Abstract: From March to May, 2006œªwe conducted extensive snow leopard surveys in the Burhanbuda Mountain Kunlun Mountains, Qinghai Province, China. 32 linear transect of 5~15 km each, which running through each vegetation type, were surveyed within the study area. A total of 72 traces of snow leopard were found along 4 transects (12.5% of total transects). The traces included pug marks or footprints, scrapes and urine marks. We estimated the average density of wild ungulates in the region was 2.88ñ0.35 individuals km-2(n=29). We emplaced 16 auto2 trigger cameras in different environments and eight photos of snow leopard were shot by four cameras and the capture rate of snow leopard was 71.4%. The minimum snow leopard population size in the Burhanbuda Mountain was two, because two snow leopards were phototrapped by different cameras at almost same time. Simultaneously, the cameras also shot 63 photos of other wild animals, including five photos are unidentified wild animals, and 20 photos of livestock. We evaluated the human attitudes towards snow leopard by interviewing with 27 Tibetan householders of 30 householders live in the study area. We propose to establish a nature reserve for protecting and managing snow leopards in the region. Snow leopard (Uncia uncia) is considered as a unique species because it lives above the snow line, it is endemic to alpines in Central Asia, inhabiting in 12 countries across Central Asia (Fox, 1992). Snow leopard ranges in alpine areas in Qinghai, Xinjiang, Inner Mongolia, Tibet, Gansu and Sichuan in western China (Liao, 1985, 1986; Zhou, 1987; Ma et al., 2002; Jiang & Xu, 2006). The total population and habitat of snow leopards in China are estimated to be 2,000~2,500 individuals and 1,824,316 km2, only 5% of which is under the protection of nature reserves. The cat's current range is fragmented (Zou & Zheng, 2003). Due to strong human persecutions, populations of snow leopards decreased significantly since the end of the 20th century. Thus, the
snow leopards are under the protection of international and domestic laws. From March to May, 2006, we conducted two field surveys in Zhiyu Village, Dulan County in Burhanbuda Mountain, Kunlun Mountains, China to determine the population, distribution and survival status of snow leopards in the area. The aim of the study was to provide ecologic data for snow leopard conservation.

Abstract: Population density estimation is integral to the effective conservation and management of wildlife. The snow leopard Panthera uncia is categorized as Vulnerable on the IUCN Red List, and reliable information on its density is a prerequisite for its conservation and management. Little is known about the status of the snow leopard in the central and eastern Qilian Mountains, China. To address this, we estimated the population density of the snow leopard using a spatially explicit capture–recapture model based on camera trapping in Machang in the central and eastern Qilian Mountains during January–March 2019. We set up
40 camera traps and recorded 84 separate snow leopard captures over 3,024 trap-days. We identified 18 individual snow leopards and estimated their density to be 2.26/100 km. Our study provides baseline information on the snow leopard and the first population estimate for the species in the central and eastern Qilian Mountains.

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: Monitoring Techniques Forest leopards have never been systematically surveyed in African forests, in spite of their potentially vital ecological role as the sole large mammalian predators in these systems. Because leopards are rarely seen in this habitat, and are difficult to survey using the most common techniques for assessing relative abundances of forest mammals, baseline knowledge of leopard ecology and responses to human disturbance in African forests remain largely unknown. This technical handbook sums up the experience gained during a two-year study of leopards by Philipp Henschel in the Lop‚ Reserve in Gabon, Central Africa, in 2001/2002, supplemented by additional experience from carnivore studies conducted by Justina Ray in southwestern Central African Republic and eastern Congo (Zaire) . The main focus of this effort has been to develop a protocol that can be used by fieldworkers across west and central Africa to estimate leopard densities in various forest types. In developing this manual, Henschel tested several indirect methods to assess leopard numbers in both logged and unlogged forests, with the main effort devoted to testing remote photography survey methods developed for tigers by Karanth (e.g., Karanth 1995, Karanth & Nichols 1998; 2000; 2002), and modifying them for the specific conditions characterizing African forest environments. This handbook summarizes the results of the field testing, and provides recommendations for techniques to assess leopard presence/absence, relative abundance, and densities in African forest sites. We briefly review the suitability of various methods for different study objectives and go into particular detail on remote photography survey methodology, adapting previously developed methods and sampling considerations specifically to the African forest environment. Finally, we briefly discuss how camera trapping may be used as a tool to survey other forest mammals. Developing a survey protocol for African leopards is a necessary first step towards a regional assessment and priority setting exercise targeted at forest leopards, similar to those carried out on large carnivores in Asian and South American forests.