Abstract: The author provides description of fauna of Kazakhstan's mountainous areas. Fauna of the mountain taiga is also typical for the forests of South Siberia. Ungulate species such as musk deer and ibex are common for rocky taiga areas. In the Altai, ibex, musk deer, and wild sheep are preyed on by dhole and snow leopard and more typical species such as glutton and wolf. Ibex, argali, and irbis are typical for Transili Ala-Tau and West Tien Shan. Tien Shan is the only area of the USSR with quite many irbis preserved. The ridges of this mountainous area located in Kazakhstan are very likely to be an area the most densely populated by snow leopards within the predator's habitat.

Abstract: Represented is fauna of big salt-marsh valleys and pre-Kyzylkum area, a tier of low desert foothill valleys, tiers of lowland ridges, deeply cut hillside midlands, and cold highlands of the watershed ridge-top tier in the Jizak region of Uzbekistan. The highest tier of the Jizak region, a habitat of snow leopard, Menzbier's marmot, Siberian ibex, sometimes wild Tajik sheep coming from the East, bear ascending from lower elevations, and wolf in summer, has the most adverse living conditions. Central Asia argali and stone marten inhabit in central part of the North Nurata ridge.

Abstract: It provides description of fauna of the Central Asia mountains. Ibex (Capra sibirica) was noticed to keep to the alpine and sub-alpine zone and never descends bellow 2,500 m. Hunting for ibex and wild sheep, snow leopard (Leopardus uncia) keeps at the same elevation.

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.