Crown condition within integrated evaluations of Level II monitoring data at the German level

Abstract

The assessment of crown condition is an annually repeated core activity within the ‘Intensive Forest Monitoring Programme’ (Level II monitoring) at selected plots. As the intensive monitoring focuses on a better understanding of direct and indirect effects of air pollution at the ecosystem level, a considerable number of parameters are collected. This allows the analysis of numerous relationships which may determine tree-crown condition with respect to different ecological situations and their temporal variation. This article describes possibilities for integrated evaluations of crown condition data within the German Level II programme. Besides descriptions of defoliation and mortality rates over time, relationships among defoliation, and stand and site characteristics and sampling circumstances are explored. Autocorrelative properties of defoliation data are also described. The results reveal that the date of the field assessment is influential in three out of four species, while the dominance structure has effects in Scots pine and oak. Temporal variation of defoliation reveals strong plot-specific characteristics with limited possibilities for generalisations. Species-specific temporal autocorrelation structures were detected. Within integrated approaches, the ‘federal state’ variable gains high coefficients of determination; however, alternative models incorporating significant ecological factors also reveal plausible explanations. Interactions between predictors complicate the statistical approaches. This is also true for models that include temporal aspects of defoliation and indicators of climatic drought stress. Methodological limitations and significant interpretations are discussed. Statistical evaluations are restricted mainly by the low number of cases, limiting the number of predictors within multiple models. In the future, both a consequent processing of the collected data according to relevant questions and the application of advanced statistical models will be needed.