Snow leopards (Panthera uncia) are elusive predators inhabiting high-altitude and mountainous rugged habitats. The current study was conducted in the Yanchiwan National Nature Reserve, Gansu Province, China, to assess the habitat suitability of snow leopards and identify key environmental factors inducing their distribution. Field data collected between 2019 and 2022 through scat sampling and camera trapping techniques provided insights into snow leopard habitat preferences. Spatial distribution and cluster analyses show distinct hotspots of high habitat suitability, mostly concentrated near mountainous landscapes. While altitude remains a critical determinant, with places above 3300 m showing increased habitat suitability, other factors such as soil type, human footprint, forest cover, prey availability, and human disturbance also play important roles. These variables influence ecological dynamics and are required to assess and manage snow leopard habitats. The MaxEnt model has helped us to better grasp these issues, particularly the enormous impact of human activities on habitat suitability. The current study highlights the importance of altitude in determining snow leopard habitat preferences and distribution patterns in the reserve. Furthermore, the study underscores the significance of considering elevation in conservation planning and management strategies for snow leopards, particularly in mountainous regions. By combining complete environmental data with innovative modeling tools, this study not only improves local conservation efforts but also serves as a model for similar wildlife conservation initiatives around the world. By understanding the environmental factors driving snow leopard distribution, conservation efforts can be more efficiently directed to ensure the long-term survival of this endangered species. This study provides valuable insights for evidence-based conservation efforts to safeguard the habitats of snow leopards amidst emerging anthropogenic pressure and environmental fluctuations.

The enteric virome, comprising a complex community of viruses inhabiting the gastrointestinal tract, plays a significant role in health and disease dynamics. In this study, the fecal sample of a wild snow leopard was subjected to viral metagenomic analysis using a double barcode Illumina MiSeq platform. The resulting reads were de novo assembled into contigs with SOAPdenovo2 version r240. Additional bioinformatic analysis of the assembled genome and genome annotation was done using the Geneious prime software (version 2022.0.2). Following viral metagenomic analysis and bioinformatic analysis, a total of 7 viral families and a novel specie of bocaparvovirus tentatively named Panthera uncia bocaparvovirus (PuBOV) with GenBank accession number OQ627713 were identified. The complete genome of PuBOV was predicted to contain 3 open reading frames (ORFs), contains 5433 nucleotides and has a G + C content of 47.40 %. BLASTx analysis and pairwise sequence comparison indicated the novel virus genome was a new species in the genus Bocaparvovirus based on the species demarcation criteria of the International Committee on the Taxonomy of Viruses. This study provides valuable insights into the diversity and composition of the enteric virome in wild endangered snow leopards. The identification and characterization of viruses in wildlife is crucial for developing effective strategies to manage and mitigate potential zoonotic and other viral disease threats to human and animal health.

Knowledge of large carnivore population abundance is essential for wildlife management and conservation, but these data are often difficult to obtain in inherently low-density species. In particular, the snow leopard, Panthera uncia, an enigmatic cat occupying remote mountains in Central Asia, has received insufficient assessments of its population abundance because of logistical and methodological challenges. Here, we aimed to develop a robust density estimation of snow leopards based on 81 days of camera trapping within a contiguous and previously unsurveyed 1950 km2 area of habitat on the Tibetan Plateau (Mayue Township, Shenzha County, Tibet Autonomous Region, China). By applying spatially explicit capture-recapture models, we produced an estimate of 1.40 (95%CI: 1.06-1.84) individuals per 100 km2 . Results also suggested sex-specific variation in the range of movement around activity centers, with male (N = 10, σ = 4.02) movement considerably greater than female (N = 8, σ = 1.84) movement. The findings can serve as a reliable baseline reference for assessing the population trends of this endangered felid species with future estimates. This study will provide context to contribute toward a better understanding of ecological factors shaping the distribution and abundance of snow leopards and the related conservation measures needed to sustain their long-term survival on the Tibetan Plateau.

