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Khatoon, R., Hussain, I., Anwar, M., Nawaz, M. A. (2017). Diet selection of snow leopard (Panthera uncia) in Chitral, Pakistan. Turkish Journal of Zoology, (14), 914–923.
Abstract: Snow leopard (Panthera uncia) is an elusive endangered carnivore found in remote mountain regions of Central Asia, with
sparse distribution in northern Pakistan, including Chitral and Baltistan. The present study determined the food habits of snow leopard, including preferred prey species and seasonal variation in diet. Fifty-six scat samples were collected and analyzed to determine the diet composition in two different seasons, i.e. summer and winter. Hair characteristics such as cuticular scale patterns and medullary structure were used to identify the prey. This evidence was further substantiated from the remains of bones, claws, feathers, and other undigested remains found in the scats. A total of 17 prey species were identified; 5 of them were large mammals, 6 were mesomammals, and the remaining 6 were small mammals. The occurrence of wild ungulates (10.4%) in the diet was low, while livestock constituted a substantial part (26.4%) of the diet, which was higher in summer and lower in winter. Mesomammals altogether comprised 33.4% of the diet, with palm civet (Paguma larvata) as a dominant (16.8%) species, followed by golden marmot (Marmota caudate) (8.8%), which was higher in winter. There was a significant difference in seasonal variation in domestic livestock and small mammals. The livestock contribution of 26.4% observed in the present study indicates a significant dependence of the population on livestock and suggests that the study area is expected to be a high-conflict area for snow leopards. The results of the current study would help improve the conservation efforts for snow leopards, contributing to conflict resolution and effective management of this endangered cat. |
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Mallon, D. P., Jackson, R. M. (2017). A downlist is not a demotion: Red List status and reality. Oryx, , 1–5.
Abstract: Assessments of biodiversity status are needed to
track trends, and the IUCN Red List has become the accepted global standard for documenting the extinction risk of species. Obtaining robust data on population size is an essential component of any assessment of a species� status, including assessments for the IUCN Red List. Obtaining such estimates is complicated by methodological and logistical issues, which are more pronounced in the case of cryptic species, such as the snow leopard Panthera uncia. Estimates of the total population size of this species have, to date, been based on little more than guesstimates, but a comprehensive summary of recent field research indicates that the conservation status of the snow leopard may be less dire than previously thought. A revised categorization, from Endangered to Vulnerable, on the IUCN Red List was proposed but met some opposition, as did a recent, similar recategorization of the giant panda Ailuropoda melanoleuca. Possible factors motivating such attitudes are discussed. Downlisting on the IUCN Red List indicates that the species concerned is further from extinction, and is always to be welcomed, whether resulting from successful conservation intervention or improved knowledge of status and trends. Celebrating success is important to reinforce the message that conservation works, and to incentivize donors. |
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Ghoshal, A., Bhatnagar, Y. V., Pandav, B., Sharma, K., Mshra, C. (2017). Assessing changes in distribution of the Endangered snow leopard Panthera uncia and its wild prey over 2 decades in the Indian Himalaya through interviewbased occupancy surveys. Oryx, , 1–13.
Abstract: Understanding species distributions, patterns of
change and threats can form the basis for assessing the conservation status of elusive species that are difficult to survey. The snow leopard Panthera uncia is the top predator of the Central and South Asian mountains. Knowledge of the distribution and status of this elusive felid and its wild prey is limited. Using recall-based key-informant interviews we estimated site use by snow leopards and their primary wild prey, blue sheep Pseudois nayaur and Asiatic ibex Capra sibirica, across two time periods (past: �; recent: �) in the state of Himachal Pradesh, India. We also conducted a threat assessment for the recent period. Probability of site use was similar across the two time periods for snow leopards, blue sheep and ibex, whereas for wild prey (blue sheep and ibex combined) overall there was an % contraction. Although our surveys were conducted in areas within the presumed distribution range of the snow leopard, we found snow leopards were using only % of the area (, km). Blue sheep and ibex had distinct distribution ranges. Snow leopards and their wild prey were not restricted to protected areas, which encompassed only % of their distribution within the study area. Migratory livestock grazing was pervasive across ibex distribution range and was the most widespread and serious conservation threat. Depredation by free-ranging dogs, and illegal hunting and wildlife trade were the other severe threats. Our results underscore the importance of community-based, landscape- scale conservation approaches and caution against reliance on geophysical and opinion-based distribution maps that have been used to estimate national and global snow leopard ranges. |
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Pal, R., Panwar, A., Goyal, S. P., Sathyakumar, S. (2022). Changes in ecological conditions may influence intraguild competition: inferring interaction patterns of snow leopard with co-predators. PeerJ, 10(e14277), 1–26.
Abstract: Background: Large-scale changes in habitat conditions due to human modifications and climate change require management practices to consider how species communities can alter amidst these changes. Understanding species interactions across the gradient of space, anthropogenic pressure, and season provide the opportunity to anticipate possible dynamics in the changing scenarios. We studied the interspecific interactions of carnivore species in a high-altitude ecosystem over seasonal (summer and winter) and resource gradients (livestock grazing) to assess the impact of changing abiotic and biotic settings on coexistence.
