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\begin{document}

\title{Depth diversity gradients of macrophytes: shape, drivers and recent
shifts}



\author[1]{Anne Lewerentz}%
\author[2]{Markus Hoffmann}%
\author[1]{Juliano Sarmento Cabral}%
\affil[1]{Julius-Maximilians-Universitat Wurzburg}%
\affil[2]{Technical University of Munich}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\selectlanguage{english}
\begin{abstract}
Investigating diversity gradients helps to understand biodiversity
drivers and threats. However, one diversity gradient is seldomly
assessed, namely how plant species distribute along the depth gradient
of lakes. Here, we provide the first in-depth characterization of depth
diversity gradients (DDG) of submerged macrophytes across different
lakes. We characterize the DDG for additive richness components (alpha,
beta, gamma), assess environmental drivers and address temporal change
over recent years. We take advantage of yet the largest dataset of
macrophyte occurrence along lake depth (274 depth transects across 28
deep lakes) as well as of physio-chemical measurements (12 deep lakes
from 2006 to 2017 across Bavaria), provided publicly online by the
Bavarian State Office for the Environment. We found a high variability
in DDG shapes across the study lakes. The DDG for alpha and gamma
richness are predominantly hump-shaped, while beta richness shows a
decreasing DDG. Generalized additive mixed-effect models indicate that
the maximum alpha richness within the depth transect
(\emph{R\textsubscript{max}}) is significantly influenced by lake area
only, whereas for the corresponding depth (\emph{D\textsubscript{max}})
are influenced by light quality, light quantity and layering depth. Most
observed DDGs seem generally stable over recent years. However, for
single lakes we found significant linear trends for Rmax and Dmax going
into different directions. The observed hump-shaped DDGs agree with
three competing hypotheses: the mid-domain effect, the mean-disturbance
hypothesis, and the mean-productivity hypothesis. The DDG amplitude
seems driven by lake area (thus following known species-area
relationships), whereas skewness depended on physio-chemical factors,
mainly water transparency and layering depth. Our results provide
insights for conservation strategies and for mechanistic frameworks to
disentangle competing explanatory hypotheses for the DDG.%
\end{abstract}%



\sloppy


\section*{Introduction}

{\label{introduction}}

Describing and explaining biodiversity gradients have been central goals
of biogeography and ecology since the beginning of the respective fields
(Gaston 2000). Improving our understanding of the biodiversity gradients
and their drivers is still an important requirement to deal with
impending species loss. Therefore, many studies have explored
environmental gradients as explanatory variables for biodiversity
patterns along different geographic scales (Rahbek 2004, Whittaker et
al. 2007) like (1) latitude (Stehli et al., 1969; Rohde, 1992; Pontarp
et al., 2019; Etienne et al. 2019), (2) elevation (Hutchinson 1953,
Rahbek 1995, Lomolino 2001, Nogu\selectlanguage{ngerman}és-Bravo et al. 2008, Colwell and Rangel
2010, Sanders and Rahbek 2012, Graham et al. 2014, Rahbek et al. 2019),
(3) tree height in forests (Petter et al. 2016), (4) depth in soils
(Rendo\selectlanguage{polish}š \selectlanguage{english}et al. 2016, Jak\selectlanguage{polish}š\selectlanguage{english}ov\selectlanguage{ngerman}á et al. 2019) or (5) depth in water (Rex and
Etter 1998, Smith and Brown 2002, Gong et al. 2015). These geographical
gradients share some environmental gradients, which are expected to
influence spatial structuring of diversity gradients, e.g. temperature,
light or seasonality. However, the shorter spatiotemporal scales are,
the less confounding biogeographical contingencies there are, such as
the legacy of the glacial cycles on latitudinal gradients, and
dispersal/connectivity limitations. Hence, studying gradients expressed
at short spatiotemporal extents may provide valuable insights on drivers
of biodiversity. On the one hand, the best studied biodiversity
gradients are the large spatiotemporal gradients like the decreasing
diversity with latitude (Pontarp et al. 2019) and the decreasing or
hump-shaped diversity with elevation (Rahbek 1995). On the other hand,
the short spatiotemporal gradients, like depth in freshwaters, are often
overlooked.

Freshwater ecosystems have a high biodiversity, but also show a high
rate of species loss (Strayer and Dudgeon 2010, Gatti 2016, He et al.
2017), exceeding those of terrestrial systems (Dudgeon et al. 2006).
Despite this, studies that focus on the diversity gradient in freshwater
are surprisingly scarce, although light gradients in freshwater must
represent a very strong driver. The few studies seem to show
predominantly a general decrease of biodiversity along the depth
gradient, e.g. for bacteria (Cantonati et al. 2014, Zhao et al. 2019),
Chironomids (Zhao et al. 2019) or diatoms (Kingsbury et al. 2012,
Stoof-Leichsenring et al. 2020)\emph{,} or hump-shaped patterns along
depth, e.g. for diatoms (Zhao et al. 2019) or submerged macrophytes (Ye
et al. 2018). Thus, it is not clear if the patterns are generalizable
for different species groups and even across different lakes.

Macrophytes play a pivotal role in lakes by reducing nutrient
concentrations (Song et al. 2019), by providing food for a lot of other
species (Bakker et al. 2016) and by giving shelter to a large number of
other aquatic organisms like zooplankton, juvenile fish and amphibians
(Jeppesen et al. 1998). However, there are several knowledge gaps on
macroecology of freshwater plants (Alahuhta et al. 2020) and especially
the depth pattern of submerged macrophytes is sparsely studied and
remains unclear (Fu et al. 2014a, b, Ye et al. 2018). The few studies
that have looked at depth distribution of macrophytes in lakes focussed
on Lake Erhai in Yunnan Province, China. They report a hump-shaped
pattern along the water depth gradient for species richness and
community biomass of submerged macrophyte species (Ye et al. 2018).
Looking at all functional types including emergent species, Lake Erhai
shows a significant decrease in taxonomic and functional diversity along
the water depth gradient and its niche differentiation (Fu et al. 2014a,
b). Hence, it remains unclear if the described pattern is generalizable
and whether it stays robust over time.

