Simulating structural forest patterns with a forest gap model: a model evaluation

Ecological models can be characterized by their degree of generality, reality and precision. Any model, being a deliberate simplification of reality, cannot excel in all three aspects. Forest gap models have been widely used for studying tree population dynamics, but their predictions have not often been tested for their local precision, but rather for their broad agreement with descriptions of near-natural vegetation. The objectives of our study were (1) to evaluate the performance of the forest gap model ForClim, which had been developed striving for generality and realism, in simulating the long-term development of structural features in Swiss mountain forests; and (2) to examine whether and how the model needs to be changed to improve its precision for a specific site. We used long-term forest data (45 years) from three different forest types in the Swiss National Park. l simulation runs for the most dominant forest type in the study area failed to reproduce the observed structural patterns. A detailed analysis of the growth performance of individual trees led to the conclusion that a modified height–diameter function was required, which presumably increases the generality of the model. The new model structure led to simulated stand features that were broadly consistent with observations. After, in addition, taking local variations of model parameters (on mortality, browsing, and seedling establishment rates) into account, we were able to considerably improve the performance of ForClim in simulating the structural features of the different mountain forest stands. We suggest that from the point of view of its revised structure, the ForClim model is principally suitable for site-specific applications, but local precision can only be achieved by site-specific parameter estimation procedures. We conclude that model evaluation and validation as conducted in this study could be quite useful for increasing the reliability of simulations performed with this class of models.