Abstract: Executive Summary: Evolved to live in some of the world’s highest and harshest habitats, the elusive and rare snow leopards (Panthera uncia) are undisputed icons of High Asia. Across their distributional range in Central and South Asia, the snow leopard’s habitat spans diverse landscapes, with livestock herding being the most dominant form of land use. As a result, areas inhabited by snow leopards and people often overlap, creating challenges as well as opportunities for their conservation. Snow leopard conservation has received increasing attention in the past two decades and global interest in protecting this unique high-mountain cat continues to rise. However, effective and efficient snow leopard conservation initiatives require multi-dimensional research and collaboration among a diverse array of actors. National governments in snow leopard range, for instance, have repeatedly pledged their support for the conservation of the animal and the breathtaking landscapes they inhabit. These landscapes house an array of unique high-altitude wildlife and provide homes and life-sustaining natural resources to hundreds of millions of people. The mountains of High Asia also form the headwaters of 20 major river basins, an important water source for 22 countries (Sindorf et al., 2014). More than 2 billion people live in these basins which overlap the snow leopard range. Given the growing interest in and commitment towards conservation of snow leopards and their habitats, it is crucial to examine the depth and breadth of knowledge currently available to inform conservation efforts and identify gaps in that knowledge. We reviewed over 100 years of published research on snow leopards to examine its temporal and spatial trends across an array of thematic areas. Snow leopard research intensified in the 1970s and studies on snow leopards have continued to increase exponentially since then. However, just four hotspots of snow leopard research (sites with continued multi-year research) have emerged, with less than 23% of the snow leopard range being researched. Nepal, India and China have conducted the most snow leopard research, followed by Mongolia and Pakistan. Our analysis revealed that snow leopard research was highly focussed on ecological research followed by studies on human-wildlife conflict. Most ecological studies focused on estimating the number and distribution of snow leopards and prey species. However, conservationists have surveyed less than 3% of the snow leopard range using rigorous and scientifically acceptable abundance estimation approaches. The lack of attention to the human dimensions of conservation was particularly stark, especially given that the majority of the snow leopard range is inhabited by local communities dependent on livestock herding. More importantly, very few studies evaluated the effectiveness of conservation actions. A lack of evidence demonstrating and quantifying the impacts of conservation interventions is a significant knowledge gap in snow leopard research. In this review, we identify and suggest the high-priority research necessary for effective conservation planning for snow leopards and their multiple-use habitat in High Asia.

Abstract: The snow leopard is one of the most endangered large mammals.
Its population, already low, is declining, most likely due to the
consequences of human activity, including a reduction in the size and
number of suitable habitats. With climate change, habitat loss may
escalate, because of an upward shift in the tree line and concomitant
loss of the alpine zone, where the snow leopard lives. Migration between
suitable areas, therefore, is important because a decline in abundance
in these areas may result in inbreeding, fragmentation of populations,
reduction in genetic variation due to habitat fragmentation, loss of
connectivity, bottlenecks or genetic drift. Here we use our data
collected in Nepal to determine the areas suitable for snow leopards, by
using habitat suitability maps, and describe the genetic structure of
the snow leopard within and between these areas. We also determine the
influence of landscape features on the genetic structure of its
populations and reveal corridors connecting suitable areas. We conclude
that it is necessary to protect these natural corridors to maintain the
possibility of snow leopards' migration between suitable areas, which
will enable gene flow between the diminishing populations and thus
maintain a viable metapopulation of snow leopards.

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.