Temperature, productivity, and habitat characteristics collectively drive lake food web structure.

While many efforts have been devoted to understand variations in food web structure among terrestrial and aquatic ecosystems, the environmental factors influencing food web structure at large spatial scales remain hardly explored. Here, we compiled biodiversity inventories to infer food web structure of 67 French lakes using an allometric niche-based model and tested how environmental variables (temperature, productivity, and habitat) influence them. By applying a multivariate analysis on 20 metrics of food web topology, we found that food web structural variations are represented by two distinct complementary and independent structural descriptors. The first is related to the overall trophic diversity, whereas the second is related to the vertical structure. Interestingly, the trophic diversity descriptor was mostly explained by habitat size (26.7% of total deviance explained) and habitat complexity (20.1%) followed by productivity (dissolved organic carbon: 16.4%; nitrate: 9.1%) and thermal variations (10.7%). Regarding the vertical structure descriptor, it was mostly explained by water thermal seasonality (39.0% of total deviance explained) and habitat depth (31.9%) followed by habitat complexity (8.5%) and size (5.5%) as well as annual mean temperature (5.6%). Overall, we found that temperature, productivity, and habitat characteristics collectively shape lake food web structure. We also found that intermediate levels of productivity, high levels of temperature (mean and seasonality), as well as large habitats are associated with the largest and most complex food webs. Our findings, therefore, highlight the importance of focusing on these three components especially in the context of global change, as significant structural changes in aquatic food webs could be expected under increased temperature, pollution, and habitat alterations.

Identification of ecological thresholds from variations in phytoplankton communities among lakes: contribution to the definition of environmental standards.

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.