Hierarchical dynamic modeling of outbreaks of mountain pine beetle using partial differential equations

Authors

  • Yanbing Zheng,

    Corresponding author
    1. Department of Statistics, University of Kentucky, Lexington, KY 40536-0001, U.S.A.
    • Department of Statistics, University of Kentucky.
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  • Brian H. Aukema

    1. Canadian Forest Service, Natural Resources Canada, Prince George, BC, Canada
    2. Ecosystem Science and Management Program, University of Northern British Columbia, Prince George, BC, Canada
    3. Department of Entomology, University of Minnesota, St. Paul, MN
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  • This article is published in Environmetrics as a special issue on TIES 2008: Quantitative Methods for Environmental Sustainability, edited by Sylvia R. Esterby, University of British Columbia Okanagan, Canada.

Abstract

In this article, we develop spatial—temporal generalized linear mixed models for spatial—temporal binary data observed on a spatial lattice and repeatedly over discrete time points. To account for spatial and temporal dependence, we introduce a spatial—temporal random effect in the link function and model by a diffusion—convection dynamic model. We propose a Bayesian hierarchical model for statistical inference and devise Markov chain Monte Carlo algorithms for computation. We illustrate the methodology by an example of outbreaks of mountain pine beetle on the Chilcotin Plateau of British Columbia, Canada. We examine the effect of environmental factors while accounting for the potential spatial and temporal dependence. Copyright © 2010 John Wiley & Sons, Ltd.

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