Assessing tiger–prey interactions in Sumatran rainforests

Abstract

Little is known about interactions between the critically endangered Sumatran tiger Panthera tigris sumatrae and its prey because of the difficulties associated with detecting these species. In this study, we quantify temporal overlap between the Sumatran tiger and five of its presumed prey species from four study areas comprising disturbed lowland to primary submontane forest. Data from 126 camera traps over 8984 camera days were used to estimate species activity patterns and, in turn, their overlap through the coefficient D (ranging from 0 to 1, i.e. no overlap to complete overlap). A newly developed statistical technique was applied to determine confidence intervals associated with respective overlap, which is important, as such measures of precision are usually not estimated in these types of study. Strong temporal overlap was found between tiger and muntjac Muntiacus muntjac (D = 0.80, 95%CI= 0.71-0.84) and tiger and sambar Cervus unicolor (D =0.81, 0.55-0.85), with the latter illustrating the importance of measuring precision. According to the foraging theory, Sumatran tigers should focus on expending lower levels of energy searching for and then capturing larger bodied prey that present the least risk. Hence, surprisingly, there was little overlap between the crepuscular tiger and the largest-bodied prey species available, the nocturnal tapir Tapirus indicus (0.52, 0.44-0.60), suggesting that it is not a principal prey species. This study provides the first insights into Sumatran tiger-prey temporal interactions. The ability to estimate overlap statistics with measures of precision has obvious and wide benefits for other predator-prey and interspecific competition studies.

CircNNTSR: AnRPackage for the Statistical Analysis of Circular, Multivariate Circular, and Spherical Data Using Nonnegative Trigonometric Sums

“…The time at which an event occurs, such as the epileptic seizures of a patient during the day, can also be analyzed as circular data. Regarding possible applications, Ridout and Linkie (2009), Linkie and Ridout (2011), and Lynam et al (2013), for example, used circular models to study the activity patterns of wild cats using camera-trap data. Fernández-Durán (2004) developed a family of univariate densities for circular data based on nonnegative trigonometric sums (Fejér 1916).…”

Activity patterns by two colour morphs of the vulnerable guiña, Leopardus guigna (Molina 1782), in temperate forests of southern Chile

“…We estimated moon luminosity by calculating a nocturnal illumination index (I norm (t)), using moon time duration, moon phase and local cloud cover (Schwitzer et al 2007). Nonparametric kernel density estimation was used to estimate the probability density function of guiña activity pattern (Linkie & Ridout 2011). We compared the activity patterns displayed by spotted and melanistic morphs using the overlap coeffi cient Δ 1 , which ranges from 0 (no overlap) to 1 (complete overlap).…”

Ecology and conservation of the northern hopping-mouse and sympatric vertebrates of Groote Eylandt

“…Activity patterns were constructed using kernel density estimates which, in this context, describe the probability of a camera trap event occurring at any given time (Linkie and Ridout 2011). We then followed the procedure developed by Ridout and Linkie (2009) to determine the overlap coefficient between activity patterns of different variables.…”