Abstract: Catching a glimpse of a snow leopard is a rare and exciting event for anyone. For researchers, hideen camera traps have become a vital tool in their work.

Abstract: Conservation and management of snow leopards (Uncia uncial) has largely relied on anecdotal evidence and presence-absence data due to their cryptic nature and the difficult terrain they inhabit. These methods generally lack the scientific rigor necessary to accurately estimate population size and monitor trends. We evaluated the use of photography in capture-mark-recapture (CMR) techniques for estimating snow leopard population abundance and density within Hemis National Park, Ladakh, India. We placed infrared camera traps along actively used travel paths, scent-sprayed rocks, and scrape sites within 16-30 kmý sampling grids in successive winters during January and March 2003-2004. We used head-on, oblique, and side-view camera configurations to obtain snow leopard photographs at varying body orientations. We calculated snow leopard abundance estimates using the program CAPTURE. We obtained a total of 66 and 49 snow leopard captures resulting in 8.91 and 5.63 individuals per 100 trap nights during 2003 and 2004, respectively. We identified snow leopards based on the distinct pelage patters located primarily on the forelimbs, flanks, and dorsal surface of the tail. Capture probabilities ranged from 0.33 to 0.67. Density estimates ranged from 8.49 (SE+0.22) individuals per 100 kmý in 2003 to 4.45 (SE+0.16) in 2004. We believe the density disparity between years is attributable to different trap density and placement rather than to an actual decline in population size. Our results suggest that photographic capture-mark-recapture sampling may be a useful tool for monitoring demographic patterns. However, we believe a larger sample size would be necessary for generating a statistically robust estimate of population density and abundance based on CMR models.

Abstract: The elusive snow leopard Panthera unica is a rare and little studied species in China. Over 1 March-15 May 2006 we conducted a survey for the snow leopard in the Gouli Region, East Burhanbuda Mountain, Kunlun Mountains, Qinghai Province, China, in an area of c. 300 km2 at altitudes of 4,000-4,700 m. We surveyed 29 linear transects with a total length of c. 440 km, and located a total of 72 traces (pug marks, scrapes and urine marks) of snow leopard along four of the transects. We obtained eight photographs of snow leopard from four of six camera traps. We also recorded 1,369 blue sheep, 156 Tibetan gazelles, 47 argali, 37 red deer and one male white-lipped deer. We evaluated human attitudes towards snow leopard by interviewing the heads of 27 of the 30 Tibetan households living in the study area. These local people did not consider that snow leopard is the main predator of their livestock, and thus there is little retaliatory killing. Prospects for the conservation of snow leopard in this area therefore appear to be good. We analysed the potential threats to the species and propose the establishment of a protected area for managing snow leopard and the fragile alpine ecosystem of this region. (c) 2008 Fauna & Flora International.

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.

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.