The lack of studies is intriguing because the environmental gradients
along lake depth represents one of the sharpest found in nature, with
strong variation in just few meters. With increasing lake depth,
multiple abiotic factors that influence the growth of macrophytes
(light, temperature, nutrients, water quality, disturbances/hydrologic
variability) drastically change (Bornette and Puijalon 2011). Light is
gradually attenuated with increasing depth due to absorption and
scattering, resulting in a specific reduction of light quality and
quantity depending on depth and on the water turbidity. Water
temperature in deep lakes does not decrease gradually, but rather
abruptly with depth (Bornette and Puijalon 2011). The formation of
thermally stratified lakes results in an abrupt thermocline, especially
during growing season. The thermocline influences the within-lake fluid
dynamics in each thermal layer, further leading to stratified gradients
in nutrients and water quality components during stratification
(Bornette and Puijalon 2011). Moreover, mechanical disturbances, like
wind or waves, lose their influence gradually with depth (Van Zuidam and
Peeters 2015). Also the probability that water level fluctuations result
in drying up the soil (Evtimova and Donohue 2016) is reduced and
browsing pressure by water fowl decreases with depth (Bakker et al.
2016). How these different environmental gradients influence the species
richness of macrophytes stays unclear, although knowing the processes
shaping species diversity might help to predict how global change will
affect biodiversity and how management strategies might mitigate
potential negative diversity responses.

This study aims to describe the depth distribution of macrophyte
diversity and assessing the relative importance of its drivers.
Specifically, we ask the following questions:

\begin{enumerate}
\tightlist
\item
  1.1. What is the general shape of the depth diversity gradient (DDG)
  of submerged macrophytes in deep lakes? 1.2. Are there differences
  between lakes and diversity components (alpha-, beta, gamma richness)?
\item
  What are the drivers for macrophyte DDG?
\item
  3.1. Has the DDG being stable over recent years? 3.2. Are temporal
  trends general or lake-specific?
\end{enumerate}

To address these questions, we use the macrophytes occurrence data of
274 transects taken along 13 years across 28 natural deep lakes in
Bavaria, that were mapped for the monitoring of the European Water
Framework Directive. We expect a hump-shaped DDG (question 1.1)
corresponding to previous punctual empirical evidence and following the
typical patterns found along elevation. We assume no broad differences
between lakes and diversity components as the pattern is supposed to be
generalisable (question 1.2). To tackle question (2), we test whether
the shape of the DDG can be explained by geographic and
physical-chemical characteristics of the lakes. We expect water quality
to have a high degree of influence, since water quality influences
resource availability (light, temperature). Finally, we assess for
question 3.1 -3.2 whether there have been detectable temporal changes in
the DDG. We suppose that the DDG is a quite stable pattern over time as
macrophytes react slowly to changes (Bakker et al. 2013). However, due
to the overall warming in annual average water temperature during the
last decades we expect that species richness increases, as invasive
species are expected, and warm-adapted species might expand. Our results
provide the most refined and extensive assessment of macrophyte
biodiversity patterns in freshwater lakes up to date, giving insights
for the development of long-term conservation strategies for freshwater
systems in general.

\section*{Material and Methods}

{\label{material-and-methods}}

\textbf{Data and study area}

Bavaria has a broad variety of lakes, which vary in size, depth,
altitude and physiochemical parameters. Information about surface area
and maximal lake depth were provided by Bayerisches Landesamt für
Wasserwirtschaft (1987). We obtained data for water level statistics
from the hydrological service of Bavaria (https://www.gkd.bayern.de/).
Physical-chemical data include monthly measurements at the deepest point
of the lake. We selected the physical-chemical parameters that were
measured for the largest number of lakes. The full list of environmental
variables is given in Table 1.

Macrophyte data was also extracted from the Bavarian State Office for
the Environment (www.lfu.bayern.de). The macrophyte data were recorded
for the EU-Water Framework Directive Monitoring and include vegetation
surveys for all large lakes of Bavaria (\textgreater{}0.5 km² surface
area) for at least one and maximum five different years. At each lake,
macrophyte data for several transects perpendicular to the shoreline at
characteristic sections are available (see sampling strategy in Fig.
1a). Each transect is subdivided along the depth gradient into four
depths (0-1m; 1-2m; 2-4m; \textgreater{}4m). At each depth all species'
frequency is sampled in five steps following the scale of Kohler (1978),
an estimate of abundance.

\textbf{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. \emph{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 \textgreater{}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 \textgreater{}4~m in a depth of -5.0 m.

\textbf{Species richness components and depth diversity gradient
measures}

As depth-independent component of species richness, we calculated gamma
richness as the total number of species per lake and year.

As depth-dependent component of species richness, we determined for
every lake and year an additive alpha richness as the number of species
per depth range averaged across of transects (Fig. 1b). The gamma
richness for every lake and year was defined as the total number of
species per depth range (Fig. 1c). We then calculated an additive beta
richness as gamma richness minus alpha richness (Tuomisto 2010) (Fig.
1d).

To further characterize the diversity depth gradient, we identified the
peak of the richness depth curve (Fig. 1c). For each transect we
filtered the depth with the maximal species number. Thereafter, we
averaged this valued across transects per lake and year, from now on
termed the depth with maximal alpha richness
(\selectlanguage{greek}\emph{D\textsubscript{(α,max)}}\selectlanguage{english} ). The corresponding maximal species
number averaged across transects is termed the maximal alpha richness
(\selectlanguage{greek}\emph{R\textsubscript{(α,max)}}\selectlanguage{english} ). Similarly, the depth with maximal
gamma richness (\selectlanguage{greek}\emph{D\textsubscript{(γ,max)}}\selectlanguage{english} ) is the depth of the
maximal gamma richness (\selectlanguage{greek}\emph{R\textsubscript{(γ,max)}}\selectlanguage{english} ) along depth,
as well as maximal beta depth (\selectlanguage{greek}\emph{D\textsubscript{(β,max)}}\selectlanguage{english} )
describes maximal beta richness (\selectlanguage{greek}\emph{R\textsubscript{(β,max)}}\selectlanguage{english} ) along
depth.

\textbf{Statistical analysis}

We addressed the study questions with several analyses, focusing on
different dataset levels dependent on data availability.
The\emph{biodiversity dataset} contains all macrophyte recordings (274
mapped transects in 100 field campaigns, mapping of lake in one year is
called \emph{field campaign} ) of the selected 28 lakes. As no complete
information is available for all mapped lakes and years, we compiled two
subsets of the \emph{biodiversity dataset} : The \emph{environmental \&
biodiversity dataset} is a subset dataset with all macrophyte recordings
for which all abiotic data (see Table 1) were available. This dataset
includes data from 12 lakes, 27 field campaigns and 147 transects. For
the \emph{biodiversity time series dataset} we selected all lakes for
which repeated mappings for at least 3 years were available. This
condition was fulfilled for 17 lakes mapped in 73 field campaigns along
194 transects. Analyses for each research question are described below.

