Bayesian Methods for Estimating Animal Abundance at Large Spatial Scales Using Data from Multiple Sources

Abstract

Estimating animal distributions and abundances over large regions is of primary interest in ecology and conservation. Specifically, integrating data from reliable but expensive surveys conducted at smaller scales with cost-effective but less reliable data generated from surveys at wider scales remains a central challenge in statistical ecology. In this study, we use a Bayesian smoothing technique based on a conditionally autoregressive (CAR) prior distribution and Bayesian regression to address this problem. We illustrate the utility of our proposed methodology by integrating (i) abundance estimates of tigers in wildlife reserves from intensive photographic capture–recapture methods, and (ii) estimates of tiger habitat occupancy from indirect sign surveys, conducted over a wider region. We also investigate whether the random effects which represent the spatial association due to the CAR structure have any confounding effect on the fixed effects of the regression coefficients.