Habitat protection and planning for indicator species using the MaxEnt model in Alborz.

Predicting and mapping appropriate habitats for endangered and threatened species is crucial for monitoring and restoring their dwindling populations in their natural surroundings. Additionally, it aids in selecting suitable conservation sites and effectively managing their habitats. An ideal approach for habitat suitability modeling involves utilizing MaxEnt machine learning techniques. The MaxEnt model was employed to forecast habitat suitability for key species, including Ursus arctos, Capra aegagrus, Ovis ammon, Lutra lutra, Martes foina, Lynx lynx, and Panthera pardus. Additionally, Linkage Pathways were employed to model ecological corridors connecting core habitats, enhancing our understanding of landscape connectivity for these species. The results showed that it is imperative to safeguard vital northern and southern areas between the prohibited hunting zones and the protected area. These areas provide the best routes for species to move between two habitats. However, settlements and rural areas pose a significant threat that can lead to the reduction or destruction of these communication areas. Therefore, protecting these regions should be a top priority. [ABSTRACT FROM AUTHOR]