For the first question, concerning the general depth distribution
pattern, we used the richness components including the different DDG
measures and determined pattern types. We plotted as general DDG curves
the mean and standard deviation of alpha, beta and gamma richness for
each depth (Question 1.1). We performed simultaneous tests for linear
models with multiple comparisons of means using Tukey contrasts that are
robust under non-normality, heteroscedasticity and variable sample size
(Herberich et al. 2010) to compare the richness across depth for
significant difference. Furthermore, we plotted the different DDG peaks
(DDG measures) for alpha, beta and gamma richness and determined the
corresponding regression line by fitting a linear model. We classified
the DDG curves for all three richness measures in four pattern types
depending on the depth of the richness curve maximum: Decreasing
(\emph{D\textsubscript{max}} \textgreater{} -1m), shallow hump-shaped
(\emph{D\textsubscript{max}} between -1 and -2 m), deep hump-shaped
(\emph{D\textsubscript{max}} between -2 and -4 m) and increasing
(\emph{D\textsubscript{max}} \textless{} -4~m) (Fig. 1d). To determine
the correlations between the different diversity components (Question
1.2) we performed a Pearson correlation test between depth dependent
richness components. Furthermore, we tested for correlations between DDG
measures across the different richness components. A Chi-square test
helped to look at associations between pattern types and biodiversity
components.

For the second question, concerning the drivers of the diversity depth
gradient, we analysed the influence of abiotic data on the DDG using
the\emph{environmental \& biodiversity dataset} . We log-transformed the
abiotic and biotic data. To show that the diversity metrics of
the\emph{environmental \& biodiversity dataset} are representative for
the diversity metrics of \emph{biodiversity dataset} we applied the
PERMANOVA test \emph{adonis2} , using the R package `vegan' which
compares centroids and the variance (Oksanen et al. 2019). A
non-significant result (\emph{p} \textgreater{}0.05) confirms that
centroids and variance of two groups are not different (Supporting
information). To identify the driving factors on the richness peaks we
used Generalized Additive Mixed-Effect Models (GAMMs), computed with the
R package 'gamm4' (Wood 2011). The
\selectlanguage{greek}\emph{D\textsubscript{(α,β,γ,max)}}\selectlanguage{english}and
\selectlanguage{greek}\emph{R\textsubscript{(α,β,γ,max)}}\selectlanguage{english} were used as response variables, the
lake as random effect. To reduce the high correlations between abiotic
factors (Pearson correlation test) we performed a Principle Component
Analysis (PCA) analysis and named the main axis (\textgreater{}80\%
variance) after the corresponding abiotic factor, whenever an axis
encompassed more than 40\% of the variation of a variable. We used the
loadings of the main PCA axes (\textgreater{}80\% variance) as
explanatory variables for the GAMM. We constructed a full model with all
PCA axes, then we stepwise excluded the least significant terms until
obtaining a minimal model (Wood 2008).

To answer the third questions about the temporal change of the depth
diversity gradient, we used the \emph{biodiversity time series dataset}
. First, we calculate the Invariability Coefficient (IC) as inverse of
the Coefficient of Variation (CV):

\begin{equation}
IC=\frac{1}{\text{CV}}=\frac{1}{\frac{\text{sd}}{\text{mean}}}\ =\frac{\text{mean}}{\text{sd}}\nonumber \\
 \end{equation}

The IC is a statistical tool to evaluate the degree of invariability
also for datasets with different means (Question 3.1). To check for
temporal trends, we built simple linear regression models for depth
independent gamma richness and the DDG
measures,\selectlanguage{greek}\emph{D\textsubscript{(α,β,γ,max)}}\selectlanguage{english}
and\selectlanguage{greek}\emph{R\textsubscript{(α,β,γ,max)},}\selectlanguage{english} as response variables and time
as explanatory variable for (a) the complete dataset and (b) each
individual lake. We identified all models that showed significant linear
trends (\emph{p} \textless{}0.1) and characterized the direction of
their slopes (Question 3.2).

\section*{Results}

{\label{results}}

For the dataset of all macrophytes recordings (\emph{biodiversity
dataset} ), a total of 75 submerged species is documented in Bavaria.
The available taxonomic groups are mainly Spermatophytes (65 species),
Charophytes (20 species), Bryophytes (5 species) and Pteridophytes (2
species). The complete abiotic and biotic data (\emph{environmental \&
biodiversity dataset} ) cover 57 different species, whereas
the\emph{biodiversity time series dataset} included 66 species. The
total (depth-independent) gamma richness per lake ranges from 5 to 34
species of submerged macrophytes. The mean gamma richness averaged
across lakes is 15.36 species with a standard deviation of 6.27 species.

\textbf{The depth diversity gradient (DDG) patterns of macrophytes}

The mean depth pattern of submerged macrophytes' alpha and gamma
richness is hump-shaped, showing a peak between -1 and -2 m respectively
(Fig. 2a,c). The mean alpha richness at the hump's peak is 4.5 species
(sd = 2.2), whereas the mean gamma richness peak is 11.4 species (sd =
5.1). In contrast, Beta richness shows a decreasing curve with its
highest richness being 7.0 species (sd = 4.0) between surface and -1~m
depth (Fig. 2b). However, all three richness components show high
standard deviations in the depth classes. They vary across depth classes
between 1.9 and 2.3 species for alpha richness, between 3.7 and 4.1 for
beta richness and from 5.1 till 5.6 for gamma richness (see individual
DDG curves for all lakes in Supporting information).

Comparing the richness components across depths revealed only
significant differences between mid and greater depths, but not for
shallower depth (Supporting information).

Plotting the DDG measures for all three richness components (Fig. 2d-f)
we find a hump-shaped pattern for alpha richness and a bi-modal pattern
for beta and gamma richness.

Looking at the DDG patterns of single field campaigns, for alpha
richness, hump-shaped curves with a peak between -2 and -4~m are most
frequent (52\%) (Fig. 2g-i). For beta richness the majority are
decreasing curves (40\%) while for gamma richness hump-shaped curves
with a peak between -1 and -2~m were slightly prevailing (39\%)., All
depth pattern types are found for all three measures.

The three depth-dependent richness measures are significantly correlated
with one another (\emph{p~} \textless{} 0.05). The strongest correlation
shows beta and gamma richness (cor~= 0.95), followed by alpha and gamma
richness (cor~= 0.85) and alpha and beta richness (cor~= 0.64).
The\emph{D\textsubscript{max}} and \emph{R\textsubscript{max}} do not
correlate within the respective richness components (\emph{p~}
\textless{} 0.05). However, the \emph{D\textsubscript{max}}values across
the three richness components correlates with each other. Similarly,
\emph{R\textsubscript{max}} also correlates across the three richness
components (\emph{p~} \textless{} 0.05). For correlation coefficients
see Supporting information. However, a Chi-square test shows, that
pattern types and richness components are statistically significantly
associated (\emph{p} ~= 0.0005).

