A predictive spatial model for gray wolf (Canis lupus) denning sites in a human‐dominated landscape in western Iran. Issue 3 (15th March 2013)

• Record Type: Journal Article
• Authors: Ahmadi, Mohsen; Kaboli, Mohammad; Nourani, Elham; Alizadeh Shabani, Afshin; Ashrafi, Sohrab
• Is Part Of: Ecological research. Volume 28:Issue 3(2013)
• Page Start: 513
• Page End: 521
• Publication Date: 2013-03-15
• Subjects: Gray wolf -- Den site selection -- Mahalanobis distance model -- Hamedan province -- Iran; Ecology -- Periodicals; Ecology -- Japan -- Periodicals; Écologie; Japon; Ecology; Japan; Ressource Internet (Descripteur de forme); Périodique électronique (Descripteur de forme); Periodicals; 577.05
• DOI: 10.1007/s11284-013-1040-2
• Languages: English
• ISSNs: 0912-3814

Abstract

Abstract: Predictive models of species denning habitat are useful for understanding distribution of core use areas and identifying areas with potential conflicts. Wolf den site selection in unmanaged areas with dominance of agriculture‐lands and human disturbance is not completely understood. We used a GIS multivariate model based on the Mahalanobis distance statistic and 11 digital habitat layers to evaluate gray wolf denning site suitability in Hamedan province (HP), western Iran. Results showed that >74 % of dens occurred in potential denning sites that occupied 14 % (2, 785 km 2 ) of the study area. Based on sensitivity analysis with ROC, we found that the distance to rangelands, elevation, and distance to roads, human settlements and streams were the most predictive variables. Our potential distribution model of wolf den sites showed a very good overall performance according to classification accuracy and discrimination capacity. Denning in elevated rugged terrains adjacent to rangelands and away from roads and villages revealed that wolf den distribution in HP is highly influenced primarily by factors associated with human disturbance. The derived predicted distribution map can be used to prioritize areas for conservation, identify areas with potential conflicts, and to implement adaptive management in regions beyond wilderness and protected areas.