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Face Value: Towards Robust Estimates of Snow Leopard Densities.

Alexander JS, Gopalaswamy AM, Shi K, Riordan P - PLoS ONE (2015)

Bottom Line: When densities of large carnivores fall below certain thresholds, dramatic ecological effects can follow, leading to oversimplified ecosystems.Our results underline the critical challenge in achieving sufficient sample sizes of snow leopard captures and recaptures.Possible performance improvements are discussed, principally by optimising effective camera capture and photographic data quality.

View Article: PubMed Central - PubMed

Affiliation: The Wildlife Institute, School of Nature Conservation, Beijing Forestry University, Beijing, China.

ABSTRACT
When densities of large carnivores fall below certain thresholds, dramatic ecological effects can follow, leading to oversimplified ecosystems. Understanding the population status of such species remains a major challenge as they occur in low densities and their ranges are wide. This paper describes the use of non-invasive data collection techniques combined with recent spatial capture-recapture methods to estimate the density of snow leopards Panthera uncia. It also investigates the influence of environmental and human activity indicators on their spatial distribution. A total of 60 camera traps were systematically set up during a three-month period over a 480 km2 study area in Qilianshan National Nature Reserve, Gansu Province, China. We recorded 76 separate snow leopard captures over 2,906 trap-days, representing an average capture success of 2.62 captures/100 trap-days. We identified a total number of 20 unique individuals from photographs and estimated snow leopard density at 3.31 (SE = 1.01) individuals per 100 km2. Results of our simulation exercise indicate that our estimates from the Spatial Capture Recapture models were not optimal to respect to bias and precision (RMSEs for density parameters less or equal to 0.87). Our results underline the critical challenge in achieving sufficient sample sizes of snow leopard captures and recaptures. Possible performance improvements are discussed, principally by optimising effective camera capture and photographic data quality.

No MeSH data available.


Related in: MedlinePlus

The map of the spatial distribution of snow leopards across the study area.A pixelated density map produced in SPACECAP showing estimated snow leopard densities per pixel of size 1.96 km2.
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pone.0134815.g004: The map of the spatial distribution of snow leopards across the study area.A pixelated density map produced in SPACECAP showing estimated snow leopard densities per pixel of size 1.96 km2.

Mentions: From the posterior density estimates for each pixel (1.96 km2), the density scale per pixel ranges from 0.028 to 0.077 (Fig 4). Higher density areas thus have a density 3 times higher than that of lower density areas. We note the spatial variation and the high snow leopard density estimated in the South-eastern area of the camera trap array, which is an area known to be more remote from human disturbance. Results of our simulation exercise indicate that our estimates from the SECR model were not within good bounds of accuracy and precision (Table 3). Our estimated RMSE for density was less or equal to 0.87 and psi parameters was less or equal to 0.12. We did however achieve a high coverage probability (>97%) indicating that the posterior distribution of the estimated parameters represented the true probability distribution well.


Face Value: Towards Robust Estimates of Snow Leopard Densities.

Alexander JS, Gopalaswamy AM, Shi K, Riordan P - PLoS ONE (2015)

The map of the spatial distribution of snow leopards across the study area.A pixelated density map produced in SPACECAP showing estimated snow leopard densities per pixel of size 1.96 km2.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4554729&req=5

pone.0134815.g004: The map of the spatial distribution of snow leopards across the study area.A pixelated density map produced in SPACECAP showing estimated snow leopard densities per pixel of size 1.96 km2.
Mentions: From the posterior density estimates for each pixel (1.96 km2), the density scale per pixel ranges from 0.028 to 0.077 (Fig 4). Higher density areas thus have a density 3 times higher than that of lower density areas. We note the spatial variation and the high snow leopard density estimated in the South-eastern area of the camera trap array, which is an area known to be more remote from human disturbance. Results of our simulation exercise indicate that our estimates from the SECR model were not within good bounds of accuracy and precision (Table 3). Our estimated RMSE for density was less or equal to 0.87 and psi parameters was less or equal to 0.12. We did however achieve a high coverage probability (>97%) indicating that the posterior distribution of the estimated parameters represented the true probability distribution well.

Bottom Line: When densities of large carnivores fall below certain thresholds, dramatic ecological effects can follow, leading to oversimplified ecosystems.Our results underline the critical challenge in achieving sufficient sample sizes of snow leopard captures and recaptures.Possible performance improvements are discussed, principally by optimising effective camera capture and photographic data quality.

View Article: PubMed Central - PubMed

Affiliation: The Wildlife Institute, School of Nature Conservation, Beijing Forestry University, Beijing, China.

ABSTRACT
When densities of large carnivores fall below certain thresholds, dramatic ecological effects can follow, leading to oversimplified ecosystems. Understanding the population status of such species remains a major challenge as they occur in low densities and their ranges are wide. This paper describes the use of non-invasive data collection techniques combined with recent spatial capture-recapture methods to estimate the density of snow leopards Panthera uncia. It also investigates the influence of environmental and human activity indicators on their spatial distribution. A total of 60 camera traps were systematically set up during a three-month period over a 480 km2 study area in Qilianshan National Nature Reserve, Gansu Province, China. We recorded 76 separate snow leopard captures over 2,906 trap-days, representing an average capture success of 2.62 captures/100 trap-days. We identified a total number of 20 unique individuals from photographs and estimated snow leopard density at 3.31 (SE = 1.01) individuals per 100 km2. Results of our simulation exercise indicate that our estimates from the Spatial Capture Recapture models were not optimal to respect to bias and precision (RMSEs for density parameters less or equal to 0.87). Our results underline the critical challenge in achieving sufficient sample sizes of snow leopard captures and recaptures. Possible performance improvements are discussed, principally by optimising effective camera capture and photographic data quality.

No MeSH data available.


Related in: MedlinePlus