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Geoadditive Bayesian models for forestry defoliation data: a case study
Author(s) -
Musio Monica,
Augustin Nicole H.,
von Wilpert Klaus
Publication year - 2008
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.903
Subject(s) - deviance information criterion , markov chain monte carlo , covariate , bayesian probability , forestry , bayesian inference , statistics , econometrics , inference , mathematics , computer science , geography , artificial intelligence
We analyze forestry defoliation data from the Emission Impact and Forest Nutrition (IWE) survey, which was carried out in Baden‐Württemberg, Germany in the year 1988. The survey contains information on individual trees such as the degree of defoliation, age, species and measurements on nutrients in the needles, as well as information on tree locations such as soil and geographical characteristics. Our goal is to find suitable predictors for tree defoliation from the above information, and to find a set of models which can explain the underlying biological and environmental processes. To model the spatial correlation in the data and possible nonlinear effects of the covariates we use a geoadditive hierarchical Bayesian model. Posterior inference and model comparison are computationally assessed via Markov Chain Monte Carlo (MCMC) techniques and deviance information criterion (DIC) respectively. Copyright © 2008 John Wiley & Sons, Ltd.