Abstract: This study was undertaken in the Phu valley of Manang district in the Annapurna Conservation Area, Nepal,
Spring, 2004 and 2005. I used the Snow Leopard Management Information System (“second order” survey technique), to determine
the relative abundance of snow leopards in delineated areas in Phu valley. Transects routes were plotted by
randomly selected feasible landforms such as along ridgelines, cliff bases and river bluffs where snow
leopards sign is likely to be found. Altogether, 16 transects (total length of 7.912 km) were laid down (mean
transect length=0.495 km). They revealed, 54 sign sites (both relic and non-relic) and altogether 88 signs (72
scrapes, 11 feces, 3 scent mark, 2 pugmarks and 1 hair) were recorded (6.8 site/km and 11.1 signs/km). There
were 61.1% non-relic and 38.9% relic sites. The density of snow leopards in Phu Valley may be 4-5 snow
leopards/100 kmý.It was found that the Ghyo block had the highest sign density (13.6 mean sign item/km)
and Phu block (9.8 mean sign item/km) and the lowest in Ngoru block (3.9 mean sign item/km.). For blue sheep, direct count method was applied from different appropriate vantage points (fixed-point
count). I counted total individuals in each herd and classified all individuals whenever possible, using 8 X24
binocular and 15-60x spotting scope. A total 37 blue sheep herds and 1209 individuals were observed in
192.25 kmý of the study area (blue sheep density, 6.3 kmý). Average herd size was 32.68. Herd size varied
from 1 to 103 animals (the largest so far recorded). The average sex ratio male to female for the entire survey
area was 0.67. Recruitment rate was 47.13. The ratio of yearlings to adult female was 0.45. In Ghyo block
had total 168 blue sheep (area, 44.08 km2 or 3.8/ km2 i.e. 137.2 kg/ kmý). Blue sheep density in Ngoru block
showed 4.7/km2 (area, 65.47 km2). Highest density of blue sheep among three blocks was recorded in Phu
block, 8.9/km2 (or 320 kg/km2) in its 82.70 km2 area. A standard questionnaire was designed, and interviews conducted for relevant information was collected on
livestock depredation patterns (total household survey). Out of 33 households surveyed, 30 reported that they
had livestock depredation by the snow leopard in 2004. Altogether 58 animals were reportedly lost to snow
leopards (3.1% of the total mortality). Out of the estimated standing available biomass (1, 83,483kg) in the
Phu valley at least 2220 kg or 1.3% of the total livestock biomass was consumed by snow leopards in the
year of our study (2004). It was estimated that in the Phu valley annually 1.8 animals were lost per household
to snow leopards. This means approx. Rs.413560 (US$ 5,908) is lost annually in the valley (US$
179/household/annum). Ghyo block, had the highest animals loss (53.4%), followed by Phu block (36.2%)
and Ngoru block (10.3%) to snow leopards. There is positive correlation among the densities of blue sheep, relative abundance of the snow leopard and
livestock depredation. Blue sheep is the main prey species of the snow leopard in Phu valley and its
conservation therefore matters to reduce livestock depredation. A general patterns appears here that shows
that blue sheep (prey) abundance determine snow leopard (predator) abundance and that livestock
depredation by snow leopards may be minimal where there is good population of blue sheep, and vice versa.

Abstract: The main purpose of this work was to study the use of infrared trapping cameras to estimate Snow Leopard population size in a specific study area. This is the first time a study of this nature has taken place in China. During 71 days of field work, a total of 36 cameras were set up in Muzat Valley adjacent to the Tomur Nature Reserve in Xinjiang Province. We expended approximately 2094 trap days total. At least 32 pictures of Snow Leopards, 22 pictures of other wild species and 72 pictures of livestock were taken in the Muzat Valley. Meanwhile, 20 transects were run and 31 feces sample were collected. We also observed the behavior of ibex for 77.3 hours and found a total of approximately 264 ibexes in the research area.

Abstract: Many mammalian herbivores show a temporal diet variation between graminoid-dominated and browse dominated diets. We determined the causes of such a diet shift and its implications for conservation of a medium sized ungulate-the bharal Pseudois nayaur. Past studies show that the bharal diet is dominated by graminoids (>80%) during summer, but the contribution of graminoids declines to about 50% in winter. We tested the predictions generated by two alternative hypotheses explaining the decline: low graminoid availability during winter causes bharal to include browse in their diet; bharal include browse, with relatively higher nutritional quality, in their diet to compensate for the poor quality of graminoids during winter. We measured winter graminoid availability in areas with no livestock grazing, areas with relatively moderate livestock grazing, and those with intense livestock grazing pressures. The chemical composition of plants contributing to the bharal diet was analysed. The bharal diet was quantiWed through signs of feeding on vegetation at feeding locations. Population structures of bharal populations were recorded using a total count method. Graminoid availability was highest in areas without livestock grazing, followed by areas with moderate and intense livestock grazing. The bharal diet was dominated by graminoids (73%) in areas with highest graminoid availability. Graminoid contribution to the bharal diet declined monotonically (50, 36%) with a decline in graminoid availability. Bharal young to female ratio was 3 times higher in areas with high graminoid availability than areas with low graminoid availability. The composition of the bharal winter diet was governed predominantly by the availability of graminoids in the rangelands. Our results suggest that bharal include more browse in their diet during winter due to competition from livestock for graminoids. Since livestock grazing reduces graminoid availability, creation of livestock-free areas is necessary for the conservation of grazing species such as the bharal and its predators including the endangered snow leopard in the Trans-Himalaya.

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.