\textbf{Drivers of the depth diversity gradients}

The DDG measures correlate with some of the abiotic variables
(Supporting information). \selectlanguage{greek}\emph{R\textsubscript{(α,max)}}\selectlanguage{english} correlates
highly significantly (\emph{p} ~\textless{} 0.01) with \emph{area}
(cor~= 0.53, \emph{p} ~\textless{} 0.01), \emph{water level
fluctuations} (cor~= 0.54, \emph{p} ~\textless{} 0.01),
\emph{conductivity} (cor~= 0.53,\emph{p} ~\textless{} 0.01),
\emph{NH\textsubscript{4}N} (cor~= -0.5,\emph{p~} \textless{}~0.01),
\emph{SiO\textsubscript{2}} (cor~= 0.62,\emph{p} ~\textless{} 0.01) and
\emph{spectral absorption coefficient}(cor~= 0.6, \emph{p} ~\textless{}
0.01). \selectlanguage{greek}\emph{R\textsubscript{(β,max)}}\selectlanguage{english} correlates highly significantly
(\emph{p} ~\textless{}~0.01) with\emph{area} (cor~= 0.55, \emph{p}
~\textless{} 0.01).\selectlanguage{greek}\emph{R\textsubscript{(γ,max)}}\selectlanguage{english} correlates highly
significantly (\emph{p} ~\textless{} 0.01) with \emph{area} (cor~=
0.57,\emph{p} ~\textless{} 0.01) and \emph{water level fluctuations}
(cor~= 0.56, \emph{p~} \textless{} 0.01).
\selectlanguage{greek}\emph{D\textsubscript{(α,max)}}\selectlanguage{english} correlates highly significantly
(\emph{p~} \textless{} 0.01) with\emph{O\textsubscript{2} dissolved}
(cor~= -0.54, \emph{p~} \textless{} 0.01), \emph{total phosporus
content} (cor~= 0.6, \emph{p~} \textless{} 0.001), \emph{transparency}
(cor~= -0.67, \emph{p~} \textless{} 0.001) and \emph{tempsd} (cor~=
0.59, \emph{p~} \textless{} 0.01).\selectlanguage{greek}\emph{D\textsubscript{(β,max)} and
D\textsubscript{(γ,max)}}\selectlanguage{english} do not correlate highly significantly
(\emph{p} \textless{}0.01) with any of the abiotic variables.

Abiotic and biotic variables are correlated with one another in a
complex fashion (Supporting information). Strongest positive
correlations (cor~\textgreater{} 0.7 or \textless{} -0.7) within abiotic
data were found between \emph{N\textsubscript{tot}}
and\emph{NO\textsubscript{3}N} (cor~= 0.92, \emph{p} ~\textless{}
0.01),\emph{conductivity} and \emph{SiO\textsubscript{2}} (cor~=
0.83,\emph{p} ~\textless{} 0.01), c\emph{hloride} and
\emph{conductivity}(cor~= 0.73, \emph{p} ~\textless{} 0.01). Strongest
negative correlations showed \emph{transparency} and \emph{total}
\emph{phosphorus content}(cor~= -0.72, \emph{p~} \textless{} 0.01) and
\emph{transparency} and\emph{conductivity} (cor~= -0.71, \emph{p~}
\textless{} 0.01).

Due to the high correlation coefficients between abiotic factors, we
performed a PCA (Supporting information). We use the first four axes
(81\% of total variation -- Fig. 3f-i) to address the DDG drivers. The
first axis, PC1, can be characterized as the\emph{``SiO\textsubscript{2}
\& Conductivity axis''} (both positive with the axis)\emph{,} explaining
30.1\% of the variance. The PC2, the second axis, can be described as
the ``\emph{Temperature \& P\textsubscript{tot} axis} '' as both abiotic
variables have the highest (negative) impact (26.1\% of variance). The
third axis, PC3, can be named the ``\emph{Temperature sd -- Chloride
axis} '' (13.3\% of variance) as it ranges from most negative variable
\emph{temp\_sd} to most positive variable c\emph{hloride} while the
fourth axis, PC4 shows the ``\emph{O\textsubscript{2diss} -- SAC axis}
'' (10.5\% of variance) spanned between \emph{O\textsubscript{2diss}}
(most negative) and\emph{SAC} (most positive).

The GAMM showed that \selectlanguage{greek}\emph{D\textsubscript{(α, max)}}\selectlanguage{english} (R\selectlanguage{ngerman}²=0.73)
significantly varies with all four PCA axes (Fig. 3a-d).
The\selectlanguage{greek}\emph{D\textsubscript{(α, max)}}\selectlanguage{english} decreases with PC2
(\emph{Temperature \& P\textsubscript{tot} axis} ) and PC3 (\emph{Tempsd
-- Chloride axis} ) axes , slightly increases with PC4 axis
(\emph{O\textsubscript{2diss} -- SAC axis} ) and increases only for
extreme positive values of PC1 axis (\emph{SiO\textsubscript{2} \&
Conductivity axis} ). The\selectlanguage{greek}\emph{R\textsubscript{(α,max)}}\selectlanguage{english} (R\selectlanguage{ngerman}²=0.44) is
only influenced by the PC1 axis (\emph{SiO\textsubscript{2} \&
Conductivity axis} ) with a positive linear relationship (Fig. 3e). The
GAMM analysis for\selectlanguage{greek}\emph{D\textsubscript{(β,max)}}\selectlanguage{english}
\textsubscript{,}\selectlanguage{greek}\emph{R\textsubscript{(β,max)}}\selectlanguage{english}
\textsubscript{,}\selectlanguage{greek}\emph{D\textsubscript{(γ,max)}}\selectlanguage{english} and
\selectlanguage{greek}\emph{R\textsubscript{(γ,max)}}\selectlanguage{english} had all R\selectlanguage{ngerman}²\textless{}2.1\% (see results
in Supporting information).

\textbf{Temporal dynamics of the depth diversity gradients}

The DDG measures of the different richness components show different
degrees of invariability (\emph{IV} ) as measure for stability.
The\emph{IV} of Gamma richness (mean=7.14, sd=3.69),
of\selectlanguage{greek}\emph{D\textsubscript{(α,max)}}\selectlanguage{english} (mean=6.62, sd=4.11),
of\selectlanguage{greek}\emph{R\textsubscript{(α,max)}}\selectlanguage{english} (mean=6.36, sd=2.87),
of\selectlanguage{greek}\emph{R\textsubscript{(β,max)}}\selectlanguage{english} (mean=5.89, sd=2.06) and
of\selectlanguage{greek}\emph{R\textsubscript{(γ,max)}}\selectlanguage{english} (mean=6.52, sd=2.97) were high in
comparison to the \emph{IV} of
\selectlanguage{greek}\emph{D\textsubscript{(β,max)}}\selectlanguage{english}(mean=1.56, sd=0.48) and of
\selectlanguage{greek}\emph{D\textsubscript{(γ,max)}}\selectlanguage{english} (mean=1.78, sd=0.53).

