Genetic diversity and structure of the threatened striped legless lizard, Delma impar: management implications for the species and a translocated population

Abstract

The striped legless lizard, Delma impar, is a specialist grassland species restricted to south-eastern Australia. Anthropogenic influences have seen the destruction of much of its habitat and the species is threatened with extinction. Known populations of D. impar in Canberra (Australia) have recently been cleared for urban development. In 2015, Bush Heritage Australia translocated 41 individuals from these populations to Scottsdale Reserve. In this study, we completed the first population genetics analysis of D. impar in Canberra, providing a baseline for assessment of the genetic success of the translocation to Scottsdale Reserve. We analysed 154 D. impar individuals from six populations in Canberra, assessing levels of genetic diversity and differentiation within and between populations, using eight highly polymorphic microsatellite loci. High levels of genetic diversity and negligible levels of genetic differentiation were observed. Measures of allelic diversity were lower in the translocated population compared to the Canberra populations and Bayesian analysis revealed a disproportionate representation of two genetic clusters identified by STRU CTU RE between the Scottsdale Reserve and Canberra populations, indicating that the initial genetic capture failed to 'capture' recommended levels of genetic diversity to support an ongoing population. If the species successfully establishes itself at Scottsdale Reserve, the data suggests that the population should be augmented with individuals from other sites in Canberra, with the aim of increasing genetic diversity to recommended levels (i.e. > 95% genetic variation). This will maximise resilience, adaptability and long-term survival potential of the Scottsdale Reserve population of striped legless lizards from a genetic context.

Conservation concerns associated with low genetic diversity for K’gari–Fraser Island dingoes

“…Evidence of linkage disequilibrium was detected in one loci (FH3591). Although eight of the loci deviated significantly from HWE after Bonferroni correction, the deviation was unsurprising given that isolated, island populations are expected to be affected by selection pressures 73 . MICRO-CHECKER and ML-NULLFREQ estimated the average potential null allele frequency to be 0.13 ± 0.02, and 0.12 ± 0.02 across all loci, respectively, which can potentially lead to underestimation of diversity and differentiation parameters.…”

“…Quality control of the allelic data was performed using a range of analyses 73 , 74 . The potential for null alleles was determined using MICRO-CHECKER v2.2.3 75 and ML-NULLFREQ 76 , based on 1000 bootstraps.…”

“…The potential for null alleles was determined using MICRO-CHECKER v2.2.3 75 and ML-NULLFREQ 76 , based on 1000 bootstraps. GenAlEx was used to estimate deviation from Hardy–Weinberg equilibrium for each locus 73 , 77 , and an assessment of linkage dis-equilibrium among pairs of loci was undertaken with FSTAT v2.9.3.2 78 . Large allele dropout and stuttering potential was assessed with MICRO-CHECKER v2.2.3 75 , based on 1000 bootstraps and a 95% confidence interval.…”

“…MICRO-CHECKER and ML-NULLFREQ estimated the average potential null allele frequency to be 0.13 ± 0.02, and 0.12 ± 0.02 across all loci, respectively, which can potentially lead to underestimation of diversity and differentiation parameters. Additionally, given the fact that loci from wild populations affected by bottlenecks, inbreeding, genetic drift, founder effects and natural selection are likely to deviate from HWE 79 , it is unlikely that these deviations will significantly impact subsequent genetic analyses 73 .…”

“…A principle coordinates analysis (PCoA) was conducted in GENALEX version 6.503 77 using standardised genetic distance measures and we examined population structure further using the Bayesian clustering algorithm implemented in STRUCTURE v2.3.4 88 . We used an admixture model with correlated allele frequencies and 10 independent runs for each value of K (number of clusters) 73 , 74 between 2 and 6, employing a burn-in of 100 000 followed by 500 000 Markov Chain Monte Carlo (MCMC) steps for each run. The geographic location of samples was not used in the clustering analysis.…”