Data preparation
Data preparation and analyses were done in R 3.5.3 (R Development Core Team 2019). The code of data analysis is provided as Research Compendium on github. It will be provided in final version and is not yet added here because of the double-blind review process.
To have comparable conditions we selected all lakes from the dataset that are deep (maximum depth >5m), not artificial and have a natural water level dynamic (i.e. not influenced by storage power plants) and at least one sampling repetition.
To describe the local water level fluctuation (WLF) we calculated for each lake the difference between mean high water (MHW) and mean low water (MLW).
\begin{equation} WLF=MHW-MLW\nonumber \\ \end{equation}
Based on the monthly abiotic measurements, we calculated annual means for all chemical-physical variables based on monthly measurements at the lake surface. For this calculation, we considered measurement campaigns with at least eight monthly values available. Values below the detection were assumed to be zero. To describe the water layering of the lakes we used the standard deviation of the water temperature measurements of surface, -2 m, -4 m and -6 m depth (Tempsd). The available geographic and chemical-physical variables and their mean, standard deviation, median, minimal and maximal values are given in Table 1.
From the macrophytes surveys, we excluded datasets with (I) just one plot or transect for a lake and year, (II) species that were identified as emergent or floating plants and (III) plants that were not identified down to the species level. For further calculations we transformed the depth ranges to decimal numbers by the mean of their limits. That is, the depth range of 0-1 m was converted to -0.5 m depth, the range of 1-2 m in -1.5 m, 2-4 m in -3.0 m and >4 m in a depth of -5.0 m.