Abstract: Parasites diversity in close-related species of hosts may be different depending on habitat use and climatic conditions. The aim of this study was to
analyze parasites fauna in four felid species inhabiting Russian Far East and South Siberia (including taiga forest and mountain treeless areas). We
have collected 272 feces samples of four felid species: Amur tiger, Amur leopard, snow leopard and Pallas� cat. Helminths (eggs and larvae) in
excrements were studied by flotation using a saturated solution of ammonium nitrate. We have described 10 helminths species in Amur tiger feces, 6
� in Amur leopard, 2 � in snow leopard and 3 � in Pallas� cat. Obviously, snow leopard and Palls� cat had lower helminths diversity than two other
species. These differences can be explained, to some extent, by climatic parameters. The climate in the snow leopard and Pallas' cat habitats is
described by sharp and significant temperature fluctuations – the annual temperature difference can exceed 90°C, which may lead to lower survival of
the number of infectious agents in Pallas' cat excrements. In addition, the snow cover that can protect helminth eggs and larvae from the cold
temperatures especially in Amur tiger and Amur leopard habitats. Possibly, another important factor is the spatial and social organization of Pallas'
cats, with a low frequency of contacts with other individuals. Such way, species-specific differences in helminths were related, probably, with the
species evolution in different habitats

Abstract: The number of large cats is reducing. These animals are hold out in the most difficult of access places. During long time snow leopard was a poorly known animal. The situation was changed with developing of mountain tourism and mountaineering. It's necessary to reduce the capturing snow leopards for zoological gardens.

Abstract: Solitary felids like tigers and snow leopards are notoriously difficult to enumerate, and indirect techniques like pugmark surveys often produce ambiguous information that is difficult to interpret because many factors influence marking behavior and frequency (Ahlborn & Jackson 1988). Considering the snow leopard's rugged habitat, it is not surprising then that information on its current status and occupied range is very limited. We adapted the camera-trapping techniques pioneered by Ullas Karanth and his associates for counting Bengal tigers to the census taking of snow leopards in the Rumbak watershed of the India's Hemis High Altitude National Park (HNP), located in Ladakh near Leh (76ø 50' to 77ø 45' East; 33ø 15' to 34ø 20'North).

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: Camera trapping is the most widely used data collection method for estimating snow leopard (Panthera uncia) abundance; however, the accuracy of this method is limited by human observer errors from misclassifying individuals in camera trap images. We evaluated the extent Whiskerbook (www.whiskerbook.org), an artificial intelligence (AI) software, could reduce this error rate and enhance the accuracy of capture-recapture abundance estimates. Using 439 images of 34 captive snow leopard individuals, classification was performed by five observers with prior experience in individual snow leopard ID (“experts”) and five observers with no such experience (“novices”). The “expert” observers classified 35 out of 34 snow leopard individuals, on average erroneously splitting one individual into two, thus resulting in a higher number than true individuals. The success rate of experts was 90 %, with less than a 3 % error in estimating the population size in capture-recapture modeling. However, the “novice” observers successfully matched 71 % of encounters, recognizing 25 out of 34 individuals, underestimating the population by 25 %. It was found that expert observers significantly outperformed novice observers, making statistically fewer errors (Mann Whitney U test P = 0.01) and finding the true number of individuals (P = 0.01). These differences were contrasted with a previous study by Johansson et al. 2020, using the same subset of 16 individuals from European zoos. With the help of AI and the Whiskerbook platform, “experts” were able to match 87 % of encounters and identify 15 out of 16 individuals, with modeled estimates of 16 ± 1 individuals. In contrast, “novices” were 63 % accurate in matching encounters and identified 12 out of 16 individuals, modeling 12 ± 1 individuals that underestimated the population size by 12 %. When comparing the performance of observers using AI and the Whiskerbook platform to observers performing the tasks manually, we found that observers using Whiskerbook made significantly fewer errors in splitting one individual into two (P = 0.04). However, there were also a significantly higher number of combination errors, where two individuals were combined into one (P = 0.01). Specifically, combination errors were found to be made by “novices” (P = 0.04). Although AI benefited both expert and novice observers, expert observers outperformed novices. Our results suggest that AI effectively reduced the misclassification of individual snow leopards in camera trap studies, improving abundance estimates. However, even with AI support, expert observers were needed to obtain the most accurate estimates.