The goal of this study is to investigate the self-organized critical state and the complexity of the historical landscape of southern Wisconsin (60,000 km2).
The landscape was classified into 27 discrete forest types using statistical cluster analysis. The data for classification was derived from the United States General Land Office Surveys that were conducted during the 19th century prior to Euro-American settlement.
We applied a two-dimensional cellular automaton model with a single adjustable parameter. The model evolves by replacing a cell that dies out at random times by a cell chosen randomly within a circular radius r (neighborhood), where r takes values between 1 (local) and 10 units (regional). Clusters measure the degree of organization.
Good agreement is found when comparing the simulated to the observed landscape using fractal dimension (spatial), cluster probability (temporal), and algorithmic complexity (interaction characteristics). All results are robust towards a variety of perturbations.
In our example, the self-organized state, in both time and space, is strongly dependent on the neighborhood size chosen for the model runs.
Ref: J. Bolliger, InterJournal Complex Systems, 558
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