For all lakes depth-independent gamma richness showed a small but
significant trend (\emph{p} ~\textless{} 0.1) towards more species
(Supporting information). Analysing all lakes together, the DDG measures
revealed no significant common trend (\emph{p} ~\textgreater{} 0.05)
(Supporting information). For individual lakes slopes of significant
linear models of the \emph{D\textsubscript{max}}
and\emph{R\textsubscript{max}} over years showed mostly positive trends
(richness increasing and peaking at shallower depths, see Supporting
information). The \selectlanguage{greek}\emph{D\textsubscript{(α, max)}}\selectlanguage{english} shows two significant
positive trends (peak shifting towards water surface -- lake Starnberg
and lake Tegernsee) and two significant negative trends (peak shifting
to deeper waters -- lakes Gro\selectlanguage{ngerman}ßer Alpsee and Woerthsee).
The\selectlanguage{greek}\emph{D\textsubscript{(β, max)}}\selectlanguage{english} increases significantly at Lake
Riegsee, while \selectlanguage{greek}\emph{D\textsubscript{(γ, max)}}\selectlanguage{english} increases for lakes
Riegsee, Staffelsee Nord and Tegernsee. For total gamma richness, lakes
Chiemsee, Staffelsee Nord, Staffelsee S\selectlanguage{ngerman}üd and lake Starnberg show
positive trends (Table 2).

\section*{Discussion}

{\label{discussion}}

\textbf{The DDG patterns of macrophytes}

We showed, that submerged macrophytes in deep lakes have in general a
hump-shaped Depth Diversity Gradients (DDG) for alpha richness, a
prevailing decreasing pattern for beta richness and a dominantly
hump-shaped pattern for gamma richness (Fig. 2a-c) (question 1.1). As we
had only significant differences between mid and greater depths for all
richness components an even coarser species mapping resolution might be
helpful. Our results are congruent to the few existing studies, that
show also hump-shaped pattern (Ye et al. 2018) for alpha richness of
submerged macrophytes. A simple explanation for the predominantly
hump-shaped pattern of alpha and gamma richness might be the mid-domain
effect (Colwell et al. 2004): Niches along environmental gradients
overlap and build a peak of richness following geometric constrains.
Furthermore, the generally decreasing beta DDG might be explained by a
change in local species between transects in shallower depths. In
shallow water, disturbances resulting from the surface might be more
diverse, which may increase coexistence with spatial partitioning of
occurrences. As disturbances are coming from the surface, we make the
hypotheses that shallow water has a higher environmental heterogeneity
which might be the reason for an increased beta diversity.

We see a high variety of DDG shapes between lakes as all these DDGs vary
in their maximum richness (\emph{R\textsubscript{max}} ) and the
corresponding depth (\emph{D\textsubscript{max}} ), but a robust
hump-shaped pattern can be seen for alpha richness (Fig. 2d-f) (question
1.2). The high variety of \emph{R\textsubscript{max}}
and\emph{D\textsubscript{max}} is not surprising, as we studied lakes
showing a range of physical-chemical properties (Table 1) and gamma
richness between 2-35 species per lake. This wide range of species
richness and environmental conditions broadens our understanding of the
DDG, before limited to one single lake in China (Fu et al. 2014a, b).
Although for alpha and gamma richness, hump-shaped curves along depth
are predominant (Fig. 2g-i), we also see increasing and decreasing
patterns at single lakes. Increasing curves must be hump-shaped as we
can safely assume that plant richness should decrease to zero further
down in deep lakes. We detected more decreasing DDGs for gamma than for
alpha richness, which reflects predominantly decreasing beta richness
curves. Nevertheless, besides geometry and disturbances, there must be
further variables affecting the DDG, as DDG shape varies between lakes,
which themselves have different properties.

\textbf{The DDG drivers}

The drivers of the macrophyte DDGs strongly differed for DDG measures
and richness components. Whereas pairwise correlations detected many
strong relationships across richness components, multiple models
revealed significant variables only for DDG measures of alpha richness.

The \emph{R\textsubscript{max}} correlates of the different richness
components with a very similar set of abiotic parameters.
All\emph{R\textsubscript{max}} correlates with area. This reflects
species-area relationships (SARs) (Connor and McCoy 1979, Lomolino 2000,
Qian et al. 2007, Patiño et al. 2014) for macrophytes, which is also
shown by high correlation of area with total gamma richness. Looking at
the GAMM results for non-linear responses
\selectlanguage{greek}\emph{R\textsubscript{(β,max)}}\selectlanguage{english} and \selectlanguage{greek}\emph{R\textsubscript{(γ,max)}}\selectlanguage{english} did
not show any significant results, but \selectlanguage{greek}\emph{R\textsubscript{(α,max)}}\selectlanguage{english} is
exclusively influenced by the \emph{``SiO\textsubscript{2} \&
Conductivity axis''} (PC1) (Fig. 3)\emph{. Area, spectral absorption
coefficient} and \emph{water level fluctuations} (WLF) also have a high
contribution to PC1 and thus,\emph{area} might be the key driving force
again. The SAR of macrophytes was already shown in a several studies
(Alahuhta et al. 2020), still a study comparing SAR of macrophytes with
terrestrial plants would be very informative\emph{.} Here, it would be
interesting to add information about lake bathymetry, as lake area is
just a proxy for the colonisable area per depth. However, it was not
shown yet that the size of lakes also influences the shape of DDG.

The \selectlanguage{greek}\emph{D\textsubscript{(β,max)}}\selectlanguage{english} and \selectlanguage{greek}\emph{D\textsubscript{(γ,max)}}\selectlanguage{english}
could not be explained with abiotic variables, neither by correlations
nor by a GAMM. Unlike \selectlanguage{greek}\emph{D\textsubscript{(α,max)}}\selectlanguage{english} , the gamma, and
consequently beta, values along DDG are more variable, indicating
spatial heterogeneity and possibly unsaturation (Karger et al. 2014).
Still, \selectlanguage{greek}\emph{D\textsubscript{(α,max)}}\selectlanguage{english} correlates positively
with\emph{phosphorus} and \emph{temperature\_sd} , and negatively
with\emph{O\textsubscript{2} dissolved} and \emph{transparency} .
Furthermore, looking at non-linear influences,
the\selectlanguage{greek}\emph{D\textsubscript{(α,max)}}\selectlanguage{english} is affected by all four PCA-axes.

