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Poyarkov, A. D., Munkhtsog, B., Korablev, M. P., Kuksin, A. N., Alexandrov, D. Y., Chistopolova, M. D., Hernandez-Blanco, J. A., Munkhtogtokh, O., Karnaukhov, A. S., Lkhamsuren, N., Bayaraa, M., Jackson, R. M., Maheshwari, A., Rozhnov, V. V. (2020). Assurance of the existence of a trans-boundary population of the snow leopard (Panthera uncia) at Tsagaanshuvuut – Tsagan- Shibetu SPA at the Mongolia-Russia border. Integrative Zoology, (15), 224–231.
Abstract: The existence of a trans-boundary population of the snow leopard (Panthera uncia) that inhabits the massifs of Tsagaanshuvuut (Mongolia) – Tsagan-Shibetu (Russia) was determined through non-invasive genetic analysis of scat samples and by studying the structure of territory use by a collared female individual. The genetic analysis included species identification of samples through sequencing of a fragment of the cytochrome b gene and individual identification using a panel of 8 microsatellites. The home range of a female snow leopard marked with a satellite Global Positioning System (GPS) collar was represented by the minimum convex polygon method (MCP) 100, the MCP 95 method and the fixed kernel 95 method. The results revealed insignificant genetic differentiation between snow leopards that inhabit both massifs (minimal fixation index [FST]), and the data testify to the unity of the cross-border group. Moreover, 5 common individuals were identified from Mongolian and Russian territories. This finding clearly shows that their home range includes territories of both countries. In addition, regular movement of a collared snow leopard in Mongolia and Russia confirmed the existence of a cross-border snow leopard group. These data support that trans-boundary conservation is important for snow leopards in both countries. We conclude that it is crucial for Russia to study the northern range of snow leopards in Asia.
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Rashid, W., Shi, J., Rahim, I. U., Dong, S., Ahmad, L. (2020). Research trends and management options in human-snow leopard conflict. Biological Conservation, 242(108413), 1–10.
Abstract: 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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Filla, M., Lama, R. P., Ghale, T. R., Signer, J., Filla, T., Aryal, R. R., Heurich, M., Waltert, M., Balkenhol, N., Khorozyan, I. (2020). In the shadows of snow leopards and the Himalayas: density and habitat selection of blue sheep in Manang, Nepal. Ecology and Evolution, 2021(11), 108–122.
Abstract: 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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Samelius, G., Suryawanshi, K., Frank, J., Agvaantseren, B., Baasandamba, E., Mijiddorj, T., Johansson, O., Tumursukh, L., Mishra, C. (2020). Keeping predators out: testing fences to reduce livestock depredation at night-time corrals. Oryx, , 1–7.
Abstract: Livestock depredation by large carnivores is a global conservation challenge, and mitigation measures to reduce livestock losses are crucial for the coexistence of large carnivores and people. Various measures are employed to reduce livestock depredation but their effectiveness has rarely been tested. In this study, we tested the effectiveness of tall fences to reduce livestock losses to snow leopards Panthera uncia and wolves Canis lupus at night-time corrals at the winter camps of livestock herders in the Tost Mountains in southern Mongolia. Self-reported livestock losses at the fenced corrals were reduced from a mean loss of 3.9 goats and sheep per family and winter prior to the study to zero losses in the two winters of the study. In contrast, self-reported livestock losses in winter pastures, and during the rest of the year, when herders used different camps, remained high, which indicates that livestock losses were reduced because of the fences, not because of temporal variation in predation pressure. Herder attitudes towards snow leopards were positive and remained positive during the study, whereas attitudes towards wolves, which attacked livestock also in summer when herders moved out on the steppes, were negative and worsened during the study. This study showed that tall fences can be very effective at reducing night-time losses at corrals and we conclude that fences can be an important tool for snow leopard conservation and for facilitating the coexistence of snow leopards and people.
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Johansson, O., Samelius, G., Wikberg, E, Chapron, G., Mishra, C., Low, M. (2020). Identification errors in camera- trap studies result in systematic population overestimation. Scientific Reports, 10(6393), 1–10.
Abstract: 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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Shrestha, B., Kindlmann, P. (2020). Implications of landscape genetics and connectivity of snow
leopard in the Nepalese Himalayas for its conservation. (Vol. 10).
Abstract: The snow leopard is one of the most endangered large mammals.
Its population, already low, is declining, most likely due to the
consequences of human activity, including a reduction in the size and
number of suitable habitats. With climate change, habitat loss may
escalate, because of an upward shift in the tree line and concomitant
loss of the alpine zone, where the snow leopard lives. Migration between
suitable areas, therefore, is important because a decline in abundance
in these areas may result in inbreeding, fragmentation of populations,
reduction in genetic variation due to habitat fragmentation, loss of
connectivity, bottlenecks or genetic drift. Here we use our data
collected in Nepal to determine the areas suitable for snow leopards, by
using habitat suitability maps, and describe the genetic structure of
the snow leopard within and between these areas. We also determine the
influence of landscape features on the genetic structure of its
populations and reveal corridors connecting suitable areas. We conclude
that it is necessary to protect these natural corridors to maintain the
possibility of snow leopards' migration between suitable areas, which
will enable gene flow between the diminishing populations and thus
maintain a viable metapopulation of snow leopards.
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Chetri, M., Odden, M., Devineau, O., McCarthy, T., Wegge, P. (2020). Multiple factors influence local perceptions of snow leopards and
Himalayan wolves in the central Himalayas, Nepal. PeerJ, , 1–18.
Abstract: 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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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.
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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. (2020). Identifying priority landscapes for conservation of snow
leopards in Pakistan. Plos One, , 1–20.
Abstract: 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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Durbach, I., Borchers, D., Sutherland, C., Sharma, K. (2020). Fast, flexible alternatives to regular grid designs for spatial
capture–recapture..
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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