Potential habitat of snow leopard (
            <i>
              Panthera uncia
            </i>
            , Felinae) in south Siberia and adjacent territories based on the maximum entropy distribution model

Kalashnikova, Y. A.; Karnaukhov, A. S.; Dubinin, Maxim; Poyarkov, A. D.; Рожнов, В. В.

doi:10.1134/s0044513419030061

Cited by 11 publications

(10 citation statements)

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“…Snow leopards occur at low densities, which in concert with their elusive nature renders distribution and habitat suitability studies difficult (Ghoshal et al, 2019;Kalashnikova et al, 2019;Watts et al, 2019). Even though studies on snow leopard distribution exist, most of them were conducted in high-quality habitats (Suryawanshi et al, 2019) and are restricted to a few countries such as Pakistan (Hammed et al, 2020), India (Watts et al, 2019;Singh et al, 2020), Nepal (Aryal et al, 2016;Shrestha and Kindlmann, 2020), China (Bai et al, 2018;Li et al, 2020), Southern Russia (Kalashnikova et al, 2019), and Kazakhstan (Holt et al, 2018), whereas in the case of Bhutan, only little is known. Such imbalances in the knowledge of the snow leopard's distribution can result in an incomplete understanding of its distribution nationally and globally.…”

Section: Introductionmentioning

confidence: 99%

Modeling Distribution and Habitat Suitability for the Snow Leopard in Bhutan

Lham

Cozzi

Sommer

et al. 2021

Front. Conserv. Sci.

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The snow leopard (Panthera uncia) is one of the world's most elusive felids. In Bhutan, which is one of the 12 countries where the species still persists, reliable information on its distribution and habitat suitability is lacking, thus impeding effective conservation planning for the species. To fill this knowledge gap, we created a country-wide species distribution model using “presence-only” data from 420 snow leopard occurrences (345 from a sign survey and 77 from a camera-trapping survey) and 12 environmental covariates consisting of biophysical and anthropogenic factors. We analyzed the data in an ensemble model framework which combines the outputs from several species distribution models. To assess the adequacy of Bhutan's network of protected areas and their potential contribution toward the conservation of the species, we overlaid the output of the ensemble model on the spatial layers of protected areas and biological corridors. The ensemble model identified 7,206 km2 of Bhutan as suitable for the snow leopard: 3,647 km2 as highly suitable, 2,681 km2 as moderately suitable, and 878 km2 as marginally suitable. Forty percent of the total suitable habitat consisted of protected areas and a further 8% of biological corridors. These suitable habitats were characterized by a mean livestock density of 1.3 individuals per hectare, and a mean slope of 25°; they closely match the distribution of the snow leopard's main wild prey, the bharal (Pseudois nayaur). Our study shows that Bhutan's northern protected areas are a centre for snow leopard conservation both at the national and regional scale.

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Section: Introductionmentioning

confidence: 99%

Modeling Distribution and Habitat Suitability for the Snow Leopard in Bhutan

Lham

Cozzi

Sommer

et al. 2021

Front. Conserv. Sci.

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“…Mongolia also borders Russia to the north which has a relatively small snow leopard population (McCarthy & Chapron, 2003). The Mongolian snow leopard population is believed to support Russian snow leopard populations as all known snow leopard habitats are along the Mongolian border (Kalashnikova et al., 2019). The country supports at least four landscapes prioritized to be secured under the Global Snow Leopard and Ecosystem Protection Program (GSLEP), including three in Mongolia and one in Russia adjacent to the national border (Zakharenka et al, 2016).…”

Section: Methodsmentioning

confidence: 99%

“…Apart from a small number of surveys that have used occupancy based estimators (Alexander et al., 2015; Ghoshal et al., 2017; Taubmann et al., 2015), previous efforts that mapped snow leopard distributions have relied on either expert opinion from scientists and conservationists with experience of working in the specific areas (Mccarthy & Chapron, 2003; Riordan et al 2015; Alexander et al., 2016; Aryal et al., 2016; Li et al 2016; Kalashnikova et al., 2019), or on the modelling of presence‐only data (e.g. with MAXENT; Li et al., 2014, 2016; Aryal et al., 2016; Bai et al 2018; Holt et al 2018; Kalashnikova et al., 2019; Shi et al 2019; Hameed et al 2020). Both approaches have specific limitations.…”

