The conservation and landscape genetics of the sand lizard Lacerta agilis

Lacerta agilis is a widespread lizard which reaches the western edge of its range in Britain where it is restricted to three geographically separated areas. Recent habitat loss and fragmentation have resulted in a significant decline and it is now a UK conservation priority. Sand lizards from across the Britain were genotyped at 15 microsatellite loci and the resulting dataset used to address questions regarding the conservation genetics, phylogeography and influence of landscape on patterns of genetic diversity. Genetic diversity of Dorset populations compared favourably to European examples. However, diversity was significantly lower in Surrey and Merseyside. Significant genetic structuring occurred across small geographical distances even in relatively unfragmented landscapes. Lacerta agilis colonised Britain via a land bridge across the North Sea and reached the limits of its current distribution approximately 5,000 years BP. Subsequent climate cooling has resulted in a range contraction to areas where the habitat is suitable for the successful incubation of eggs. A resistance surface was used to investigate the effect of landscape configuration on patterns of genetic diversity at multiple scales in Dorset. At a local scale, habitat type and rivers were the best predictors of genetic diversity. At a regional scale, rivers were most important, whereas habitat type and artificial barriers were less important. Artificial barriers may be more significant than the results suggest as their true effect has not yet been realised due to a genetic time-lag. Male lizards from Merseyside exhibited significant differences in colour and pattern to the Dorset and Surrey populations. However, despite difference in colour, all populations were equally green, which is in keeping with the importance of ‘greenness’ as a sexual signal. The implications of these findings for the conservation of L. agilis are discussed in the context of current challenges and predicted future global climate change.