An evaluation of methods for modelling distribution of Patagonian insects Una evaluación de los métodos para modelizar la distribución de insectos patagónicos

Various studies have shown that model performance may vary depending on the species being modelled, the study área, 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 evalúate 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 overeóme the problem of not having reliable absence data with which to evalúate model performance, we used randomly selected pseudo-absences located outside of the polygon área 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.Varios estudios han mostrado que el desempeño de los modelos de distribución puede variar dependiendo de la especie modelizada, el área de estudio o el número de localidades de presencia utilizadas, sugiriendo que es necesario evaluar cuál es la mejor técnica de modelización en cada situación concreta. En este estudio evaluamos distintas técnicas de modelización para la distribución de los insectos patagónicos. Hemos aplicado ocho de los métodos más ampliamente usados (redes neuronales, BIOCLIM, árboles de clasificación y regresión, DOMAIN, Modelos Aditivos Generalizados, GARP, Modelos Lineares Generaliza