Abstract

We show how a recent framework combining Markov chain Monte Carlo (MCMC) with particle filters (PFMCMC) may be used to estimate population state-space models. With the purpose of utilizing the strengths of each method, PFMCMC explores hidden states by particle filters, while process and observation parameters are estimated using an MCMC algorithm. PFMCMC is exemplified by analyzing time series data on a red kangaroo (Macropus rufus) population in New South Wales, Australia, using MCMC over model parameters based on an adaptive Metropolis-Hastings algorithm. We fit three population models to these data; a density-dependent logistic diffusion model with environmental variance, an unregulated stochastic exponential growth model, and a random-walk model. Bayes factors and posterior model probabilities show that there is little support for density dependence and that the random-walk model is the most parsimonious model. The particle filter Metropolis-Hastings algorithm is a brute-force method that may be used to fit a range of complex population models. Implementation is straightforward and less involved than standard MCMC for many models, and marginal densities for model selection can be obtained with little additional effort. The cost is mainly computational, resulting in long running times that may be improved by parallelizing the algorithm.

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Authors

Knape J, de Valpine P

Institution

Department of Environmental Science, Policy and Management, 137 Mulford Hall Number 3114, University of California, Berkeley, California 94720, USA. jknape@berkeley.edu

Source

Ecology 93:2 2012 Feb pg 256-63

MeSH

Animals
Computer Simulation
Macropodidae
Markov Chains
Models, Biological
Models, Statistical
Monte Carlo Method
Phytoplankton
Population Dynamics
Time Factors
Zooplankton

Pub Type(s)

Journal Article

Language

eng

PubMed ID

22624307