Field practices: Assessing tiger population dynamics using photographic captures
From these histories, capture frequency statistics and estimates of capture probabilities can be derived.
Book chapter↗
Geology topics
N. Samba Kumar
Publications and source records attributed to N. Samba Kumar.
Concepts and practices: Estimating abundance of prey species using hierarchical model-based approaches
Tigers predominantly prey on large ungulate species, such as sambar (Cervus unicolor), red deer (Cervus elaphus), gaur (Bos gaurus), banteng (Bos javanicus), chital (Axis axis), muntjac (Muntiacus muntjak), wild pig (Sus scrofa), and bearded pig (Sus barbatus). The density of a tiger population is strongly correlated with the density of such prey species (Karanth et al. 2004). In the absence of direct hunting of tigers, abundance of prey in an area is the key determinant of the “carrying capacity” of that area for tigers (Chap. 2). Accurate estimates of prey abundance are often needed to assess the potential number of tigers a conservation area can support.
Book chapter↗
Program SPACECAP: software for estimating animal density using spatially explicit capture-recapture models
1. The advent of spatially explicit capture-recapture models is changing the way ecologists analyse capture-recapture data. However, the advantages offered by these new models are not fully exploited because they can be difficult to implement. 2. To address this need, we developed a user-friendly software package, created within the R programming environment, called SPACECAP. This package implements Bayesian spatially explicit hierarchical models to analyse spatial capture-recapture data. 3. Given that a large number of field biologists prefer software with graphical user interfaces for analysing their data, SPACECAP is particularly useful as a tool to increase the adoption of Bayesian spatially explicit capture-recapture methods in practice.
Methods in Ecology and Evolution↗
Counting India's wild tigers reliably
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Science↗
Bayesian inference in camera trapping studies for a class of spatial capture-recapture models
We develop a class of models for inference about abundance or density using spatial capture-recapture data from studies based on camera trapping and related methods. The model is a hierarchical model composed of two components: a point process model describing the distribution of individuals in space (or their home range centers) and a model describing the observation of individuals in traps. We suppose that trap- and individual-specific capture probabilities are a function of distance between individual home range centers and trap locations. We show that the models can be regarded as generalized linear mixed models, where the individual home range centers are random effects. We adopt a Bayesian framework for inference under these models using a formulation based on data augmentation. We apply the models to camera trapping data on tigers from the Nagarahole Reserve, India, collected over 48 nights in 2006. For this study, 120 camera locations were used, but cameras were only operational at 30 locations during any given sample occasion. Movement of traps is common in many camera-trapping studies and represents an important feature of the observation model that we address explicitly in our application.
Ecology↗