Understanding how microbial diversity influences ecosystem properties is of paramount importance. Cellular traits-which determine responses to the abiotic and biotic environment-may help us rigorously link them. However, our capacity to measure traits in natural communities has thus far been limited. Here we compared the predictive power of trait richness (trait space coverage), evenness (regularity in trait distribution) and divergence (prevalence of extreme phenotypes) derived from individual-based measurements with two species-level metrics (taxonomic richness and evenness) when modelling the productivity of natural phytoplankton communities. Using phytoplankton data obtained from 28 lakes sampled at different spatial and temporal scales, we found that the diversity in individual-level morphophysiological traits strongly improved our ability to predict community resource-use and biomass yield. Trait evenness-the regularity in distribution of individual cells/colonies within the trait space-was the strongest predictor, exhibiting a robust negative relationship across scales. Our study suggests that quantifying individual microbial phenotypes in trait space may help us understand how to link physiology to ecosystem-scale processes. Elucidating the mechanisms scaling individual-level trait variation to microbial community dynamics could there improve our ability to forecast changes in ecosystem properties across environmental gradients.

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Eutrophication of shallow lakes often triggers a series of cascading ecological effects. Among these are shifts in the zooplankton community structure due to phytoplankton changes, or shifts in the fish community reducing size-selective feeding of planktivorous fish. In such conditions larger zooplankton (e.g. Daphnia) can have a selective advantage. Re-oligotrophication can reverse such food web interactions. Europe's largest wetland system (the Danube Delta) went through a period of eutrophication and is now slowly recovering again. However, changes in the Daphnia populations triggered by eutrophication in this system have remained unstudied. We used different sampling strategies to screen 24 lakes (which differ in their ecological state) for the presence of Daphnia as well as for biotic and abiotic parameters potentially explaining Daphnia abundance. Daphnia densities were surprisingly low. We found D. magna ephippia in the sediment, but no D. magna in the water column. Microsatellite analyses on pelagic Daphnia populations confirmed the presence of the Daphnia longispina complex and provided evidence for significant hybridisation events. FluoroProbe data showed that Daphnia was mainly present in lakes with a higher phytoplankton production. Our study provides insights into the spatial and temporal distribution of Daphnia in a very dynamic wetland system.

Danube Delta shallow lakes experience cyanobacteria blooms that can negatively affect the aquatic ecosystem. Although there are several studies on Danube Delta cyanobacteria, little is known about their spatial-temporal patterns and the potential predictive role they can offer.We therefore analyzed the distribution of cyanobacteria in 19 lakes belonging to three lake complexes, and tested whether their seasonal dynamics are in line with the predictions of the PEG model. Furthermore, we investigated to which extent cyanobacteria diversity and abundance were related to lake hydrogeomorphological characteristics such as: surface, water level, connectivity, water retention, flood risk, transparency. Although lakes had different seasonal cyanobacterial assemblages, the biovolume and genus richness had a geographical pattern, decreasing from south-east (lakes forming the fluvial delta) towards north-west (lakes forming the maritime delta). Cyanobacteria biovolume reflected largely the PEG model peaking in summer (the fluvial delta) and autumn ( the maritime delta). Genus richness followed the same pattern. Cyanobacteria distribution was predicted by various abiotic (e.g. risk of flooding, connectivity) and biotic factors (e.g. submersed macrophytes, phytoplankton diversity, peat deposits). Our study contributes to the understanding of cyanobacteria diversity and distribution in shallow interconnected lakes by revealing the complexity of predictors for geographical and seasonal patterns.

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