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Blomqvist, L. (1998). The snow leopard EEP in 1996. In L. Blomqvist (Ed.), International Pedigree Book of Snow Leopards (Vol. 7, pp. 26–29). Helsinki: Helsinki Zoo.
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Blomqvist, L. (1982). The 3. Snow Leopard Register. In L. Blomqvist (Ed.), International Pedigree Book of Snow Leopards (Vol. 3, pp. 145–164). Helsinki: Helsinki Zoo.
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Blomqvist, L. (1980). The snow leopard register. In L. Blomqvist (Ed.), International Pedigree Book of Snow Leopards (Vol. 2, pp. 218–238). Helsinki: Helsinki Zoo.
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Blomqvist, L. (1980). Photos of snow leopards. In L. Blomqvist (Ed.), International Pedigree Book of Snow Leopards (Vol. 2, pp. 239–257). Helsinki: Helsinki Zoo.
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Blomqvist, L. (1978). Photos of snow leopards. In L. Blomqvist (Ed.), International Pedigree Book of Snow Leopards, Vol. 1 (Vol. 1, pp. 141–151). Helsinki: Helsinki Zoo.
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Blomqvist, L. (1978). Resolution from the first international snow leopard conference in Helsinki on March 7-8, 1978. In L. Blomqvist (Ed.), International Pedigree Book of Snow Leopards, Vol. 1 (Vol. 1, pp. 3–5). Helsinki: Helsinki Zoo.
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Blomqvist, L., & Rieger, I. (1980). Snow leopard references. In L. Blomqvist (Ed.), International Pedigree Book of Snow Leopards (Vol. 2, pp. 258–262). Helsinki: Helsinki Zoo.
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Bobrinskiy, N. A. (1935). Subgenus Leopardus.
Abstract: Snow Leopard Felis (Leopardus) uncia S c h r † b., 1778 is distributed in the mountains of Central Asia, Turkmenistan (very rare) and Turkestan, on Tarbagatay, Altay, Sayans and in Uriankhay area. Subspecies haven't been described. Body length is about 130 cm, tail length 90 cm.
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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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Chalise, M. K. (2008). Nepalka Samrakshit Banyajantu (Nepal's Protected Wildlife in Nepali language). Lalitpur, Kathmandu: Shajha Prakashan.
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