Development of artificial neural network models predictingmacroinvertebrate taxa in the river Axios (Northern Greece)

Abstract

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