INVESTIGADORES
ROIG Sergio Alberto
artículos
Título:
Una evaluación de los métodos para modelizar la distribución de insectos patagónicos
Autor/es:
TOGNELLI, M.; ROIG JUÑENT, SERGIO; A. MARVALDI; G.E. FLORES; LOBO, JM
Revista:
REVISTA CHILENA DE HISTORIA NATURAL
Editorial:
SOC BIOLGIA CHILE
Referencias:
Lugar: Santiago de Chile; Año: 2009 vol. 82 p. 347 - 347
ISSN:
0716-078X
Resumen:
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 groupis 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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