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Fakultät für Biologie, Chemie und Geowissenschaften

Lehrstuhl für Ökologische Modellbildung - Prof. Dr. Michael Hauhs

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Hauhs, M; Lange, H: Modeling the complexity of environmental and ecological systems in Klonowski W: Proceedings of the 3rd European Interdisciplinary School on Nonlinear Dynamics for System and Signal Analysis, Euroattractor 2002, Simplicity Behind Complexity, Lengerich: Pabst Science Publishers, 85-109 (2004)
Abstract:
Ecosystems as objects of natural sciences are often difficult to understand, as an object of traditional management they are sometimes easy to utilize. Computer-based modeling offers new tools to study this apparent paradox. We propose an interactive framework from which the traditional approach based on dynamic system theory can be challenged for living systems: Models derived on the basis of the state concept have not (yet?) allowed predictions that derive novel management competence relevant for the altered boundary conditions of ecosystems. Here a concept of interaction as currently used in information sciences serves as starting point for deriving models more appropriate for ecosystems. An application and test of this concept consists in a search for signatures of interaction in environmental and ecological time series. Confronted with the notorious lack of detailed process understanding, it is plausible to rely on time series analysis techniques. The intricate nature of typical multivariate data sets from ecosystems immediately suggests a preference for nonlinear techniques, and among them temporally local methods, able to detect even subtle changes in the underlying “dynamics”. We shortly introduce a couple of these methods, which have been demonstrated to be appropriate for time series exceeding minimal length requirements. This is exemplified by recurrence quantification analysis. In addition we present methods to quantify the memory content (Hurst analysis) and complexity of data sets (defined in an information-theoretic context). Time series analysis of extended environmental and ecological data sets can give detailed structural insights, monitors subtle changes undetectable otherwise, forms the basis for further inferences and provides rigorous model testing on all scales. The success of dynamic system theory when applied to non-living environmental data is strikingly contrasted by the difficulties of the same method when dealing with ecological data We conjecture that this difference reflects the extent to which interaction has been disregarded for ecosystems.
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