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Face Value: Towards Robust Estimates of Snow Leopard Densities.

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Estimating snow leopard (Panthera uncia) density is challenging. This study used camera traps and spatial capture-recapture methods, estimating 3.31 individuals per 100 km2, highlighting the need for improved data collection.

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Area of Science:

  • Ecology
  • Wildlife Conservation
  • Population Dynamics

Background:

  • Large carnivore populations are crucial for ecosystem health but difficult to monitor due to low densities and wide ranges.
  • Understanding snow leopard (Panthera uncia) population status is vital for conservation efforts.
  • Traditional survey methods are often insufficient for accurately estimating densities of wide-ranging, low-density species.

Purpose of the Study:

  • To estimate the density of snow leopards (Panthera uncia) in the Qilianshan National Nature Reserve, China.
  • To investigate the influence of environmental and human activity on snow leopard spatial distribution.
  • To evaluate the effectiveness of non-invasive data collection and spatial capture-recapture methods for snow leopard population assessment.

Main Methods:

  • Deployment of 60 camera traps over a 480 km2 area for three months.
  • Utilizing non-invasive photographic data for individual identification and spatial capture-recapture (SCR) modeling.
  • Recording 76 snow leopard captures over 2,906 trap-days, achieving a 2.62 captures/100 trap-days success rate.

Main Results:

  • Identification of 20 unique snow leopards from photographic data.
  • Estimation of snow leopard density at 3.31 (SE = 1.01) individuals per 100 km2.
  • Simulation results indicated potential bias and precision limitations in SCR model estimates (RMSEs ≤ 0.87).

Conclusions:

  • Achieving sufficient sample sizes for snow leopard capture-recapture remains a significant challenge.
  • The study underscores the difficulties in accurately estimating densities of elusive, low-density species.
  • Optimizing camera trap effectiveness and photographic data quality is crucial for improving future population estimates.