Abstract: In 2001 the King Mahendra Trust for Nature Conservation (KMTNC), Annapurna Conservation Area (ACAP), Snow Leopard Conservancy (SLC) and WWF-Nepal initiated a collaborative project aimed at enhancing ecotourism in the Manang area, in ways that strengthen benefits to local communities while also protecting the environment and the local culture. Manang is known for its relatively dense snow leopard population, along with supporting good numbers of blue sheep, the endangered cat's principal prey through much of the Himalaya. However, snow leopards periodically kill many livestock, leading to retributive killing by herders along with other associated people-wildlife conflict. In order to encourage the local people to better co-exist with snow leopards and other wildlife, SLC, WWF-Nepal and ACAP agreed to explore ways of providing tourism benefits to local communities as an incentive to protect this rare predator and conserve its alpine habitat. Key in this regard is the possibility of developing locally guided nature treks, and accordingly, this survey was conducted in order to assess existing market opportunities and constraints to such ecotourism enterprise.

Abstract: The current study in Mongolia is truly groundbreaking, in that it shows that the problem of commercial wildlife trade is also vast, unsustainable, and a major threat to wildlife populations in other areas. This paper's Executive Summary briefs the topics of wildlife trade in Mongolia, fur trade, medicinal trade, game meat trade, trophy and sport hunting, trade chains and markets, trade sustainability, impacts of wildlife trade on biodiversity conservation, impacts of trade on rural livelihoods, enabling wildlife management, and management recommendations. The main content of the paper includes: wildlife trade survey methods, a history of wildlife trade in Mongolia, wildlife take and trade today, enabling wildlife management, and recommendations and priority actions. The recommendations have been divided into six separate sections, including (1) cross-cutting recommendations, (2) international trade enforcement, (3) domestic trade enforcement, (4) hunting management, (5) trophy and sport hunting management, and (6) community-based approaches. Each section identifies short-term, long-term, and regulatory goals in order of priority within each subsection.

Abstract: Accurate assessments of the status of threatened species and their conservation
planning require reliable estimation of their global populations and robust monitoring
of local population trends. We assessed the adequacy and suitability of studies
in reliably estimating the global snow leopard (Panthera uncia) population. We
compiled a dataset of all the peer-reviewed published literature on snow leopard
population estimation. Metadata analysis showed estimates of snow leopard density
to be a negative exponential function of area, suggesting that study areas have generally
been too small for accurate density estimation, and sampling has often been
biased towards the best habitats. Published studies are restricted to six of the
12 range countries, covering only 0.3�0.9% of the presumed global range of the
species. Re-sampling of camera trap data from a relatively large study site
(c.1684 km2) showed that small-sized study areas together with a bias towards
good quality habitats in existing studies may have overestimated densities by up to
five times. We conclude that current information is biased and inadequate for generating
a reliable global population estimate of snow leopards. To develop a rigorous
and useful baseline and to avoid pitfalls, there is an urgent need for
(a) refinement of sampling and analytical protocols for population estimation of
snow leopards (b) agreement and coordinated use of standardized sampling protocols
amongst researchers and governments across the range, and (c) sampling
larger and under-represented areas of the snow leopard's global range.

Abstract: Snow leopards (Panthera uncia) are a keystone species of Central Asia’s high mountain ecosystem. The species is listed as vulnerable and is elusive, preventing accurate population assessments that could inform conservation actions. Non-invasive genetic monitoring conducted by citizen scientists offers avenues to provide key data on this species that would otherwise be inaccessible. From 2011 to 2015, OSI-Panthera citizen science expeditions tracked signs of presence of snow leopards along transects in the main valleys and crests of the Sarychat-Ertash State Reserve (Kyrgyzstan). Scat samples were genotyped at seven autosomal microsatellite loci and at a X/Y locus for sex identification, which allowed estimating a minimum of 11 individuals present in the reserve from 2011 to 2015. The genetic recapture of 7 of these individuals enabled diachronic monitoring, providing indications of individuals’ movements throughout the reserve. We found putative family relationships between several individuals. Our results demonstrate the potential of this citizen science program to get a precise description of a snow leopard population through time.

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.