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Sharma, R. K., Sharma, K., Borchers, D., Bhatnagar, Y. V., Suryawanshi, K. S., Mishra, C. |
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Title |
Spatial variation in population-density, movement and detectability of snow leopards in
2 a multiple use landscape in Spiti Valley, Trans-Himalaya |
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2020 |
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bioRxiv |
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Co-existence; land sharing; population-density; spatial capture recapture; Pseudois nayaur
Capra sibirica; ungulates; livestock. |
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The endangered snow leopard Panthera uncia occurs in human use landscapes in the mountains of South and Central Asia. Conservationists generally agree that snow leopards must be conserved through a land-sharing approach, rather than land-sparing in the form of strictly protected areas. Effective conservation through land-sharing requires a good understanding of how snow leopards respond to human use of the landscape. Snow leopard density is expected to show spatial variation within a landscape because of variation in the intensity of human use and the quality of habitat. However, snow leopards have been difficult to enumerate and monitor. Variation in the density of snow leopards remains undocumented, and the impact of human use on their populations is poorly understood. We examined spatial variation in snow leopard density in Spiti Valley, an important snow leopard landscape in India, via spatially explicit capture recapture analysis of camera trap data. We camera trapped an area encompassing a minimum convex polygon of 953 km . We estimated an overall density of 0.49 (95% CI: 0.39-0.73) adult snow leopards per 100 km . Using AIC, our best model showed the density of snow leopards to depend on wild prey density, movement about activity centres to depend on altitude, and the expected number of encounters at the activity centre to depend on topography. Models that also used livestock biomass as a density covariate ranked second, but the effect of livestock was weak. Our results highlight the importance of maintaining high density pockets of wild prey populations in multiple use landscapes to enhance snow leopard conservation. |
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1620 |
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Zhang, L., Lian, X., Yang, X |
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Title |
Population density of snow leopards (Panthera Uncia) in the Yage Valley Region of the Sanjiangyuan National Park: Conservation Implications and future directions |
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2020 |
Publication |
Artic, Antartic and Alpine Research |
Abbreviated Journal |
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52 |
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1 |
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541-550 |
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Snow leopard; population density; camera trapping; Tibetan Plateau; alpine ecosystem |
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Population-based studies on snow leopard (Panthera uncia) are of theoretical and practical sig- nificance for the conservation of alpine ecosystems, though geographic remoteness and isolation hinder surveys in many promising regions. The Sanjiangyuan National Park on the Tibetan Plateau is acknowledged as a main snow leopard habitat, but most of the region remains unexplored and unknown. We adopted a combined approach of route survey and camera trapping survey to explore the population density of snow leopard in the Yage Valley region of the Sanjiangyuan National Park. Results indicated that (1) large populations of blue sheep contributed to the major food supply for snow leopards, along with diverse prey species as dietary supplementations, and (2) a population density of four to six snow leopards per 100 km2 on the north bank was estimated, and nine to fourteen individuals within the valley core areas were identified. We also argue that under the potential impacts of hydropower dams, this valley ecosystem should be symbolized as a conservation hotspot and therefore merits prioritized conservation. We recommend further surveys combined with novel methods/techniques and advocate a sustainable ecotourism model for the first V-shaped valley along the Yangtze mainstream. |
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1619 |
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Durbach, I., Borchers, D., Sutherland, C., Sharma, K. |
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Fast, flexible alternatives to regular grid designs for spatial
capture–recapture. |
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Research Article |
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2020 |
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Methods in Ecology and Evolution |
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1-13 |
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camera trap, population ecology,sampling, spatial capture-recapture, surveys |
