Cost effective prediction of the eutrophication status of lakes and reservoirs

1. Eutrophication is a serious threat in many parts of the world, and identifying the environmental factors that determine the spatial distribution of eutrophicated waterbodies as well as the development of management tools is a challenge. 2. In this study, data from the Ile‐de‐France region were analysed to determine if catchment scale environmental variables could predict concentrations of chlorophyll a (used as a proxy for eutrophication status) of artificial lakes and reservoirs. 3. General additive models (GAM) and random forest models (RF) displayed greater predictive power than generalised linear models, indicating the importance of non‐monotonic relationships. Using RF modelling, very high predictive accuracy was achieved for both continuous and binomial (eutrophic or not) response variables (continuous: R² = 0.715; binomial: kappa = 0.764, 89% of waterbodies were accurately predicted). The better predictive power and robustness of RF versus GAM was attributed to the formers ability to better handle complex interactions between predictors and to account for threshold effects. 4. Our results confirmed the close link between the water quality of lakes and reservoirs and the characteristics of their catchments. Moreover, we also showed that (i) simple (e.g. linear and/or monotonic) relationships between catchment land use and water quality were only found for sub‐regional datasets, and (ii) land use needs to be considered in association with complementary environmental variables (hydromorphological variables) to best assess its impact on water quality.