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The overuse of rainforests in the last century and its consequences necessitate a rethinking of logging policies. To this end models have been developed to simulate rainforest dynamics and to allow optional management strategies to be evaluated. Parameterisation of presently existing models for a certain site needs a lot of work, thus the parameterisation effort is too high to apply the models to a wide range of rainforests. Hence, in this paper we introduce the simplified model FORREG using the knowledge we have gained from a more complex model, FORMIX3-Q. The FORREG model uses differential equations to determine the volume growth of three successional species groups. Parameterisation is simplified by a genetic algorithm, which determines the required internal model parameters from characteristics of the forest dynamics. The new model is employed to assess the sustainability of various logging policies in terms of yield and damage. Results for three forests are discussed: (1) the tropical lowland rain forest in the Deramakot Forest Reserve, (2) the Lambir National Park in Malaysia and (3) a subtropical forest in Paraguay. Our model reproduces both undisturbed forest dynamics and dynamics of logged forests simulated with FORMIX3-Q very well. However, the resultant volumes of yield and damage differ slightly from those gained by FORMIX3-Q if short logging cycles are simulated. Choosing longer logging cycles leads to a good correspondence of both models. For the Deramakot Forest Reserve different logging cycles are compared and discussed. (c) 2006 Elsevier B.V. All rights reserved.
Describing the heterogeneous structure of forests is often challenging.
One possibility is to analyze forest biomass in different plots and to derive plot-based frequency distributions.
However, these frequency distributions depend on the plot size and thus are scale dependent.
This study provides insights about transferring them between scales. Understanding the effects of scale on distributions of biomass is particularly important for comparing information from different sources such as inventories, remote sensing and modeling, all of which can operate at different spatial resolutions. Reliable methods to compare results of vegetation models at a grid scale with field data collected at smaller scales are still missing.
The scaling of biomass and variables, which determine the forest biomass, was investigated for a tropical forest in Panama. Based on field inventory data from Barro Colorado Island, spanning 50 ha over 30 years, the distributions of aboveground biomass, biomass gain and mortality were derived at different spatial resolutions, ranging from 10 to 100 m. Methods for fitting parametric distribution functions were compared.
Further, it was tested under which assumptions about the distributions a simple stochastic simulation forest model could best reproduce observed biomass distributions at all scales. Also, an analytical forest model for calculating biomass distributions at equilibrium and assuming mortality as a white shot noise process was tested.
Scaling exponents of about 0.47 were found for the standard deviations of the biomass and gain distributions, while mortality showed a different scaling relationship with an exponent of 0.3. Lognormal and gamma distribution functions fitted with the moment matching estimation method allowed for consistent parameter transfers between scales. Both forest models (stochastic simulation and analytical solution) were able to reproduce observed biomass distributions across scales, when combined with the derived scaling relationships.
The study demonstrates a way of how to approach the scaling problem in model-data comparisons by providing a transfer relationship. Further research is needed for a better understanding of the mechanisms that shape the frequency distributions at the different scales.