Section: Introductionmentioning

confidence: 99%

Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia

Bayandonoi¹,

Sharma²,

Alexander

et al. 2021

Diversity and Distributions

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Aim Snow leopards are distributed across the mountains of 12 countries spread across 1.8 million km2 in Central and South Asia. Previous efforts to map snow leopard distributions have relied on expert opinions and modelling of presence‐only data. Expert opinion is subjective and its reliability is difficult to assess, while analyses of presence‐only data have tended to ignore the imperfect detectability of this elusive species. The study was conducted to prepare the first ever probabilistic distribution map of snow leopards across Mongolia addressing the challenge of imperfect detection. Location We conducted sign‐based occupancy surveys across 1,017 grid‐cells covering 406,800 km2 of Mongolia's potential snow leopard range. Methods Using a candidate model set of 31 ecologically meaningful models that used six site and seven sampling covariates, we estimate the probability of sites being used by snow leopards across the entire country. Results Occupancy probability increased with greater terrain ruggedness, with lower values of vegetation indices, with less forest cover, and were highest at intermediate altitudes. Detection probability was higher for segments walked on foot, and for those in more rugged terrain. Our results showed broad agreement with maps developed using expert opinion and presence‐only data but also highlighted important differences, for example in northern areas of Mongolia deemed largely unfavourable by previous expert opinion and presence‐only analyses. Main conclusions This study reports the first national‐level occupancy survey of snow leopards in Mongolia and highlights methodological opportunities that can be taken to scale and support national‐level conservation planning. Our assessments indicated that <12% of its entire land mass, and 42% of the potential snow leopard habitat in Mongolia has a high (>0.5) probability of being used by snow leopards. We emphasize the utility of occupancy modelling, which jointly models detection and site use, in achieving these goals.

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“…Site occupancy modelling using presence/absence data has emerged as a go-to method in monitoring of difficult-to-detect species such as snow leopards ( Fleishman et al, 2001 , MacKenzie et al, 2002 ). However, snow leopard specific studies are meagre and have been conducted rather sporadically within its vast range including China ( Alexander et al, 2016 , Li et al, 2013 , Wolf and Ale, 2009 ), Nepal ( Aryal et al, 2016 ), India ( Bhatnagar et al, 2016 , Watts et al, 2019 ), and Russia ( Kalashnikova et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

On the snow leopard Trails: Occupancy pattern and implications for management in the Pamir

Din

Hameed

Ali

et al. 2022

Saudi Journal of Biological Sciences

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The snow leopard ( Panthera uncia ) inhabits one of the most challenging environments on Earth, referred to as the ‘third pole’. Only a fraction of its vast range has been explored thus far, owing to myriad of barriers inflicted by the remote terrain and socio-ecological realities of the landscapes. Understanding distribution patterns of species is essential to devise practical management measures. This study aimed to understand the distribution pattern and factors influencing occupancy of snow leopard in the Pamir Mountain range through sign-based occupancy modelling. Our study confirmed that the Pamir range is a snow leopard stronghold, with occupancy estimated at 0.57 ± 0.02. The topographic features positively influenced the detection probability (p = 0.37 ± 0.005) of snow leopards. Occupancy was influenced by mean annual precipitation (β = -6.12 ± 1.8), density of roads (β = -1.61 ± 0.6) and water sources (β = 0.74 ± 0.4). Our findings underpin that sign-based distribution surveys provide vigorous scientific knowledge about elusive species and merit replication being used for other species. We propose to redefine the protected area boundaries based on ecological knowledge and encourage transboundary cooperation to safeguard snow leopards at a landscape scale.

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Potential habitat of snow leopard ( Panthera uncia , Felinae) in south Siberia and adjacent territories based on the maximum entropy distribution model

Cited by 11 publications

References 0 publications

Modeling Distribution and Habitat Suitability for the Snow Leopard in Bhutan

Modeling Distribution and Habitat Suitability for the Snow Leopard in Bhutan

Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia

On the snow leopard Trails: Occupancy pattern and implications for management in the Pamir

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