Density of tiger and leopard in a tropical deciduous forest of Mudumalai Tiger Reserve, southern India, as estimated using photographic capture–recapture sampling

Abstract

Density of tiger Panthera tigris and leopard Panthera pardus was estimated using photographic capturerecapture sampling in a tropical deciduous forest of Mudumalai Tiger Reserve, southern India, from November 2008 to February 2009. A total of 2,000 camera trap nights for 100 days yielded 19 tigers and 29 leopards within an intensive sampling area of 107 km 2 . Population size of tiger from closed population estimator model M b Zippin was 19 tigers (SE=±0.9) and for leopards M h Jackknife estimated 53 (SE=±11) individuals. Spatially explicit maximum likelihood and Bayesian model estimates were 8.31 (SE=±2.73) and 8.9 (SE=±2.56) per 100 km 2 for tigers and 13.17 (SE=±3.15) and 13.01 (SE=±2.31) per 100 km 2 for leopards, respectively. Tiger density for MMDM models ranged from 6.07 (SE=±1.74) to 9.72 (SE=±2.94) per 100 km 2 and leopard density ranged from 13.41 (SE=±2.67) to 28.91 (SE=±7.22) per 100 km 2 . Spatially explicit models were more appropriate as they handle information at capture locations in a more specific manner than some generalizations assumed in the classical approach. Results revealed high density of tiger and leopard in Mudumalai which is unusual for other high density tiger areas. The tiger population in Mudumalai is a part of the largest population at present in India and a source for the surrounding Reserved Forest.

Impact of farmland use on population density and activity patterns of serval in South Africa

“…Using an ad hoc buffer, the ½MMDM method resulted in a much higher estimation of serval density than the maximum-likelihood SECR and Bayesian SECR models, whereas the full MMDM density estimate was similar and not statistically different from either of the SECR density estimates. Given the uncertainties of using an ad hoc buffer, these SECR methods are ideal for the study of rare species (Obbard et al 2010;Kalle et al 2011;Gerber et al 2012). Hence we suggest considering SECR model estimates for conservation decision making.…”

“…We generated systematic home range centers in an area contained within a 5-km buffer, which was larger than the mean maximum distance moved around camera traps. The large buffer around the sampled area ensured inclusion of home ranges of individuals exposed to cameras (Royle and Dorazio 2008;Kalle et al 2011). Detailed description of all methods can be viewed elsewhere (Karanth 1995;Karanth and Nichols 1998;Efford et al 2004;Efford 2009;Royle et al 2009;Ramesh 2010;Ramesh et al 2012b).…”

“…Camera traps have become an important tool to document cryptic carnivores (Karanth and Nichols 1998;Silver et al 2004; Kalle et al 2011;Ramesh et al 2012aRamesh et al , 2012b. Individual servals can be identified by their unique spot and stripe patterns on flanks, legs, and neck (Thiel 2011).…”

“…Servals are shy, elusive, crepuscular, and difficult to recapture in livetrapping studies (Thiel 2011). Mark-recapture methods in conjunction with camera-trap technique have been used in studies of several felid species and have been recommended for use with other individually identifiable species as well (Kalle et al 2011;Karanth 1995;Trolle and Kery 2005;Soisalo and Cavalcanti 2006;Pereira et al 2011;Ramesh et al 2012b). A camera-trap study of servals in Zambia (Thiel 2011) resulted in recapture rates too low for statistical analyses under systematic mark-recapture methods.…”

Estimating tiger and its prey abundance in Bardia National Park, Nepal

“…The density was 1.31 (SE 0.32) and 0.87 (SE 0.28) tigers/100 km 2 from ½ MMDM and MMDM of Program DENSITY (Table 1). The density from Spatial Explicit Capture-recapture of Maximum likelihood provided 0.61(SE 0.15) For density analysis, the likelihood approach (Efford et al, 2004) seems to be appropriate being comparatively less sensitive to buffer width as it is directly based on parameters estimating density unlike Bayesian and is faster, and both spatial methods (Royle et al, 2009) have not shown any significant difference in terms of density estimation (Kalle et al, 2011). Thus, the likelihood approach was interpreted for discussion.…”