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.

Abstract: Snow leopards (Panthera uncia) frequently prey on livestock throughout their range, posing a potential threat to human livelihoods and endangering the predator’s own survival. In this study, we document seven incidents of snow leopards intruding into livestock corrals and engaging in surplus killing in three districts of Badakhshan, Afghanistan. Six of the predation incidents were attributed to a single individual, occurring in five locations of Wakhan District and eventually in Yumgan District, where the captured animal was relocated. The remaining predation incident occurred in Keran-wa Munjan District, marking the first recorded evidence of snow leopards in this area. In all but one of the incidents, the predator was trapped in the corral it intruded and safely released back to the wild with the support of the National Environmental Protection Agency (NEPA) and Wildlife Conservation Society (WCS) team in Afghanistan. Local communities have been supportive in releasing the snow leopard despite losses of over 50 livestock. To mitigate negative interactions between snow leopards and livestock, conservation efforts should focus on conserving prey species, implementing predator- proof measures for livestock corrals, and utilizing collar tracking when a trapped snow leopard is found in a corral. Unfortunately, when an individual repeatedly enters livestock corrals and continues killing livestock, capture and relocation to captivity often become the only viable option to address the problem and ensure the animal’s safety from retaliatory action by affected herders.

Abstract: Snow leopard Pardus uncia Schreber recorded in Trans Caspian region (in Sumbar rive) very rare and occasionally.

Abstract: Snow leopard (Uncia uncial) is a flagship species of the Himalaya. Very few studies have been done on the ecology of this species in the Himalaya. This paper presents an overview of four studies conducted on snow leopards in Nepal and India, dealing with various aspects of snow leopard ecology including their status assessment, making behaviour, habitat selection, food habits, and impact on livestock. The information generated by these studies is useful in planning effective conservation and management strategies for this endangered top predator of high mountains.