Simulating complex landscapes with a generic model: Sensitivity to qualitative and quantitative classifications [An article from: Ecological Complexity]

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Description:
The sensitivity of a generic cellular automaton model to qualitative and quantitative landscape classifications is tested regarding the spatio-temporal self-organization of the historical landscape pattern of southern Wisconsin (USA). The qualitative classification is R.W. Finley's ''Original Vegetation of Wisconsin'' and relies on criteria determined by an expert individual. The classification accounts for descriptive information and expert knowledge, but is not easily reproducible due to locally subjective decisions of class delineations. The quantitative classifications rely on a numerical-objective algorithm that ensures classification reproducibility and tests for robustness, but do not account for local ecosystem knowledge or qualitative detail. For model development, a cell in the forest-landscape lattice is chosen according to three generic and stochastic rules. The ''uncorrelated'' rule chooses a cell randomly, the ''correlated'' rule picks a cell within two distances of random length, and the ''raster'' rule chooses randomly one of four immediate neighbors in the lattice. The so chosen cell is then replaced by a cell randomly identified within a circular neighborhood of radius r (1