Research Area: Metaheuristic Computing
Accurate assessments of the status of threatened species and their conservation planning require reliable estimation of their global populations and robust monitoring of local population trends. We assessed the adequacy and suitability of studies in reliably estimating the global snow leopard (Panthera uncia) population. We compiled a dataset of all the peer-reviewed published literature on snow leopard population estimation. Metadata analysis showed estimates of snow leopard density to be a negative exponential function of area, suggesting that study areas have generally been too small for accurate density estimation, and sampling has often been biased towards the best habitats. Published studies are restricted to six of the 12 range countries, covering only 0.3–0.9% of the presumed global range of the species. Re-sampling of camera trap data from a relatively large study site (c.1684 km2) showed that small-sized study areas together with a bias towards good quality habitats in existing studies may have overestimated densities by up to five times. We conclude that current information is biased and inadequate for generating a reliable global population estimate of snow leopards. To develop a rigorous and useful baseline and to avoid pitfalls, there is an urgent need for (a) refinement of sampling and analytical protocols for population estimation of snow leopards (b) agreement and coordinated use of standardized sampling protocols amongst researchers and governments across the range, and (c) sampling larger and under-represented areas of the snow leopards global range.
Keywords:
camera trap
Central Asia
Himalaya
meta-analysis
monitoring
Panthera uncia
population ecology
Author(s) Name: Kulbhushansingh R. Suryawanshi, Munib Khanyari, Koustubh Sharma, Purevjav Lkhagvajav, Charudutt Mishra
Journal name: Population Ecology
Conferrence name:
Publisher name: WILEY
DOI: 10.1002/1438-390X.1027
Volume Information: Volume61, Issue 3
