COST Action FP0603
Forest models for research and decision support in sustainable forest management

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Since the development of the first yield tables in the 19th century, growth models have played an important role in research and as tools to support management decisions. However, important changes in society demands on forests as well as changing growing conditions associated with climate change and other environmental changes, made descriptive growth and yield models less relevant and outdated. At the same time, increasing emphasis on processbased models relying more on causal relationships have been developed by forest scientists and in ecology in general. The latter models are increasingly used in decision support, sometimes without sufficient validation against available growth and yield data. Thus it appears that much is to be gained by comparing and combining approaches and by sharing data and models. This will broaden the applicability of models, and will give a push to dynamic forest resource assessment and decision support for forest management.

Forest growth models are necessary for a wide range of forest uses. Even though timber production has been the main objective of much research in the past, the understanding gained with respect to the underlying mechanisms governing forest growth is crucial for proper understanding of forest growth under changing environmental conditions and management.

During the last two decades, and in particular since the Earth Summit in Rio de Janeiro in 1992, forest management has become increasingly complex. At present, multifunctional sustainable forest management (MSFM), aims at optimization of the production of multiple products – timber, wood for fuel, cork, berries, game, medicinal plants, etc… while safeguarding the quality and the sustainability of the forest ecosystem and satisfying society’s needs and demands for recreation, biodiversity, landscape and environmental conservation. This requires detailed information on tree growth and forest dynamics, including structural development, biodiversity indicators, and vulnerability and adaptation to disturbances. This is reflected in increasing emphasis, throughout Europe, on more detailed, versatile forest growth models that can account for changes in growing conditions and for adaptive management strategies. Due to the complex nature of forest ecosystems, the data needs of the models, and the increasing complexity of programming languages, no research group can achieve this in isolation. Within Europe, traditionally there is good collaboration in forest research and forest growth modelling but the relationship between traditional modellers and process-based modellers and physiologists needs to be strengthened.

Decision support models have evolved with the evolution of forest management objectives, and in so doing they have made use of increased knowledge about ecosystem functioning and technology development. Such models were originally empirical growth and yield models, but today have evolved into a spectrum of models that ranges from state-space stand-level models, through distribution-based models, and individual-tree models to complex process-based ecophysiological models.

No single model can meet all the requirements for evaluating the sustainability of multifunctional forest management, and it is of paramount importance to ensure exchange of concepts and software between research groups to avoid duplication of efforts in model development. Most of the currently existing models are addressing one or more of these requirements. These models should be compared, evaluated and documented. This includes the following:

- Finding out if and how forest inventory data can be used as its input

- Use of permanent sample plot data for evaluation and validation

- Documentation and exchange of modelling concepts, both with respect to forest growth and with respect to inclusion of changes in growing conditions due to environmental change (e.g. by using climate change scenarios)

- Parameterization of European, relevant full species list, including its genetic variability and the respective consequences for growth and adaptation to changing conditions

- Sensitivity and uncertainty analysis of the different forest models

- Simulation of alternative silvicultural strategies with quantification of all forest outputs (not only on tree growth, but also on all forest products and services)

- Analysis of predictive capacity of the models for the various forest characteristics and indicators of interest (e.g. stand structure and wood quality)

- Options to include the effects of several different types of forest damage event – insect defoliations, acidic deposition, fire, wind-throw, etc…

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