The PC2 (\emph{Temperature \& P\textsubscript{tot} axis} ) shows the
highest influence (Fig. 3). This means that in lakes with
high\emph{phosphorus} concentration and/or high \emph{temperature} the
DDG peaks in shallower waters. \emph{Phosphorus} is the limiting factor
for phytoplankton growth and phytoplankton reduces the light
availability for macrophytes. In contrast for macrophytes, it is still
debatable whether the \emph{phosphorus} concentration in the water is a
limiting growth factor (Carr et al. 1997). One important point to
consider in this debate is that rooted submerged macrophytes can also
take up nutrients from the sediments (Lacoul and Freedman 2006).
Hence,\emph{phosphorus} might affect macrophytes by promoting
phytoplankton growth, which then reduces light availability and shifts
DDG to shallower depths. Besides \emph{phosphorus} , \emph{temperature}
is a major factor influencing metabolic processes as photosynthesis and
respiration. Additionally in lakes, higher \emph{temperature} result in
higher nutrient levels due to increased mineralization and internal
fertilization processes (Moss 2012). Internal fertilization processes
occur when higher water temperatures lead to increased layering
stability, prolonged oxygen consumption, anoxia in deep waters,
resulting in anoxic resuspension of \emph{phosphorus} from the lake
sediments. These resuspended nutrients promote phytoplankton growth,
thus reducing light for macrophytes. Therefore, the PC2
(\emph{Temperature \& P\textsubscript{tot} axis} ) describes the
productivity gradient in lakes, caused by lower light availability
leading to a shallower maximum of species richness.

Besides light quantity, light quality also
influences\selectlanguage{greek}\emph{D\textsubscript{(α,max)}}\selectlanguage{english} , which is indicated by the
influence of the PC4 (\emph{O\textsubscript{2}diss-- SAC axis} ). With
high\emph{O\textsubscript{2}diss} content and low spectral absorption
coefficient at 254nm (SAC, a measure of coloured dissolved organic
matter \emph{-- CDOM} ) we observe richness peaks at deeper waters. On
the one hand, \emph{CDOM} reduces damaging UV-B radiation. On the other
hand, it reduces light availability. Thus, we see a diametrically
opposed effect of light quantity and light quality which might
contribute to the prevailing pattern of highest species richness at
medium depth level. In general, if light resource represents the main
component of productivity in lakes, the mid-depth DDG might follow the
intermediate productivity hypothesis (VanderMeulen et al. 2001,
Rajaniemi 2003, Huston 2014).

Besides light, also temperature seems to
influence\selectlanguage{greek}\emph{D\textsubscript{(α,max)}}\selectlanguage{english} , via surface water temperature
and its influence on light availability (as explained above) and via the
lake's layering depth. This second mechanism by which temperature
layering affects DDG is demonstrated by that along PC3 (\emph{Tempsd --
Chloride axis} ) \selectlanguage{greek}\emph{D\textsubscript{(α,max)}}\selectlanguage{english} decreases. A
high\emph{temperature\_sd} (shallow epilimnion -- the upper temperature
layer in a stratified lake) promotes a
shallow\selectlanguage{greek}\emph{D\textsubscript{(α,max)}}\selectlanguage{english} \textsubscript{,} while a
low\emph{temperature\_sd} (broad epilimnion) allows
deeper\selectlanguage{greek}\emph{D\textsubscript{(α,max)}}\selectlanguage{english} \textsubscript{.}
\emph{Temperature\_sd}is positively correlated to \emph{temperature}
demonstrating that higher temperatures can lead to a shallower upper
warm layer in water bodies as the stratification is more stable (Adrian
et al. 2009).

The weakest influencing effect (lowest drop contribution) is provided by
PC1 (\emph{NH4N -- SiO2 \& Conductivity axis} ). Just at very high
values of PC1 that \selectlanguage{greek}\emph{D\textsubscript{(α,max)}}\selectlanguage{english} becomes shallower.
As\emph{conductivity} is negatively correlated with
\emph{transparency}(cor=-0.71, \emph{p} \textless{}0.001), we speculate
that also here transparency is the actual mechanism that
influences\selectlanguage{greek}\emph{D\textsubscript{(α,max)}}\selectlanguage{english} .

In summary, the main influences on \selectlanguage{greek}\emph{D\textsubscript{(α,max)}}\selectlanguage{english} seem
to be as expected factors of water quality that influence light quantity
(transparency, influenced by phosphorus and temperature), light quality
(CDOM) and layering depth (temperature). The main influence
on\selectlanguage{greek}\emph{R\textsubscript{(α,max)}}\selectlanguage{english} is the lake surface area.

\textbf{DDG temporal change}

We showed that the stability of the pattern depends on the DDG measure
(question 3.1). \selectlanguage{greek}\emph{D\textsubscript{(β,max)}}\selectlanguage{english}
and\selectlanguage{greek}\emph{D\textsubscript{(γ,max)}}\selectlanguage{english} were quite variable measures over
years, while \selectlanguage{greek}\emph{D\textsubscript{(α,max)}, R\textsubscript{(α,max)},
R\textsubscript{(β,max)}}\selectlanguage{english} and \selectlanguage{greek}\emph{R\textsubscript{(γ,max)}}\selectlanguage{english} are
comparatively stable measures. This may be related to the fact that
there is neither pair-wise correlation nor explaining model
for\selectlanguage{greek}\emph{D\textsubscript{(β,max)}}\selectlanguage{english} and \selectlanguage{greek}\emph{D\textsubscript{(γ,max)}}\selectlanguage{english} .

Contrary to our expectations we see no general trend of increasing
species richness or decreasing\emph{D\textsubscript{max}}
\textsubscript{.} (question 3.2). Although we observe high variety in
DDG temporal change between lakes, the DDG temporal change for single
lakes, especially for\selectlanguage{greek}\emph{D\textsubscript{(α,max)}}\selectlanguage{english} , is low and
develops into different directions for different lakes. Still, we see
linear trends that are consistent over time within lakes. These patterns
suggest that global change effects will be more complex than
anticipated. In fact, climate and land use change influence all the
highly connected chemical and physical gradients known to significantly
affect DDG (Hossain et al. 2017). Thus, the following hypothesis can be
set (Fig. 4): (1) As temperatures rise, so do lake surface water
temperatures as well (O'Reilly et al. 2015). This seems to result in
shallower epilimnion (Kraemer et al. 2015) and generally
shallower\emph{D\textsubscript{max}} and a lower
\emph{R\textsubscript{max}} . (2) Furthermore, rising temperature entail
higher P content, as they promote internal fertilization. But also
extreme weather events combined with enriched fertilization in
agriculture can cause fertilization events (Rose et al. 2016), which
might result in shallower light depth consequently in shallower DDG. (3)
Browning, which is generally increasing due to temperature induced
decomposition rates and changes in precipitation events (Sobek et al.
2007, Weyhenmeyer and Karlsson 2009, Guarch-Ribot and Butturini 2016),
leads to a shallower\emph{D\textsubscript{max}} . (4) However, water
management reduced the external nutrient loading of European lakes
enormously during the last decades (Eigemann et al. 2016, Murphy et al.
2018). This trend is still ongoing and might still lead towards lower
nutrient contents and thus to deeper \emph{D\textsubscript{max}} . All
these opposing environmental trends make it hard to draw a general trend
for multiple lakes for short timespans. However, for long timespans it
seems to be a race between climate change impacts (Hypothesis 1-3 in
Fig. 4) that might lead to a shallower \emph{D\textsubscript{max}} and
thus generally less macrophytes and water management impacts that might
deepen the\emph{D\textsubscript{max}} via improved water quality
(Hypothesis 4 in Fig. 4).

