Predictive modelling of macroinvertebrate assemblages for stream habitat assessments in Queensland (Australia)

This paper describes the iterative approach towards predictive Artificial Neural Network (ANN) models for 37 macroinvertebrate taxa based on 896 stream data sets from the Queensland stream system. Data preprocessing and sensitivity analyses proved to be crucial in order to create data consistency and non-redundancy in the context of this approach. The model validation by means of 167 independent data sets revealed 73% as lowest rate and 82% as average rate of correct ANN predictions of stream site habitats. The increase of correct predictions was 30%, if ANNs and the statistical stream model AusRivAS were compared based on the same data sets. The validation of the ANN models justified their application to the prediction and assessment of stream habitats based on an independent database for test sites. Implications to stream management and research were drawn from prediction results.