Methods: The study was conducted in the Upper Bhagirathi basin, Western Himalaya, India. We analyzed around 4 years of camera trap monitoring data to understand seasonal spatial and temporal interactions of the snow leopard with common leopard and woolly wolf were assessed in the greater and trans-Himalayan habitats, respectively. We used two species occupancy models to assess spatial interactions, and circadian activity patterns were used to assess seasonal temporal overlap amongst carnivores. In addition, we examined scats to understand the commonalities in prey selection. Results: The result showed that although snow leopard and wolves depend on the same limited prey species and show high temporal overlap, habitat heterogeneity and differential habitat use facilitate co-occurrence between these two predators. Snow leopard and common leopard were spatially independent in the summer. Conversely, the common leopard negatively influences the space use of snow leopard in the winter. Limited prey resources (lack of livestock), restricted space (due to snow cover), and similar activity patterns in winter might result in strong competition, causing these species to avoid each other on a spatial scale. The study showed that in addition to species traits and size, ecological settings also play a significant role in deciding the intensity of competition between large carnivores. Climate change and habitat shifts are predicted to increase the spatial overlap between snow leopard and co-predators in the future. In such scenarios, wolves and snow leopards may coexist in a topographically diverse environment, provided sufficient prey are available. However, shifts in tree line might lead to severe competition between common leopards and snow leopards, which could be detrimental to the latter. Further monitoring of resource use across abiotic and biotic environments may improve our understanding of how changing ecological conditions can affect resource partitioning between snow leopards and predators. |
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Zhang, C., Ma, T., Ma, D. (2023). Status of the snow leopard Panthera uncia in the Qilian Mountains, Gansu Province, China. Oryx, , 1–6.
Abstract: Population density estimation is integral to the effective conservation and management of wildlife. The snow leopard Panthera uncia is categorized as Vulnerable on the IUCN Red List, and reliable information on its density is a prerequisite for its conservation and management. Little is known about the status of the snow leopard in the central and eastern Qilian Mountains, China. To address this, we estimated the population density of the snow leopard using a spatially explicit capture–recapture model based on camera trapping in Machang in the central and eastern Qilian Mountains during January–March 2019. We set up
40 camera traps and recorded 84 separate snow leopard captures over 3,024 trap-days. We identified 18 individual snow leopards and estimated their density to be 2.26/100 km. Our study provides baseline information on the snow leopard and the first population estimate for the species in the central and eastern Qilian Mountains. |
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Kichloo, M. A., Sharma, K., Sharma, N. (2023). Climate casualties or human disturbance? Shrinking distribution of the three large carnivores in the Greater Himalaya. Springer – Climatic Change, 176(118), 1–17.
Abstract: Mammalian carnivores are key to our understanding of ecosystem dynamics, but most of them are threatened with extinction all over the world. Conservating large carnivores is often an arduous task considering the complex relationship between humans and carnivores, and the diverse range and reasons of threats they face. Climate change is exacerbating the situation further by interacting with most existing threats and amplifying their impacts. The Mountains of Central and South Asia are warming twice as rapidly as the rest of the northern hemisphere. There has been limited research on the effect of climate change and other variables on large carnivores. We studied the patterns in spatio-temporal distribution of three sympatric carnivores, common leopard, snow leopard, and Asiatic black bear in Kishtwar high altitude National Park, a protected area in the Great Himalayan region of Jammu and Kashmir. We investigated the effects of key habitat characteristics as well as human disturbance and climatic factors to understand the spatio-temporal change in their distributions between the early 1990s and around the year 2016–2017. We found a marked contraction in the distribution of the three carnivores between the two time periods. While snow leopard shifted upwards and further away from human settlements, common leopard and Asiatic black bear suffered higher rates of local extinctions at higher altitudes and shifted to lower areas with more vegetation, even if that brought them closer to settlements. We also found some evidence that snow leopards were less likely to have faced range contraction in areas with permanent glaciers. Our study underscores the importance of climate adaptive conservation practices for long-term management in the Greater Himalaya, including the monitoring of changes in habitat, and space-use patterns by human communities and wildlife.
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Bohnett, E., Holmberg, J., Faryabi, S. P., An, L., Ahmad, B., Rashid, W., Ostrowski, S. (2023). Comparison of two individual identification algorithms for snow leopards (Panthera uncia) after automated detection. Ecological Informatics, 77(102214), 1–14.
