Assessing habitat suitability for aoudad (Ammotragus lervia) reintroduction in southeastern morocco to promote ecotourism

Abstract

The objective of this study is to address the complex task of identifying optimal locations for reintroducing Ammotragus lervia in a semi-arid area in the Eastern High Atlas of Morocco, considering three topographical factors. The study assesses the effectiveness of a commonly used machine learning classifier (MLC) in mapping potential areas for introducing these species, which is crucial for promoting and enhancing local biodiversity. To begin with, an extensive inventory of 88 remaining sites where these Barbary sheep still living was conducted, and precise measurements of three topographical parameters were collected at each site. Subsequently, a machine learning algorithm called Bagging was employed to develop a predictive model. The predictive model demonstrated a high level of performance, as evidenced by an area under the curve (AUC) value of 0.929. Bagging effectively identified favorable areas, encompassing around 13.8 % of the study region, which were predominantly located in the western part. These areas were characterized by mountainous terrain, shorter slopes, and higher altitudes. The research findings provide valuable guidance to decision-makers, offering a roadmap to reintroduce these species for enhancing the local biodiversity in the region.