Abstract
Simplifying large ecosystem models is essential if we are to understand the underlying causes of observed behaviours. However, such understanding is often employed to achieve simplification. This paper introduces a method for model simplification that can be applied without requiring intimate prior knowledge of the system. Its utility is measured by the resulting values of given model diagnostics relative to those of the original model. The method uses a least-squares criterion to identify sets of state variables that can be aggregated, and then generates a modified model structure and accompanying parameters that enable these variables to be replaced with the aggregates. The method is applied to a model of the nitrogen cycle in Port Phillip Bay, Victoria, Australia. Aside from reducing the model’s order, the method enables the reduced model to retain an ecological interpretation, and reveals insights into the system’s structure.
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