Aggregation of individual trees and patches in forest succession models: capturing variability with height structured, random, spatial distributions.

Individual based, stochastic forest patch models have the potential to realistically describe forest dynamics. However, they are mathematically intransparent and need long computing times. We simplified such a forest patch model by aggregating the individual trees on many patches to height-structured tree populations with theoretical random dispersions over the whole simulated forest area. The resulting distribution-based model produced results similar to those of the patch model under a wide range of conditions. We concluded that the height- structured tree dispersion is an adequate population descriptor to capture the stochastic variability in a forest and that the new approach is generally applicable to any patch model. The simplified model required only 4.1% of the computing time needed by the patch model. Hence, this new model type is well-suited for applications where a large number of dynamic forest simulations is required.

Sensitivity of a forest ecosystem model to climate parametrization schemes.

An analysis of the climate parametrization scheme adopted by conventional forest gap models revealed that most models assume a constant climate and are difficult to calibrate consistently. Tree growth showed unrealistically sensitive threshold effects along ecological gradients of temperature and precipitation. A new parametrization was compared with its predecessors in terms of the model's capability to predict realistic steady state species compositions at three test sites in the Alps. Applying the new model variant ForClim to some climate-change scenarios suggests that forest gap models are highly sensitive to climate pametrizations, regardless of the realism with which they simulate forests for the current climate. Moreover, the precision of climate scenarios based on General Circulation Models (GCM), for example, falls short of ForClim's sensitivity. Climate-dependent processes in forest gap models should be rehearsed before these models are used in impact studies of climatic change.