A spatial model for the needle losses of pine‐trees in the forests of Baden‐Württemberg: an application of Bayesian structured additive regression

Nicole H. Augustin, Stefan Lang, Monica Musio and Klaus Von Wilpert

Journal of the Royal Statistical Society Series C, 2007, vol. 56, issue 1, 29-50

Abstract: Summary. The data that are analysed are from a monitoring survey which was carried out in 1994 in the forests of Baden‐Württemberg, a federal state in the south‐western region of Germany. The survey is part of a large monitoring scheme that has been carried out since the 1980s at different spatial and temporal resolutions to observe the increase in forest damage. One indicator for tree vitality is tree defoliation, which is mainly caused by intrinsic factors, age and stand conditions, but also by biotic (e.g. insects) and abiotic stresses (e.g. industrial emissions). In the survey, needle loss of pine‐trees and many potential covariates are recorded at about 580 grid points of a 4 km × 4 km grid. The aim is to identify a set of predictors for needle loss and to investigate the relationships between the needle loss and the predictors. The response variable needle loss is recorded as a percentage in 5% steps estimated by eye using binoculars and categorized into healthy trees (10% or less), intermediate trees (10–25%) and damaged trees (25% or more). We use a Bayesian cumulative threshold model with non‐linear functions of continuous variables and a random effect for spatial heterogeneity. For both the non‐linear functions and the spatial random effect we use Bayesian versions of P‐splines as priors. Our method is novel in that it deals with several non‐standard data requirements: the ordinal response variable (the categorized version of needle loss), non‐linear effects of covariates, spatial heterogeneity and prediction with missing covariates. The model is a special case of models with a geoadditive or more generally structured additive predictor. Inference can be based on Markov chain Monte Carlo techniques or mixed model technology.

Date: 2007
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