Olfaction is a complicated process that begins with the specific binding of volatile odorant molecules to dedicated olfactory receptors (ORs) in the olfactory epithelium and plays a pivotal role in the survival of mammals. The OR subgenome of the snow leopard has remained largely unexplored, and thus, investigation of the OR system would shed light on the evolutionary dynamics of the snow leopard OR repertoires and genetic evidence for environmental adaptation. In this study, we conducted genome-wide identification and characterization of OR genes in the snow leopard and compared them to all other Panthera species. A total of 213, 294, 624, 305, and 253 functional OR genes were identified in the snow leopard, lion, jaguar, leopard, and tiger, respectively. The phylogenetic relationships of functional Panthera OR genes were illustrated, which comprised 69 families and 350 subfamilies distributed in two classes (Class I and Class II). Comparative analysis of the five Panthera species indicated 115 shared and 5 snow leopard-specific clusters. The potential odorant specificity of certain snow leopard OR genes was identified by similarities to human protein sequences and we identified odorants such as eugenol methyl ether that had the most OR genes. Since our references for odorants were from human studies, possible odorants from snow leopard-specific OR genes need further investigation. The lowest number of OR genes for the snow leopard among Panthera species possibly revealed the association between OR gene family contraction and high-altitude adaptation, which needed further and deeper investigation. This systematic study of OR genes in the snow leopard will provide a solid foundation for further study of olfactory function and variation in the snow leopard.

Replicated multiple scale species distribution models (SDMs) have become increasingly important to identify the correct variables determining species distribution and their influences on ecological responses. This study explores multi-scale habitat relationships of the snow leopard (Panthera uncia) in two study areas on the Qinghai-Tibetan Plateau of western China. Our primary objectives were to evaluate the degree to which snow leopard habitat relationships, expressed by predictors, scales of response, and magnitude of effects, were consistent across study areas or locally landcape-specific. We coupled univariate scale optimization and the maximum entropy algorithm to produce multivariate SDMs, inferring the relative suitability for the species by ensembling top performing models. We optimized the SDMs based on average omission rate across the top models and ensembles\' overlap with a simulated reference model. Comparison of SDMs in the two study areas highlighted landscape-specific responses to limiting factors. These were dependent on the effects of the hydrological network, anthropogenic features, topographic complexity, and the heterogeneity of the landcover patch mosaic. Overall, even accounting for specific local differences, we found general landscape attributes associated with snow leopard ecological requirements, consisting of a positive association with uplands and ridges, aggregated low-contrast landscapes, and large extents of grassy and herbaceous vegetation. As a means to evaluate the performance of two bias correction methods, we explored their effects on three datasets showing a range of bias intensities. The performance of corrections depends on the bias intensity; however, density kernels offered a reliable correction strategy under all circumstances. This study reveals the multi-scale response of snow leopards to environmental attributes and confirms the role of meta-replicated study designs for the identification of spatially varying limiting factors. Furthermore, this study makes important contributions to the ongoing discussion about the best approaches for sampling bias correction.

The existence of a trans-boundary population of the snow leopard (Panthera uncia) that inhabits the massifs of Tsagaanshuvuut (Mongolia) - Tsagan-Shibetu (Russia) was determined through non-invasive genetic analysis of scat samples and by studying the structure of territory use by a collared female individual. The genetic analysis included species identification of samples through sequencing of a fragment of the cytochrome b gene and individual identification using a panel of 8 microsatellites. The home range of a female snow leopard marked with a satellite Global Positioning System (GPS) collar was represented by the minimum convex polygon method (MCP) 100, the MCP 95 method and the fixed kernel 95 method. The results revealed insignificant genetic differentiation between snow leopards that inhabit both massifs (minimal fixation index [FST ]), and the data testify to the unity of the cross-border group. Moreover, 5 common individuals were identified from Mongolian and Russian territories. This finding clearly shows that their home range includes territories of both countries. In addition, regular movement of a collared snow leopard in Mongolia and Russia confirmed the existence of a cross-border snow leopard group. These data support that trans-boundary conservation is important for snow leopards in both countries. We conclude that it is crucial for Russia to study the northern range of snow leopards in Asia.

