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Korablev, M. P., Poyarkov, A. D., Karnaukhov, A. S., Zvychaynaya, E. Y., Kuksin, A. N., Malykh, S. V., Istomov, S. V., Spitsyn, S. V., Aleksandrov, D. Y., Hernandez-Blanco, J. A., Munkhtsog, B., Munkhtogtokh, O., Putintsev, N. I., Vereshchagin, A. S., Becmurody, A., Afzunov, S., Rozhnov, V. V. (2021). Large-scale and fine-grain population structure and genetic diversity of snow leopards (Panthera uncia Schreber, 1776) from the northern and western parts of the range with an emphasis on the Russian population. Conservation Genetics, .
Abstract: The snow leopard (Panthera uncia Schreber, 1776) population in Russia and Mongolia is situated at the northern edge of the range, where instability of ecological conditions and of prey availability may serve as prerequisites for demographic instability and, consequently, for reducing the genetic diversity. Moreover, this northern area of the species distribution is connected with the western and central parts by only a few small fragments of potential habitats in the Tian-Shan spurs in China and Kazakhstan. Given this structure of the range, the restriction of gene flow between the northern and other regions of snow leopard distribution can be expected. Under these conditions, data on population genetics would be extremely important for assessment of genetic diversity, population structure and gene flow both at regional and large-scale level. To investigate large-scale and fine-grain population structure and levels of genetic diversity we analyzed 108 snow leopards identified from noninvasively collected scat samples from Russia and Mongolia (the northern part of the range) as well as from Kyrgyzstan and Tajikistan (the western part of the range) using panel of eight polymorphic microsatellites. We found low to moderate levels of genetic diversity in the studied populations. Among local habitats, the highest heterozygosity and allelic richness were recorded in Kyrgyzstan (He = 0.66 ± 0.03, Ho = 0.70 ± 0.04, Ar = 3.17) whereas the lowest diversity was found in a periphery subpopulation in Buryatia Republic of Russia (He = 0.41 ± 0.12, Ho = 0.29 ± 0.05, Ar = 2.33). In general, snow leopards from the western range exhibit greater genetic diversity (He = 0.68 ± 0.04, Ho = 0.66 ± 0.03, Ar = 4.95) compared to those from the northern range (He = 0.60 ± 0.06, Ho = 0.49 ± 0.02, Ar = 4.45). In addition, we have identified signs of fragmentation in the northern habitat, which have led to significant genetic divergence between subpopulations in Russia. Multiple analyses of genetic structure support considerable genetic differentiation between the northern and western range parts, which may testify to subspecies subdivision of snow leopards from these regions. The observed patterns of genetic structure are evidence for delineation of several management units within the studied populations, requiring individual approaches for conservation initiatives, particularly related to translocation events. The causes for the revealed patterns of genetic structure and levels of genetic diversity are discussed.
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Filla, M., Lama, R. P., Filla, T., Heurich, M., Balkenhol, N., Waltert, M., Khorozyan, I. (2022). Patterns of livestock depredation by snow leopards and effects of intervention strategies: lessons from the Nepalese Himalaya. Wildlife Research, .
Abstract: Context: Large carnivores are increasingly threatened by anthropogenic activities, and their protection is among the main goals of biodiversity conservation. The snow leopard (Panthera uncia) inhabits high-mountain landscapes where livestock depredation drives it into conflicts with local people and poses an obstacle for its conservation.
Aims: The aim of this study was to identify the livestock groups most vulnerable to depredation, target them in implementation of practical interventions, and assess the effectiveness of intervention strategies for conflict mitigation. We present a novel attempt to evaluate intervention strategies for particularly vulnerable species, age groups, time, and seasons.
Methods: In 2020, we conducted questionnaire surveys in two regions of the Annapurna Conservation Area, Nepal (Manang, n = 146 respondents and Upper Mustang, n = 183). We applied sample comparison testing, Jacobs’ selectivity index, and generalised linear models (GLMs) to assess rates and spatio-temporal heterogeneity of depredation, reveal vulnerable livestock groups, analyse potential effects of applied intervention strategies, and identify husbandry factors relevant to depredation.
Key results: Snow leopard predation was a major cause of livestock mortality in both regions (25.4–39.8%), resulting in an estimated annual loss of 3.2–3.6% of all livestock. The main intervention strategies (e.g. corrals during night-time and herding during daytime) were applied inconsistently and not associated with decreases in reported livestock losses. In contrast, we found some evidence that dogs, deterrents (light, music playing, flapping tape, and dung burning), and the use of multiple interventions were associated with a reduction in reported night-time depredation of yaks.
Conclusions and implications: We suggest conducting controlled randomised experiments for quantitative assessment of the effectiveness of dogs, deterrents, and the use of multiple interventions, and widely applying the most effective ones in local communities. This would benefit the long-term co-existence of snow leopards and humans in the Annapurna region and beyond.
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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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