RESUMO
In aquatic ecosystems, the identification of ecological thresholds may be useful for managers as it can help to diagnose ecosystem health and to identify key levers to enable the success of preservation and restoration measures. A recent statistical method, gradient forest, based on random forests, was used to detect thresholds of phytoplankton community change in lakes along different environmental gradients. It performs exploratory analyses of multivariate biological and environmental data to estimate the location and importance of community thresholds along gradients. The method was applied to a data set of 224 French lakes which were characterized by 29 environmental variables and the mean abundances of 196 phytoplankton species. Results showed the high importance of geographic variables for the prediction of species abundances at the scale of the study. A second analysis was performed on a subset of lakes defined by geographic thresholds and presenting a higher biological homogeneity. Community thresholds were identified for the most important physico-chemical variables including water transparency, total phosphorus, ammonia, nitrates, and dissolved organic carbon. Gradient forest appeared as a powerful method at a first exploratory step, to detect ecological thresholds at large spatial scale. The thresholds that were identified here must be reinforced by the separate analysis of other aquatic communities and may be used then to set protective environmental standards after consideration of natural variability among lakes.