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In this paper we perform an endemicity analysis using ca 4,000 distributional records from 426 carabid species/subspecies distributed along austral South America. We used the program NDM/VNDM which implements a fill function (R. fill) to heuristically assign species' potential occurrence from observed presences. In the present analysis we use different grid sizes, and also we explore different values for the fill function (R. fill). The areas of endemism identified by NDM/VNDM were compared with previous biogeographical hypotheses. Some resulting areas of endemism were recognized through the whole range of grid sizes, while others could only be identified by using a particular grid size. In general, the use of small cells helped identify disjunct areas of endemism, as well as small ones, whereas big cells were convenient for the identification of broad areas that appeared as fragmented if smaller cells were used. In general, as R.fill function increased, the number of recognized areas of endemism and endemic species also increased. Our results show that areas of endemism with diverse traits can be derived from a singular combination of R. fill and grid size, emphasizing the importance of exploring different analytical options during the identification of distributional patterns.

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Various studies have shown that model performance may vary depending on the species being modelled, the study area, or the number of sampled localities, and suggest that it is necessary to assess which model is better for a particular situation. Thus, in this study we evaluate the performance of different techniques for modelling the distribution of Patagonian insects. We applied eight of the most widely used modelling methods (artificial neural networks, BIOCLIM, classification and regression trees, DOMAIN, generalized additive models, GARP, generalized linear models, and Maxent) to the distribution of ten Patagonian insect species. We compared model performance with five accuracy measures. To overcome the problem of not having reliable absence data with which to evaluate model performance, we used randomly selected pseudo-absences located outside of the polygon area defined by taxonomic experts. Our analyses show significant differences among modelling methods depending on the chosen accuracy measure. Maxent performed the best according to four out of the five accuracy measures, although its accuracy did not differ significantly from that obtained with artificial neural networks. When assessed on per species basis, Maxent was also one of the strongest performing methods, particularly for species sampled from a relatively low number of localities. Overall, our study identified four groups of modelling techniques based on model performance. The top-performing group is composed of Maxent and artificial neural networks, followed closely by the DOMAIN technique. The third group includes GARP, GAM, GLM, and CART, and the fourth best performer is the BIOCLIM technique. Although these results may allow obtaining better distributional predictions for reserve selection, it is necessary to be cautious in their use due to the provisional nature of these simulations.

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