2017/11/01 by Eric Ariel L. Salas, Raul Valdéz, Stefan Michel · 2 citations
Environmental Science · #Rangeland Management and Livestock Ecology #Ecology and biodiversity studies #Species Distribution and Climate Change #Habitat #Geography #Ecology #Biology
paper · pdf · doi:10.1016/j.heliyon.2017.e00445
openalex publication_date 2017/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We modeled summer and winter habitat suitability of Marco Polo argali in the Pamir Mountains in southeastern Tajikistan using these statistical algorithms: Generalized Linear Model, Random Forest, Boosted Regression Tree, Maxent, and Multivariate Adaptive Regression Splines. Using sheep occurrence data collected from 2009 to 2015 and a set of selected habitat predictors, we produced summer and winter habitat suitability maps and determined the important habitat suitability predictors for both seasons. Our results demonstrated that argali selected proximity to riparian areas and greenness as the two most relevant variables for summer, and the degree of slope (gentler slopes between 0° to 20°) and Landsat temperature band for winter. The terrain roughness was also among the most important variables in summer and winter models. Aspect was only significant for winter habitat, with argali preferring south-facing mountain slopes. We evaluated various measures of model performance such as the Area Under the Curve (AUC) and the True Skill Statistic (TSS). Comparing the five algorithms, the AUC scored highest for Boosted Regression Tree in summer (AUC = 0.94) and winter model runs (AUC = 0.94). In contrast, Random Forest underperformed in both model runs.