Abstract: Photo-identification of individual snow leopards (Panthera uncia) is the primary data source for density estimation via capture-recapture statistical methods. To identify individual snow leopards in camera trap imagery, it is necessary to match individuals from a large number of images from multiple cameras and historical catalogues, which is both time-consuming and costly. The camouflaged snow leopards also make it difficult for machine learning to classify photos, as they blend in so well with the surrounding mountain environment, rendering applicable software solutions unavailable for the species. To potentially make snow leopard individual identification available via an artificial intelligence (AI) software interface, we first trained and evaluated image classification techniques for a convolutional neural network, pose invariant embeddings (PIE) (a triplet loss network), and compared the accuracy of PIE to that of the HotSpotter algorithm (a SIFT-based algorithm). Data were acquired from a curated library of free-ranging snow leopards taken in Afghanistan between 2012 and 2019 and from captive animals in zoos in Finland, Sweden, Germany, and the United States. We discovered several flaws in the initial PIE model, such as a small amount of background matching, that was addressed, albeit likely not fixed, using background subtraction (BGS) and left-right mirroring (LR) techniques which demonstrated reasonable accuracy (Rank 1: 74% Rank-5: 92%) comparable to the Hotspotter results (Rank 1: 74% Rank 2: 84%)The PIE BGS LR model, in conjunction with Hotspotter, yielded the following results: Rank-1: 85%, Rank-5: 95%, Rank-20: 99%. In general, our findings indicate that PIE BGS LR, in conjunction with HotSpotter, can classify snow leopards more accurately than using either algorithm alone.
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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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Rashid, W., Shi, J., Rahim, I. U., Dong, S., Ahmad, L. (2020). Research trends and management options in human-snow leopard conflict. Biological Conservation, 242(108413), 1–10.
Abstract: Conservation of the snow leopard (Panthera uncia) is challenging because of its threatened status and increase in human-snow leopard conflict (HSC). The area of occupancy of the snow leopard comprises mountainous regions of Asia that are confronted with various environmental pressures including climate change. HSCs have increased with a burgeoning human population and economic activities that enhance competition between human and snow leopard or its preys. Here we systematically review the peer-reviewed literature from 1994 to 2018 in Web of Science, Google Scholar, Science Direct and PubMed (30 articles), to evaluate the current state of scholarship about HSCs and their management. We determine: 1) the spatio-temporal distribution of relevant researches; 2) the methodologies to assess HSCs; 3) and evaluate existing interventions for conflict management; and 4) the potential options for HSC management. The aim of the current study is thus to identify key research gaps and future research requirements. Of the articles in this review, 60% evaluated the mitigation of HSCs, while only 37% provided actionable and decisive results. Compensation programs and livestock management strategies had high success rates for mitigating HSCs through direct or community-managed interventions. Further research is required to evaluate the efficacy of existing HSC mitigation strategies, many of which, while recommended, lack proper support. In spite of the progress made in HSC studies, research is needed to examine ecological and sociocultural context of HSCs. We suggest future work focus on rangeland management for HSC mitigation, thus ultimately fostering a co-existence between human and snow leopard.
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Bohnett, E., Faryabi, S. P., Lewison, R., An, L., Bian, X., Rajabi, A. M., Jahed, N., Rooyesh, H., Mills, E., Ramos, S., Mesnildrey, N., Perez, C. M. S., Taylor, J., Terentyev, V., Ostrowski, S. (2023). Human expertise combined with artificial intelligence improves performance of snow leopard camera trap studies. Global Ecology & Conservation, 41(e02350), 1–13.
Abstract: Camera trapping is the most widely used data collection method for estimating snow leopard (Panthera uncia) abundance; however, the accuracy of this method is limited by human observer errors from misclassifying individuals in camera trap images. We evaluated the extent Whiskerbook (www.whiskerbook.org), an artificial intelligence (AI) software, could reduce this error rate and enhance the accuracy of capture-recapture abundance estimates. Using 439 images of 34 captive snow leopard individuals, classification was performed by five observers with prior experience in individual snow leopard ID (“experts”) and five observers with no such experience (“novices”). The “expert” observers classified 35 out of 34 snow leopard individuals, on average erroneously splitting one individual into two, thus resulting in a higher number than true individuals. The success rate of experts was 90 %, with less than a 3 % error in estimating the population size in capture-recapture modeling. However, the “novice” observers successfully matched 71 % of encounters, recognizing 25 out of 34 individuals, underestimating the population by 25 %. It was found that expert observers significantly outperformed novice observers, making statistically fewer errors (Mann Whitney U test P = 0.01) and finding the true number of individuals (P = 0.01). These differences were contrasted with a previous study by Johansson et al. 2020, using the same subset of 16 individuals from European zoos. With the help of AI and the Whiskerbook platform, “experts” were able to match 87 % of encounters and identify 15 out of 16 individuals, with modeled estimates of 16 ± 1 individuals. In contrast, “novices” were 63 % accurate in matching encounters and identified 12 out of 16 individuals, modeling 12 ± 1 individuals that underestimated the population size by 12 %. When comparing the performance of observers using AI and the Whiskerbook platform to observers performing the tasks manually, we found that observers using Whiskerbook made significantly fewer errors in splitting one individual into two (P = 0.04). However, there were also a significantly higher number of combination errors, where two individuals were combined into one (P = 0.01). Specifically, combination errors were found to be made by “novices” (P = 0.04). Although AI benefited both expert and novice observers, expert observers outperformed novices. Our results suggest that AI effectively reduced the misclassification of individual snow leopards in camera trap studies, improving abundance estimates. However, even with AI support, expert observers were needed to obtain the most accurate estimates.
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