Introduction: Biodiversity has gained significant attention in recent decades due to growing awareness of its ecological role, global importance, and projected large-scale declines. The rapid pace of environmental degradation and biodiversity loss underscores the urgent need to protect areas that comprehensively represent regional biodiversity. Effective conservation requires establishing adequate networks of protected areas within each country, aligned with global strategies, to serve as refuges safeguarding diverse flora and fauna while ensuring their long-term survival. At a global scale, amphibians are the most threatened group of vertebrates. Currently, habitat degradation and destruction constitute the primary drivers of species and population declines. This study evaluates the efficacy of existing protected areas in conserving the Lorestan newt (Neurergus kaiseri), an endangered endemic species, across Lorestan and Khuzestan provinces in Iran.
Materials and Methods: This study utilized 62 presence points collected from historical literature, official records of the Lorestan Department of Environment, and local ecological knowledge. Habitat suitability was modeled using Maximum Entropy (MaxEnt) in RStudio. Spatial statistical methods—including the Getis-Ord Gi* hotspot analysis, Anselin Local Moran’s I (for spatial autocorrelation), and Natural Breaks (Jenks) classification—were compared to identify critical habitat patches. The efficacy of existing protected areas for conserving the Lorestan newt was then evaluated by spatially overlaying these habitat hotspots with current protected area boundaries.
Results: The MaxEnt habitat suitability model demonstrated high reliability and predictive accuracy for Lorestan newt distribution, as evidenced by an AUC (Area Under the Curve) value of 0.95. Jackknife analysis identified the most influential environmental variables as: minimum temperature of the coldest month (Bio6), annual precipitation (Bio12), precipitation of the coldest quarter (Bio19), isothermality (Bio3), and elevation. Comparative evaluation of spatial analysis methods revealed that the Getis-Ord Gi* approach achieved superior performance in hotspot detection, with an AUC of 0.96.
Species distribution modeling has become an increasingly vital tool for supporting biodiversity conservation management. This study integrated maximum entropy modeling with spatial data mining techniques to identify habitat hotspots for the Lorestan newt and evaluate their overlap with existing protected areas. The results revealed that only 6% of critical habitat hotspots (31,520.16 out of 579,106.35 hectares) currently fall within protected area boundaries. Notably, Khuzestan Province - containing 54% of all identified hotspots - lacks any designated protected areas for this species. In contrast, two regions in Lorestan Province demonstrated high conservation efficacy: Shadabkuh 1 (98% overlap) and Tang-e Haft (93% overlap). These findings highlight three urgent conservation priorities: (1) comprehensive revision of the current protected area network, (2) establishment of new protected zones in northern Khuzestan, and (3) creation of habitat connectivity corridors to ensure long-term species survival...... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... .....