Habitat suitability modeling of Goitered gazelle (Gazella subgutturosa): A Maximum Entropy approach from Samelghan plain, Iran

The spatial distribution modeling can simulate the suitability of species habitats on different spatial scales, based on species records and site characteristics to gain insight into ecological, and evolutionary drivers or help predict habitat suitability across large scales. Species distribution models (SDMs) based on presence-absence or presence-only data are widely used in biogeography to indicate the ecological niche and predict the geographical distribution of species habitats. Although presence-absence data is generally of higher quality, it is also less common than presence-only data because it requires more rigorous planning to visit a set of pre-determined sites. Among the algorithms available, the MaxEnt approach is one of the most widely used methods of developing habitat modeling. The MaxEnt uses maximum entropy to generalize specific observations of presence-only data and does not require data where the species is absent within the theoretical framework. This study aims to predict the suitable habitat for Goitered gazelle (Gazella subgutturosa) in the Samelghan plain in northeastern Iran. The results showed that the variables of the Mediterranean climate classes, slope 0-5% class, and semi-dense pastures with type Acantholimon-Astragalus are more important than other environmental variables used in modeling. The area under the curve (AUC), Receiver Operating Characteristic (ROC), and the classification threshold illustrate the model performance. Based on the ROC (AUC=0.99) results in this study, it was found that Maxent's performance was very good