showing the genetic identity of individual samples generated using STRUCTURE 2.3.2. <b>c</b> Bar plot output from TESS with each subpopulation's labelling assignment (%) from GENECLASS2 shown below. The box gives details of each subpopulation (Subpop): vegetation (Veg) was classified as exotic campeche forest (C), exotic eucalyptus forest (E), exotic mango orchard (M), native forest (N) and exotic terminalia forest (T); size is the area (km²) occupied by each subpopulation.</p

Population statistics of the concatenated mtDNA sequences for 13 subpopulations of <i>Phelsuma guimbeaui</i>.

<p>* denotes significant at P<0.05.</p><p>Subpop = subpopulation; Area = area of subpopulation in km²; A = number of individuals per subpopulation; H_N = number of haplotypes; H = haplotype diversity; π = nucleotide diversity; D = Tajima's D; Fs = Fu's Fs statistic; r = raggedness index.</p

Phylogenetic relationships of mtDNA haplotypes in <i>Phelsuma guimbeaui</i>.

<p>The Bayesian tree was produced in MRBAYES with subpopulation identity (L1 to L13) shown at the end of each branch. In the parsimony network, the circles represent different haplotypes, with their size proportional to the number of geckos. Open circles represent predicted but missing or unsampled haplotypes.</p

Pattern of deforestation in Mauritius from 1773 to 1997.

<p>The red dots indicate the 10 subpopulations for which both microsatellite and mtDNA analyses were conducted, and the yellow dots the three subpopulations for which only mtDNA analyses were carried out. The blue stars mark subpopulations not sampled and the black region within the purple dotted line on the 1997 map shows the Black River mountains. All subpopulation locations were transposed by 1</p

Simulation showing the probability of survival and retaining rare alleles, with 95% confidence intervals in parentheses, over 50 years in ten subpopulations of <i>Phelsuma guimbeaui</i> in the absence of migration.

<p>The estimated population size (N_e ×10) with a default rare allele frequency of 0.05 was implemented into the starting parameters of all models.</p

Population genetic indices (± standard deviation) for ten subpopulations of <i>Phelsuma guimbeaui</i>.

<p>Subpop = subpopulation; A = number of individuals sampled; H_E = mean expected heterozygosity and standard deviation; H_O = mean observed heterozygosity and standard deviation; N_A = mean number of alleles and standard deviation; A_R = mean allelic diversity and standard deviation; P_A = mean private allelic richness and standard deviation; F_IS = inbreeding coefficient. N_A, A_R and P_A were based on a minimum sample of 22 geckos per subpopulation.</p

Pairwise F_st values among the 10 subpopulations of <i>Phelsuma guimbeaui</i>; all values were significant at P<0.001 based on 999 permutations.

<p>Subpop = subpopulation.</p

Isolation by distance (IBD) in <i>Phelsuma guimbeaui</i> using microsatellite markers.

<p>Genetic distance F_st/(1-F_st) is plotted against log spatial distance (km).</p

Mean effective population sizes with 95% confidence intervals in parentheses for ten subpopulations of <i>Phelsuma guimbeaui</i> estimated using N_e ESTIMATOR and MIGRATE.

<p>Mean effective population sizes with 95% confidence intervals in parentheses for ten subpopulations of <i>Phelsuma guimbeaui</i> estimated using N_e ESTIMATOR and MIGRATE.</p

Model selection results. Abundance was modelled with habitat and status as site-level covariates.

<p>K = number of parameters used.</p><p>Delta AICc = difference between lowest AICc model and model AICc.</p><p>AICc weight = model probability among all candidate models.</p><p>Detection probability was modelled with observation-level covariates: cloud = cloud percentage cover; habitat = building, non-palm or palm; status = presence or absence of <i>P. grandis</i>; and temp = temperature. We used the corrected Akaike Information Criterion (AICc) to determine the best supported model.</p