Goitered Gazelle's (Gazella Subgutturosa) Habitat Desirability Modeling by Using Maximum Entropy (Maxent) Method

Abstract

The models predicting the spatial distribution of species can simulate the suitability of species habitats on different spatial scales, based on species records and site characteristics to gain insight into ecological or evolutionary drivers or to help predict habitat suitability across large scales. Species distribution models (SDMs) based on presence-absence or presence-only data use widely in biogeography to characterize the ecological niche of species and to predict the geographical distribution of their habitat. 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, one of the most widely used methods of developing SDMs is the Maximum Entropy (MaxEnt) method. The MaxEnt uses entropy to generalize specific observations of presence-only data and does not require or even incorporate points where the species is absent within the theoretical framework. The purpose of this study is 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 curve (AUC), Receiver Operating Characteristic (ROC), and the classification threshold shows model performance. Based on the ROC (AUC=0.99) results in this study, it was found that Maxents performance was very good. Desirability habitat was classified based on the threshold value (0.0277) and the ROC, which approx 11% of the area, predicted suitable habitat for Goitered gazelle.