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In this study, decision tree models were induced to predict the habitat suitability of six macroinvertebrate taxa: Asellidae, Baetidae, Caenidae, Gammaridae, Gomphidae and Heptageniidae. The modelling techniques were applied on a dataset of 102 samples collected in 31 sites along the river Axios in Northern Greece. The database consisted of eight physical-chemical and seven structural variables, as well as the abundances of 90 macroinvertebrate taxa. A seasonal variable was included allowing the description of potential temporal changes in the macroinvertebrate taxa. Rules relating the presence/absence of six benthic macroinvertebrate taxa with the 15 physical-chemical and structural river characteristics and the seasonal variable were induced using the J48 algorithm. In order to improve the performance and the interpretability of the induced models, three optimisation techniques were applied: tree-pruning, bagging and boosting. The predictive performance of the decision tree models was assessed on the basis of the percentage of Correctly Classified Instances (CCI) and the Cohen's kappa statistic. The results of the present study demonstrated that although the models had a relatively high predictive performance, noise in the dataset and inappropriate input variables prevented to some extent, the models from making reliable predictions. Although tree-pruning did not improve significantly the reliability of the induced models, it reduced considerably the tree complexity and in this way increased the transparency of the trees. Consequently, the induced models allowed for a correct ecological interpretation. The effect of bagging and boosting on the other hand varied considerably between the different models, as well as within different repetitions of 10-fold cross-validation in an individual model. In some cases the predictive performance was improved, in others stable or even worsened. The effect of bagging and boosting seemed to be strongly dependent on the dataset on which the two techniques were applied. Tree-pruning thus proved to have a high potential when applied in models used for decision-making of river restoration and conservation management.

This study aimed at analysing the relationship between river characteristics and abundance of Gammarus pulex. To this end, four methods which can identify the relative contribution and/or the contribution profile of the input variables in neural networks describing the habitat preferences of this species were compared: (i) the "PaD" ("Partial Derivatives") method consists of a calculation of the partial derivatives of the output in relation to the input variables; (ii) the "Weights" method is a computation using the connection weights of the backpropagation Artificial Neural Networks; (iii) the "Perturb" method analyses the effect of a perturbation of the input variables on the output variable; (iv) the "Profile" method is a successive variation of one input variable while the others are kept constant at a fixed set of values. The dataset consisted of 179 samples, collected over a three-year period in the Zwalm river basin in Flanders, Belgium. Twenty-four environmental variables as well as the log-transformed abundance of Gammarus pulex were used in this study. The different contribution methods gave similar results concerning the order of importance of the input variables. Moreover, the stability of the methods was confirmed by gradually removing variables. Only in a limited number of cases a shift in the relative importance of the remaining input variables could be observed. Nevertheless, differences in sensitivity and stability of the methods were detected, probably as a result of the different calculation procedures. In this respect, the "PaD" method made a more severe discrimination between minor and major contributing environmental variables in comparison to the "Weights", "Profile" and "Perturb" methods. From an ecological point of view, the input variables "Ammonium" and to a smaller extent "COD", were selected by these methods as dominant river characteristics for the prediction of the abundance of Gammarus pulex in this study area.

Ecological models can act as interesting tools to support decision-making in river restoration management. In particular models which are capable of predicting the habitat requirements of species are of considerable importance to ensure that the planned actions have the desired effects on the aquatic ecosystem. To this end, Artificial Neural Network (ANN) models were tested and optimized for the prediction of the habitat suitability for Gammarus pulex, a relevant indicator species in water quality assessment. Although ANN models are in general quite robust with a rather high predictive reliability, the model performance had to be increased with regard to simulations for river restoration management. In particular, it has been shown that spatial and temporal expert-rules could possibly be included. Migration dynamics of downstream drift and upstream migration of the organisms and migration barriers along the river (weirs, culverted river sections, ...) might indeed deliver important additional information on the effectiveness of the restoration plans, and also on the timing of the expected effects. In this context, an additional in-stream migration model for Gammarus pulex was developed. This migration model, implemented in a Geographical Information System (GIS), has been used to simulate a practical river restoration scenario for a river in Flanders, Belgium. The case study illustrated that the removal of a weir, at a particular site, resulted in the improvement of the habitat suitability for Gammarus pulex. The ANN models predicted that after restoration the habitat was suitable again for Gammarus pulex. The migration model indicated that the restored parts of the river would be recolonized within about 2 months. In this way, decision makers can have an idea whether and when a restoration option will have a desired effect.