Alexander, J. S., Johansson, O., Xiao, L., Chetri, M., Lkhagvajav, P., Karumbaya, R., Wright, B., Modaqiq, W., Lovari, S. (2023). Snow Leopard Network: 20 years of collaboration among practitioners. Oryx, 57(5), 559–560.
Abstract: The Snow Leopard Network (snowleopardnetwork.org), a global group dedicated to snow leopard Panthera uncia conservation, is commemorating 2 decades of accomplishments since its inception in 2002. Initiated at the Snow Leopard Survival Summit in Seattle, USA, with 58 experts from 17 countries, the Network continues to grow and to play a pivotal role in safeguarding the snow leopard in High Asia. Current membership stands at 621 individuals and 31 organizations. As new challenges and opportunities arise, collaborative and innovative solutions are more crucial than ever.
|
Alexander, J. S., Murali, R., Mijiddorj, T. N., Agvaantseren, B., Lhamo, C., Sharma, D., Suryawanshi, K. R., Zhi, L., Sharma, K., Young, J. C. (2023). Applying a gender lens to biodiversity conservation in High Asia. Frontiers in Conservation Science, , 1–8.
Abstract: Community-based conservation efforts represent an important approach to facilitate the coexistence of people and wildlife. A concern, however, is that these efforts build on existing community structures and social norms, which are commonly dominated by men. Some biodiversity conservation approaches may consequently neglect women’s voices and deepen existing inequalities and inequities. This paper presents two community case studies that draw upon the knowledge and experience gained in our snow leopard conservation practice in pastoral and agro-pastoral settings in Mongolia and India to better understand women’s roles and responsibilities. In these settings, roles and responsibilities in livestock management and agriculture are strongly differentiated along gender lines, and significant gaps remain in women’s decision-making power about natural resources at the community level. We argue that context-specific and gender-responsive approaches are needed to build community support for conservation actions and leverage women’s potential contributions to conservation outcomes.
|
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.
|
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.
|
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.
|
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.
|
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.
|
Esson, C., Samelius, G., Strand, T. M., Lundkvist, A., Michaux, J. R., Rasback, T., Wahab, T., Mijiddorj, T. N., Berger, L., Skerratt, L. F., Low, M. (2023). The prevalence of rodent-borne zoonotic pathogens in the South Gobi desert region of Mongolia. Infection Ecology & Epidemiology, 13(2270258), 1–10.
Abstract: The alpine ecosystems and communities of central Asia are currently undergoing large-scale ecological and socio-ecological changes likely to affect wildlife-livestock-human disease interactions and zoonosis transmission risk. However, relatively little is known about the prevalence of pathogens in this region. Between 2012 and 2015 we screened 142 rodents in Mongolia’s Gobi desert for exposure to important zoonotic and livestock pathogens. Rodent seroprevalence to Leptospira spp. was>1/3 of tested animals, Toxoplasma gondii and Coxiella burnetii approximately 1/8 animals, and the hantaviruses being between 1/20 (Puumala-like hantavirus) and<1/100 (Seoul-like hantavirus). Gerbils trapped inside local dwellings were one of the species seropositive to Puumala-like hantavirus, suggesting a potential zoonotic transmission pathway. Seventeen genera of zoonotic bacteria were also detected in the faeces and ticks collected from these rodents, with one tick testing positive to Yersinia. Our study helps provide baseline patterns of disease prevalence needed to infer potential transmission between source and target populations in this region, and to help shift the focus of epidemiological research towards understanding disease transmission among species and proactive disease mitigation strategies within a broader One Health framework.
|
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.
|
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.
|
