Comparing methods for identifying optimal forest management
The past few decades have seen a considerable increase in the number and level of detail of individual-based models (IBMs) of forest dynamics. These models forecast dynamics by predicting each individual’s birth, dispersal, reproduction, and death and how these events are affected by spatial competition for resources with neighbors. Individual-based forest simulators have also been used for forest management. Despite their usefulness, IBMs have one important disadvantage: they require too much computational resources to be used at a large scale. For this reason, a number of approaches has recently been developed based on differential equations, rather than on more complex algorithms. A notable advance in this area has been the development of the perfect-plasticity approximation (PPA; Strigul et al. 2008), which builds on the assumption that trees experience full light above the canopy height, defined as the maximum height at which the canopy can be closed, and reduced light below the canopy height. In turn, this height is a dynamic quantity, which depends on demographic parameters. The PPA is a promising tool for understanding forest dynamics. Until now, however, it has been developed and studied only for natural forests. In this project, we will explore its validity for describing managed forests under different types of harvesting. This will be done in three steps. First, we will develop a spatially explicit IBM of forest dynamics that accounts for crown plasticity and management. Second, we will derive a PPA corresponding to this IBM. Finally, we will compare and critically evaluate the results of these two approaches.