Multi-scale analysis identifies habitat suitability and conservation priorities for snow leopards (Panthera uncia) in the Qilian Mountains

Snow leopards (Panthera uncia) are regarded as the most charismatic apex predator in alpine Asia, yet their populations are under serious threat from human activities and habitat fragmentation. Ensuring the effectiveness of current protected areas is critical for their conservation, which necessitates a comprehensive understanding of their habitat selection patterns at different spatial scales. We aimed to use an optimized multi-scale habitat selection model to determine habitat suitability and connectivity, and identify core habitats and corridors with the aim of providing critical conservation recommendations. First, we integrated snow leopard occurrence data from camera traps deployed in the Qilian Mountains (China) from 2017 to 2023 with environmental variables to construct univariate models for scale optimization. Then, we screened the variables for the optimal scale, and modeled snow leopard habitat suitability using a multi-scale multivariate model. Finally, we identified core patches and potential corridors through a connectivity analysis. Multi-scale analyses demonstrated that snow leopards prefer to select unused land with high terrain ruggedness and precipitation. Within the suitable habitat range, we identified 51 core patches and 86 potential corridors. Optimized models highlighted critical conservation information: core habitats and key connectivity corridors had over one-third of their territory outside the protected areas. Subei County is the primary concentration area of most important patches, though connectivity between the patches remains poor. Tianjun County has the most corridors, yet they are almost entirely outside the protected areas. Our analysis indicates that conservation or management planning should prioritize previously neglected core habitats and essential corridors. Subei, Tianjun and Gangcha counties will become priority areas for future conservation.