Improving the predictive assessment of water biological quality using macrophytes: Empirical testing and method selection

Abstract Bioassessment in southern European rivers has been hampered by difficulties in reference data availability and the unknown effect of the interacting multiple stressors on plant communities. Predictive modelling may help to overcome this limitation. This study aims to develop and evaluate macrophyte-based predictive models of the biological status of rivers using various modelling techniques. We compared models based on multiple linear regression (MLR), boosted regression trees (BRT) and artificial neural networks (ANNs). Secondarily, we investigated the relationship between two macrophyte indices grounded in distinct conceptual premises (the Riparian Vegetation Index – RVI, and the Macrophyte Biological Index for Rivers – IBMR) and a set of environmental variables, including climatic conditions, geographical characteristics, land use, water chemistry and habitat quality of rivers. We assembled a dataset of 292 Mediterranean sampling locations on perennial rivers and streams (mainland Portugal) with macrophyte and environmental data. The quality of models for the IBMR was higher than for the RVI for all cases, which indicates a better ecological linkage of IBMR with the stressor and abiotic variables. The IBMR using ANN outperformed the BRT models, for which the r-Pearson correlation coefficients were 0.877 and 0.801, and the normalised root mean square errors were 10.0 and 11.3, respectively. Variable importance analysis revealed that longitude and geology, hydrological/climatic conditions, water body size, and land use had the highest impact on the IBMR model predictions. Despite the differences in the quality of the models, all showed similar importance to individual input variables, although in a different order. Despite some difficulties in model training for ANNs, our findings suggest that BRT and ANNs can be used to assess ecological quality, and for decision-making on the environmental management of rivers.