Global sensitivity analysis of a dynamic vegetation model: Model sensitivity depends on successional time, climate and competitive interactions

Accurately predicting the dynamics of tree species productivity as well as their ranges at large scales is of key importance for assessing the impact of global change on forests. Dynamic vegetation models (DVMs), particularly forest gap models (FGMs), have been suggested as suitable tools for such joint predictions. However, DVMs generally feature a large number of parameters that need to be estimated and may cause considerable uncertainty in model outputs. In addition, model sensitivity may depend on environmental conditions, stand composition and development stage. We systematically evaluated the parameter sensitivity on simulated basal area of the state-of-the art FGM ForClim along a wide ecological gradient to analyze model behavior and identify key parameters and processes that cause the highest variability in model output. We applied the revised Morris screening method at 30 representative sites across Europe, and compared results for monospecific and mixed stands at two system states in time, i.e. early and late succession (dynamic equilibrium). The most influential parameters were related to tree establishment, the water and light regimes, growth and temperature, whereby the relative parameter influence of the latter strongly varied with climate. Further, model sensitivity differed between monospecific and mixed stands as well as between early and late succession, reflecting the differential influence of ecological processes with stand structure. We conclude that the parameter sensitivity of complex models should be analyzed individually for several system states of interest. We recommend to focus the further development (process representation and calibration) and analysis of FGMs on process representations related to establishment, water limitations and phenology to improve the robustness of model predictions. We provide recommendations for specific improvements of FGMs to better represent range dynamics.