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

Sinks as saviors: why flawed inference cannot assist tiger recovery

A recent study of tigers in Chitwan, Nepal (1) stirred controversy by challenging the “source-sink” approach that underlies current global tiger conservation strategies (2). The observed lack of difference in tiger density estimates inside the protected area compared with a multiple-use area outside is offered as evidence. Based on this result, the study questions the relevance of strictly protected tiger reserves involving regulation of extractive uses and relocation of human settlements. The study offers an alternate vision of sustainable, syntopic “coexistence” of tigers and humans as a solution to increasing human resource demands on tiger habitats

How “science” can facilitate the politicization of charismatic megafauna counts

Ideally, the practice of science stays independent, informs policy in real time, and facilitates learning. However, when large uncertainties go unreported or are not effectively communicated, science can, inadvertently, facilitate inappropriate politics.http://www.pnas.orgam2023Mammal Research InstituteZoology and Entomolog

Nationwide abundance and distribution of African forest elephants across Gabon using non-invasive SNP genotyping

Robust monitoring programs are essential for understanding changes in wildlife population dynamics and distribution over time, especially for species of conservation concern. In this study, we applied a rapid non-invasive sampling approach to the Critically Endangered African forest elephant (Loxodonta cyclotis), at nationwide scale in its principal remaining population strongholds in Gabon. We used a species-specific customized genetic panel and spatial capture-recapture (SCR) approach, which gave a snapshot of current abundance and density distribution of forest elephants across the country. We estimated mean forest elephant density at 0.38 (95% Confidence Interval 0.24–0.52) per km2 from 18 surveyed sites. We confirm that Gabon is the main forest elephant stronghold, both in terms of estimated population size: 95,110 (95% CI 58,872–131,349) and spatial distribution (250,782 km2). Predicted elephant densities were highest in relatively flat areas with a high proportion of suitable habitat not in proximity to the national border. Protected areas and human pressure were not strong predictors of elephant densities in this study. Our nationwide systematic survey of forest elephants of Gabon serves as a proof-of-concept of application of noninvasive genetic sampling for rigorous population monitoring at large spatial scales. To our knowledge, it is the first nationwide DNA-based assessment of a free-ranging large mammal in Africa. Our findings offer a useful national baseline and status update for forest elephants in Gabon. It will inform adaptive management and stewardship of elephants and forests in the most important national forest elephant stronghold in Africa

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

Lecture écriture au féminin

Many ecological theories and species conservation programmes rely on accurate estimates of population density. Accurate density estimation, especially for species facing rapid declines, requires the application of rigorous field and analytical methods. However, obtaining accurate density estimates of carnivores can be challenging as carnivores naturally exist at relatively low densities and are often elusive and wide-ranging. In this study, we employ an unstructured spatial sampling field design along with a Bayesian sex-specific spatially explicit capture-recapture (SECR) analysis, to provide the first rigorous population density estimates of cheetahs (Acinonyx jubatus) in the Maasai Mara, Kenya. We estimate adult cheetah density to be between 1.22 ± 0.301 and 1.28 ± 0.322 individuals/100km2 across four candidate models specified in our analysis. Our spatially explicit approach revealed 'hotspots' of cheetah density, highlighting that cheetah are distributed heterogeneously across the landscape. The SECR models incorporated a movement range parameter which indicated that male cheetah moved four times as much as females, possibly because female movement was restricted by their reproductive status and/or the spatial distribution of prey. We show that SECR can be used for spatially unstructured data to successfully characterise the spatial distribution of a low density species and also estimate population density when sample size is small. Our sampling and modelling framework will help determine spatial and temporal variation in cheetah densities, providing a foundation for their conservation and management. Based on our results we encourage other researchers to adopt a similar approach in estimating densities of individually recognisable species

maracheetahSex

The ID and sex (1 = male and 0 = female) of each cheetah sighted during the sampling perio