Abstract
Predicting the probability of species occurrence based on environmental variables across landscapes makes it possible to determine suitable habitats and constitutes an important tool for identifying priority areas for conservation. Sand Dune Lizards (Liolaemus multimaculatus) are endemic to the temperate coastal dunes from Argentina and were recently classified as Endangered by the International Union for Conservation of Nature. We studied the influence of landscape metrics on this lizard’s distribution and built predictive distribution maps from habitat suitability models. Additionally, we analyzed the representation of suitable habitats within natural protected areas (NPAs). We conducted lizard surveys in two successive seasons and obtained landscape metrics from thematic maps in two circular landscape units. All information collected was compiled into a Geographical Information System (GIS). The relationships between lizard presence or absence and the landscape metrics were analyzed using Generalized Linear Models, and the results were transferred using GIS to habitat suitability maps. Digitalized NPAs were overlapped on these maps to estimate the representation of suitable habitats. Within the main habitat categories of the sand dunes, L. multimaculatus was found exclusively in active dunes, which means that these habitats are of vital importance for this species. The habitat suitability models indicated that (1) patch size of active dunes and distance to the coastline were the main drivers of the distribution of this species, and (2) homogeneous landscapes composed of active dunes and low or null percentages of semi-fixed and forested dunes were the preferred habitat. The predictive maps showed that highly suitable habitats encompass <5% (2059.7 ha) of total active dunes and had a linear distribution near the coastline and far away from areas modified by human activities. Currently, only 19.8% (408.8 ha) of highly suitable habitats fall within NPAs boundaries. The high habitat association of the species and the very small remaining suitable habitat warrants urgent conservation measures, which can be supported by habitat suitability models based on landscape metrics.