Spatial density estimate of the snow leopard,<i>Panthera uncia</i>, in the Central Tibetan Plateau, China

Bian, Xiaoxing; Liang, Xinmiao; Weckworth, Byron V.; JYAL, Dorje; Hull, Vanessa; Yang, Lu

doi:10.1111/1749-4877.12672

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…2), thereby ensuring a relatively good representation across different strata of snow leopard habitats in the Sanjiangyuan region. Our modeling framework could be further improved by involving prey density as snow leopard density covariates (Suryawanshi et al 2021), as well as covariates in σ and λ simulation (Bian et al 2023). We tested models adding covariates to explain heterogeneity of sigma and lambda; however, the models did not perform well due to insu cient detection number.…”

Section: Discussionmentioning

confidence: 99%

First large-scale assessment of snow leopard population in China using existing data from multiple organizations

Li,

Wei,

Chen

et al. 2023

Preprint

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Accurate population data of large carnivores is essential for their effective conservation planning, yet estimating population size is challenging due to their elusive and wide-ranging nature. China is estimated to encompass 60% of the snow leopard Panthera uncia habitat, marking it a crucial pillar for global snow leopard conservation. However, no large-scale population assessment has been conducted despite scattered survey effort accumulating rapidly in recent years. This study combined and standardized existing camera trap survey data from 12 sites collected by four organizations during 2015 ~ 2021 to estimate snow leopard population in an area of 0.36 million km2. The representativeness of existing survey was evaluated based on habitat stratification to achieve unbiased population estimation. Spatially explicit capture-recapture (SECR) models were applied for density estimation and the most optimal model only included habitat rank as density covariate, showing a significant positive correlation with density. An average snow leopard density of 0.90/100 km2 (0.68 ~ 1.21/100 km2) and a population size of 1,002 individuals (755 ~ 1,341) was estimated for the entire region. Two more conservative estimates of 971 and 978 individuals were generated within two defined survey regions, in which our data has higher representativity. This study presents a practical approach to synthesize existing population survey data for large-scale population assessments of individually identifiable species. The estimated number represents 11 ~ 21% of the global snow leopard population, indicating high conservation value of this region.

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Section: Discussionmentioning

confidence: 99%

First large-scale assessment of snow leopard population in China using existing data from multiple organizations

Li,

Wei,

Chen

et al. 2023

Preprint

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Next-generation snow leopard population assessment tool: multiplex-PCR SNP panel for individual identification from feces

Solari,

Ahmad,

Armstrong

et al. 2024

Preprint

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Snow leopards, Panthera uncia, are under threat from numerous pressures and are the focus of a great deal of conservation efforts. However, their elusive nature makes it difficult to estimate population sizes. Current methods used to monitor local population sizes include visually identifying individuals from camera trap photos and genetically identifying individuals from fecal samples using microsatellite loci. Here, we present a new method for identifying snow leopard individuals from fecal samples using a multiplex PCR single nucleotide polymorphism (SNP) panel method. The SNP panel we present consists of 144 SNPs and utilizes next-generation sequencing technology, making it cheaper and easier than current microsatellite methods. We validate our SNP panel with paired tissue and fecal samples from zoo individuals, showing a minimum of 96.7% accuracy in allele calls per run. We then generate SNP data from 235 field-collected fecal samples from across Pakistan to show that the panel can reliably identify individuals from low-quality fecal samples of unknown age and is robust to contamination. We also show that our SNP panel has the capability to identify first-order relatives and provides insights into the geographic origin of samples. This SNP panel will empower the snow leopard research community in their efforts to assess local and global snow leopard population sizes. More broadly, we present a method for developing a SNP panel that utilizes open source software for SNP selection and primer design, Illumina sequencing technology, and a streamlined lab and bioinformatics protocol which can be used to create similar SNP panels for any species of interest for which adequate genomic reference data is available.

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Effects of Livestock Grazing on Spatiotemporal Interactions Between Snow Leopards and Ungulate Prey

Xu,

Xiao,

et al. 2024

Integr. Zool.

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Spatiotemporal interactions between predators and prey are central to maintaining sustainable functioning ecosystems and community stability. For wild ungulates and their predators, livestock grazing is an important anthropogenic disturbance causing population declines and modifying their interactions over time and space. However, it is poorly understood how fine‐scale grazing affects the spatiotemporal responses of predators, prey, and their interactions. Two opposing hypotheses describe a dichotomy of possible effects. The human shield hypothesis states that people can protect prey because predators avoid areas with high human‐induced mortality risk, whereas in the human competitor hypothesis, humans compete for prey and negatively impact predators through reduced prey availability. We used camera‐trapping data from the Gansu Qilianshan National Nature Reserve in Northwest China to measure occupancy, daily activity patterns, and spatiotemporal interactions between snow leopards (Panthera uncia), the dominant predator, and their ungulate prey in areas with contrasting grazing intensities. The results of grazing were consistent with both the human‐shield and human‐competitor hypotheses, affecting spatiotemporal patterns and interactions of predators and prey. For the primary prey species, blue sheep (Pseudois nayaur), their spatial and temporal patterns were affected by grazing, which led to a reduction in interaction frequencies with snow leopards. For secondary prey, grazing led to reduced interaction frequencies with snow leopards for white‐lipped deer (Przewalskium albirostris) and red deer (Cervus yarkandensis), but increased frequencies for alpine musk deer (Moschus chrysogaster). Our results indicate how both competition among livestock and prey and predator or prey avoidance of grazed areas can impact populations and predator–prey interactions. Our findings are relevant to grazing management and snow leopard conservation.

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Spatial density estimate of the snow leopard,Panthera uncia, in the Central Tibetan Plateau, China

Cited by 3 publications

References 47 publications

First large-scale assessment of snow leopard population in China using existing data from multiple organizations

First large-scale assessment of snow leopard population in China using existing data from multiple organizations

Next-generation snow leopard population assessment tool: multiplex-PCR SNP panel for individual identification from feces

Effects of Livestock Grazing on Spatiotemporal Interactions Between Snow Leopards and Ungulate Prey

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