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44 results for “Snow leopard”
Data for "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia"
<p>Data and code used for a country-wide occupancy survey of snow leopards in Mongolia, accompanying the paper "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia".</p> <p>This data contains the results of a survey of 1017 20x20km sampling units, out of a total of 1200 sampling units identified as potential snow leopard habitat (183 could not be sampled for various reasons), a near complete survey of potential snow leopard habitat in Mongolia, nearly 500,000 square kilometers, and an enormous effort by many researchers. If you make use of the data, please cite the following sources:</p> <ul> <li><em>Data for "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia".</em> (2021). Gantulga Bayandonoi, Koustubh Sharma, Justine Shanti Alexander, Purevjav Lkhagvajav, Ian Durbach, Darryl MacKenzie, Chimeddorj Buyanaa, Bariushaa Munkhtsog, Munkhtogtokh Ochirjav, Sergelen Erdenebaatar, Bilguun Batkhuyag, Nyamzav Battulga, Choidogjamts Byambasuren, Bayartsaikhan Uudus, Shar Setev, Lkhagvasuren Davaa, Khurel-Erdene Agchbayar, Naranbaatar Galsandorj, David Borchers. doi: https://doi.org/10.5281/zenodo.5257572</li> <li><em>Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia. </em>(2021). Gantulga Bayandonoi, Koustubh Sharma, Justine Shanti Alexander, Purevjav Lkhagvajav, Ian Durbach, Darryl MacKenzie, Chimeddorj Buyanaa, Bariushaa Munkhtsog, Munkhtogtokh Ochirjav, Sergelen Erdenebaatar, Bilguun Batkhuyag, Nyamzav Battulga, Choidogjamts Byambasuren, Bayartsaikhan Uudus, Shar Setev, Lkhagvasuren Davaa, Khurel-Erdene Agchbayar, Naranbaatar Galsandorj, David Borchers. To appear in <em>Diversity and Distributions</em></li> </ul> <p><strong>Contents of zip file</strong></p> <p><em>Data</em></p> <p>The main dataset is contained in `data\Mongolia_occupancy_inputs.Rdata` . Please see the paper for more detail on data collection. The following objects are contained in the file:</p> <p>- Pres: presence/absence occupancy survey results, used for model fitting<br> - Site_Cov: unit-specific covariates, used for model fitting<br> - SurvCov: survey-specific covariates, used for model fitting<br> - Mongolia_studyarea: covariates for whole survey area, used for prediction<br> - Mongolia_fullrange: covariates across whole expected snow leopard range, used for prediction</p> <p><em>Code</em></p> <p>Code is cloned from the GitHub repository <a href="https://github.com/iandurbach/mongolia-occupancy">https://github.com/iandurbach/mongolia-occupancy</a>, which may contain updates. The version here reproduces the analyses in the paper above. The run these analyses:</p> <p>- run *occupancy-analysis.R* to fit the main occupancy models (these are also saved in the `\output` folder), do model selection, and plot covariate effects<br> - run *occupancy-goodness-of-fit.R* to calculate the c-hat statistic giving an indication of model fit for the best model<br> - run *comparing-maps.R* to compare the occupancy results with similar metrics generated using a presence-only analysis (using MaxEnt) or an expert map generated through qualitative discussion (reproduces Figure 3 in the paper).</p> <p>Code in *occupancy-data-preproc.R* is not needed but included for completeness. It converts the csv files in `data\csv`, which contain various input datasets used by the occupancy model, into a single .Rdata file (`data\Mongolia_occupancy_inputs.Rdata`), which is then used by the scripts above. Some minimal pre-processing (excluding ununsed variables, renaming for consistency, etc) is performed. </p>
Data from: Metabarcoding analysis provides insight into the link between prey and plant intake in a large alpine cat carnivore, the snow leopard
<p>Species of the family Felidae (a group represented by cats) are thought to be obligate carnivores, specialized for hunting and consuming other animals. However, the detection of plants in the feces of felids raises questions about the role of plants in their diet. This is particularly true for the snow leopard (Panthera uncia), a big cat native to central and South Asia's high mountains. Our study aimed to comprehensively identify the prey and plants consumed by snow leopards as well as six other sympatric mammals. We applied DNA metabarcoding methods on 126 fecal samples collected from the Sarychat-Ertash Nature Reserve in Kyrgyzstan. We found that among the three most common plant families in snow leopard feces, Tamaricaceae (genus Myricaraia) was consumed often by snow leopards. The genus Myricaria frequently appeared in samples lacking any animal prey DNA, indicating that snow leopards might have consumed this plant especially when their digestive tracts were empty. We also observed a significant difference in plant composition between male and female snow leopards, and potentially between sampling seasons. We provide a comprehensive overview of the prey and plants detected in the feces of snow leopards and sympatric mammals. We believe our findings will help in formulating hypotheses and guiding future research to understand the adaptive significance of plant-eating behavior in felids and animal-plant relationships in the ecosystem.</p>
Figure 2 in Diet selection of snow leopard (Panthera uncia) in Chitral, Pakistan
Figure 2. Microphotographs of hair scale pattern of Cape hare (Lepus capensis): a) reference hair (10 × 100×); b) hair found in scat sample (10 × 100×).
