title: Approximate Bayesian inference for complex ecosystems.
abstract: Mathematical models have been central to ecology for nearly a century. Simple models of population dynamics have allowed us to understand fundamental aspects underlying the dynamics and stability of ecological systems. What has remained a challenge, however, is to meaningfully interpret experimental or observational data in light of mathematical models. Here, we review recent developments, notably in the growing field of approximate Bayesian computation (ABC), that allow us to calibrate mathematical models against available data. Estimating the population demographic parameters from data remains a formidable statistical challenge. Here, we attempt to give a flavor and overview of ABC and its applications in population biology and ecology and eschew a detailed technical discussion in favor of a general discussion of the advantages and potential pitfalls this framework offers to population biologists.
authors.affiliation: Centre for Integrative Systems Biology and Bioinformatics Department of Life Sciences, Sir Ernst Chain Building, Imperial College London, London, SW7 2AZ UK.
authors.forename: Michael P H
authors.initials: MP
authors.lastname: Stumpf