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Zhang, C., Ma, T., Ma, D. |
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Status of the snow leopard Panthera uncia in the Qilian Mountains, Gansu Province, China |
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2023 |
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Oryx |
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1-6 |
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Camera trapping, China, density, Panthera uncia, Qilian Mountains, snow leopard, spatially explicit capture–recapture |
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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. |
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SLN @ rakhee @ |
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1725 |
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Kichloo, M. A., Sharma, K., Sharma, N. |
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Climate casualties or human disturbance? Shrinking distribution of the three large carnivores in the Greater Himalaya |
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Journal Article |
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2023 |
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Springer – Climatic Change |
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176 |
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118 |
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1-17 |
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Snow leopard · Common leopard · Asiatic black bear · Climate change · Multi- season occupancy analysis · Local ecological knowledge · Imperfect detection |
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Mammalian carnivores are key to our understanding of ecosystem dynamics, but most of them are threatened with extinction all over the world. Conservating large carnivores is often an arduous task considering the complex relationship between humans and carnivores, and the diverse range and reasons of threats they face. Climate change is exacerbating the situation further by interacting with most existing threats and amplifying their impacts. The Mountains of Central and South Asia are warming twice as rapidly as the rest of the northern hemisphere. There has been limited research on the effect of climate change and other variables on large carnivores. We studied the patterns in spatio-temporal distribution of three sympatric carnivores, common leopard, snow leopard, and Asiatic black bear in Kishtwar high altitude National Park, a protected area in the Great Himalayan region of Jammu and Kashmir. We investigated the effects of key habitat characteristics as well as human disturbance and climatic factors to understand the spatio-temporal change in their distributions between the early 1990s and around the year 2016–2017. We found a marked contraction in the distribution of the three carnivores between the two time periods. While snow leopard shifted upwards and further away from human settlements, common leopard and Asiatic black bear suffered higher rates of local extinctions at higher altitudes and shifted to lower areas with more vegetation, even if that brought them closer to settlements. We also found some evidence that snow leopards were less likely to have faced range contraction in areas with permanent glaciers. Our study underscores the importance of climate adaptive conservation practices for long-term management in the Greater Himalaya, including the monitoring of changes in habitat, and space-use patterns by human communities and wildlife. |
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1724 |
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Bohnett, E., Holmberg, J., Faryabi, S. P., An, L., Ahmad, B., Rashid, W., Ostrowski, S. |
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Comparison of two individual identification algorithms for snow leopards (Panthera uncia) after automated detection |
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Journal Article |
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2023 |
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Ecological Informatics |
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77 |
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102214 |
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1-14 |
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Background subtraction, Deep learning, Hotspotter, Individual identification, PIE v2, Snow leopards |
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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. |
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SLN @ rakhee @ |
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1723 |
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Atzeni, L., Wang, J., Riordan, P., Shi, K., Cushman, S. A. |
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Title |
Landscape resistance to gene flow in a snow leopard population from Qilianshan National Park, Gansu, China |
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Journal Article |
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2023 |
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Landscape Ecology |
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Landscape genetics · MLPE · Gene flow · Genetic distance · Isolation by distance · Isolation by resistance · Landscape resistance · Snow leopard · Principal component analysis |
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Context: The accurate estimation of landscape resistance to movement is important for ecological understanding and conservation applications. Rigorous estimation of resistance requires validation and optimization. One approach uses genetic data for the optimization or validation of resistance models. Objectives We used a genetic dataset of snow leopards from China to evaluate how landscape genetics resistance models varied across genetic distances and spatial scales of analysis. We evaluated whether landscape genetics models were superior to models of resistance derived from habitat suitability or isolation-by-distance.
Methods: We regressed genetically optimized, habitat-based, and isolation-by-distance hypotheses against genetic distances using mixed effect models. We explored all subset combinations of genetically optimized variables to find the most supported resistance scenario for each genetic distance.
Results: Genetically optimized models always out-performed habitat-based and isolation-by-distance hypotheses. The choice of genetic distances influenced the apparent influence of variables, their spatial scales and their functional response shapes, producing divergent resistance scenarios. Gene flow in snow leopards was largely facilitated by areas of intermediate ruggedness at intermediate elevations corresponding to small-to-large valleys within and between the mountain ranges.
