Artificial Neural Network models (ANNs) were used to predict habitat suitability for 12 macroinvertebrate taxa, using
environmental input variables. This modelling technique was applied to a dataset of 102 measurement series collected in 31
sampling sites in the Greek river Axios. The database consisted of seven physical-chemical and seven structural variables, as well
as abundances of 90 macroinvertebrate taxa. A seasonal variable was included to allow the description of potential temporal
changes in the macroinvertebrate communities. The induced models performed well for predicting habitat suitability of the
macroinvertebrate taxa. Senso-nets and sensitivity analyses revealed that dissolved oxygen concentration and the substrate
composition always played a crucial role in predicting habitat suitability of the macroinvertebrates. Although ANNs are often
referred to as black box prediction techniques, it was demonstrated that ANNs combined with sensitivity analyses can provide
insight in the relationship between river conditions and the occurrence of macroinvertebrates, and thus deliver new ecological
knowledge. Consequently, these models can be useful in decision-making for river restoration and conservation management.