Habitat evaluation constitutes an important and fundamental step in the management of wildlife populations and conservation policy planning. Geographic information system (GIS) and species presence data provide the means by which such evaluation can be done. Maximum Entropy (MaxEnt) is widely used in habitat suitability modeling due to its power of accuracy and additional descriptive properties. To survey snow leopard populations in Qomolangma (Mt. Everest, QNNR) National Nature Reserve, Tibet, China, we pooled 127 pugmarks, 415 scrape marks, and 127 non-invasive identifications of the animal along line transects and recorded 87 occurrences through camera traps from 2014-2017. We adopted the MaxEnt model to generate a map highlighting the extent of suitable snow leopard habitat in QNNR. Results showed that the accuracy of the MaxEnt model was excellent (mean AUC=0.921). Precipitation in the driest quarter, ruggedness, elevation, maximum temperature of the warmest month, and annual mean temperature were the main environmental factors influencing habitat suitability for snow leopards, with contribution rates of 20.0%, 14.4%, 13.3%, 8.7%, and 8.2% respectively. The suitable habitat area extended for 7001.93 km2, representing 22.72% of the whole reserve. The regions bordering Nepal were the main suitable snow leopard habitats and consisted of three separate habitat patches. Our findings revealed that precipitation, temperature conditions, ruggedness, and elevations of around 4000 m influenced snow leopard preferences at the landscape level in QNNR. We advocate further research and cooperation with Nepal to evaluate habitat connectivity and to explore possible proxies of population isolation among these patches. Furthermore, evaluation of subdivisions within the protection zones of QNNR is necessary to improve conservation strategies and enhance protection.

The snow leopard, Panthera uncia, is an elusive high-altitude specialist that inhabits vast, inaccessible habitat across Asia. We conducted the first range-wide genetic assessment of snow leopards based on noninvasive scat surveys. Thirty-three microsatellites were genotyped and a total of 683 bp of mitochondrial DNA sequenced in 70 individuals. Snow leopards exhibited low genetic diversity at microsatellites (AN = 5.8, HO = 0.433, HE = 0.568), virtually no mtDNA variation, and underwent a bottleneck in the Holocene (∼8000 years ago) coinciding with increased temperatures, precipitation, and upward treeline shift in the Tibetan Plateau. Multiple analyses supported 3 primary genetic clusters: (1) Northern (the Altai region), (2) Central (core Himalaya and Tibetan Plateau), and (3) Western (Tian Shan, Pamir, trans-Himalaya regions). Accordingly, we recognize 3 subspecies, Panthera uncia irbis (Northern group), Panthera uncia uncia (Western group), and Panthera uncia uncioides (Central group) based upon genetic distinctness, low levels of admixture, unambiguous population assignment, and geographic separation. The patterns of variation were consistent with desert-basin \"barrier effects\" of the Gobi isolating the northern subspecies (Mongolia), and the trans-Himalaya dividing the central (Qinghai, Tibet, Bhutan, and Nepal) and western subspecies (India, Pakistan, Tajikistan, and Kyrgyzstan). Hierarchical Bayesian clustering analysis revealed additional subdivision into a minimum of 6 proposed management units: western Mongolia, southern Mongolia, Tian Shan, Pamir-Himalaya, Tibet-Himalaya, and Qinghai, with spatial autocorrelation suggesting potential connectivity by dispersing individuals up to ∼400 km. We provide a foundation for global conservation of snow leopard subspecies, and set the stage for in-depth landscape genetics and genomic studies.

Many ecological studies and conservation management plans employ noninvasive scat sampling based on the assumption that species\' scats can be correctly identified in the field. However, in habitats with sympatric similarly sized carnivores, misidentification of scats is frequent and can lead to bias in research results. To address the scat identification dilemma, molecular scatology techniques have been developed to extract DNA from the donor cells present on the outer lining of the scat samples. A total of 100 samples were collected in the winter of 2009 and 2011 in Taxkorgan region of Xinjiang, China. DNA was extracted successfully from 88% of samples and genetic species identification showed that more than half the scats identified in the field as snow leopard (Panthera uncia) actually belonged to fox (Vulpes vulpes). Correlation between scat characteristics and species were investigated, showing that diameter and dry weight of the scat were significantly different between the species. However it was not possible to define a precise range of values for each species because of extensive overlap between the morphological values. This preliminary study confirms that identification of snow leopard feces in the field is misleading. Research that relies upon scat samples to assess distribution or diet of the snow leopard should therefore employ molecular scatology techniques. These methods are financially accessible and employ relatively simple laboratory procedures that can give an indisputable response to species identification from scats.