|
Aromov B. (1995). The Biology of the Snow Leopard in the Hissar Nature Reserve.
Abstract: The work contains data on biology snow leopard in Hissar nature reserve, Uzbekistan. The number of snow leopards in this reserve has increased from two or four in 1981 to between 13 and 17 individuals in 1994. Since 1981, snow leopards have been sighted 72 times and their tracks or pugmarks 223 times. In the Hissar Nature Reserve snow leopards largely feed on ibex. Over a period of 14 years, 92 kills and remains of ibex aged from one to thirteen years of age have been examined. Other records of predation, by the number of events observed, include 33 cases of juvenile and mature horses, 25 long-tailed marmot (Marmota caudata). 18 Himalayan snowcock (Tetraogallus himalayemis), 17 domestic goat, 13 wild boar (Sus scrofa), five domestic sheep and three incidents involving cattle. Twenty-two attacks on domestic flocks were reported, and these occurred during both the daytime and at night. Snow leopards usually mate between the 20th of February and March 20th. The offspring are born in late April to May, and there are usually two per litter (23 encounters), although a single litter of three has also been recorded.
|
|
|
Aryal, A. (2009). Final Report On Demography and Causes of Mortality of Blue Sheep (Pseudois nayaur) in Dhorpatan Hunting Reserve in Nepal.
Abstract: A total of 206 individual Blue sheep Pseudois nayaur were estimated in Barse and Phagune blocks of Dhorpatan Hunting Reserve (DHR) and population density was 1.8 Blue sheep/sq.km. There was not significant change in population density from last 4 decades. An average 7 animals/herd (SD-5.5) were classified from twenty nine herds, sheep per herds varying from 1 to 37. Blue sheep has classified into sex ratio on an average 75 male/100females was recorded in study area. The sex ratio was slightly lower but not significantly different from the previous study. Population of Blue sheep was seen stable or not decrease even there was high poaching pressure, the reason may be reducing the number of predators by poison and poaching which has
supported to increase blue sheep population. Because of reducing the predators Wolf Canis lupus, Wild boar population was increasing drastically in high rate and we can observed wild boar above the tree line of DHR. The frequency of occurrence of different prey species in scats of different predators shows that, excluding zero values, the frequencies of different prey species were no significantly different (ö2= 10.3, df = 49, p > 0.05). Most of the scats samples (74%) of Snow leopard, Wolf, Common Leopard, Red fox's cover one prey species while two and three species were present in 18% and 8%, respectively. Barking deer Muntiacus muntjak was the most frequent (18%) of total diet composition of common leopards. Pika Ochotona roylei was the most frequent (28%), and Blue sheep was in second position for diet of snow leopards which cover 21% of total diet composition. 13% of diet covered non-food item such as soil, stones, and vegetable. Pika was most frequent on Wolf and Red fox diet which covered 32% and 30% respectively. There was good positive relationship between the scat density and Blue sheep consumption rate, increasing the scat density, increasing the Blue sheep consumption rate. Blue sheep preference by different predators such as Snow leopard, Common leopard, Wolf and Red fox were 20%, 6%, 13% and 2% of total prey species respectively.
|
|
|
Atzeni, L., Cushman, S. A., Bai, D., Wang, J., Chen, P., Shi,
K., Riordan, P. (2020). Meta-replication, sampling bias, and multi-scale model selection:
A case study on snow leopard (Panthera uncia) in western China. Ecology and Evolution, , 1–27.
Abstract: Replicated multiple scale species distribution models (SDMs)
have become increasingly important to identify the correct variables
determining species distribution and their influences on ecological
responses. This study explores multi-scale habitat relationships of the
snow leopard (Panthera uncia) in two study areas on the Qinghai–Tibetan
Plateau of western China. Our primary objectives were to evaluate the
degree to which snow leopard habitat relationships, expressed by
predictors, scales of response, and magnitude of effects, were
consistent across study areas or locally landcape-specific. We coupled
univariate scale optimization and the maximum entropy algorithm to
produce multivariate SDMs, inferring the relative suitability for the
species by ensembling top performing models. We optimized the SDMs based
on average omission rate across the top models and ensembles’ overlap
with a simulated reference model. Comparison of SDMs in the two study
areas highlighted landscape-specific responses to limiting factors.
