Document details for 'Bayesian inference for the spatio-temporal invasion of alien species'

Authors Cook, A., Marion, G., Butler, A. and Gibson, G.
Publication details Bulletin of Mathematical Biology 69(6), 2005-2025.
Keywords Bayesian inference Markov chain Monte Carlo spatial and temporal model stochastic model species distribution invasive species ecology ALARM project
Abstract In this paper we develop a Bayesian approach to parameter estimation in a stochastic spatio-temporal model of the spread of invasive species across a landscape. To date, statistical techniques, such as logistic and autologistic regression, have outstripped stochastic spatio-temporal models in their ability to handle large numbers of covariates. Here we seek to address this problem by making use of a range of covariates describing the bio-geographical features of the landscape. Relative to regression techniques, stochastic spatio-temporal models are more transparent in their representation of biological processes. They also explicitly model temporal change, and therefore do not require the assumption that the species' distribution (or other spatial pattern) has already reached equilibrium as is often the case with standard statistical approaches. In order to illustrate the use of such techniques we apply them to the analysis of data detailing the spread of an invasive plant, \Hm, across Britain in the 20$^\mathrm{th}$ Century using geo-referenced covariate information describing local temperature, elevation and habitat type. The use of Markov chain Monte Carlo sampling within a Bayesian framework facilitates statistical tests of differences in the suitability of different habitat classes for \hm, and enables predictions of future spread to account for parametric uncertainty and system variability. Our results show that ignoring such covariate information may lead to biased estimates of key processes and implausible predictions of future distributions.
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