\textbf{Implications for diversity gradients in general}

Looking at different diversity gradients and generalising the findings
might be a chance to get a deeper insight into mechanisms of species
richness. The depth diversity gradient (DDG) of macrophytes brings some
advantages compared the latitudinal diversity gradient (LDG) or the
elevational diversity gradient (EDG). The DDG is formed for shorter
scales (few meters) than the EDG, which implies a lower importance of
dispersal or connectivity processes. Another big advantage looking at
freshwater depth pattern is that it is much easier to use replicates for
DDG than for LDG and EDG. For LDG, the options for replicates are
restricted as there are mainly two replicates for the two hemispheres,
which can be further (pseudo)replicated when studying different taxa or
continents (Pontarp et al. 2019). For EDG, comparative studies require a
high logistic sampling effort. Thus, studies addressing the overall
shape across EDGs and the variables affecting them are not yet clear
(Nogu\selectlanguage{ngerman}és-Bravo et al. 2008, McCain and Grytnes 2010, Kessler et al. 2011,
Sanders and Rahbek 2012). Hence, the smaller scales of DDG seem an
advantage to do such replicate studies to address the role of energy or
productivity in shaping the diversity gradients. Moreover, a comparative
study across different gradients (EDG vs. DDG) could yield further
insights in understanding diversity drivers.

Besides DDG, there are other small-scale diversity gradients that get
studied. One example is the height or vertical diversity gradient (VDG)
from forest floor to tree crowns following environmental gradients of
light intensity, temperature and humidity. Here, species richness
distribution depends on the study species group. Hump-shaped patterns
for ferns and lycophytes (Acebey et al. 2017) or vascular epiphytes
(Krömer et al. 2007) or decreasing patterns for vascular epiphytes (Wang
et al. 2016) were found. The pattern does not seem to be very clear and
are strongly influenced by host crown characteristics (Wang et al.
2016). Still, VDG could be the most comparable gradient to DDG for
contrasting the role of light and temperature, but paired data with both
diversity and environmental data for VDG is even scarcer than for DDG.

The LDG for species generally peak in the tropics (Pontarp et al. 2019).
However, looking at freshwater plants, the diversity gradient does not
decrease linearly (Alahuhta et al. 2020), but seems to have at
sub-tropical to low tropical latitudes (Murphy et al. 2019). Thus, along
latitude, the species richness peak is at a high to intermediate level
of solar productivity, which is reflected in our analysis of DDG. This
might be explained by the Intermediate Productivity Hypothesis (IPH).
The IPH, stating that at low productivity level (deep waters with low
light quantity and low temperature) only few species can survive and at
high productivity level (shallow waters with high light quantity and
high temperature) only few competitive species survive, is highly
discussed (Adler et al. 2011, Fraser et al. 2015) and mostly used along
LDG. Besides competition, mid-gradient peaks can be explained from an
evolutionary perspective as a maximal difference between speciation and
extinction rates is expected at intermediate productivity (VanderMeulen
et al. 2001). Although quantification of productivity along depth should
be attempted, our findings already indicate a key role of light quantity
and temperature in shaping DDG.

The hump-shaped curve of the DDG might mirror most elevational diversity
gradients (EDG), which often peak at intermediate levels (Kessler et al.
2001, Nogués-Bravo et al. 2008). For both gradients one explanation
might be geometry. The mid-domain effect (MDE) explains richness peaks
in the middle of gradients by species range overlaps simply via
geometric constraints (Colwell et al. 2004). This concept is mainly used
to explain the EDG (McCain 2004, Cardelús et al. 2006, Brehm et al.
2007). Also along the depth gradient niches of single species might
overlap and create an area of enhanced richness. Nevertheless, to
adequately verify the influence of the mid-domain effect, it would be
necessary to study single species in relation to environmental variables
via mesocosm experiments without competition. Potential niche ranges
associated with lake depth (e.g. light or temperature conditions) could
then be determined and the overlap of these ranges for all co-occurring
species of a particular lake would provide at which depth lies the
MDE-predicted maximum number of species. This would make it possible to
compare observed vs. MDE-predicted DDGs. Moreover, another important
explanatory theory of EDG is the Intermediate Disturbance Hypothesis
(IDH). It suggests that species richness is highest at mid-levels of
disturbance as species of early and late successional phases can coexist
(Connell 1978). Whereas disturbances along EDG are caused by enhanced
human activities at lower elevation (Nogués-Bravo et al. 2008),
disturbances along DDG are caused by waves, grazing and water level
fluctuations at shallower depth. These disturbances act on two different
depth patterns: 1) evenly distributed over depth like herbivory by fish
or 2) gradually distributed (loosing strength with depth), which can be
caused by anthropogenic use, herbivory by birds or mammals, wind, waves
(Van Zuidam and Peeters 2015), mechanical disturbance by ice cover, or
water level fluctuations (Evtimova and Donohue 2016). The latter was
already integrated in our study in a very simple way of taking long term
indicators. Further monitoring schemes should target other disturbances,
quantifying herbivory, wave height, ice cover or finer resolved water
level fluctuations. As herbivores are supposed to eliminate on average
40-48\% of plant biomass in freshwaters (Bakker et al. 2016) their
influence might be outstanding. The long-term water level fluctuation
showed no strong effect on richness measures and it might not be the
main explanation of the hump-shaped DDG. Nevertheless, although our
findings tend to not support the intermediate productivity/disturbance
hypotheses, considering that human (or natural) disturbances in shallow
waters should be more prevalent in our data, the intermediate
productivity/disturbance hypotheses could still play a role along DDG.

\textbf{Limitations and Perspectives}

The main limitation is that, in some lakes, the deeper end of the DDG
was not clearly quantified. This is, however, most critical for the
lakes with increasing DDG (For alpha richness: Eibsee 2016; for beta
richness: Eibsee 2011, Grosser Ostersee 2008 \& 2014, Tachinger See
2006, Woerthsee 2008, Eibsee 2016, Schliersee 2008; for gamma richness:
Eibsee 2011 \& 2016, Grosser Ostersee 2008, Tachinger See 2006 and
Woerthsee 2008). For these lakes, which are mostly lakes with a high
water transparency, it might be interesting to extend sampling to deeper
water to quantify a metric termed `the lower macrophyte limit'
(Søndergaard et al. 2013). This metric is often used as indicator for
water quality and might be useful to further characterize the DDG as it
defines the lower limit and the occupied space.

