Abstract: The Snow Leopard Panthera uncia is a rare top predator of high-altitude ecosystems and insufficiently surveyed outside of protected areas in Nepal. We conducted a rapid camera-trapping survey to assess the presence of Snow Leopard in the Limi valley of Humla District. Three individuals were recorded in two camera locations offering the first photographic evidence of this elusive cat outside the protected area network of Nepal. In addition to Snow Leopard, the Blue Sheep Pseudois nayaur, Beech Marten Martes foina, Pika Ochotona spp. and different species of birds were also detected by camera-traps. More extensive surveys and monitoring are needed for reliably estimating the population size of Snow Leopard in the area. The most urgent needs are community-based conservation activities aimed at mitigating immediate threats of poaching, retaliatory killing, and rapid prey depletion to ensure the survival of this top predator in the Himalaya.

Abstract: The main purpose of this work was to study the use of infrared trapping cameras to estimate Snow Leopard population size in a specific study area. This is the first time a study of this nature has taken place in China. During 71 days of field work, a total of 36 cameras were set up in Muzat Valley adjacent to the Tomur Nature Reserve in Xinjiang Province. We expended approximately 2094 trap days total. At least 32 pictures of Snow Leopards, 22 pictures of other wild species and 72 pictures of livestock were taken in the Muzat Valley. Meanwhile, 20 transects were run and 31 feces sample were collected. We also observed the behavior of ibex for 77.3 hours and found a total of approximately 264 ibexes in the research area.

Abstract: Successful conservation of the endangered snow leopard (Panthera uncia) relies on the effectiveness of monitoring programmes. We present the results of a 19-month camera trap survey effort, conducted as part of a longterm study of the snow leopard population in Qilianshan National Nature Reserve of Gansu Province, China. Weassessed the minimumnumber of individual snowleopards and population density across different sampling periods using spatial capture–recapture methods. Between 2013–2014, we deployed 34 camera traps across an area of 375 km2, investing a total of 7133 trap-days effort. Weidentified a total number of 17–19 unique individuals
from photographs (10–12 adults, five sub-adults and two cubs). The total number of individuals identified and estimated density varied across sampling periods, between 10–15 individuals and 1.46–3.29 snow leopards per 100 km2 respectively. We demonstrate that snow leopard surveys of limited scale and conducted over short sampling periods only present partial views of a dynamic and transient system.We also underline the challenges in achieving a sufficient sample size of captures and recaptures to assess trends in snow leopard population size and/or density for policy and conservation decision-making

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: Snow leopards Panthera uncia are under threat across their range and require urgent conservation actions based on sound science. However, their remote habitat and cryptic nature make them inherently difficult to study and past attempts have provided insufficient information upon which to base effective conservation. Further, there has been no statistically-reliable and cost-effective method available to monitor snow leopard populations, focus conservation effort on key populations, or assess conservation impacts. To address these multiple information needs, Panthera, Snow Leopard Trust, and Snow Leopard Conservation Fund, launched an ambitious long-term study in Mongolia’s South Gobi province in 2008. To date, 10 snow leo-pards have been fitted with GPS-satellite collars to provide information on basic snow leopard ecology. Using 2,443 locations we calculated MCP home ranges of 150 – 938 km2, with substantial overlap between individuals. Exploratory movements outside typical snow leopard habitat have been observed. Trials of camera trapping, fecal genetics, and occupancy modeling, have been completed. Each method ex-hibits promise, and limitations, as potential monitoring tools for this elusive species.