These were dependent on the effects of the hydrological network,
anthropogenic features, topographic complexity, and the heterogeneity of
the landcover patch mosaic. Overall, even accounting for specific local
differences, we found general landscape attributes associated with snow
leopard ecological requirements, consisting of a positive association
with uplands and ridges, aggregated low-contrast landscapes, and large
extents of grassy and herbaceous vegetation. As a means to evaluate the
performance of two bias correction methods, we explored their effects on
three datasets showing a range of bias intensities. The performance of
corrections depends on the bias intensity; however, density kernels
offered a reliable correction strategy under all circumstances. This
study reveals the multi-scale response of snow leopards to environmental
attributes and confirms the role of meta-replicated study designs for
the identification of spatially varying limiting factors. Furthermore,
this study makes important contributions to the ongoing discussion about
the best approaches for sampling bias correction.
|
|
|
Bagchi, S., Mishra, C., & Bhatnagar, Y. (2004). Conflicts between traditional pastoralism and conservation of Himalayan ibex (Capra sibirica) in the Trans-Himalayan mountains. Animal Conservation, 7, 121–128.
Abstract: There is recent evidence to suggest that domestic livestock deplete the density and diversity of wild herbivores in the cold deserts of the Trans-Himalaya by imposing resource limitations. To ascertain the degree and nature of threats faced by Himalayan ibex (Capra sibirica) from seven livestock species, we studied their resource use patterns over space, habitat and food dimensions in the pastures of Pin Valley National Park in the Spiti region of the Indian Himalaya. Species diet profiles were obtained by direct observations. We assessed the similarity in habitat use and diets of ibex and livestock using Non-Metric Multidimensional Scaling. We estimated the influence of the spatial distribution of livestock on habitat and diet choice of ibex by examining their co-occurrence patterns in cells overlaid on the pastures. The observed co-occurrence of ibex and livestock in cells was compared with null-models generated through Monte Carlo simulations. The results suggest that goats and sheep impose resource limitations on ibex and exclude them from certain pastures. In the remaining suitable habitat, ibex share forage with horses. Ibex remained relatively unaffected by other livestock such as yaks, donkeys and cattle. However, most livestock removed large amounts of forage from the pastures (nearly 250 kg of dry matter/day by certain species), thereby reducing forage availability for ibex. Pertinent conservation issues are discussed in the light of multiple-use of parks and current socio-economic transitions in the region, which call for integrating social and ecological feedback into management planning.
|
|
|
Bekenov A.B. (2002). About the IUCN categories and criteria for animals inclusion in Red Data Books and lists (project INTAS 99-1483).
Abstract: Uncia uncia in Kazakhstan is defined as EN C 2a(i); D1. The International Red List (2000) attributes this species to EN C 2a, which is an example of concurrence in the assessments at regional and global levels.
|
|
|
Berezovikov N.N. (1990). The Markakol nature reserve.
Abstract: It provides general information about the Markakol nature reserve (Kazakhstan), physico-geographical characteristic, and description of flora and fauna. Snow leopards were noticed to enter the nature reserve from time to time, which seems to be very small for the predator to inhabit it permanently.
|
|
|
Bhatnagar, Y. V. (2008). Relocation from wildlife reserves in the Greater and Trans-Himalayas: Is it necessary? (Vol. 6).
