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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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Journal Article |
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2020 |
Publication |
bioRxiv |
Abbreviated Journal |
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Co-existence; land sharing; population-density; spatial capture recapture; Pseudois nayaur
Capra sibirica; ungulates; livestock. |
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Abstract  |
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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Durbach, I., Borchers, D., Sutherland, C., Sharma, K. |
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Title |
Fast, flexible alternatives to regular grid designs for spatial
capture–recapture. |
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Research Article |
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2020 |
Publication |
Methods in Ecology and Evolution |
Abbreviated Journal |
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1-13 |
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camera trap, population ecology,sampling, spatial capture-recapture, surveys |
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Abstract |
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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Johansson, O., Ullman, K., Lkhagvajav, P., Wiseman, M.,
Malmsten, J., Leijon, M. |
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Title |
Detection and Genetic Characterization of Viruses Present in
Free-Ranging Snow Leopards Using Next-Generation Sequencing |
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Journal Article |
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2020 |
Publication |
Frontiers in Veterinary Science |
Abbreviated Journal |
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7 |
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645 |
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1-9 |
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snow leopard, free-ranging, virome, Mongolia, rectal swabs, next-generating sequencing, Panthera unica |
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Abstract |
Snow leopards inhabit the cold, arid environments of the high
mountains of South and Central Asia. These living conditions likely
affect the abundance and composition of microbes with the capacity to
infect these animals. It is important to investigate the microbes that
snow leopards are exposed to detect infectious disease threats and
define a baseline for future changes that may impact the health of this
endangered felid. In this work, next-generation sequencing is used to
investigate the fecal (and in a few cases serum) virome of seven snow
leopards from the Tost Mountains of Mongolia. The viral species to which
the greatest number of sequences reads showed high similarity was
rotavirus. Excluding one animal with overall very few sequence reads,
four of six animals (67%) displayed evidence of rotavirus infection. A
serum sample of a male and a rectal swab of a female snow leopard
produced sequence reads identical or closely similar to felid
herpesvirus 1, providing the first evidence that this virus infects snow
leopards. In addition, the rectal swab from the same female also
displayed sequence reads most similar to feline papillomavirus 2, which
is the first evidence for this virus infecting snow leopards. The rectal
swabs from all animals also showed evidence for the presence of small
circular DNA viruses, predominantly Circular Rep-Encoding
Single-Stranded (CRESS) DNA viruses and in one case feline anellovirus.
Several of the viruses implicated in the present study could affect the
health of snow leopards. In animals which are under environmental
stress, for example, young dispersing individuals and lactating females,
health issues may be exacerbated by latent virus infections. |
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1612 |
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Rode, J., Pelletier, A., Fumey, J., Rode, S., Cabanat, A. L., Ouvrard, A., Chaix, B., White, B., Harnden, M., Xuan, N. T., Vereshagin, A., Casane, D. |
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Diachronic monitoring of snow leopards at Sarychat-Ertash State Reserve (Kyrgyzstan) through scat genotyping: a pilot study |
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Journal Article |
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2020 |
Publication |
bioRxiv |
Abbreviated Journal |
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1-21 |
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Keywords |
snow leopard, noninvasive genotyping, population dynamics, microsatellite, relatedness, diachronic monitoring, citizen science, Central Asia |
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Snow leopards (Panthera uncia) are a keystone species of Central Asia’s high mountain ecosystem. The species is listed as vulnerable and is elusive, preventing accurate population assessments that could inform conservation actions. Non-invasive genetic monitoring conducted by citizen scientists offers avenues to provide key data on this species that would otherwise be inaccessible. From 2011 to 2015, OSI-Panthera citizen science expeditions tracked signs of presence of snow leopards along transects in the main valleys and crests of the Sarychat-Ertash State Reserve (Kyrgyzstan). Scat samples were genotyped at seven autosomal microsatellite loci and at a X/Y locus for sex identification, which allowed estimating a minimum of 11 individuals present in the reserve from 2011 to 2015. The genetic recapture of 7 of these individuals enabled diachronic monitoring, providing indications of individuals’ movements throughout the reserve. We found putative family relationships between several individuals. Our results demonstrate the potential of this citizen science program to get a precise description of a snow leopard population through time. |
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1602 |
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Johansson, O., Ausilio, G., Low, M., Lkhagvajav, P., Weckworth,
B., Sharma, K. |
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Title |
The timing of breeding and independence for snow leopard females
and their cubs. |
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2020 |
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Mammalian Biology |
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Age of independence; Life-history trade-offs; Panthera uncia; Parental care; Pre-dispersal behavior; Separation; Subadult |
