Snow leopards exhibit non-stationarity in scale-dependent habitat selection between two national protected areas in China

The rapidly changing environments resulting from anthropogenic activities and climate change presented challenges to biodiversity protection efforts. China, in its pursuit of sustainable economic growth and urban development, grappled with the increasing challenge of designating national protected areas for biodiversity conservation in a proactive and ecologically effective manner. In this study, focusing on the snow leopard (Panthera uncia), a species of high conservation concern, from the A'nyamaqen and Bortala protected areas in China's Qinghai and Xinjiang provinces, respectively. Using random forest analysis, we conducted multi-scale habitat selection modeling to quantify and compared the habitats between the two sites. Three models were created utilizing data from either Qinghai, Xinjiang, or a combination of both sites, enabling investigation of nonstationarity in habitat limiting factors in different landscapes. Although there were minor differences in variable ranking and optimal scales among the models, they consistently indicated a strong negative relationship between proximity to roads and habitat suitability at broader scales. These findings provided insights into spatially varying limiting factors leading to divergent snow leopard realized habitat niches in different parts of their Chinese range. Understanding these context-dependent habitat preferences was vital for assessing the impact of infrastructure development on snow leopard populations. Overall, this study underscored the importance of understanding snow leopard habitat selection in the face of changing environments. The findings contributed to ongoing conservation efforts and emphasize the need for adaptive approaches that addressed challenges posed by urban development and environmental transformations. By integrating spatial analysis and modeling techniques, we enhanced our understanding of snow leopard ecology, enabling effective conservation strategies in China and beyond.