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dc.creatorTOGNELLI,MARCELO F
dc.creatorROIG-JUÑENT,SERGIO A
dc.creatorMARVALDI,ADRIANA E
dc.creatorFLORES,GUSTAVO E
dc.creatorLOBO,JORGE M
dc.date2009-01-01
dc.date.accessioned2019-04-25T12:53:52Z
dc.date.available2019-04-25T12:53:52Z
dc.identifierhttps://scielo.conicyt.cl/scielo.php?script=sci_arttext&pid=S0716-078X2009000300003
dc.identifier.urihttp://revistaschilenas.uchile.cl/handle/2250/62926
dc.descriptionVarious 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.
dc.formattext/html
dc.languageen
dc.publisherSociedad de Biología de Chile
dc.relation10.4067/S0716-078X2009000300003
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourceRevista chilena de historia natural v.82 n.3 2009
dc.subjectExpert opinión
dc.subjectmodel evaluation
dc.subjectPatagonia
dc.subjectpseudo-absence data
dc.subjectspecies distribution modelling
dc.titleAn evaluation of methods for modelling distribution of Patagonian insects


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