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Significant knowledge gaps persist on snow leopard demography
and reproductive behavior. From a GPS-collared population in Mongolia,
we estimated the timing of mating, parturition and independence. Based
on three mother–cub pairs, we describe the separation phase of the cub
from its mother as it gains independence. Snow leopards mated from
January–March and gave birth from April–June. Cubs remained with their
mother until their second winter (20–22 months of age) when cubs started
showing movements away from their mother for days at a time. This
initiation of independence appeared to coincide with their mother mating
with the territorial male. Two female cubs remained in their mothers’
territory for several months after initial separation, whereas the male
cub quickly dispersed. By comparing the relationship between body size
and age of independence across 11 solitary, medium-to-large felid
species, it was clear that snow leopards have a delayed timing of
separation compared to other species. We suggest this may be related to
their mating behavior and the difficulty of the habitat and prey capture
for juvenile snow leopards. Our results, while limited, provide
empirical estimates for understanding snow leopard ecology and for
parameterizing population models. |
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1613 |
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Bhatia, S., Suryawanshi, K., Redpath, S. M., Mishra, C |
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Understanding people's responses toward predators in the Indian Himalaya |
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2020 |
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Animal Conservation |
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1-8 |
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human-wildlife conflict; human attitudes to wildlife; value orientation; carnivores; tolerance; human-wildlife relationships; risk perception; Himalaya |
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Abstract |
Research on human–wildlife interactions has largely focused on the magnitude of wildlife‐caused damage, and the patterns and correlates of human attitudes and behaviors. We assessed the role of five pathways through which various correlates potentially influence human responses toward wild animals, namely, value orientation, social interactions (i.e. social cohesion and support), dependence on resources such as agriculture and livestock, risk perception and nature of interaction with the wild animal. We specifically evaluated their influence on people's responses toward two large carnivores, the snow leopard Panthera uncia and the wolf Canis lupus in an agropastoral landscape in the Indian Trans‐Himalaya. We found that the nature of the interaction (location, impact and length of time since an encounter or depredation event), and risk perception (cognitive and affective evaluation of the threat posed by the animal) had a significant influence on attitudes and behaviors toward the snow leopard. For wolves, risk perception and social interactions (the relationship of people with local institutions and inter‐community dynamics) were significant. Our findings underscore the importance of interventions that reduce people's threat perceptions from carnivores, improve their connection with nature and strengthen the conservation capacity of local institutions especially in the context of wolves. |
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1630 |
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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 |
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Ecology and Evolution |
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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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Johansson, O., Samelius, G., Wikberg, E, Chapron, G., Mishra, C., Low, M |
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Identification errors in camera- trap studies result in systematic population overestimation |
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Journal Article |
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2020 |
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Scientific Reports |
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10 |
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6393 |
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1-10 |
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Reliable assessments of animal abundance are key for successful conservation of endangered species. For elusive animals with individually-unique markings, camera-trap surveys are a benchmark standard for estimating local and global population abundance. Central to the reliability of resulting abundance estimates is the assumption that individuals are accurately identified from photographic captures. To quantify the risk of individual misidentification and its impact on population abundance estimates we performed an experiment under controlled conditions in which 16 captive snow leopards (Panthera uncia) were camera-trapped on 40 occasions and eight observers independently identified individuals and recaptures. Observers misclassified 12.5% of all capture occasions, resulting in systematically inflated population abundance estimates on average by one third (mean ± SD = 35 ± 21%). Our results show that identifying individually-unique individuals from camera-trap photos may not be as reliable as previously believed, implying that elusive and endangered species could be less abundant than current estimates indicate. |
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SLN @ rakhee @ |
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1496 |
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Zhang, L., Lian, X., Yang, X |
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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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Journal Article |
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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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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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Journal Article |
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2020 |
Publication |
Plos One |
Abbreviated Journal |
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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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