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Schaller, G. (1986). Surveys of Mountain Wildlife in China, Report # 4.
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Schaller, G. (1987). Surveys of Mountain Wildlife in China, Report # 6.
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Schaller, G. B. (1987). Status of large mammals in the Taxkorgan Reserve, Xinjiang, China. Biological-Conservation, 42(1), 53–71.
Abstract: A status survey of large mammals was conducted in the W half of 14 000 km“SUP 2” Taxkorgan Reserve. Only one viable population of fewer than 150 Marco Polo sheep Ovis ammon poli survives; it appears to be augmented by adult males from Russia and Afghanistan during the winter rut. Asiatic ibex Capra ibex occur primarily in the western part of the reserve and blue sheep Pseudois nayaur – the most abundant wild ungulate – in the E and SE parts. The 2 species overlap in the area of contact. Counts revealed an average wild ungulate density of 0.34 animals km“SUP -2”. Snow leopard Panthera uncia were rare, with possibly 50-75 in the reserve, as were wolves Canis lupus and brown bear Ursus arctos. The principal spring food of snow leopard was blue sheep (60%) and marmot (29%). Local people have greatly decimated wildlife. Overgrazing by livestock and overuse of shrubs for fuelwood is turning this arid steppe habitat into desert. -from Authors
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Schaller, G. B., Tserendeleg, J., & Amarsana, G. (1994). Observations on snow leopards in Mongolia. In J.Fox, & D.Jizeng (Eds.), (pp. 33–42). Usa: Islt.
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Schaller, G. B., Hong, L., Talipu, J., & Mingjiang, R. Q. (1989). The Snow Leopard in Xinjiang, China (Vol. winter). Seattle: Islt.
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Schaller, G. (1988). Wildlife Survey in Tibet, Report #8.
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Tserendeleg, J. (1994). On Protection and Survey of Snow Leopards in Mongolia. In J.L.Fox, & D.Jizeng (Eds.), (pp. 43–46). Usa: Islt.
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Wikramanayake, E. Tracking snow leopard and blue sheep, WWF conservationist Eric Wikramanayake goes on a wildlife survey in Bhutan.
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Yanfa, L., & Bangjie, T. (1988). A Preliminary Study on the Geographical Distribution of Snow Leopards in China. In H.Freeman (Ed.), (pp. 51–63). Interanational Snow Leopard Trust and The Wildlife Institute of India.
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Durbach, I., Borchers, D., Sutherland, C., Sharma, K. (2020). Fast, flexible alternatives to regular grid designs for spatial
capture–recapture..
Abstract: Spatial capture–recapture (SCR) methods use the location of
detectors (camera traps, hair snares and live-capture traps) and the
locations at which animals were detected (their spatial capture
histories) to estimate animal density. Despite the often large expense
and effort involved in placing detectors in a landscape, there has been
relatively little work on how detectors should be located. A natural
criterion is to place traps so as to maximize the precision of density
estimators, but the lack of a closed-form expression for precision has
made optimizing this criterion computationally demanding. 2. Recent
results by Efford and Boulanger (2019) show that precision can be well
approximated by a function of the expected number of detected
individuals and expected number of recapture events, both of which can
be evaluated at low computational cost. We use these results to develop
a method for obtaining survey designs that optimize this approximate
precision for SCR studies using count or binary proximity detectors, or
multi-catch traps. 3. We show how the basic design protocol can be
extended to incorporate spatially varying distributions of activity
centres and animal detectability. We illustrate our approach by
simulating from a camera trap study of snow leopards in Mongolia and
comparing estimates from our designs to those generated by regular or
optimized grid designs. Optimizing detector placement increased the
number of detected individuals and recaptures, but this did not always
lead to more precise density estimators due to less precise estimation
of the effective sampling area. In most cases, the precision of density
estimators was comparable to that obtained with grid designs, with
improvement in some scenarios where approximate CV(¬D) < 20% and density
varied spatially. 4. Designs generated using our approach are
transparent and statistically grounded. They can be produced for survey
regions of any shape, adapt to known information about animal density
and detectability, and are potentially easier and less costly to
implement. We recommend their use as good, flexible candidate designs
for SCR surveys when reasonable knowledge of model parameters exists. We
provide software for researchers to construct their own designs, in the
form of updates to design functions in the r package oSCR.
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