The snow leopard (*Panthera uncia*) is a critically endangered species, with populations declining due to human activities, habitat loss, and climate change. The reduction in suitable habitats and the increasing fragmentation of remaining areas threaten genetic diversity through inbreeding and reduced gene flow. Climate change further exacerbates the situation by shifting the tree line and reducing the alpine zone, the snow leopard's primary habitat. Understanding the spatial distribution of suitable habitats and the genetic connectivity between these areas is crucial for effective conservation planning. This study focuses on Nepal, a region with significant snow leopard populations and strong community involvement in conservation efforts, to map suitable habitats, analyze the genetic structure of snow leopard populations, and identify key migration corridors that connect these habitats. The aim is to provide crucial information for the development of effective conservation strategies that ensure the long-term survival of snow leopards in Nepal.

Previous research has highlighted the declining snow leopard population and its vulnerability to human activities and climate change. Studies have focused on habitat suitability modeling in various regions, including Nepal and Tibet, but connectivity analyses remain scarce. While some studies have analyzed descriptive genetic diversity in Nepal, research on spatial population genetic structure using Bayesian clustering—a crucial tool for long-term conservation planning—has been lacking. Existing literature underscores the importance of maintaining genetic variability and connectivity within snow leopard metapopulations for their long-term survival.

This study utilized a combination of field data collection, genetic analysis, and habitat suitability modeling. A total of 268 samples (scat, hair, urine) were collected from four study sites in Nepal: Lower Mustang (LM), Upper Manang (UM), North Sagarmatha (N-S), and South-West Sagarmatha (SW-S). Mitochondrial DNA analysis was used for species identification, and microsatellite genotyping was employed for individual identification and population genetic structure analysis. Bayesian clustering in Structure was used to determine the number of genetic clusters and the assignment probabilities of individuals to these clusters. Habitat suitability was modeled using Maxent, incorporating environmental variables such as altitude, temperature, precipitation, and distance from roads. Connectivity analysis was performed using Circuitscape to identify migration corridors between suitable habitats. The relationship between genetic distance (FST) and geographic and resistance distances was analyzed using Mantel tests to assess isolation by distance (IBD) and isolation by resistance (IBR).

Out of 268 samples, 108 microsatellite genotypes were obtained, representing 22 individuals. Bayesian clustering (Structure) revealed three main genetic clusters, with samples from Sagarmatha showing greater spatial separation than those from Upper Manang and Lower Mustang. Genetic differentiation was moderate between some areas, but overall statistically significant. The Maxent model showed high predictive ability (AUC = 0.974), indicating excellent model performance. Altitude, annual mean temperature, annual precipitation, and distance from roads were identified as the most important environmental variables influencing snow leopard habitat suitability. The most suitable habitat was found in a narrow belt between 3500 and 4500 m altitude. Circuitscape analysis revealed potential migration corridors connecting the study areas, some of which are narrow and potentially vulnerable. Analysis of IBD and IBR showed weak effects of geographic and resistance distances on genetic differentiation, indicating the influence of factors beyond simple distance in shaping genetic structure.

The study findings confirm the importance of altitude, temperature, and precipitation in determining snow leopard habitat suitability, aligning with previous research. The lack of a significant effect of ruggedness on habitat suitability in this study might be due to limitations in the resolution of available ruggedness data. Genetic analysis revealed gene flow between Manang and Mustang, facilitated by identified corridors. However, connectivity between western (Manang, Mustang) and eastern (Sagarmatha) areas appears more limited, potentially due to narrow corridors and human disturbances. The genetic isolation of North and South-West Sagarmatha might be related to recent recolonization and the barrier effect of rivers and tourist routes. The study highlights the significant impact of human activities, particularly proximity to roads and tourist trails, on snow leopard dispersal and genetic structure, underscoring the need for mitigation strategies.

This study successfully delineated suitable snow leopard habitats in Nepal and identified key migration corridors. The analysis revealed the strong influence of landscape features, particularly human disturbances, on the genetic structure of snow leopard populations. Protecting these corridors is essential for maintaining gene flow and ensuring the long-term survival of snow leopard metapopulations. Future research should focus on detailed assessments of corridor functionality, investigating the effects of specific human activities on snow leopard movement, and developing targeted conservation strategies to mitigate human-wildlife conflict.

The study's sample size, though substantial, may not fully represent the entire snow leopard population in Nepal. The accuracy of the habitat suitability model depends on the quality and resolution of environmental data used. Further research is needed to fully understand the relative influence of various environmental factors on snow leopard distribution and movement, especially the interaction between human activity and topography.