Abstract: The snow leopard faces multiple threats in the Himalayan region, from habitat degradation, loss of prey, the trade in pelts, parts and live animals, and conflict with humans, primarily pastoralists. Consequently, the populations are considered to be in decline and the species is listed as Endangered in the IUCN's Red List. As a 'flagship' and 'umbrella' species the snow leopard can be a unifying biological feature to raise awareness of its plight and the need for conservation, which will benefit other facets of Himalayan biodiversity as well. Some studies of snow leopards have been conducted in the Himalayan region. But, because of its elusive nature and preference for remote and inaccessible habitat, knowledge of the ecology and behaviour of this mystical montane predator is scant. The available information, however, suggests that snow leopards occur at low densities and large areas of habitat are required to conserve a viable population. Thus, many researchers and conservationists have advocated landscape-scale approaches to conservation within a regional context, rather than focusing on individual protected areas.This regional strategy for WWF's snow leopard conservation program is built on such an approach. The following were identified as important regional issues: 1) international trade in snow leopards and parts; 2) the human-snow leopard conflict; 3) the need for a landscape approach to conservation to provide large spatial areas that can support demographically and ecologically viable snow leopard metapopulations; 4) research on snow leopard ecology to develop long-term, science-based conservation management plans; and 5) regional coordination and dialog. While the issues are regional, the WWF's in the region have developed 5-year strategic actions and activities, using the regional strategies as a touchstone, which will be implemented at national levels. The WWF's will develop proposals based on these strategic actions, with estimated budgets, for use by the network for funding and fund-raising. WWF also recognizes the need to collaborate and coordinate within the network and with other organizations in the region to achieve conservation goals in an efficient manner, and will form a working group to coordinate activities and monitor progress.

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.