Conclusions: This study highlights that landscape genetics models provide superior estimation of functional dispersal than habitat surrogates and suggests that optimization of genetic distance should be included as an optimization routine in landscape genetics, along with variables, scales, effect size and functional response shape. Furthermore, our study provides new insights on the ecological conditions that promote gene flow in snow leopards, which expands ecological knowledge, and we hope will improve conservation planning. |
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SLN @ rakhee @ |
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1720 |
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Rashid, W., Shi, J., Rahim, I. U., Dong, S., Ahmad, L. |
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Title |
Research trends and management options in human-snow leopard conflict |
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Journal Article |
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2020 |
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Biological Conservation |
Abbreviated Journal |
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242 |
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108413 |
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1-10 |
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Snow leopards, Systematic review, Compensation, Co-existence, Livestock, Human-wildlife conflict, Mitigation |
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Conservation of the snow leopard (Panthera uncia) is challenging because of its threatened status and increase in human-snow leopard conflict (HSC). The area of occupancy of the snow leopard comprises mountainous regions of Asia that are confronted with various environmental pressures including climate change. HSCs have increased with a burgeoning human population and economic activities that enhance competition between human and snow leopard or its preys. Here we systematically review the peer-reviewed literature from 1994 to 2018 in Web of Science, Google Scholar, Science Direct and PubMed (30 articles), to evaluate the current state of scholarship about HSCs and their management. We determine: 1) the spatio-temporal distribution of relevant researches; 2) the methodologies to assess HSCs; 3) and evaluate existing interventions for conflict management; and 4) the potential options for HSC management. The aim of the current study is thus to identify key research gaps and future research requirements. Of the articles in this review, 60% evaluated the mitigation of HSCs, while only 37% provided actionable and decisive results. Compensation programs and livestock management strategies had high success rates for mitigating HSCs through direct or community-managed interventions. Further research is required to evaluate the efficacy of existing HSC mitigation strategies, many of which, while recommended, lack proper support. In spite of the progress made in HSC studies, research is needed to examine ecological and sociocultural context of HSCs. We suggest future work focus on rangeland management for HSC mitigation, thus ultimately fostering a co-existence between human and snow leopard. |
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SLN @ rakhee @ |
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1716 |
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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. |
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Human expertise combined with artificial intelligence improves performance of snow leopard camera trap studies |
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Journal Article |
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2023 |
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Global Ecology & Conservation |
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41 |
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e02350 |
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1-13 |
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Snow leopard, Artificial intelligence, Camera trap misclassification, individual ID, HotSpotter |
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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. |
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1715 |
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Kachel, S., Bayrakcismith, R., Kubanychbekov, Z., Kulenbekov, R., McCarthy, T., Weckworth, B., Wirsing, A. |
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Ungulate spatiotemporal responses to contrasting predation risk from wolves and snow leopards |
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Journal Article |
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2022 |
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Journal of Animal Ecology |
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1-16 |
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landscape of fear, multiple-predator effects, non-consumptive effects, predation-risk effects, predator facilitation, risk allocation, snow leopard, wolf |
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1. Spatial responses to risk from multiple predators can precipitate emergent consequences for prey (i.e. multiple-predator effects, MPEs) and mediate indirect interactions between predators. How prey navigate risk from multiple predators may therefore have important ramifications for understanding the propagation of predation-risk effects (PREs) through ecosystems.
2. The interaction of predator and prey traits has emerged as a potentially key driver of antipredator behaviour but remains underexplored in large vertebrate systems, particularly where sympatric prey share multiple predators. We sought to better generalize our understanding of how predators influence their ecosystems by considering how multiple sources of contingency drive prey distribution in a multi-predator–multi-prey system.
3. Specifically, we explored how two sympatric ungulates with different escape tactics—vertically agile, scrambling ibex Capra sibirica and sprinting argali Ovis ammon—responded to predation risk from shared predators with contrasting hunting modes—cursorial wolves Canis lupus and vertical-ambushing, stalking snow leopards Panthera uncia.
4. Contrasting risk posed by the two predators presented prey with clear trade-offs. Ibex selected for greater exposure to chronic long-term risk from snow leopards, and argali for wolves, in a nearly symmetrical manner that was predictable based on the compatibility of their respective traits. Yet, acute short-term risk from the same predator upended these long-term strategies, increasing each ungulates' exposure to risk from the alternate predator in a manner consistent with a scenario in which conflicting antipredator behaviours precipitate risk-enhancing MPEs and mediate predator facilitation. By contrast, reactive responses to wolves led ibex to reduce their exposure to risk from both predators—a risk-reducing MPE. Evidence of a similar reactive risk-reducing effect for argali vis-à-vis snow leopards was lacking.
5. Our results suggest that prey spatial responses and any resulting MPEs and prey-mediated interactions between predators are contingent on the interplay of hunting mode and escape tactics. Further investigation of interactions among various drivers of contingency in PREs will contribute to a more comprehensive understanding and improved forecasting of the ecological effects of predators. |
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SLN @ rakhee @ |
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1704 |
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Weiskopf, S. R., Kachel, S. M., McCarthy, K. P. |
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Title |
What Are Snow Leopards Really Eating? Identifying Bias in Food-Habit Studies |
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Journal Article |
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2016 |
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Wildlife Society Bulletin |
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1-8 |
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diet, DNA genotyping, feces, Panthera uncia, scat, snow leopard. Lack of |
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Declining prey populations are widely recognized as a primary threat to snow leopard (Panthera
uncia) populations throughout their range. Effective snow leopard conservation will depend upon reliable
knowledge of food habits. Unfortunately, past food-habit studies may be biased by inclusion of nontarget
species in fecal analysis, potentially misinforming managers about snow leopard prey requirements.
