Applications of the self-organising feature map neural network in community data analysis

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Freedom from restrictive assumptions that underlie many quantitative techniques make neural networks attractive for ecological investigations. The potential of the self organising feature map (SOFM) neural network for the classification, and to a lesser extent, ordination of vegetation data was investigated. The SOFM output was shown to correspond closely to classifications obtained from three alternative clustering algorithms, with similar samples located close together in the SOFM output space. Moreover, the classes were distributed spatially in the SOFM output by their relative similarity. This was evident with comparison against classifications derived at various levels of a hierarchical classification that revealed that the classes aggregated during each step of the hierarchical classification also tended to lie close together in the SOFM output space. As a consequence, the spatial distribution of classes in the SOFM output may represent the data in a manner similar to an ordination analysis. Some evidence for this inference is provided by comparison with the results of a standard ordination analysis.