Biogeographical Modelling

Prof. Dr. Björn Reineking

Elkin, Ch; Reineking, B; Bigler, C; Bugmann, H: Do small-grain processes matter for landscape scale questions? Sensitivity of a forest landscape model to the formulation of tree growth rate, Landscape Ecology (2012), doi:DOI 10.1007/s10980-012-9718-3

Process-based forest landscape models are valuable tools for testing basic ecological theory and for projecting how forest landscapes may respond to climate change and other environmental shifts. However, the ability of these models to accurately predict environmentally-induced shifts in species distributions as well as changes in forest composition and structure is often contingent on the phenomenological representation of individual-level processes accurately scaling-up to landscape-level community dynamics. We use a spatially explicit landscape forest model (LandClim) to examine how three alternative formulations of individual tree growth (logistic, Gompertz, and von Bertalanffy) influence model results. Interactions between growth models and landscape characteristics (landscape heterogeneity and disturbance intensity) were tested to determine in what type of landscape simulation results were most sensitive to growth model structure. We found that simulation results were robust to growth function formulation when the results were assessed at a large spatial extent (landscape) and when coarse response variables, such as total forest biomass, were examined. However, results diverged when more detailed response variables, such as species composition within elevation bands, were considered. These differences were particularly prevalent in regions that included environmental transition zones where forest composition is strongly driven by growth-dependent competition. We found that neither landscape heterogeneity nor the intensity of landscape disturbances accentuated simulation sensitivity to growth model formulation. Our results indicate that at the landscape extent, simulation results are robust, but the reliability of model results at a finer resolution depends critically on accurate tree growth functions.










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