Differentiation between snow leopard and sympatric carnivore scat is now cost-effective and reliable using
genetics. We used fecal mitochondrial DNA sequencing to identify scat depositors and assessment bias in
snow leopard food-habit studies. We compared presumed, via field identification, and genetically confirmed
snow leopard scats collected during 2005 and 2012 from 4 sites in Central Asia, using standard forensic
microscopy to identify prey species. Field identification success varied across study sites, ranging from 21% to
64% genetically confirmed snow leopard scats. Our results confirm the importance of large ungulate prey for
snow leopards. Studies that fail to account for potentially commonplace misidentification of snow leopard
scat may mistakenly include a large percentage of scats originating from other carnivores and report
inaccurate dietary assessments. Relying on field identification of scats led to overestimation of percent
occurrence, biomass, and number of small mammals consumed, but underestimated values of these measures for large ungulates in snow leopard diet. This clarification suggests that the conservation value of secondary prey, such as marmots (Marmota spp.) and other small mammals, may be overstated in the literature; stable snow leopard populations are perhaps more reliant upon large ungulate prey than previously understood. |
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SLN @ rakhee @ |
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1445 |
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Khatoon, R., Hussain, I., Anwar, M., Nawaz, M. A. |
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Diet selection of snow leopard (Panthera uncia) in Chitral, Pakistan |
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Journal Article |
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2017 |
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Turkish Journal of Zoology |
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14 |
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914-923 |
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Snow leopard, conservation, seasonal variation, diet composition, Chitral, Pakistan |
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Snow leopard (Panthera uncia) is an elusive endangered carnivore found in remote mountain regions of Central Asia, with
sparse distribution in northern Pakistan, including Chitral and Baltistan. The present study determined the food habits of snow leopard,
including preferred prey species and seasonal variation in diet. Fifty-six scat samples were collected and analyzed to determine the
diet composition in two different seasons, i.e. summer and winter. Hair characteristics such as cuticular scale patterns and medullary
structure were used to identify the prey. This evidence was further substantiated from the remains of bones, claws, feathers, and other
undigested remains found in the scats. A total of 17 prey species were identified; 5 of them were large mammals, 6 were mesomammals,
and the remaining 6 were small mammals. The occurrence of wild ungulates (10.4%) in the diet was low, while livestock constituted a
substantial part (26.4%) of the diet, which was higher in summer and lower in winter. Mesomammals altogether comprised 33.4% of
the diet, with palm civet (Paguma larvata) as a dominant (16.8%) species, followed by golden marmot (Marmota caudate) (8.8%), which
was higher in winter. There was a significant difference in seasonal variation in domestic livestock and small mammals. The livestock
contribution of 26.4% observed in the present study indicates a significant dependence of the population on livestock and suggests
that the study area is expected to be a high-conflict area for snow leopards. The results of the current study would help improve the
conservation efforts for snow leopards, contributing to conflict resolution and effective management of this endangered cat. |
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SLN @ rakhee @ |
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1459 |
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Suryawanshi, K. R., Redpath, S., Bhatnagar, Y. V., Ramakrishnan, U., Chaturvedi, V., Smout, S. C., Mishra, C. |
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Impact of wild prey availability on livestock predation by snow leopards |
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Journal Article |
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2017 |
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Royal Society Open Science |
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1-11 |
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apparent competition, apparent facilitation, conservation conflicts, indirect interactions, predator� prey interactions, snow leopard |
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Abstract |
An increasing proportion of the world�s poor is rearing
livestock today, and the global livestock population is growing.
Livestock predation by large carnivores and their retaliatory
killing is becoming an economic and conservation concern.
A common recommendation for carnivore conservation and
for reducing predation on livestock is to increase wild prey
populations based on the assumption that the carnivores
will consume this alternative food. Livestock predation,
however, could either reduce or intensify with increases
in wild prey depending on prey choice and trends in
carnivore abundance. We show that the extent of livestock
predation by the endangered snow leopard Panthera uncia
intensifies with increases in the density of wild ungulate
prey, and subsequently stabilizes. We found that snow leopard
density, estimated at seven sites, was a positive linear
function of the density of wild ungulates�the preferred
prey�and showed no discernible relationship with livestock
density. We also found that modelled livestock predation
increased with livestock density. Our results suggest that
snow leopard conservation would benefit from an increase
in wild ungulates, but that would intensify the problem of
livestock predation for pastoralists. The potential benefits of
increased wild prey abundance in reducing livestock predation
can be overwhelmed by a resultant increase in snow leopard
populations. Snow leopard conservation efforts aimed at
facilitating increases in wild prey must be accompanied by greater assistance for better livestock
protection and offsetting the economic damage caused by carnivores. |
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no |
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SLN @ rakhee @ |
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1457 |
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