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The knowledge about potential climate change impacts on forests is continuously expanding and some changes in growth, drought induced mortality and species distribution have been observed. However despite a significant body of research, a knowledge and communication gap exists between scientists and non-scientists as to how climate change impact scenarios can be interpreted and what they imply for European forests. It is still challenging to advise forest decision makers on how best to plan for climate change as many uncertainties and unknowns remain and it is difficult to communicate these to practitioners and other decision makers while retaining emphasis on the importance of planning for adaptation. In this paper, recent developments in climate change observations and projections, observed and projected impacts on European forests and the associated uncertainties are reviewed and synthesised with a view to understanding the implications for forest management. Current impact assessments with simulation models contain several simplifications, which explain the discrepancy between results of many simulation studies and the rapidly increasing body of evidence about already observed changes in forest productivity and species distribution. In simulation models uncertainties tend to cascade onto one another; from estimating what future societies will be like and general circulation models (GCMs) at the global level, down to forest models and forest management at the local level. Individual climate change impact studies should not be uncritically used for decision-making without reflection on possible shortcomings in system understanding, model accuracy and other assumptions made. It is important for decision makers in forest management to realise that they have to take long-lasting management decisions while uncertainty about climate change impacts are still large. We discuss how to communicate about uncertainty – which is imperative for decision making – without diluting the overall message. Considering the range of possible trends and uncertainties in adaptive forest management requires expert knowledge and enhanced efforts for providing science-based decision support. [ABSTRACT FROM AUTHOR]

The paper deals with the management problem how to decide on tree species suitability under changing environmental conditions. It presents an algorithm that classifies the output of a range shift model for major tree species in Europe into multiple classes that can be linked to qualities characterizing the ecological niche of the species. The classes: i) Core distribution area, ii) Extended distribution area, iii) Occasional occurrence area, and iv) No occurrence area are first theoretically developed and then statistically described. The classes are interpreted from an ecological point of view using criteria like population structure, competitive strength, site spectrum and vulnerability to biotic hazards. The functioning of the algorithm is demonstrated using the example of a generalized linear model that was fitted to a pan-European dataset of presence/absence of major tree species with downscaled climate data from a General Circulation Model (GCM). Applications of the algorithm to tree species suitability classification on a European and regional level are shown. The thresholds that are used by the algorithm are precision-based and include Cohen's Kappa. A validation of the algorithm using an independent dataset of the German National Forest Inventory shows good accordance of the statistically derived classes with ecological traits for Norway spruce, while the differentiation especially between core and extended distribution for European beech that is in the centre of its natural range in this area is less accurate. We hypothesize that for species in the core of their range regional factors like forest history superimpose climatic factors. Problems of uncertainty issued from potentially applying a multitude of modelling approaches and/or climate realizations within the range shift model are discussed and a way to deal with the uncertainty by revealing the underlying attitude towards risk of the decision maker is proposed. [Copyright &y& Elsevier]