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.

Abstract: From March to May, 2006œªwe conducted extensive snow leopard surveys in the Burhanbuda Mountain Kunlun Mountains, Qinghai Province, China. 32 linear transect of 5~15 km each, which running through each vegetation type, were surveyed within the study area. A total of 72 traces of snow leopard were found along 4 transects (12.5% of total transects). The traces included pug marks or footprints, scrapes and urine marks. We estimated the average density of wild ungulates in the region was 2.88ñ0.35 individuals km-2(n=29). We emplaced 16 auto2 trigger cameras in different environments and eight photos of snow leopard were shot by four cameras and the capture rate of snow leopard was 71.4%. The minimum snow leopard population size in the Burhanbuda Mountain was two, because two snow leopards were phototrapped by different cameras at almost same time. Simultaneously, the cameras also shot 63 photos of other wild animals, including five photos are unidentified wild animals, and 20 photos of livestock. We evaluated the human attitudes towards snow leopard by interviewing with 27 Tibetan householders of 30 householders live in the study area. We propose to establish a nature reserve for protecting and managing snow leopards in the region. Snow leopard (Uncia uncia) is considered as a unique species because it lives above the snow line, it is endemic to alpines in Central Asia, inhabiting in 12 countries across Central Asia (Fox, 1992). Snow leopard ranges in alpine areas in Qinghai, Xinjiang, Inner Mongolia, Tibet, Gansu and Sichuan in western China (Liao, 1985, 1986; Zhou, 1987; Ma et al., 2002; Jiang & Xu, 2006). The total population and habitat of snow leopards in China are estimated to be 2,000~2,500 individuals and 1,824,316 km2, only 5% of which is under the protection of nature reserves. The cat's current range is fragmented (Zou & Zheng, 2003). Due to strong human persecutions, populations of snow leopards decreased significantly since the end of the 20th century. Thus, the
snow leopards are under the protection of international and domestic laws. From March to May, 2006, we conducted two field surveys in Zhiyu Village, Dulan County in Burhanbuda Mountain, Kunlun Mountains, China to determine the population, distribution and survival status of snow leopards in the area. The aim of the study was to provide ecologic data for snow leopard conservation.