masoud salari; Fereydoon Sarmadian; Ali Salajegheh
Abstract
Wild sheep (Ovis orientalis) are a critical component of wildlife biodiversity in Iran and are categorized as Vulnerable (VU) on the IUCN Red List. This species plays a crucial role in maintaining the integrity of rangeland ecosystems and contributes to ecological balance within their habitats. ...
Read More
Wild sheep (Ovis orientalis) are a critical component of wildlife biodiversity in Iran and are categorized as Vulnerable (VU) on the IUCN Red List. This species plays a crucial role in maintaining the integrity of rangeland ecosystems and contributes to ecological balance within their habitats. Variations in land characteristics (including climate, topography, soil, vegetation, hydrological factors, and land use) result in distinct habitat suitability classifications for this species. This study involved long-term observational research on wild sheep behavior over a decade, aiming to identify the most influential factors affecting habitat suitability and to generate a habitat suitability map using machine learning algorithms alongside the Analytical Hierarchy Process (AHP) in Khabr National Park. The findings indicate that the region has relatively high suitability for this species, with elevation, slope, vegetation cover, and proximity to water resources emerging as the most significant factors. Validation of the results using the kappa coefficient and the overall accuracy index confirms the high precision of the findings. This underscores the value of integrating machine learning models with AHP in habitat suitability assessments, aiding management in understanding the species’ ecological requirements and identifying priority conservation areas.
