Abstract: Describes the range, behaviour and ecology of lion Panthera leo, tiger P. tigris, leopard P. pardus, snow leopard P. uncia, clouded leopard Neofelis nebylosa and cheetah Acinonyx jubatus. -P.J.Jarvis

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: 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: 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: The World Conservation Union listed the snow leopard (Uncia uncia) as endangered in 1974. With as few as 3,500 snow leopards left in the wild, scientists placed the snow leopard on the IUCN Red List of critically endangered species shared by animals such as the giant panda and tiger. In an effort to save the snow leopard from extinction, former zoo employee Helen Freeman founded the Snow Leopard Trust in 1981. The Snow Leopard Trust works to save this elusive cat by incorporating community-based conservation projects. One of these project Leopard Enterprise (SLE), impacts poverty stricken communities in Mongolia, Kyrgyz Republic, and Pakistan. It assists over 300 families in its conservation efforts. The economic incentives provided via SLE have led participating communities not to harm the snow leopard or its prey, and to practice sustainable herding. Since the project began in 1997, the number of snow leopards harmed around the communities' territories has dropped to near zero. Additionally, the annual income of families that utilize the benefits of SLE has increased by 25% to 40%. SLE creates this economic benefit by providing the training and equipment necessary to make desirable products from the wool of herd animals. Snow Leopard Trust then purchases these handicraft items from the local people and them globally. Zoos can expand their conservation efforts by simply offering these items in their gift shops. Woodland Park Zoo (WPZ) was the first zoological institution to sell the products, and WPZ continues to generate revenue from them. SLE is a golden opportunity for zoos to increase revenue, assist poor families, and save an endangered species and fragile ecosystem.