Abstract: China holds the greatest proportion of the snow leopard’s (Panthera uncia) global range and is central to their conservation. The country is also undergoing unprecedented economic growth, which increases both the threats to the snow leopard and the opportunities for its conservation. In this paper we aim to review published literature (from 1950 to 2014) in English and Mandarin on snow leopard ecology and conservation in China in order to identify thematic and geographic research gaps and propose research priorities. We first retrieved all publish items that considered snow leopards in China (n = 106). We extracted from these papers 274 reports of snow leopard presence in China. We then reviewed a subset of papers (n = 33) of this literature, which specifically focused on snow leopard ecology and conservation within China. We introduced a thematic framework that allows a structured and comprehensive assessment of findings. This framework recognizes 4 critical and interrelated topics underpinning snow leopard ecology and conservation: habitat (distribution and protected area coverage); prey (distribution and abundance, predator–prey relationships); human interactions (hunting and trade, livestock interactions and conflicts); and the underlying policy context. Significant gains in knowledge as well as research gaps and priorities are discussed with reference to our framework. The modest quantity and limited scope of published research on the snow leopard in China calls for a continued and intensified effort to inform and support national conservation policies.

Abstract: The enchanting state of Uttaranchal, carved out of Uttar Pradesh on 9th November 2000, has a total area of ca. 53,485 km2 with a population density of 160 persons/ km2, much lower than the national average of 324/km2. This young state can take pride in the fact that 13.42% of its area is under protected areas. The state has varied landscapes: snow-capped and conifer forest covered mountains in the north, forest covered foothills with numerous perennial rivers and streams, locally known as the bhabar tract which includes the Himalayan foothills and the Shivalik range. As a result, the land is home to a variety of fascinating wildlife such as the golden mahseer (Tor putitora), king cobra (Ophiophagus hanna), Himalayan monal (Lophophorus impejanus), great hornbill (Buceros bicornis), Himalayan tahr (Hemitragus jemlahicus), bharal (Pseudois nayaur), Himalayan musk deer (Moschus chrysogaster), goral (Nemorhaedus goral), elephant (Elephas maximus), snow leopard (Panthera uncia), leopard (P. pardus), black bear (Ursus thibetanus), and tiger (P. tigris). All across their range, most of these species are endangered. The potential of this state, with about 800 kilometers of riverine habitat, can only be surpassed by Arunachal Pradesh in terms of golden mahseer conservation. The mountains, bedecked with the scarlet flowers of rhododendron (Rhododendron arboreum) in the summer months, can be a veritable home to many forms of pheasants, mountain ungulates and carnivores, provided poaching for trade is eliminated and hunting for the pot is brought under control. The bhabar forests of this state, ca. 7,500 km2, extending between Yamuna and Sharda rivers (Fig. 1.), can easily support a population of about 1000 elephants and 200 tigers as long as this large habitat, now fragmented in three blocks, is managed and protected as one continuous habitat for wildlife. Six villages, gujjar settlements and encroachments need to be moved away from the main wildlife habitat which goes along the bhabar tract. Although the conservation of these habitats can eventually bring in immense benefits through well-planned ecotourism programmes that are rapidly catching up in the state, initial conservation efforts would need a substantial amount of funds.

Abstract: The Vulnerable snow leopard Panthera uncia experiences
persecution across its habitat in Central Asia, particularly
from herders because of livestock losses. Given the
popularity of snow leopards worldwide, transferring some
of the value attributed by the international community to
these predators may secure funds and support for their conservation.
We administered contingent valuation surveys to
 international visitors to the Annapurna Conservation
Area, Nepal, between May and June , to determine
their willingness to pay a fee to support the implementation
of a Snow Leopard Conservation Action Plan. Of the %of
visitors who stated they would pay a snow leopard conservation
fee in addition to the existing entry fee, the mean
amount that they were willing to pay was USD  per trip.
The logit regression model showed that the bid amount, the
level of support for implementing the Action Plan, and the
number of days spent in the Conservation Area were significant
predictors of visitors’ willingness to pay. The main reasons
stated by visitors for their willingness to pay were a
desire to protect the environment and an affordable fee. A
major reason for visitors’ unwillingness to pay was that
the proposed conservation fee was too expensive for them.
This study represents the first application of economic valuation
to snow leopards, and is relevant to the conservation of
threatened species in the Annapurna Conservation Area
and elsewhere.

Abstract: This study presents the testing of two unsupervised classification methods for their ability to accurately identify unknown individual tigers, Panthera tigris, and snow leopards, Panthera uncia, from their footprints. A neural-network based method, the Kohonen self-organizing map (SOM), and a Bayesian method, AutoClass, were assessed using hind footprints taken from captive animals under standardized conditions. AutoClass successfully discriminated individuals of both species from their footprints. Classification accuracy was greatest for tigers, with more misclassification of individuals occurring for snow leopards. Examination of variable influence on class formations failed to identify consistently influential measurements for either species. The self-organizing map did not provide accurate classification of individuals for either species. Results were not substantially improved by altering map dimensions nor by using principal components derived from the original data. The interpretation of resulting classifications and the importance of using such techniques in the study of wild animal populations are discussed. The need for further testing in the field is highlighted.