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The distribution of biodiversity at the landscape level is shaped by biotic, abiotic and anthropogenic factors. Biodiversity maps provide the basis for defining management and conservation strategies that can minimize human impacts. The objective was to elaborate a map of potential biodiversity of lizards based on habitat suitability maps of individual species in Santa Cruz (Argentina). Also, we analysed desertification influence and the representativeness of the current network of protected areas on the lizard biodiversity. For this, we used a database of eight lizard species and we explored 41 potential explanatory variables to develop habitat suitability maps, which were combined to obtain one single map of the potential biodiversity. We analysed the outputs in a GIS project using the marginality and the specialization indexes and the normalized difference vegetation index of each species. Also, we characterized the potential biodiversity using the following variables: desertification, ecological areas and current network of protected areas. We detected differences in the occupied niches for the different species throughout the landscape. The map of potential biodiversity uncovered hotspots of biodiversity in the north-east study area, where the prevalence of unique climatic conditions showed a dry steppe and a high degree of desertification due to the human impacts (e.g. livestock). These results can be readily used as a support system for conservation and management strategies at different scale levels in areas with higher human impacts or to develop new protection areas. Fil: Rosas, Yamina Micaela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; Argentina Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral; Argentina Fil: Martínez Pastur, Guillermo José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; Argentina

Many techniques for predicting species potential distribution were recently developed. Despite the international interest for these procedures, applications of predictive approaches to the study of Italian fauna distribution are exceptionally rare. This paper aimed at: (a) detecting climatic exigencies of A. bedriagae in Sardinia; (b) predicting the Archaeolacerta bedriagae Sardinian potential distribution; (c) identifying the most vulnerable Italian populations of the species. Literature and field data were utilized as presence records. Six modelling procedures (BIOCLIM, DOMAIN, ENFA, GAM, GLM, and MAXENT) were adopted. The species climatic requirements were defined using the WorldClim databank for deriving the environmental predictors. AUC and Kappa values were calculated for models validation. AUC values were compared by using Anova Monte Carlo. The best four models were combined through the weighted average consensus method for producing a univocal output. GAM and MAXENT had the best performances (respectively: AUC = 0.93 ± 0.03, Kappa = 0.77 ± 0.08; AUC = 0.93 ± 0.03, Kappa = 0.78 ± 0.07). Good results were also obtained by GLM and DOMAIN (respectively: AUC = 0.89 ± 0.04, Kappa = 0.72 ± 0.05; AUC = 0.88 ± 0.04, Kappa = 0.69 ± 0.07). BIOCLIM and ENFA gained relatively low performances (respectively: AUC = 0.78 ± 0.07, Kappa = 0.57 ± 0.14; AUC = 0.75 ± 0.06; Kappa = 0.49 ± 0.10). In Sardinia A. bedriagae is mainly influenced by seasonality, which causes the evidenced range fragmentation. Moreover, the general importance of multi-methods approaches and consensus techniques in predicting species distribution was highlighted.