Forest monitoring: substantiating cause-effect relationships

Seidling, Walter GND

Monitoring of forest condition and tree performance is a long-termactivity to provide data, substantiated causeeffects relationships and conclusions for environmental policies and forestmanagement.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 actualmeanings may differ considerably. For the sake of amore 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 fromthe role of aworking 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 problemswith 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 plotrelated 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 geostatisticsmay 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.



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Seidling, Walter: Forest monitoring: substantiating cause-effect relationships. 2019.


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