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Parker, B. G., Khanyari, M., Ambarli, H., Buuveibaatar, B., Kabir, M., Khanal, G., Mirzadeh, H. R., Onon, Y., Farhadinia, M. S. (2023). A review of the ecological and socioeconomic characteristics of trophy hunting across Asia. Animal Conservation, , 1–16.
Abstract: The continuing debates about trophy hunting should be underpinned by an understanding of at least the basic characteristics of the practice (e.g. species, quotas, areas, prices). Whilst many countries in Asia have established trophy hunting programmes of considerable importance to conservation and local livelihoods, there remains some ambiguity over the extent of trophy hunting in Asia as its basic characteristics in each country have not been compiled. In this study, we compile information on various ecological and socioeconomic characteristics of trophy hunting of mammals for countries across Asia by reviewing published and unpublished literature, analysing trade data, and obtaining contributions from in-country contacts. Across Asia, established trophy hunting programmes exist in at least 11 countries and target at least 30 species and one hybrid (incl., five Vulnerable and one Endangered species). Trophy hunting in these countries varies markedly in areas (e.g. >1 million km2 in Kazakhstan, 37% of country, vs. 1325 km2 in Nepal, <1% of country) and annual offtakes (e.g. Kazakhstan: 4500 individuals from 4 of 5 trophy species; Pakistan: 229 from 4 of 7; Mongolia: 155 from 6 of 9; Tajikistan: 126 from 3 of 6; Nepal: 22 from 3 of the 4 that are trophy hunted in practice). Permit prices also vary across species and countries, with domestic and international hunters sometimes charged different rates. Hunters from the USA appear overwhelmingly prominent among international clients. National legislations typically mandate a proportion of trophy hunting revenue to accrue locally (range: 40–100%). We provide five key recommendations for research to inform trophy hunting policy in Asia: (1) Ecological impact assessments; (2) Socioeconomic impact assessments; (3) Evaluations of the contributions of trophy hunting to conservation spending; (4) Evaluations of the contributions of trophy hunting to the post-2020 Global Biodiversity Framework; (5) Further examinations of perceptions of trophy hunting.
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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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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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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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Chimed, O., Lkhagvasuren, D., Alexander, J. S., Barclay, D., Bayasgalan, N., Lkhagvajav, P., Nygren, E., Robinson, S. L., Samelius, G. (2023). Delivery of educational material increased awareness of the elusive Pallas’s cat in Southern Mongolia. Applied Environmental Education & Comunication, , 1–13.
Abstract: Public engagement and awareness programs are important components of many conservation programs today as such engagements are often critical for successful conservation. In this study, we examined if delivery of educational material increased awareness of the Pallas’s cat and its environment in a southern Mongolia herder community. We found that knowledge was greater and attitudes toward the Pallas’s cat and wildlife in general were more positive one year after the delivery of the educational material. This study thus suggests that educational material can be effective at increasing awareness of small and elusive species such as the Pallas’s cat.
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Islam, M., Sahana, M., Areendran, G., Jamir, C., Raj, K., Sajjad, H. (2023). Prediction of potential habitat suitability of snow leopard (Panthera uncia) and blue sheep (Pseudois nayaur) and niche overlap in the parts of western Himalayan region. Geo: Geography and Environment, 10(e00121), 1–15.
Abstract: The snow leopard (Panthera uncia) and blue sheep (Pseudois nayaur) are the inhabitants of remote areas at higher altitudes with extreme geographic and climatic conditions. The habitats of these least-studied species are crucial for sustaining the Himalayan ecosystem. We employed the Maximum Entropy (MaxEnt) species distribution model to predict the potential habitat suitability of snow leopards and blue sheep and extracted common overlapped niches. For this, we utilised presence location, bio-climatic and environmental variables, and correlation analysis was applied to reduce the negative impact of multicollinearity. A total of 134 presence locations of snow leopards and 64 for blue sheep were selected from the Global Biodiversity Information Facility (GBIF). The annual mean temperature (Bio1) was found to be the most useful and highly influential factor to predict the potential habitat suitability of snow leopards. Annual mean temperature, annual precipitation and isothermality were the major influencing factors for blue sheep habitat suitability. Highly influential bio-climatic, topographic and environmental variables were integrated to construct the model for predicting habitat suitability. The area under the curve (AUC) values for snow leopard (0.87) and blue sheep (0.82) showed that the models are under good representation. Of the total area investigated, 47% was suitable for the blue sheep and 38% for the snow leopards. Spatial habitat assessment revealed that nearly 11% area from the predicted suitable habitat class of both species was spatially matched (overlapped), 48.6% area was unsuitable under niche overlap and 40.5% area was spatially mismatched niche. The presence of snow leopards and blue sheep in some highly suitable areas was not observed, yet such areas have the potential to sustain these elusive species. The other geographical regions interested in exploring habitat suitability may find the methodological framework adopted in this study useful for formulating an effective conservation policy and management strategy.
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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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Allen, M. L., Rovero, F., Oberosler, V., Augugliaro, C., Krofel, M. (2023). Effects of snow leopards (Panthera uncia) on olfactory communication of Pallas’s cats (Otocolobus manul) in the Altai Mountains, Mongolia. Behaviour, , 1–9.
Abstract: Olfactory communication is important for many solitary carnivores to delineate territories and communicate with potential mates and competitors. Pallas’s cats (Otocolobus manul) are small felids with little published research on their ecology and behaviour, including if they avoid or change behaviours due to dominant carnivores. We studied their olfactory communication and visitation at scent-marking sites using camera traps in two study areas in Mongolia. We documented four types of olfactory communication behaviours, and olfaction (sniffing) was the most frequent. Pallas’s cats used olfactory communication most frequently at sites that were not visited by snow leopards (Panthera uncia) and when they used communal scent-marking sites, they were more likely to use olfactory communication when a longer time had elapsed since the last visit by a snow leopard. This suggests that Pallas’s cats may reduce advertising their presence in response to occurrence of snow leopards, possibly to limit predation risk.
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Golla, T. R., Tensen, L., Vipin, Kumar, K., Kumar, S., Gaur, A. (2023). Neutral and adaptive genetic variation in Indian snow leopards, Panthera uncia. Current Science, 125(2), 204–209.
Abstract: In this study, we reveal patterns of genetic variation in snow leopards (Panthera uncia) by combining neutral (mtDNA, microsatellites) and adaptive (MHC II-DRB) genes. We collected 56 faecal samples from three locations in India. We observed moderate levels of microsatellite diversity (N = 30; A = 5.6; HO = 0.559). Nine unique MHC II-DRB sequences were identified in four snow leopard samples, of which 8 were novel. We found low levels of polymorphism in MHC class II-DRB exon, which was higher in captive (VA = 9.4%) compared to wild individuals (VA = 7.8%), likely as a result of a population bottleneck.
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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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