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Spatial capture–recapture (SCR) methods use the location of
detectors (camera traps, hair snares and live-capture traps) and the
locations at which animals were detected (their spatial capture
histories) to estimate animal density. Despite the often large expense
and effort involved in placing detectors in a landscape, there has been
relatively little work on how detectors should be located. A natural
criterion is to place traps so as to maximize the precision of density
estimators, but the lack of a closed-form expression for precision has
made optimizing this criterion computationally demanding. 2. Recent
results by Efford and Boulanger (2019) show that precision can be well
approximated by a function of the expected number of detected
individuals and expected number of recapture events, both of which can
be evaluated at low computational cost. We use these results to develop
a method for obtaining survey designs that optimize this approximate
precision for SCR studies using count or binary proximity detectors, or
multi-catch traps. 3. We show how the basic design protocol can be
extended to incorporate spatially varying distributions of activity
centres and animal detectability. We illustrate our approach by
simulating from a camera trap study of snow leopards in Mongolia and
comparing estimates from our designs to those generated by regular or
optimized grid designs. Optimizing detector placement increased the
number of detected individuals and recaptures, but this did not always
lead to more precise density estimators due to less precise estimation
of the effective sampling area. In most cases, the precision of density
estimators was comparable to that obtained with grid designs, with
improvement in some scenarios where approximate CV(¬D) < 20% and density
varied spatially. 4. Designs generated using our approach are
transparent and statistically grounded. They can be produced for survey
regions of any shape, adapt to known information about animal density
and detectability, and are potentially easier and less costly to
implement. We recommend their use as good, flexible candidate designs
for SCR surveys when reasonable knowledge of model parameters exists. We
provide software for researchers to construct their own designs, in the
form of updates to design functions in the r package oSCR. |
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1618 |
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Hameed, S., Din, J. U., Ali, H., Kabir, M., Younas, M., Rehman,
E. U., Bari, F., Hao, W., Bischof, R., Nawaz, M. A. |
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Title |
Identifying priority landscapes for conservation of snow
leopards in Pakistan |
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2020 |
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Plos One |
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1-20 |
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Pakistan’s total estimated snow leopard habitat is about
80,000 km2 of which about half is considered prime habitat. However,
this preliminary demarcation was not always in close agreement with the
actual distribution the discrepancy may be huge at the local and
regional level. Recent technological developments like camera trapping
and molecular genetics allow for collecting reliable presence records
that could be used to construct realistic species distribution based on
empirical data and advanced mathematical approaches like MaxEnt. The
current study followed this approach to construct an accurate
distribution of the species in Pakistan. Moreover, movement corridors,
among different landscapes, were also identified through circuit theory.
The probability of habitat suitability, generated from 98 presence
points and 11 environmental variables, scored the snow leopard’s assumed
range in Pakistan, from 0 to 0.97. A large portion of the known range
represented low-quality habitat, including areas in lower Chitral, Swat,
Astore, and Kashmir. Conversely, Khunjerab, Misgar, Chapursan, Qurumber,
Broghil, and Central Karakoram represented high-quality habitats.
Variables with higher contributions in the MaxEnt model were
precipitation during the driest month (34%), annual mean temperature
(19.5%), mean diurnal range of temperature (9.8%), annual precipitation
(9.4%), and river density (9.2). The model was validated through
receiver operating characteristic (ROC) plots and defined thresholds.
The average test AUC in Maxent for the replicate runs was 0.933 while
the value of AUC by ROC curve calculated at 0.15 threshold was 1.00.
These validation tests suggested a good model fit and strong predictive
power. The connectivity analysis revealed that the population in the
Hindukush landscape appears to be more connected with the population in
Afghani- stan as compared to other populations in Pakistan. Similarly,
the Pamir-Karakoram population is better connected with China and
Tajikistan, while the Himalayan population was connected with the
population in India. Based on our findings we propose three model
landscapes to be considered under the Global Snow Leopard Ecosystem
Protection Program (GSLEP) agenda as regional priority areas, to
safeguard the future of the snow leopard in Pakistan and the region.
