The traditional focus on timber production in the forestry sector has been replaced by multi-purpose forest management considering economic, social and environmental aspects. There has been also a growing concern in the increase of human pressure on forest ecosystems and its overexploitation as well as possible climate change effects. Thus, multi-purpose sustainable forest management (MSFM) aiming sustainable ecosystem management has become an important paradigm for forestry. The potential effects of future changes in global environment (such as climate, land-use and fire disturbance) on the sustainability of forest ecosystems require “new” models of forest growth and dynamics able to predict forest reactions to a changing environment.
Classes of forest models
Basic approaches for modelling forest growth have their advantages and limitations:
- Empirical forest growth and yield models.
- Ecological Models
- Process-based/mechanistic models
- Hybrid models
One major challenge is to resolve the apparent lack of compatibility between different model types and between predictions at different temporal and spatial scales. A second major challenge is to quantify the predictive uncertainty between models and between models and reality, and include such uncertainty in decision-support systems for operational multi-purpose forest management. Simple growth models can easily be used in forest management and for scenario simulations and such models are the essential decision-support tools for forestry practice and for policy making. Such models do not however fulfil all the requirements for the evaluation of sustainability. The development of methodologies for the integration of simulation outputs from different models and the quantification of their uncertainty is therefore in strong demand.