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Wasser, S. (1998). Snow Leopard Genetics: New Techniques (Vol. xvi). Seattle: Islt.
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Aruge, S., Batool, H., Khan, F. M., Abbas, F. I., Janjua, S. (2019). A pilot study�genetic diversity and population structure of snow leopards of Gilgit-Baltistan, Pakistan, using molecular techniques. PeerJ, (7672), 1–14.
Abstract: Background: The Hindu Kush and Karakoram mountain ranges in Pakistan�s northern areas are a natural habitat of the snow leopard (Panthera uncia syn. Uncia uncia) but the ecological studies on this animal are scarce since it is human shy by nature and lives in dif!cult mountainous tracts. The pilot study is conducted to exploit the genetic diversity and population structure of the snow leopard in this selected natural habitat of the member of the wildcat family in Pakistan.
Method: About 50 putative scat samples of snow leopard from !ve localities of Gilgit-Baltistan (Pakistan) along with a control sample of zoo maintained male snow leopard were collected for comparison. Signi!cant quality and quantity of genomic DNA was extracted from scat samples using combined Zhang�phenol�chloroform method and successful ampli!cation of cytochrome c oxidase I gene (190 bp) using mini-barcode primers, seven simple sequence repeats (SSR) markers and Y-linked AMELY gene (200 bp) was done.
Results: Cytochrome c oxidase I gene sequencing suggested that 33/50 (66%) scat samples were of snow leopard. AMELY primer suggested that out of 33 ampli!ed samples, 21 (63.63%) scats were from male and 12 (36.36%) from female leopards. Through successful ampli!cation of DNA of 25 out of 33 (75.75%) scat samples using SSR markers, a total of 68 alleles on seven SSR loci were identi!ed, showing low heterozygosity, while high gene "ow between population.
Discussion: The low gene flow rate among the population results in low genetic diversity causing decreased diversi!cation. This affects the adaptability to climatic changes, thus ultimately resulting in decreased population size of the species.
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Hacker, C., Atzeni, L., Munkhtsog, B., Munkhtsog, B., Galsandorj, N., Zhang, Y., Liu, Y., Buyanaa, C., Bayandonoi, G., Ochirjav, M., Farrington, J. D., Jevit, M., Zhang, Y., Wu, L. Cong, W., Li, D., Gavette, C., Jackson, R., Janecka, J. E. (2022). Genetic diversity and spatial structures of snow leopards (Panthera uncia) reveal proxies of connectivity across Mongolia and northwestern China. Landscape Ecology, , 1–19.
Abstract: Understanding landscape connectivity and population genetic parameters is imperative for threatened species management. However, such information is lacking for the snow leopard (Panthera uncia). This study sought to explore hierarchical snow leopard gene flow patterns and drivers of genetic structure in Mongolia and China. A total of 97 individuals from across Mongolia and from the north-eastern edge of the Qinghai-Tibetan Plateau in Gansu Province to the middle of Qinghai Province in China were genotyped across 24 microsatellite loci. Distance-based frameworks were used to determine a landscape scenario best explaining observed genetic structure. Spatial and non-spatial methods were used to investigate fine-scale autocorrelation and similarity patterns as well as genetic structure and admixture. A genetic macro-division between populations in China and Mongolia was observed, suggesting that the Gobi Desert is a substantial barrier to gene flow. However, admixture and support for a resistance-based mode of isolation suggests connective routes that could facilitate movement. Populations in Mongolia had greater connectivity, indicative of more continuous habitat. Drivers of genetic structure in China were difficult to discern, and fine-scale sampling is needed. This study elucidates snow leopard landscape connectivity and helps to prioritize conservation areas. Although contact zones may have existed and occasional crossings can occur, establishing corridors to connect these areas should not be a priority. Focus should be placed on maintaining the relatively high connectivity for snow leopard populations within Mongolia and increasing research efforts in China.
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Rodgers, T. W., Janecka, J. E. (2013). Applications and techniques for non-invasive faecal genetics research in felid conservation.
Abstract: Non-invasive genetic techniques utilising DNA extracted from faeces hold great promise for felid conservation research. These methods can be used to establish species
distributions, model habitat requirements, analyse diet, estimate abundance and population density, and form the basis for population, landscape and conservation genetic analyses. Due to the elusive nature of most felid species, non-invasive genetic methods have the potential to provide
valuable data that cannot be obtained with traditional observational or capture techniques. Thus, these methods are particularly valuable for research and conservation of endangered
felid species. Here, we review recent studies that use non-invasive faecal genetic techniques to survey or study wild felids; provide an overview of field, laboratory and analysis techniques; and offer suggestions on how future non-invasive genetic studies can be expanded or improved to more effectively support conservation.
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Atzeni, L., Wang, J., Riordan, P., Shi, K., Cushman, S. A. (2023). Landscape resistance to gene flow in a snow leopard population from Qilianshan National Park, Gansu, China. Landscape Ecology, .
Abstract: Context: The accurate estimation of landscape resistance to movement is important for ecological understanding and conservation applications. Rigorous estimation of resistance requires validation and optimization. One approach uses genetic data for the optimization or validation of resistance models. Objectives We used a genetic dataset of snow leopards from China to evaluate how landscape genetics resistance models varied across genetic distances and spatial scales of analysis. We evaluated whether landscape genetics models were superior to models of resistance derived from habitat suitability or isolation-by-distance.
Methods: We regressed genetically optimized, habitat-based, and isolation-by-distance hypotheses against genetic distances using mixed effect models. We explored all subset combinations of genetically optimized variables to find the most supported resistance scenario for each genetic distance.
Results: Genetically optimized models always out-performed habitat-based and isolation-by-distance hypotheses. The choice of genetic distances influenced the apparent influence of variables, their spatial scales and their functional response shapes, producing divergent resistance scenarios. Gene flow in snow leopards was largely facilitated by areas of intermediate ruggedness at intermediate elevations corresponding to small-to-large valleys within and between the mountain ranges.
Conclusions: This study highlights that landscape genetics models provide superior estimation of functional dispersal than habitat surrogates and suggests that optimization of genetic distance should be included as an optimization routine in landscape genetics, along with variables, scales, effect size and functional response shape. Furthermore, our study provides new insights on the ecological conditions that promote gene flow in snow leopards, which expands ecological knowledge, and we hope will improve conservation planning.
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