These landscapes fall within mountain ranges of the Himalaya, Hindu Kush
and Karakoram-Pamir, respectively. We also identified gaps in the
existing protected areas network and suggest new protected areas in
Chitral and Gilgit-Baltistan to protect critical habitats of snow
leopard in Pakistan. |
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1617 |
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Atzeni, L., Cushman, S. A., Bai, D., Wang, J., Chen, P., Shi,
K., Riordan, P. |
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Title |
Meta-replication, sampling bias, and multi-scale model selection:
A case study on snow leopard (Panthera uncia) in western China. |
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Journal Article |
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2020 |
Publication |
Ecology and Evolution |
Abbreviated Journal |
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1-27 |
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MaxEnt, meta-replication, multi-scale, Panthera uncia, sampling bias, scale selection, snow leopard, species distribution model |
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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. |
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1616 |
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Chetri, M., Odden, M., Devineau, O., McCarthy, T., Wegge, P. |
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Multiple factors influence local perceptions of snow leopards and
Himalayan wolves in the central Himalayas, Nepal. |
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Journal Article |
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2020 |
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PeerJ |
Abbreviated Journal |
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1-18 |
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Panthera uncia, Canis lupus chanco, Perceptions, Large carnivores, Trans-Himalayas |
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An understanding of local perceptions of carnivores is
important for conservation and management planning. In the central
Himalayas, Nepal, we interviewed 428 individuals from 85 settlements
using a semi-structured questionnaire to quantitatively assess local
perceptions and tolerance of snow leopards and wolves. We used
generalized linear mixed effect models to assess influential factors,
and found that tolerance of snow leopards was much higher than of
wolves. Interestingly, having experienced livestock losses had a minor
impact on perceptions of the carnivores. Occupation of the respondents
had a strong effect on perceptions of snow leopards but not of wolves.
Literacy and age had weak impacts on snow leopard perceptions, but the
interaction among these terms showed a marked effect, that is, being
illiterate had a more marked negative impact among older respondents.
Among the various factors affecting perceptions of wolves, numbers of
livestock owned and gender were the most important predictors. People
with larger livestock herds were more negative towards wolves. In terms
of gender, males were more positive to wolves than females, but no such
pattern was observed for snow leopards. People’s negative perceptions
towards wolves were also related to the remoteness of the villages.
Factors affecting people’s perceptions could not be generalized for the
two species, and thus need to be addressed separately. We suggest future
conservation projects and programs should prioritize remote settlements. |
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1615 |
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Filla, M., Lama, R. P., Ghale, T. R., Signer, J., Filla, T., Aryal, R. R., Heurich, M., Waltert, M., Balkenhol, N., Khorozyan, I. |
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In the shadows of snow leopards and the Himalayas: density and habitat selection of blue sheep in Manang, Nepal |
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Journal Article |
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2020 |
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Ecology and Evolution |
Abbreviated Journal |
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2021 |
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11 |
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108-122 |
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Annapurna Conservation Area, bharal, Panthera uncia, predator-prey, Pseudois nayaur |
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There is a growing agreement that conservation needs to be proactive and pay increased attention to common species and to the threats they face. The blue sheep (Pseudois nayaur) plays a key ecological role in sensitive high-altitude ecosystems of Central Asia and is among the main prey species for the globally vulnerable snow leopard (Panthera uncia). As the blue sheep has been increasingly exposed to human pressures, it is vital to estimate its population dynamics, protect the key populations, identify important habitats, and secure a balance between conservation and local livelihoods. We conducted a study in Manang, Annapurna Conservation Area (Nepal), to survey blue sheep on 60 transects in spring (127.9 km) and 61 transects in autumn (134.7 km) of 2019, estimate their minimum densities from total counts, compare these densities with previous estimates, and assess blue sheep habitat selection by the application of generalized additive models (GAMs). Total counts yielded minimum density estimates of 6.0–7.7 and 6.9–7.8 individuals/km2 in spring and autumn, respectively, which are relatively high compared to other areas. Elevation and, to a lesser extent, land cover indicated by the normalized difference vegetation index (NDVI) strongly affected habitat selection by blue sheep, whereas the effects of anthropogenic variables were insignificant. Animals were found mainly in habitats associated with grasslands and shrublands at elevations between 4,200 and 4,700 m. We show that the blue sheep population size in Manang has been largely maintained over the past three decades, indicating the success of the integrated conservation and development efforts in this area. Considering a strong dependence of snow leopards on blue sheep, these findings give hope for the long-term conservation of this big cat in Manang. We suggest that long-term population monitoring and a better understanding of blue sheep–livestock interactions are crucial to maintain healthy populations of blue sheep and, as a consequence, of snow leopards. |
