Predicting freshwater biological quality using macrophytes: A comparison of empirical modelling approaches

Abstract

Difficulties have hampered bioassessment in southern European rivers due to limited reference data and the unclear impact of multiple interacting stressors on plant communities. Predictive modelling may help overcome this limitation by aggregating different pressures affecting aquatic organisms and showing the most influential factors. We assembled a dataset of 292 Mediterranean sampling locations on perennial rivers and streams (mainland Portugal) with macrophyte and environmental data. 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. The quality of models for the IBMR was superior to those for the RVI in 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.

Keywords: Artificial neural networks; Bioindication; Boosted regression trees; Linear regression; Macrophytes; River assessment.

Fig. 1

Map of the distribution of sampling sites (n = 292) in Portugal, SW Europe

Fig. 2

Values of IBMR and RVI (validation dataset) predicted and observed by different models

Fig. 3

Relative contribution of input variables (factors derived in FA) in different IBMR (a) and RVI (b) models