Crown condition as a function of soil, site and tree characteristics

One of the aims of this work is to describe how the target variable ‘‘tree vitality’’ in terms of needle loss is affected by other explanatory variables. To describe such a relationship in a realistic way, we use generalized additive mixed models (GAMMs) which allow to take spatial correlation of the data into account and in addition allow the inclusion of explanatory variables as predictors with the possibility of having non-linear effects. The GAMMs are fitted in a Bayesian framework using Markov chain Monte Carlo techniques. Data are available for two years 1988 and 1994. We select a set of best explanatory variables from a large set of variables including tree-specific variables, such as species, age, nutrients in the needles and site-specific variables such as altitude, relief type, soil depth and content of different nutrients in the top soil. In the two models for 1988 and 1994, different sets of explanatory variables were selected as best predictors. In both models, the effects of explanatory variables allowed a plausible interpretation. For example, the site-specific variables such as relief and soil depth were significant predictors, since these factors determine how well water and nutrient supply is balanced at a specific site. The selected sets of explanatory variables differed between 1988 and 1994, giving an indication of a possible change in the main causes of forest deterioration between 1988 and 1994. From the set of nutrient variables measured in the soil and in the needles, in 1988 altitude a.s.l. and magnesium supply were among the explanatory variables, in 1994 a combination of Al in the soil and the N/K-ratio (in the needles) was selected in the model. In 1988 altitude a.s.l. was among the most important predictors in the model. This is in contrast to 1994 where altitude was not selected. This may have to do with the fact that in the early phase of forest health monitoring (1988) one of the main causes of forest deterioration was magnesium deficiency. Later on this may have changed to a combination of soil acidification and nitrogen eutrophication. Thus by using an adequate model such as the GAMM, sets of explanatory variables for needle loss may be identified. By fitting two GAMMs, with different sets of ‘‘best’’ predictors, at two time points 1988 and 1994, we can detect changes in these sets of ‘‘best’’ predictors over time. This allows us to use the monitoring data with the tree vitality indicator crown condition/needle loss as a tool for forest health management, which may involve decisions about concrete counter measures like e.g. forest liming.