Iranian Journal of Applied Ecology

Status determination of wildlife habitats is very important in conservation programs and management of wildlife. So, in this study Ursus arctos habitat suitability was modeled using maximum entropy algorithm (MaxEnt) in Shimbar protected area. In order to model the habitat suitability, after investigating and resolving the spatial autocorrelation of occurrence records, spatially independent localities were divided into the calibration and evaluation sets and then were combined with 10 environmental variables (VIF<10) selected by MMS software. The performance of predictive models was tested using AUC and jackknife validation test. So, we applied two different thresholds, the LPT threshold and 10% threshold to generate presence/absence map. Also other Jackknife tests applied to measure variables importance. The results showed that predictive model was more efficient than random model (AUC=0.980). In addition, the potential suitable areas cover 20.75% of study area. The MaxEnt model had 88.46% success rate and was statistically significant (P = 0.000). Results of Jackknife showed that ‘plant type’ variable alone contains valuable information for modelling. Our study demonstrated that habitat suitability was successfully predicted by MaxEnt modelling, so this methodology might provide a powerful tool for improving the wildlife habitats information.