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Burgener, N., Gusset, M., & Schmid, H. (2008). Frustrated appetitive foraging behavior, stereotypic pacing, and fecal glucocorticoid levels in snow leopards (Uncia uncia) in the Zurich Zoo (Vol. 11).
Abstract: This study hypothesized that permanently frustrated, appetitive-foraging behavior caused the stereotypic pacing regularly observed in captive carnivores. Using 2 adult female snow leopards (Uncia uncia), solitarily housed in the Zurich Zoo, the study tested this hypothesis experimentally with a novel feeding method: electronically controlled, time-regulated feeding boxes. The expected result of employing this active foraging device as a successful coping strategy was reduced behavioral and physiological measures of stress, compared with a control-feeding regime without feeding boxes. The study assessed this through behavioral observations and by evaluating glucocorticoid levels noninvasively from feces. Results indicated that the 2 snow leopards did not perform successful coping behavior through exercising active foraging behavior or through displaying the stereotypic pacing. The data support a possible explanation: The box-feeding method did not provide the 2 snow leopards with the external stimuli to satisfy their appetitive behavioral needs. Moreover, numerous other factors not necessarily or exclusively related to appetitive behavior could have caused and influenced the stereotypic pacing.
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Burgelo T.B. (1986). Brief information of snow leopard.
Abstract: This article describes the encounters with snow leopard and their traces in various areas of Kazakhstan. In the Aksu Djabagly nature reserve, population of snow leopard does not exceed 10-12 animals. There were found remains of moral, argali, ibex, small birds, red-tailed marmot, hare (Lepus talai), mouse rodents and plants. One encounter with snow leopard is known to have occurred in the Greater Almaty Canyon in 1971-1981. There are no less than 25 snow leopards in the Jungar Ala-Tau. Snow leopard was found in the Aksu river valley, ridge Saur, and South Altai. The following number of snow leopards was kept in Kazakhstan's zoos, as of January 1, 1984: two males in Alma-Ata, one female in Chimkent. In 1976, one cub was born in the Alma-Ata zoo.
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Brown, J. L., Wasser, S. K., Wildt, D. E., & Graham, L. H. (1994). Steroid Metabolism and the Effectiveness of Fecal Assays for Assessing Reproductive Status in Felids. Biology of Reproduction, 50(suppl 1), 185.
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Broder, J., MacFadden, A., Cosens, L., Rosenstein, D., & Harrison, T. (2008). Use of Positive Reinforcement Conditioning to Monitor Pregnancy in an Unanesthetized Snow Leopard
(Uncia uncia) via Transabdominal Ultrasound (Vol. 27).
Abstract: Closely monitoring snow leopard (Uncia uncia) fetal developments via transabdominal ultrasound, with minimal stress to the animal, was the goal of this project. The staff at Potter Park Zoo has used the principles of habituation, desensitization, and positive reinforcement to train a female snow leopard (U. uncia). Ultrasound examinations were preformed on an unanesthetized feline at 63 and 84 days. The animal remained calm and compliant throughout both procedures. Fetuses were observed and measured on both occasions. The absence of anesthesia eliminated components of psychologic and physiologic stress associated with sedation. This was the first recorded instance of transabdominal ultrasound being carried out on an unanesthetized snow leopard. It documents the feasibility of detecting pregnancy and monitoring fetal development via ultrasound.
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Brem A.E. (1992). Irbis, or snow leopard (Felis uncia) (Vol. Vol.1. Mammals.).
Abstract: Snow leopard is met in the mountains of Turkistan, Altai, Bukhara, Pamir, Kashmir, and Tibet, and probably in South-East Siberia and along Sungari. In 1871, two animals were living in the Moscow Zoo Garden.
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Braden, K. (1988). Snow leopard conservation in the USSR. Snow Line, Fall, 2.
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Bowling, B. (2004). The Legal Status of Snow Leopards in Afghanistan. United Nations Environment Programme.
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Bower, J. N. (1983). Shy, elusive, struggling to survive: the snow leopard. The Explorer, , 9–11.
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Bower, J. N. (1980). For some endangered species, captive breeding programs are their last chance for life. National Parks and Conservation Magazine, (June), 16–19.
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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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