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: After 30 years of searching for the mysterious Beringian snow cat in vast space of Koryakia and Chukotka
one of the five cameras recorded finally this beast at night in September 2014. This is not so much a
sensation as a real scientific discovery, saying that the hearts of the snow leopard population resettlement are
not in 5000 km from the main range boundaries, but much closer. Where? � will show further studies.
In addition to the snow leopard in the North-Eastern Asia, it found two more endangered large
mammal species: the Amur tiger and the relict of the Ice Age � the Irkuyem-bear. Author has given these
animals his life and his article devoted to this topic.

Abstract: In this study, we investigated the cross-reactivity pattern of IgE and IgG4 antibodies to the major feline allergen, Fel d I. We studied the IgE and IgG4 response of 11 cat-allergic patients against Fel d I-like structures in eight members of the Felidae family: ocelot, puma, serval, siberian tiger, lion, jaguar, snow leopard, and caracal. Hair from these “big cats” was collected, extracted, and used in a RAST system and histamine-release test. By means of a RAST-inhibition assay with affinity-purified Fel d I from cat dander, it was established that, in the Felidae species, a Fel d I equivalent is present that reacts with IgE and IgG4 antibodies. We found that all patients had cross-reacting IgE antibodies to seven of the Felidae tested; no IgE antibodies reactive with the caracal were found. Eight of 10 patients with IgG4 antibodies directed to cat dander also had IgG4 antibodies directed to several Felidae species, including the caracal. However, the correlation between the IgE and the IgG4 antibody specificity was low, indicating that, in the case of Fel d I IgE and IgG4, antibodies do not necessarily have the same specificity.

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.

Abstract: Spondyloarthropathy was documented for the first time in 14 (3.7%) of 386 large cats, affecting eight species belonging to three genera. The limited distribution of joint erosions, associated with spine and sacroiliac joint pathology, was indistinguishable from that occurring in humans with spondyloarthropathy of the reactive type. This form of inflammatory arthritis is almost twice as common as osteoarthritis (for felids as a whole), and animal well-being may be enhanced by its recognition and by initiation of specific treatment.