Fox, J. L. (1986). Indo-US Snow Leopard Project, Progress Report on Field Work as of December 30, 1985 (Vol. 9). Seattle: Islt.
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Fox, J. L., Sinya, S. P., Chundawat, R. S., & Das, P. K. (1986). A Survey of Snow Leopard and Associated Species in the Himalaya of Northwestern India, Project Completion Report.
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Jackson, R. (1995). Third Slims Workshop held in Mongolia (Vol. xiii). Seattle: Islt.
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Jackson, R., Hunter, D., & Emmerich, C. (1997). SLIMS: An Information Management System for Promoting the Conservation of Snow Leopards and Biodiversity in the Mountains of Central Asia. In R.Jackson, & A.Ahmad (Eds.), (pp. 75–91). Lahore, Pakistan: Islt.
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Jackson, R., & Fox, J. L. (1997). Snow Leopard Conservation: Accomplishments and Research Priorities. In R.Jackson, & A.Ahmad (Eds.), (pp. 128–144). Pakistan: Islt.
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Jackson, R. (1997). Bhutan Workshop: Thimpu, Land of the Thunder Dragon (Vol. xv). Seattle, Wa: Islt.
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Jackson, R., & Fox, J. L. (2000). Report on Fifth Slims Training Workshop (Nepal) (Vol. xvii). Seattle: International Snow Leopard Trust.
Abstract: Nepal's snow leopards (Uncia uncia) are mostly found along the northern border with Tibet (China). The largest populations are in Dolpa, Mugu, Manang, and Myagdi Districts. Potential habitat totals about 30,000 square kilometers. Numbers are estimated at 300-500, but surveys are urgently needed to confirm this rough guess. Like elsewhere, the primary threats center on poaching, depletion of natural prey, livestock depredation and resultant retributive killing of snow leopards by herders, and the lack of public awareness and support for conserving snow leoaprds, especially among local herders.
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Ferguson, D. A. (1997). International Cooperation for Snow Leopard and Biodiversity Conservation: The Government Perspective. In R.Jackson, & A.Ahmad (Eds.), (pp. 178–193). Lahore, Pakistan: Islt.
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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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Wangchuk, R., & Jackson, R. (2009). A Community-based Approach to Mitigating Livestock-Wildlife Conflict in Ladakh, India.
Abstract: Livestock depredation by snow leopard and wolf is widespread across the Himalayan region (Jackson et al. 1996, Jackson and Wangchuk 2001; Mishra 1997, Oli et al 1994). For example, in India's Kibber Wildlife Sanctuary, Mishra (1997) reported losses amounting to 18% of the livestock holdings and valued at about US $138 per household. The villagers claimed predation rates increased after establishment of the sanctuary, but
surveys indicated a dramatic increase in livestock numbers accompanying changes in animal husbandry systems (Mishra 2000).
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