Assessing Estimators of Snow Leopard Abundance

By K. Mccarthy, T. Fuller, M. Ming, T. McCarthy, L. Waits, Kubanych Jumabaev. 2008

97 citations recorded by the source corpus.

Abstract The secretive nature of snow leopards (Uncia uncia) makes them difficult to monitor, yet conservation efforts require accurate and precise methods to estimate abundance. We assessed accuracy of Snow Leopard Information Management System (SLIMS) sign surveys by comparing them with 4 methods for estimating snow leopard abundance: predator:prey biomass ratios, capture–recapture density estimation, photo-capture rate, and individual identification through genetic analysis. We recorded snow leopard sign during standardized surveys in the SaryChat Zapovednik, the Jangart hunting reserve, and the Tomur Strictly Protected Area, in the Tien Shan Mountains of Kyrgyzstan and China. During June–December 2005, adjusted sign averaged 46.3 (SaryChat), 94.6 (Jangart), and 150.8 (Tomur) occurrences/km. We used counts of ibex (Capra ibex) and argali (Ovis ammon) to estimate available prey biomass and subsequent potential snow leopard densities of 8.7 (SaryChat), 1.0 (Jangart), and 1.1 (Tomur) snow leopards/100 km2. Photo capture–recapture density estimates were 0.15 (n = 1 identified individual/1 photo), 0.87 (n = 4/13), and 0.74 (n = 5/6) individuals/100 km2 in SaryChat, Jangart, and Tomur, respectively. Photo-capture rates (photos/100 trap-nights) were 0.09 (SaryChat), 0.93 (Jangart), and 2.37 (Tomur). Genetic analysis of snow leopard fecal samples provided minimum population sizes of 3 (SaryChat), 5 (Jangart), and 9 (Tomur) snow leopards. These results suggest SLIMS sign surveys may be affected by observer bias and environmental variance. However, when such bias and variation are accounted for, sign surveys indicate relative abundances similar to photo rates and genetic individual identification results. Density or abundance estimates based on capture–recapture or ungulate biomass did not agree with other indices of abundance. Confidence in estimated densities, or even detection of significant changes in abundance of snow leopard, will require more effort and better documentation.

Subfields: Ecology, Ecology, Evolution, Behavior and Systematics, Small Animals

View Paper: https://www.semanticscholar.org/paper/c08a3fc19f73b972013ee3c56673aef80cc7cac8

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In Ecology

In Ecology, Evolution, Behavior and Systematics

In Small Animals

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Citations in the corpus

Climate refugia of snow leopards in High Asia (2016, methodology): “An improved BIOME model, BIOME3China, showed a better ability to simulate the vegetation patterns on the Tibetan Plateau (Ni, 2000).”
Snow leopard Panthera uncia predation on livestock and wild prey in a mountain valley in northern Nepal: implications for conservation management (2012, methodology): “…into a density estimate by use of the ’radial’method (Otis et al. 1978), using amean radius of 2.06 km derived from home-range studies as shown by McCarthy et al. (2008), produced an area of 98 km2, which corresponds closely to our ca 125 km2 study area occupied by the sampled bharal…”
Camera-trapping forest–woodland wildlife of western Uganda reveals how gregariousness biases estimates of relative abundance and distribution (2010, background): “Studies of highly solitary animals, such as jaguars and tigers, may not be as strongly affected by this bias (Harmsen et al., 2009; Karanth and Nichols, 1998; Maffei et al., 2004; Mccarthy et al., 2008), except when traveling with mates or dependent young.”
Comparison of noninvasive genetic and camera-trapping techniques for surveying snow leopards (2011, background): “Snow leopard densities have ranged from a low of 0.2 individuals/100 km2 in SaryChat Zapovednik, Kyrgyzstan (McCarthy et al. 2008) to 4.5 individuals/100 km2 (2002) and 8.5 individuals/100 km2 (2003) in Hemis National Park, India (Jackson et al. 2006).”