Abstract: The Greater and Trans-Himalayan tracts are cold deserts that have severe seasonal and resource scarce environments. Covering the bulk of Indian Himalayas, they are a rich repository of biodiversity values and ecosystem services. The region has a large protected area (PA) network which has not been completely effective in conserving these unique values. The human population densities are much lower (usually < 1 per sq km) than in most other parts of the country (over 300 to a sq km). However, even such small populations can come into conflict with strict PA laws that demand large inviolate areas, which can mainly be achieved through relocation of the scattered settlements. In this paper, I reason that in this landscape relocation is not a tenable strategy for conservation due to a variety of reasons. The primary ones are that wildlife, including highly endangered ones are pervasive in the larger landscape (unlike the habitat 'islands' of the forested ecosystems) and existing large PAs usually encompass only a small proportion of this range. Similarly, traditional use by people for marginal cultivation, biomass extraction and pastoralism is also as pervasive in this landscape. There does exist pockets of conflict and these are probably increasing owing to a variety of changes relating to modernisation. However, scarce resources, the lack of alternatives and the traditional practice of clear-cut division of all usable areas and pastures between communities make resettlement of people outside PAs extremely difficult. It is reasoned that given the widespread nature of the wildlife and pockets of relatively high density, it is important to prioritise these smaller areas for conservation in a scenario where they form a mosaic of small 'cores' that are more effectively maintained with local support and that enable wildlife to persist. These ideas have recently gained widespread acceptance in both government and conservation circles and may soon become part of national strategy for these areas.
|
|
|
Bohnett, E., Faryabi, S. P., Lewison, R., An, L., Bian, X., Rajabi, A. M., Jahed, N., Rooyesh, H., Mills, E., Ramos, S., Mesnildrey, N., Perez, C. M. S., Taylor, J., Terentyev, V., Ostrowski, S. (2023). Human expertise combined with artificial intelligence improves performance of snow leopard camera trap studies. Global Ecology & Conservation, 41(e02350), 1–13.
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.
|
|
|
Bohnett, E., Holmberg, J., Faryabi, S. P., An, L., Ahmad, B., Rashid, W., Ostrowski, S. (2023). Comparison of two individual identification algorithms for snow leopards (Panthera uncia) after automated detection. Ecological Informatics, 77(102214), 1–14.
Abstract: Photo-identification of individual snow leopards (Panthera uncia) is the primary data source for density estimation via capture-recapture statistical methods. To identify individual snow leopards in camera trap imagery, it is necessary to match individuals from a large number of images from multiple cameras and historical catalogues, which is both time-consuming and costly. The camouflaged snow leopards also make it difficult for machine learning to classify photos, as they blend in so well with the surrounding mountain environment, rendering applicable software solutions unavailable for the species. To potentially make snow leopard individual identification available via an artificial intelligence (AI) software interface, we first trained and evaluated image classification techniques for a convolutional neural network, pose invariant embeddings (PIE) (a triplet loss network), and compared the accuracy of PIE to that of the HotSpotter algorithm (a SIFT-based algorithm). Data were acquired from a curated library of free-ranging snow leopards taken in Afghanistan between 2012 and 2019 and from captive animals in zoos in Finland, Sweden, Germany, and the United States. We discovered several flaws in the initial PIE model, such as a small amount of background matching, that was addressed, albeit likely not fixed, using background subtraction (BGS) and left-right mirroring (LR) techniques which demonstrated reasonable accuracy (Rank 1: 74% Rank-5: 92%) comparable to the Hotspotter results (Rank 1: 74% Rank 2: 84%)The PIE BGS LR model, in conjunction with Hotspotter, yielded the following results: Rank-1: 85%, Rank-5: 95%, Rank-20: 99%. In general, our findings indicate that PIE BGS LR, in conjunction with HotSpotter, can classify snow leopards more accurately than using either algorithm alone.
|
|
|
Brem A.E. (1992). Irbis, or snow leopard (Felis uncia) (Vol. Vol.1. Mammals.).
Abstract: Snow leopard is met in the mountains of Turkistan, Altai, Bukhara, Pamir, Kashmir, and Tibet, and probably in South-East Siberia and along Sungari. In 1871, two animals were living in the Moscow Zoo Garden.
|
|