Addressing methodological issues in the study of tiger metapopulation dynamics in Western Ghats, India

The tiger (Panthera tigris) is a globally endangered large carnivore of high conservation importance. It is cryptic, wide-ranging and naturally occurs at low population densities. Any study of its metapopulation in real landscapes is faced with a suite of methodological constraints because the temporal and spatial scale of study is large. The scope presented for developing, testing and reviewing methods, whether they are analytical, practical or conceptual, in such a context is wide: ranging from statistical methods to laboratory techniques to field methodologies to software products and methods, all relevant to tiger research and conservation endeavours. Framed within the ecological theme of assessing population dynamics of species within patchy environments, and with the source-sink view of tiger metapopulations, this thesis investigates the abundance estimation question of tigers and their prey at multiple scales within a 38,000 square kilometre tiger landscape in the Western Ghats, India. While the emphasis is firmly rooted in the development of statistical methods, software products and field methodologies, it also brings in research in laboratory techniques to bear on the abundance and distribution questions on hand. In specific, this thesis: 1. Advances our understanding of spatial capture-recapture methods - a statistical methodology being widely used over the last five years for density estimation. 2. Develops a new field-based method for estimating the density of tiger prey, specifically when they occur at low densities, using an occupancy-based approach. The results of this exercise show much promise and offers a way of tackling the abundance estimation question in unmarked, rare species. 3. Develops a software product called SPACECAP to make novel spatial capture-recapture methods accessible to wildlife biologists, ecologists and park managers. The software is adequately tested and provides users with the necessary summary statistics of density and related parameters, along with the necessary diagnostic tools in order facilitate accurate interpretation of parameters. 4. Develops a Bayesian inferential approach to estimating tiger density in areas of low abundance by bringing together data sets from multiple sources (camera trapping images and faecal DNA samples, in our case) to strengthen inference about tiger density. Results from this approach suggests that it takes a relatively small increase in sampling effort to bring about large reductions in the variance of density estimates. 5. Investigates a long-standing controversy of index-calibration experiments at large scales. The theoretical models and the empirical testing with tiger sign-encounter data at macroecological scales demonstrate the relative futility in employing simplified and direct linear models using the R-squared statistics, because there are latent sampling process parameters which considerably weaken inference. 6. Develops a spatial capture-recapture model that facilitates investigation of unanswered questions lying at the interface between behavioural and population ecology of carnivores. This is done by introducing the attraction-repulsion spatial arrangements of carnivores into spatial capture-recapture models. Nearly 70 percent of the world’s tigers remain in less than 100,000 square kilometres of habitat today. Yet, we lack sufficient methodological tools to reliably estimate their population dynamic parameters. This thesis provides a toolbox of advanced methods to make these assessments more reliable and, more importantly, accessible. It is envisioned that researchers and practical conservation managers will both benefit with this toolbox.</p

Towards accurate and precise estimates of lion density

Reliable estimates of animal density are fundamental to our understanding of ecological processes and population dynamics. Furthermore, their accuracy is vital to conservation biology since wildlife authorities rely on these figures to make decisions. However, it is notoriously difficult to accurately estimate density for wide-ranging species such as carnivores that occur at low densities. In recent years, significant progress has been made in density estimation of Asian carnivores, but the methods have not been widely adapted to African carnivores. African lions (Panthera leo) provide an excellent example as although abundance indices have been shown to produce poor inferences, they continue to be used to estimate lion density and inform management and policy. In this study we adapt a Bayesian spatially explicit capture-recapture model to estimate lion density in the Maasai Mara National Reserve (MMNR) and surrounding conservancies in Kenya. We utilize sightings data from a three-month survey period to produce statistically rigorous spatial density estimates. Overall posterior mean lion density was estimated to be 16.85 (posterior standard deviation = 1.30) lions over one year of age per 100km2 with a sex ratio of 2.2♀:1♂. We argue that such methods should be developed, improved and favored over less reliable methods such as track and call-up surveys. We caution against trend analyses based on surveys of differing reliability and call for a unified framework to assess lion numbers across their range in order for better informed management and policy decisions to be made

Site-occupancy modelling as a novel framework for assessing test sensitivity and estimating wildlife disease prevalence from imperfect diagnostic tests

1. Reliable assessments of infection status and population prevalence are critical for epidemiological modelling and disease management, but can be greatly biased when disease state is determined from imperfect diagnostic tests. Available statistical methods to adjust test-based prevalence estimates by correcting for test accuracy demand that many stringent requirements and assumptions be met (knowledge about underlying population prevalence or multiple diagnostic methods), limiting their utility for wildlife disease surveys. 2. In this paper, we present site-occupancy modelling as a flexible approach to derive estimates of population prevalence and test sensitivity under imperfect pathogen detection without a need for restrictive requirements or assumptions. We extend the utility of the standard site-occupancy framework for pathogen detection data by novel application of abundance-induced heterogeneity (AIH) models (Royle and Nichols 2003) that allow test sensitivity to vary with host pathogen load or infection intensity. 3. We demonstrate the utility of this approach for wildlife disease studies by applying site-occupancy models to a data set consisting of replicate quantitative (q)PCR diagnoses of malaria parasites (Plasmodium spp.) in blood samples from wild blue tits (Cyanistes caeruleus). 4. Model selection revealed that Plasmodium detection rates by qPCR were strongly dependent on host parasite load. Estimates of parasite detection rates revealed the qPCR assay to be highly sensitive, with accordingly, a very low probability of false negative diagnosis for the majority of infected hosts in our population and little bias in naive estimates of population prevalence, although this will be a system-specific result. 5. Our results demonstrate the utility of a site-occupancy approach for deriving estimates of population prevalence under imperfect pathogen detection and reveal that accounting for host variation in pathogen load allows a more accurate assessment of diagnostic test sensitivity. 6. By identifying factors that influence pathogen detection rates, and revealing optimal protocols for obtaining unbiased prevalence estimates, while minimising the probability of false negative diagnoses, we also show that this approach can enhance both diagnostic accuracy and cost-efficiency in wildlife disease surveys. © 2011 The Authors. Methods in Ecology and Evolution © 2011 British Ecological Society