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
K. Ullas Karanth
Publications and source records attributed to K. Ullas Karanth.
A hierarchical model for estimating the spatial distribution and abundance of animals detected by continuous-time recorders
Motivation Several spatial capture-recapture (SCR) models have been developed to estimate animal abundance by analyzing the detections of individuals in a spatial array of traps. Most of these models do not use the actual dates and times of detection, even though this information is readily available when using continuous-time recorders, such as microphones or motion-activated cameras. Instead most SCR models either partition the period of trap operation into a set of subjectively chosen discrete intervals and ignore multiple detections of the same individual within each interval, or they simply use the frequency of detections during the period of trap operation and ignore the observed times of detection. Both practices make inefficient use of potentially important information in the data. Model and data analysis We developed a hierarchical SCR model to estimate the spatial distribution and abundance of animals detected with continuous-time recorders. Our model includes two kinds of point processes: a spatial process to specify the distribution of latent activity centers of individuals within the region of sampling and a temporal process to specify temporal patterns in the detections of individuals. We illustrated this SCR model by analyzing spatial and temporal patterns evident in the camera-trap detections of tigers living in and around the Nagarahole Tiger Reserve in India. We also conducted a simulation study to examine the performance of our model when analyzing data sets of greater complexity than the tiger data. Benefits Our approach provides three important benefits: First, it exploits all of the information in SCR data obtained using continuous-time recorders. Second, it is sufficiently versatile to allow the effects of both space use and behavior of animals to be specified as functions of covariates that vary over space and time. Third, it allows both the spatial distribution and abundance of individuals to be estimated, effectively providing a species distribution model, even in cases where spatial covariates of abundance are unknown or unavailable. We illustrated these benefits in the analysis of our data, which allowed us to quantify differences between nocturnal and diurnal activities of tigers and to estimate their spatial distribution and abundance across the study area. Our continuous-time SCR model allows an analyst to specify many of the ecological processes thought to be involved in the distribution, movement, and behavior of animals detected in a spatial trapping array of continuous-time recorders. We plan to extend this model to estimate the population dynamics of animals detected during multiple years of SCR surveys.
Western Ghats, Nagarahole Tiger Reserve↗
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↗
Density estimation in tiger populations: combining information for strong inference
A productive way forward in studies of animal populations is to efficiently make use of all the information available, either as raw data or as published sources, on critical parameters of interest. In this study, we demonstrate two approaches to the use of multiple sources of information on a parameter of fundamental interest to ecologists: animal density. The first approach produces estimates simultaneously from two different sources of data. The second approach was developed for situations in which initial data collection and analysis are followed up by subsequent data collection and prior knowledge is updated with new data using a stepwise process. Both approaches are used to estimate density of a rare and elusive predator, the tiger, by combining photographic and fecal DNA spatial capture–recapture data. The model, which combined information, provided the most precise estimate of density (8.5 ± 1.95 tigers/100 km 2 [posterior mean ± SD]) relative to a model that utilized only one data source (photographic, 12.02 ± 3.02 tigers/100 km 2 and fecal DNA, 6.65 ± 2.37 tigers/100 km 2 ). Our study demonstrates that, by accounting for multiple sources of available information, estimates of animal density can be significantly improved.
Ecology↗
Science, conservation, and camera traps
Biologists commonly perceive camera traps as a new tool that enables them to enter the hitherto secret world of wild animals. Camera traps are being used in a wide range of studies dealing with animal ecology, behavior, and conservation. Our intention in this volume is not to simply present the various uses of camera traps, but to focus on their use in the conduct of science and conservation. In this chapter, we provide an overview of these two broad classes of endeavor and sketch the manner in which camera traps are likely to be able to contribute to them. Our main point here is that neither photographs of individual animals, nor detection history data, nor parameter estimates generated from detection histories are the ultimate objective of a camera trap study directed at either science or management. Instead, the ultimate objectives are best viewed as either gaining an understanding of how ecological systems work (science) or trying to make wise decisions that move systems from less desirable to more desirable states (conservation, management). Therefore, we briefly describe here basic approaches to science and management, emphasizing the role of field data and associated analyses in these processes. We provide examples of ways in which camera trap data can inform science and management.
Book chapter↗
Estimating tiger abundance from camera trap data: Field surveys and analytical issues
Automated photography of tigers Panthera tigris for purely illustrative purposes was pioneered by British forester Fred Champion (1927, 1933) in India in the early part of the Twentieth Century. However, it was McDougal (1977) in Nepal who first used camera traps, equipped with single-lens reflex cameras activated by pressure pads, to identify individual tigers and study their social and predatory behaviors. These attempts involved a small number of expensive, cumbersome camera traps, and were not, in any formal sense, directed at “sampling” tiger populations.
Book chapter↗
Estimation of demographic parameters in a tiger population from long-term camera trap data
Chapter 7 (Karanth et al.) illustrated the use of camera trapping in combination with closed population capture–recapture (CR) models to estimate densities of tigers Panthera tigris. Such estimates can be very useful for investigating variation across space for a particular species (e.g., Karanth et al. 2004) or variation among species at a specific location. In addition, estimates of density continued at the same site(s) over multiple years are very useful for understanding and managing populations of large carnivores. Such multi-year studies can yield estimates of rates of change in abundance. Additionally, because the fates of marked individuals are tracked through time, biologists can delve deeper into factors driving changes in abundance such as rates of survival, recruitment and movement (Williams et al. 2002). Fortunately, modern CR approaches permit the modeling of populations that change between sampling occasions as a result of births, deaths, immigration and emigration (Pollock et al. 1990; Nichols 1992). Some of these early “open population” models focused on estimation of survival rates and, to a lesser extent, abundance, but more recent models permit estimation of recruitment and movement rates as well.
Book chapter↗
Counting India's wild tigers reliably
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Science↗
Camera traps in animal ecology and conservation: What's next?
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Book chapter↗
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↗
Estimation of tiger densities in India using photographic captures and recaptures
The tiger ( Panthera tigris ) is an endangered, large felid whose demographic status is poorly known across its distributional range in Asia. Previously applied methods for estimating tiger abundance, using total counts based on tracks, have proved unreliable. Lack of reliable data on tiger densities not only has constrained our ability to understand the ecological factors shaping communities of large, solitary felids, but also has undermined the effective conservation of these animals. In this paper, we describe the use of a field method proposed by Karanth (1995), which combines camera-trap photography, to identify individual tigers, with theoretically well-founded capture–recapture models. We developed a sampling design for camera-trapping and used the approach to estimate tiger population size and density in four representative tiger habitats in different parts of India. The field method worked well and provided data suitable for analysis using closed capture–recapture models. The results suggest the potential for applying this methodology to rigorously estimate abundances, survival rates, and other population parameters for tigers and other low-density, secretive animal species in which individuals can be identified based on natural markings. Estimated probabilities of photo-capturing tigers present in the study sites ranged from 0.75 to 1.00. Estimated densities of tigers >1 yr old ranged from 4.1 ± 1.31 to 16.8 ± 2.96 tigers/100 km 2 (mean ± 1 se ). Simultaneously, we used line-transect sampling to determine that mean densities of principal tiger prey at these sites ranged from 56.1 to 63.8 ungulates/km 2 . Tiger densities appear to be positively associated with prey densities, except at one site influenced by tiger poaching. Our results generally support the prediction that relative abundances of large felid species may be governed primarily by the abundance and structure of their prey communities.
National Parks of Pench, Kanha, Kaziranga, and Nag↗