To visualise the spatial patterns of genetic diversity at both nuclear and mitochondrial markers across the study area, we produced genetic distance synthetic maps, also named ‘Genetic Landscape Shape’ interpolations(Miller, 2005; Miller et al., 2006). We used Allelesin Space v1.0 (Miller, 2005) to construct a connectivity network based on Delaunay triangulations (Delaunay, 1934) among all of the sampling locations and to calculate the genetic distances between observations connected in the network. The analyses were conductedusing residual genetic distances derived from the linear regression of all pairwise genetic and geographical distances as recommended by Manni et al.(2004) to overcome the potential problems caused by correlations between genetic and geographical distances.The genetic distances and respective geographical coordinates of midpoints of each connection were then imported to the Geographical Information System Arc Gis 9.0 (ESRI, Redland, CA, USA). The ‘Inverse DistanceWeighted’ (IDW) procedure with a power of one was used for the interpolation of the genetic distance surfaces as suggested by Miller (2005). IDW assumes that each input point has a local influence that diminishes with distance. The resulting genetic distances maps were plotted as 3-dimensional surfaces over the study area. The spatial representation of the ‘GeneticLandscape Shape’ interpolation areas was adjusted with a mask of the species’ range in order to exclude areas outside its known distribution.