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SLN @ rakhee @ |
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1683 |
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Rashid, W., Shi, J., Rahim, I. U., Dong, S., Ahmad, L. |
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Research trends and management options in human-snow leopard conflict |
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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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Suryawanshi, K. R., Khanyari, M., Sharma, K., Lkhagvajav, P., Mishra, C. |
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Sampling bias in snow leopard population estimation studies |
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2019 |
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Population Eccology |
Abbreviated Journal |
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1-9 |
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camera trap, Central Asia, Himalaya, meta-analysis, monitoring, Panthera uncia, population ecology |
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Abstract |
Accurate assessments of the status of threatened species and their conservation
planning require reliable estimation of their global populations and robust monitoring
of local population trends. We assessed the adequacy and suitability of studies
in reliably estimating the global snow leopard (Panthera uncia) population. We
compiled a dataset of all the peer-reviewed published literature on snow leopard
population estimation. Metadata analysis showed estimates of snow leopard density
to be a negative exponential function of area, suggesting that study areas have generally
been too small for accurate density estimation, and sampling has often been
biased towards the best habitats. Published studies are restricted to six of the
12 range countries, covering only 0.3�0.9% of the presumed global range of the
species. Re-sampling of camera trap data from a relatively large study site
(c.1684 km2) showed that small-sized study areas together with a bias towards
good quality habitats in existing studies may have overestimated densities by up to
five times. We conclude that current information is biased and inadequate for generating
a reliable global population estimate of snow leopards. To develop a rigorous
and useful baseline and to avoid pitfalls, there is an urgent need for
(a) refinement of sampling and analytical protocols for population estimation of
snow leopards (b) agreement and coordinated use of standardized sampling protocols
amongst researchers and governments across the range, and (c) sampling
larger and under-represented areas of the snow leopard's global range. |
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1477 |
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Chetri, M, Odden, M., Sharma, K., Flagstad, O., Wegge, P |
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Estimating snow leopard density using fecal DNA in a large landscape in north-central Nepal |
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Journal Article |
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Year |
2019 |
Publication |
Global Ecology and Conservation |
Abbreviated Journal |
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17 |
Pages |
1-8 |
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Keywords |
Panthera uncia, Density, Annapurna-Manaslu landscape, Noninvasive, Spatial scale |
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Abstract |
Although abundance estimates have a strong bearing on the conservation status of a
species, less than 2% of the global snow leopard distribution range has been sampled
systematically, mostly in small survey areas. In order to estimate snow leopard density
across a large landscape, we collected 347 putative snow leopard scats from 246 transects
(490 km) in twenty-six 5 5km sized sampling grid cells within 4393 km2 in Annapurna-
Manaslu, Nepal. From 182 confirmed snow leopard scats, 81 were identified as belonging
to 34 individuals; the remaining were discarded for their low (<0.625) quality index. Using
maximum likelihood based spatial capture recapture analysis, we developed candidate
model sets to test effects of various covariates on density and detection of scats on transects.
The best models described the variation in density as a quadratic function of
elevation and detection as a linear function of topography. The average density estimate of
snow leopards for the area of interest within Nepal was 0.95 (SE 0.19) animals per 100 km2
(0.66e1.41 95% CL) with predicted densities varying between 0.1 and 1.9 in different parts,
thus highlighting the heterogeneity in densities as a function of habitat types. Our density
estimate was low compared to previous estimates from smaller study areas. Probably,
estimates from some of these areas were inflated due to locally high abundances in overlap
zones (hotspots) of neighboring individuals, whose territories probably range far beyond
study area borders. Our results highlight the need for a large-scale approach in snow
leopard monitoring, and we recommend that methodological problems related to spatial
scale are taken into account in future snow leopard research. |
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no |
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Call Number |
SLN @ rakhee @ |
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1478 |
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