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.