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Freeman, H., & Hutchins, M. (1978). Captive Management of Snow Leopard Cubs. Der Zoologischer Garten, 48, 49–62.
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Robinson, J. J., Crichlow, A. D., Hacker, C. E., Munkhtsog, B., Munkhtsog, B., Zhang, Y., Swanson, W. F., Lyons, L. A., Janecka, J. E. (2024). Genetic Variation in the Pallas’s Cat (Otocolobus manul) in Zoo-Managed and Wild Populations. Diversity, 16(228), 1–13.
Abstract: The Pallas’s cat (Otocolobus manul) is one of the most understudied taxa in the Felidae family. The species is currently assessed as being of “Least Concern” in the IUCN Red List, but this assessment is based on incomplete data. Additional ecological and genetic information is necessary for the long-term in situ and ex situ conservation of this species. We identified 29 microsatellite loci with sufficient diversity to enable studies into the individual identification, population structure, and phylogeography of Pallas’s cats. These microsatellites were genotyped on six wild Pallas’s cats from the Tibet Autonomous Region and Mongolia and ten cats from a United States zoo-managed population that originated in Russia and Mongolia. Additionally, we examined diversity in a 91 bp segment of the mitochondrial 12S ribosomal RNA (MT-RNR1) locus and a hypoxia-related gene, endothelial PAS domain protein 1 (EPAS1). Based on the microsatellite and MT-RNR1 loci, we established that the Pallas’s cat displays moderate genetic diversity. Intriguingly, we found that the Pallas’s cats had one unique nonsynonymous substitution in EPAS1 not present in snow leopards (Panthera uncia) or domestic cats (Felis catus). The analysis of the zoo-managed population indicated reduced genetic diversity compared to wild individuals. The genetic information from this study is a valuable resource for future research into and the conservation of the Pallas’s cat.
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Watanabe, M., Sugano, S., Togashi, T., Imai, J., Uchida, K., Yamaguchi, R., et al. (2000). Molecular cloning and phylogenetic analysis of canine beta-casein. DNA Seq, 11(3-4), 295–300.
Abstract: A canine beta-casein cDNA was isolated from mammary tissue by polymerase chain reaction (PCR) using degenerate primers. It encodes 250 amino acids protein containing the conserved sequence motif of beta- casein. It showed the highest homology with snow-leopard (Uncia uncia (55-62% identity). It also showed 44-53% identity with human, 33-42%, identity with mouse, 29-37%, identity with rat, 43-53% identity with rabbit, 41-48% identity with pig, 44-51% identity with cattle and 44- 50% identity with sheep. A 1.2-kb mRNA was detected in mammary tissue by Northern blot analysis. Phylogenetic analysis revealed that canine beta-casein formed a branch with lesser panda and snow leopard, which were grouped into carnivore.
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Subbotin, A. E., & Istomov, S. V. (2009). The population status of snow leopards Uncia uncia (Felidae, Carnivora) in the western Sayan Mountain Ridge. Doklady Biologicl Sciences, 425, 183–186.
Abstract: The snow leopard (Uncia uncial Schreber, 1776) is the most poorly studied species of the cat family in the world and, in particular, in Russia, where the northern periphery of the species area (no more than 3% of it) is located in the Altai-Hangai-Sayan range [1]. It is generally known that the existing data on the Russian part of the snow leopard population have never been a result of targeted studies; at best, they have been based on recording the traces of the snow leopard vital activity [2]. This is explained by the snow leopard's elusive behavior, inaccessibility of its habitats for humans, and its naturally small total numbers in the entire species area. All published data on the population status of the snow leopard in Russia, from the first descriptions of the species [3-6] to the latest studies [7, 8] are subjective, often speculative, and are not confirmed by
quantitative estimates. It is obvious, however, that every accurate observation of this animal is of particular interest [9]. The purpose of our study was to determine the structure and size of the population group presumably inhabiting the Western Sayan mountain ridge at the northern boundary of the species area
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Schneider, V. K. M. (1936). Einige bilder zur Aufzucht eines schneeleoparden. Dresden Zoological Garden, , 37–39.
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Krumbiegel, V. I. (1936). Die schneeleoparden (Felis uncia Schreb.) des Dresdner Zoologischen Gartens. Dresdner Zoologischen Gartens, , 34–37.
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Smith, G. (1992). Mongolia at the crossroads. Earth Island Journal, 7(4), 1.
Abstract: Abstract: Assesses foreign investment laws adopted by the government of Mongolia which have been deemed extremely flexible and favorable for Americans. Economic benefits presented by the big game hunt industry; Consultation with Secretary of State James Baker in the formulation of said laws during his July The Mongolian government is trying its best to make the country attractive to foreign investors. Big game hunts are still Mongolia's primary source of foreign cash. European and American hunters are willing to pay as much as $90,000 for rare game such as the ibex or the snow leopard. However, a recent US Fish and Wildlife Service ruling giving protection to the Argal, a wild sheep, could mean the cutting of cash inflows from foreign hunters.
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Braden, K. (1999). Endangered species protection and economic change in the former USSR.
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Blomqvist, L., & Dexel, B. (2006). In Focus: Declining numbers of wild snow leopards.
Abstract: International collaboration to ensure the long-term survival of snow leopards (Uncia uncia) in the wild is today more acutely needed than ever! Trade in live snow leopards, their skins and bones, has during the last decade reached such extensiveness that the species is in danger of being wiped out from many of its former habitats. All recent surveys support declining populations throughout most of their range.
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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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