Comparison of deductive classification techniques for predicting potential spatial distribution of quarantine insects

The objective of this paper was to evaluate the performance of crisp and fuzzy classification criteria in the construction of deductive potential distribution models of exotic insects. As case studies, Bactrocera oleae (Gmelin) (Diptera: Tephritidae) and Cerotoma arcuatus (Olivier) (Coleoptera: Chrysomelidae) were selected. Considering crisp and fuzzy classification for raster layers of maximum, average and minimum daily temperature, a relative bioclimatic risk index was generated. The number of days with optimal conditions for pests' development was considered. Sensitivity analyses of both models were performed. Considering each case evaluated and the variables used, deductive pest distribution models made by fuzzy classification was more robust and less conservative in the determination of potential phytosanitary risk areas than those made with crisp classification criteria. This last case was more sensitive and would have a greater capacity to discriminate areas with different environmental risk profiles.