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: Replicated multiple scale species distribution models (SDMs)
have become increasingly important to identify the correct variables
determining species distribution and their influences on ecological
responses. This study explores multi-scale habitat relationships of the
snow leopard (Panthera uncia) in two study areas on the Qinghai–Tibetan
Plateau of western China. Our primary objectives were to evaluate the
degree to which snow leopard habitat relationships, expressed by
predictors, scales of response, and magnitude of effects, were
consistent across study areas or locally landcape-specific. We coupled
univariate scale optimization and the maximum entropy algorithm to
produce multivariate SDMs, inferring the relative suitability for the
species by ensembling top performing models. We optimized the SDMs based
on average omission rate across the top models and ensembles’ overlap
with a simulated reference model. Comparison of SDMs in the two study
areas highlighted landscape-specific responses to limiting factors.
These were dependent on the effects of the hydrological network,
anthropogenic features, topographic complexity, and the heterogeneity of
the landcover patch mosaic. Overall, even accounting for specific local
differences, we found general landscape attributes associated with snow
leopard ecological requirements, consisting of a positive association
with uplands and ridges, aggregated low-contrast landscapes, and large
extents of grassy and herbaceous vegetation. As a means to evaluate the
performance of two bias correction methods, we explored their effects on
three datasets showing a range of bias intensities. The performance of
corrections depends on the bias intensity; however, density kernels
offered a reliable correction strategy under all circumstances. This
study reveals the multi-scale response of snow leopards to environmental
attributes and confirms the role of meta-replicated study designs for
the identification of spatially varying limiting factors. Furthermore,
this study makes important contributions to the ongoing discussion about
the best approaches for sampling bias correction.

Abstract: There is a growing agreement that conservation needs to be proactive and pay increased attention to common species and to the threats they face. The blue sheep (Pseudois nayaur) plays a key ecological role in sensitive high-altitude ecosystems of Central Asia and is among the main prey species for the globally vulnerable snow leopard (Panthera uncia). As the blue sheep has been increasingly exposed to human pressures, it is vital to estimate its population dynamics, protect the key populations, identify important habitats, and secure a balance between conservation and local livelihoods. We conducted a study in Manang, Annapurna Conservation Area (Nepal), to survey blue sheep on 60 transects in spring (127.9 km) and 61 transects in autumn (134.7 km) of 2019, estimate their minimum densities from total counts, compare these densities with previous estimates, and assess blue sheep habitat selection by the application of generalized additive models (GAMs). Total counts yielded minimum density estimates of 6.0–7.7 and 6.9–7.8 individuals/km2 in spring and autumn, respectively, which are relatively high compared to other areas. Elevation and, to a lesser extent, land cover indicated by the normalized difference vegetation index (NDVI) strongly affected habitat selection by blue sheep, whereas the effects of anthropogenic variables were insignificant. Animals were found mainly in habitats associated with grasslands and shrublands at elevations between 4,200 and 4,700 m. We show that the blue sheep population size in Manang has been largely maintained over the past three decades, indicating the success of the integrated conservation and development efforts in this area. Considering a strong dependence of snow leopards on blue sheep, these findings give hope for the long-term conservation of this big cat in Manang. We suggest that long-term population monitoring and a better understanding of blue sheep–livestock interactions are crucial to maintain healthy populations of blue sheep and, as a consequence, of snow leopards.

Abstract: The snow leopard (Panthera uncia) is an apex predator of mountainous ecosystems in Central Asia, characterised by relatively long feeding times and low kill rates (Johansson et al. 2015; Mallon et al. 2016). Predation is mainly focused on wild ungulates and the vast majority of animals killed by snow leopards are smaller than 100 kg (Lovari et al. 2013). Throughout most of their range, Siberian ibex (Capra sibirica), blue sheep (Pseudois nayaur), and argali (Ovis ammon) represent the most important prey (Hunter 2015). These species weigh up to 180 kg, which was suggested to be near the maximum limit of the prey size that snow leopard can handle (i.e. about 3 times its size) (e.g. Schaller 1977; Hunter 2015). Accordingly, researchers generally assume that prey like adult yaks (Bos grunniens) with an average body weight of 250 kg (Bagchi & Mishra 2006), are too large to be killed by snow leopards (e.g. Devkota et al. 2013; Chetri et al. 2017). In contrast, local livestock herders report that snow leopard can also kill larger prey, including adult yaks (e.g. Li et al. 2013; Suryawanshi et al. 2013), but confirmed records of snow leopard killing prey of this size appear to be lacking in the literature. We also have very limited knowledge about the consumption of snow leopard kills, and the scavengers, including conspecifics, that are using them (Fox & Chundawat 2016; Schaller 2016). Here we report on a predation event and the following consumption process of a snow leopard kill, a free-roaming adult female yak, which we studied in 2019 using snow tracking, direct observation and camera trapping in the Mongolian Altai.

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.