Relating Biodiversity of River Communities to Physical and Chemical Water Properties

Abstract

We address the problem of finding relationships between the physical and chemical properties of river water and the biodiversity of the community present in that water . We apply the machine learning approach of induction of regression trees to biological and chemical data collected through regular monitoring of rivers in Slovenia. A predictive model is built, which identifies the most important parameters for predicting the species richness (the number oftaxa) of the community: these include biological oxygen demand (an overall indicator of pollution), water temperature, the season (month), total hardness, NO3, SiO2 and alkalinity.