Pattern-oriented modelling in population ecology

Abstract Ecological modelling should take its orientation more from real patterns observed in nature, than has been the case up to now, to overcome the deficiencies of the present strategies. Firstly, the orientation towards patterns provides guidelines about the manner and extent of the aggregation of biological information in the model. Modelling thereby loses much of its arbitrariness; secondly, pattern-oriented models are not ‘scale-free’, i.e. they relate explicitly to spatial and temporal scales; and finally, they produce comparative predictions which are better suited for testing than the predictions achieved by models, which are only either complex or generalizing. To demonstrate the strategy of pattern-oriented modelling and its advantages, three examples from population ecology are presented: (1) In a model concerning density dependence and individual variability, the orientation towards a pattern in weight distributions requires explicit inclusion of the within-generation time scale in the model. (2) Orientation towards a pattern in the dispersal capabilities of small organisms leads to a metapopulation model, where environmental correlations about certain distances are taken into account. The survival of the metapopulation depends mainly on the ratio of the correlation distance to the range of dispersal of the organisms. (3) The wave-like pattern of the spread of rabies is reproduced by an extremely simple one-dimensional model, which is based on an extended cellular automaton approach. From this basic model, a description on finer spatial and temporal scales can be developed with the aim of constructing a model which allows for the investigation of spatial barriers against the spread of rabies. A comparison of the three example models shows that the main features of pattern-oriented models are generic. In producing comparative predictions which are related explicitly to scales, pattern-oriented modelling seems to be a strategy well suited to the ‘scaling up’ from population ecology to community and ecosystem ecology.

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