Forest monitoring: Substantiating cause-effect relationships.

Monitoring of forest condition and tree performance is a long-term activity to provide data, substantiated cause-effects relationships and conclusions for environmental policies and forest management. Within this context the concept of tree and forest health, selection of response and predictor variables and challenges during statistical analyses are addressed. The terms tree and forest health are often used to characterise the performance of trees or the condition of forest ecosystems, however, the actual meanings may differ considerably. For the sake of a more coherent perception of the term health in scientific contexts and taking into account the meaning of disease(s) a more adjusted use of 'health' is recommended. Apart from the role of a working hypothesis, the selection process of meaningful response and predicting parameters is treated. On the response site the focus is on tree-related parameters like radial stem increment, crown condition, and foliar element concentrations. Each parameter reveals problems with specific implications for statistical model building. As drivers chemical properties of deposition, soil solution and soil solid phase, further foliar element concentrations, meteorological and air quality parameters are adduced. Additionally modelled plot-related values derived from external networks can be considered. Multiple regression as one of the core methods calls for unstructured residuals. To find optimal solutions especially in more intensive monitoring programmes with limited numbers of plots and many parameters is a challenge. Longitudinal and time series analyses may offer alternatives and widen the scope. While classical geostatistics may help to control spatial autocorrelation, possibilities to enlarge ecological and climatic gradients due to the inclusion of plots from similar programmes in suitable regions have to be considered as well.

Author Correction: Environment and host as large-scale controls of ectomycorrhizal fungi.

Change history: In the HTML version of this Article, author 'Filipa Cox' had no affiliation in the author list, although she was correctly associated with affiliation 3 in the PDF. In addition, the blue circles for 'oak' were missing from Extended Data Fig. 1. These errors have been corrected online.

Environment and host as large-scale controls of ectomycorrhizal fungi.

Explaining the large-scale diversity of soil organisms that drive biogeochemical processes-and their responses to environmental change-is critical. However, identifying consistent drivers of belowground diversity and abundance for some soil organisms at large spatial scales remains problematic. Here we investigate a major guild, the ectomycorrhizal fungi, across European forests at a spatial scale and resolution that is-to our knowledge-unprecedented, to explore key biotic and abiotic predictors of ectomycorrhizal diversity and to identify dominant responses and thresholds for change across complex environmental gradients. We show the effect of 38 host, environment, climate and geographical variables on ectomycorrhizal diversity, and define thresholds of community change for key variables. We quantify host specificity and reveal plasticity in functional traits involved in soil foraging across gradients. We conclude that environmental and host factors explain most of the variation in ectomycorrhizal diversity, that the environmental thresholds used as major ecosystem assessment tools need adjustment and that the importance of belowground specificity and plasticity has previously been underappreciated.

Statistical and geostatistical modelling of preliminarily adjusted defoliation on an European scale.

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.