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Tallian, A., Mattisson, J., Samelius, G., Odden, J., Mishra, C., Linnell, J. D. C., Lkhagvajav, P., Johansson, O. (2023). Wild versus domestic prey: Variation in the kill-site behavior of two large felids. Global Ecology and Conservation, 47(e026750), 1–13.
Abstract: Livestock depredation is an important source of conflict for many terrestrial large carnivore
species. Understanding the foraging behavior of large carnivores on domestic prey is therefore
important for both mitigating conflict and conserving threatened carnivore populations. Handling
time is an important, albeit often overlooked, component of predatory behavior, as it directly
influences access to food biomass, which can affect predator foraging efficiency and subsequent
kill rates. We used long-term data on snow leopards (Panthera uncia) in Mongolia (Asia) and
Eurasian lynx (Lynx lynx) in Norway (Europe) to examine how large carnivore foraging patterns
varied between domestic and wild prey, and how the different landscape characteristics affected
those patterns. Our results suggest handling time was generally shorter for domestic compared to
wild prey. For snow leopards, rugged terrain was linked to increased handling time for larger
prey. For lynx, handling time increased with terrain ruggedness for domestic, but not wild, prey,
and was greater in closed compared to open habitats. There were also other differences in snow
leopard and lynx foraging behavior, e.g., snow leopards also stayed longer at, and remained closer
to, their kill sites than lynx. Shorter handling time suggests that felids may have utilized domestic
prey less effectively than wild prey, i.e., they spent less time consuming their prey. This could a)
result in an energetic or fitness cost related to decreased felid foraging efficiency caused by the
risk of anthropogenic disturbance, or b) exacerbate conflict if reduced handling time associated
with easy prey results in increased livestock depredation.
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Gao, Y., Wang, Y., Lee, A. T. L., Liu, Y., Luo, Y., Orrick, K., Alexander, J. S., Sangpo, J. T., Clark, S. G. (2023). Contextualizing sociodemographic differences in Tibetan attitudes toward large carnivores. Conservation Science and Practice, (e13049), 1–15.
Abstract: Fostering human–wildlife coexistence necessitates a thorough and nuanced grasp of local attitudes toward wildlife. Attitudes can vary substantially based on the sociodemographic backgrounds of individuals within a society. This study examines Tibetan attitudes toward large carnivores, emphasizing the importance of contextualization in discerning the effects of sociodemographic factors on attitudes. We began by analyzing existing research on Tibetan attitudes toward wildlife in China, identifying previously studied sociodemo- graphic variables. We then executed an online survey to evaluate the affective, behavioral, cognitive, and overall attitudes of ethnic Tibetans in China toward snow leopards (Panthera uncia), gray wolves (Canis lupus), and brown bears (Ursus arctos). Our findings show that while factors such as gender, age, religious identity, and level of education shape these attitudes, their influence differs depending on the specific attitude component and the target animal under examination. Therefore, making broad generalizations about sociodemographic differences in attitudes can be misleading. It is imperative for attitude research to clearly define the attitude component (what type of attitude), object (attitude toward what), and circumstance (attitude in which situation) being studied. Conducting ethnographic fieldwork in collaboration with local cultural experts can deepen our understanding of local perspectives and the ways sociodemographic factors influence attitudes. Such insights are pivotal for developing conservation strategies attuned to local sociocultural contexts.
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Thapa, K., Baral, S., Rahamajhi, S. (2023). Effectiveness of Human-Snow leopard co-existence measure- a systematic analysis. Journal for Nature Conservation, 76(126511), 1–11.
Abstract: Snow leopards and agropastoral communities have co-existed in snow leopard range countries for centuries. The vulnerable snow leopard forms and maintains the entire ecosystem, serving as an indicator species of a healthy alpine ecosystem. However, snow leopards, on the other hand, habitually kill livestock, occasionally killing 100 or more livestock in a single night, resulting in snow leopard retaliation. Thus, the snow leopard is becoming more threatened, so more attention should be paid. Therefore, numerous conservation mitigation strategies have been applied to maintain human-snow leopard coexistence in countries of the snow leopard range. However, such implemented conservation strategies lacked a thorough assessment of their achievements or shortcomings in protecting the snow leopard and enhancing community tolerance. Therefore, we systematically examined and evaluated peer-reviewed articles and book chapters on existing and implemented mitigation measures. We use the software Publish or Perish to achieve this, and we assess using the Preferred Reporting of Items for Systematic Review and Meta-Analysis (PRISMA) review approach. We thoroughly analyzed 42 papers and book chapters that were condensed human- snow leopard co-existence-related literature published in English from 2010 to 2023. Almost 90% of the papers were country-specific, with the remaining papers covering regional or snow leopard ranges countries. Nepal had the most papers, followed by China, India, and Mongolia; however, Afghanistan, Bhutan, Pakistan, Russia, and Tajikistan each had<10%, but there was no single document from Kazakhstan or Kyrgyzstan. Predator-proof corral, improved herding practices, and community-based insurance programs were three of the key recommendations that were more than 10 to 22 times proposed interventions. There are site-specific sociocultural situations and environments that require long-term action-oriented research that is area-specific rather than short-term and generic interventions. We identified a large knowledge gap in snow leopard research, specifically a lack of evidence that demonstrates and quantifies the effects of conservation actions, and strongly advise that it be further researched.
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Thapa, K., Rayamajhi, S. (2023). Anti-predator strategies of blue sheep (naur) under varied predator compositions: a comparison of snow leopard-inhabited valleys with and without wolves in Nepal. Wildlife Research, , 1–9.
Abstract: In Nepal, naur are usually the staple wild prey for the snow leopard, a solitary stalker hunter, and in some cases, for the wolf who hunts in a pack. We assumed that naur would adapt their anti-predatory responses to the presence of chasing and ambushing predators in the Manang Valley, where there are snow leopards and wolves, and in the Nar Phu valley, an area where there is only the snow leopard.
Aims. The aim of this study was to determine if there were differences in anti-predator strategies (vigilance, habitat selection and escape terrain) of naur in two valleys over two seasons, spring and autumn.
Methods. In spring 2019, we conducted a reconnaissance survey on the status of the naur and its habitat in the Manang and Nar Phu valleys of the Annapurna Conservation Area, Nepal. In spring and autumn 2020 and 2021, we observed 360 focal naur individuals (180 individuals in each valley), using the vigilance behaviour methodology to examine the behaviour of the naur.
Key results. There was little difference in the size of the naur groups between the Manang and Nar Phu valleys. The naur were twice as vigilant in Manang (15%), where there are snow leopards and wolves, as they were in Nar Phu (9%), with only snow leopards. The distance from the naur to escape cover was significantly shorter in Manang than in Nar Phu valley. Naur used significantly more rolling terrain in Nar Phu than in Manang. Conclusions. The return of wolves to the Manang valley may have resulted in an increase in the level of naur vigilance. Most likely, the wolves in Manang have already had an effect on the female-to-young-ratio, and this effect will possibly have important consequences for the naur population, as well as at the ecosystem level in the future. Other key determining factors, such as the climate crisis and changes in local resources, could have a significant impact on the naur population, indicating the need for more research. Implications. The findings of this study would provide valuable baseline information for the design of a science-based conservation strategy for conservation managers and scientists on naur, snow leopards and wolves.
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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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Ismaili, R. R. R., Peng, X., Li., Y, Ali, A., Ahmad, T., Rahman, A. U., Ahmad, S., Shi, K. (2024). Modeling Habitat Suitability of Snow Leopards in Yanchiwan National Reserve, China. Animals, 14(1938), 1–21.
Abstract: 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.
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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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