Revista Chilena de Historia Natural 82 (3): 347-360, 2009
RESEARCH ARTICLE
An evaluation of methods for modelling distribution of Patagonian insects
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.
Key words:
expert opinion, model
evaluation, Patagonia, pseudo-absence data, species distribution Modelling