Figure 4 in Diet selection of snow leopard (Panthera uncia) in Chitral, Pakistan
Figure 4. Microphotographs of hair scale pattern of palm civet (Paguma larvata): a) reference hair (10 × 100×); b) hair found in scat sample (10 × 100×).
Figure 3 in Diet selection of snow leopard (Panthera uncia) in Chitral, Pakistan
Figure 3. Microphotographs of hair scale pattern of markhor (Capra falconeri): a) reference hair (10 × 40×); b) hair found in scat sample (10 × 40×).
Ungulate spatiotemporal responses to contrasting predation risk from wolves and snow leopards
<p>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. The interaction of predator and prey traits has emerged as a potentially key driver of anti-predator 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. Specifically, we explored how two sympatric ungulates with different escape tactics – vertically agile, scrambling ibex (<em>Capra sibirica</em>) and sprinting argali (<em>Ovis ammon</em>) – responded to predation risk from shared predators with contrasting hunting modes – cursorial wolves (<em>Canis lupus</em>) and vertical-ambushing, stalking snow leopards (<em>Panthera uncia</em>). 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 ungulate's exposure to risk from the alternate predator in a manner consistent with a scenario in which conflicting anti-predator 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.<strong> </strong>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. </p>
Figure 4 in The spatial structure of а snow leopard population (Panthera uncia, Felidae, Carnivora) in east Kyrgyzstan
Figure 4. Relationship between the males of the Sarychat-Ertash Reserve (as inferred from the DNA microsatellite profiles).
Figure 3 in The spatial structure of а snow leopard population (Panthera uncia, Felidae, Carnivora) in east Kyrgyzstan
Figure 3. Relationship between the females of the Sarychat–Ertash Reserve (as inferred from the DNA microsatellite profiles).
Figure 2. Sections 1–3 in The spatial structure of а snow leopard population (Panthera uncia, Felidae, Carnivora) in east Kyrgyzstan
Figure 2. Sections 1–3 of different intensity of marking activity of the snow leopard. For a description, see text (polygons A, B, C).
Figure 1 in The spatial structure of а snow leopard population (Panthera uncia, Felidae, Carnivora) in east Kyrgyzstan
Figure 1. Areas of snow leopard study in the East Kyrgyzstan. Blue squares-surveyed areas; red points, traces of snow leopard activity.
Data from: Metabarcoding analysis provides insight into the link between prey and plant intake in a large alpine cat carnivore, the snow leopard
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Ungulate spatiotemporal responses to contrasting predation risk from wolves and snow leopards
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Figure 1 in Diet selection of snow leopard (Panthera uncia) in Chitral, Pakistan
Figure 1. Location of study area in Chitral District, Pakistan.
Figure 5 in The spatial structure of а snow leopard population (Panthera uncia, Felidae, Carnivora) in east Kyrgyzstan
Figure 5. Spatial distribution of snow leopards in the Sarychat–Ertash Reserve.
Data from: Trophic interaction and livestock dependence of snow leopard and sympatric carnivores in Tianshan, Northwest China
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Big cat vcf files from: Exceedingly low genetic diversity in snow leopards due to persistently small population size
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Narrow dietary niche with high overlap between snow leopards and Himalayan wolves indicates potential for resource competition in Shey Phoksundo National Park, Nepal
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Data from: From shadows to data: First robust population assessment of snow leopards in Pakistan
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Spatial variation in population-density, movement and detectability of snow leopards in a multiple use landscape in Spiti Valley, Trans-Himalaya
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A chromosome-level genome assembly of the snow leopard, Panthera uncia
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OpenNeuro
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