Statistical and Geostatistical Modelling of Preliminarily Adjusted Defoliation on an European Scale

Abstract

Since 1987 tree crown condition is surveyed annually in large parts of Europe mainly in terms of defoliation. The plot-wise means of defoliation from 1994 to 2000 were evaluated by General Linear Models (GLM) in order to describe country-specific levels of defoliation and age-trends. Additional runs with estimates for influences of insects and fungi were performed. The amount of variance of defoliation explained by country, age and its interaction was between 35% and 59% for the main tree species, except for Quercus ilex. Additionally, up to 10% could be explained by the inclusion of estimates for infestations by insects and fungi. Residuals of the GLMs were taken as a measure of forest condition not biased by country or age effects and interpreted as 'preliminarily adjusted defoliation' (PAD). PAD values were analysed using geostatistical methods. The modelled spatial autocorrelations were used for kriging. The resulting maps give an overview on regions with elevated defoliation, which may pinpoint regional causes of defoliation. The elimination of methodologically caused variance is a precondition of any cause-effect oriented analyses. The combination of explorative modelling and geostatistics will promote the choice of further promising predictors.

Signals of summer drought in crown condition data from the German Level I network

“…However, the results widely corroborate the influence of age, found in many evaluations of different tree species at different spatial scales (e.g. Innes and Boswell 1988;Göttlein and Pruscha 1996;Klap et al 2000;Musio et al 2004;Seidling and Mues 2005;). Stand age has been brought in connection with a general accumulation of stress from different sources within tree stands (Dobbertin 2005), however, aging is also a process that changes individual physiological conditions of trees.…”

“…Even if a significant coincidence between observations of detrimental insects and crown transparency was found only in 1994, there may be more hidden influences (cf. Seidling and Mues 2005). Similar complex relationships can be assumed for influences of fungi.…”

“…Nevertheless, these influences have been shown in large-scale statistics (e.g. Hendriks et al 1997;Seidling and Mues 2005), however, findings in this longitudinal perspective are rather vague, probably due to incomplete information.…”

“…Dobbertin et al 1997;Dammann et al 2001). Crosscalibration courses (Mues and Seidling 2003), technical systems (Mizoue and Dobbertin 2004) and respective statistical treatment (Dobbertin et al 1997;Seidling and Mues 2005) could at least partly lead to a quantification of respective errors. At present, in the best case a higher amount of unexplained variance in respective models has to be accepted, and in the worst case some spurious relationships.…”

Outline and examples for integrated evaluations of data from the intensive (Level II) monitoring of forest ecosystems in Germany

“…This question was already brought up, e.g. by Ko¨hl (1991) or Ferretti (1998) for national inventories or by Innes (1988), Landmann and BouhotDelduc (1995), or Seidling and Mues (2005) at the European level. Dammann et al (2001) assume that at the German level occur no systematic deviances (bias) between observer teams due to intensive training.…”