Additional limitations of our analyses can be viewed rather as
perspectives for further studies focusing on explaining the underlying
causes of the DDG (see previous section) and to disentangle the
presented hypothesis, as these limitations require data yet unavailable.
This includes 1) depth measurements of the variables that also show
depth gradients (i.e. light, temperature or nutrients) and 2) further
variables that vary across transects and lakes, such as productivity,
slope, soil properties (components, grain size distribution and nutrient
content), ice cover duration and different disturbance factors like
anthropogenic use intensity (boats, mowing, swimming) or herbivory
pressure (fish, water birds). Nevertheless, our analyses already
indicate that light quality and quantity may play a main role in forming
the DDG in freshwater lakes and will inspire further empirical studies
on the DDG as well as comparative studies with other diversity
gradients.

A promising direction for future research might be combining
eco-physiological experiments with mechanistic modelling to test the
different species richness hypotheses. Such an approach might help to
clarify the influencing force of disturbances or geometry on DDG on
small scales.

\section*{Conclusion}

{\label{conclusion}}

Our study makes a first step towards a generalizable understanding of
the Depth Diversity Gradient (DDG) of submerged macrophytes, their
regional and temporal heterogeneity as well as the drivers of the DDG
shape. Submerged macrophytes richness peaks predominantly at
intermediate depth forming a hump-shaped pattern for alpha and gamma
richness, but a decreasing pattern for beta richness (Fig. 4).
Well-known hypotheses of biogeography that may shape diversity gradients
in general, such as mid-domain effect and mean productivity hypothesis.
The latter is already supported by our findings on the role of light and
temperature as DDG drivers. The key advantage of DDG in contrasting
these hypotheses is the logistic feasibility of short-distance scales
and the exclusion of confounding effects associated with dispersal
constraints. The key drivers of DDG we determined were area influencing
the species richness peak hight (\selectlanguage{greek}\emph{R\textsubscript{(α,max)}}\selectlanguage{english} ) and
light quality, light quantity and layering depth influencing the species
richness peak depth (\selectlanguage{greek}\emph{D\textsubscript{(α,max)}}\selectlanguage{english} ). Although we
found that the DDG in general remained stable over past few years for
most lakes, we still found shifting trends for richness metrices for
some lakes. However, these trends showed to be diverse across lakes.
Whereas climate change might be more ubiquitous, land use change may be
lake specific. This suggests that water management strategies should
consider besides global warming, also lake characteristics and change in
the surrounding land use. The interaction of these aspects also means
that although higher temperatures lead to a reduction in the quantity of
light available to aquatic plants in lakes, land use measures can be
taken to counteract this. Nevertheless, our findings already indicate
that warmer water temperatures may still lead to a shift in species
along depth dependent on further efforts to hold or increase water
quality of lakes.

\section*{Declaration}

{\label{declaration}}

Acknowledgements --

blinded for submission

Funding -- blinded for submission

Author contributions -- blinded for submission

Conflicts of interest -- \emph{none}

Permit(s) -- \emph{none required}

\section*{Data \& Code}

{\label{data-code}}

Original raw data were publicly available, provided by Bayerisches
Landesamt f\selectlanguage{ngerman}ür Umwelt, www.lfu.bayern.de

Code of data analysis are provided as Research Compendium on
github:\emph{link} \emph{will be provided in final version; it is not
added here because of the double-blind review process}

\section*{Supporting information}

{\label{supporting-information}}

Supporting analysis as a separate (R markdown) file

Study overview table

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\section*{Tables}

{\label{tables}}

Table 1 Overview of the number of datapoints (N), minimum values (Min),
maximum values (Max), mean, standard deviation (SD) and median of the
abiotic data (geographic variables: fixed values per lake;
physical-chemical variables: changing over years, annual means were
used) used as indicators in the studied lakes.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
\begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Variable (unit)\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Abbrev.\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
N\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Min (annual mean)\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Max (annual mean)\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Mean (annual mean)\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
SD (annual mean)\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Median (annual mean)\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Geographic variables\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Lake area (ha)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{Area}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
135\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
7990.00\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1840\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
2654\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
624.50\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Water level fluctuation (m)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{WLF}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.2\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.18\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.58\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.28\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.59\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Phyical-chemical variables\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Chloride (mg/l)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{Chl}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.33\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
14.98\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
7.58\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
3.94\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
7.61\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Conductivity (\selectlanguage{greek}µ\selectlanguage{english}S/cm)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{Cond}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
158.55\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
372.44\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
292.57\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
61.96\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
309.20\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Total Nitrogen (mg/l)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{N\textsubscript{tot}}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.19\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.22\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.66\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.22\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.65\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Ammonium (mg/l)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{NH\textsubscript{4}N}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.00\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.09\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.02\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.02\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.01\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Nitrogen (mg/l)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{NO\textsubscript{3}N}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.00\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.09\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.37\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.28\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.30\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Dissolved Oxygen (mg/l)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{O\textsubscript{2}diss}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
9.16\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
11.41\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
10.23\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.51\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
10.39\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Total Phosphorus (mg/l)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{P\textsubscript{tot}}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.00\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.02\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.01\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.00\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.01\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
pH (-)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{pH}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
8.04\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
8.49\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
8.32\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.11\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
8.35\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Silicate (mg/l)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{SiO\textsubscript{2}}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.00\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
2.86\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.68\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.88\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.64\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Water temperature (\selectlanguage{ngerman}°C)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{Temp}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
9.64\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
19.24\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
13.38\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
2.09\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
13.32\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Transparency (cm\^{}-1)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{Transp}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
242.50\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1197.27\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
451.74\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
198.39\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
410.00\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Spectral absorption coefficient at 254 nm (1/m)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{SAC}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.00\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
24.83\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
8.85\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
7.84\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
5.35\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Standard deviation of temperature measured at 0~m, -2~m, -4~m and -6~m
depth (\selectlanguage{ngerman}°C)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{Tempsd}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.23\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
2.24\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.91\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.58\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.62\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Table 2 Linear model results of the time series analysis of DDG
variables (D\textsubscript{max} and R\textsubscript{max}) across
richness components for each lake. Here, +: positive slope of linear
model (Meaning for D\textsubscript{max}: becomes shallower; Meaning for
R\textsubscript{max}: more species); -: negative slope of linear model
(Meaning for D\textsubscript{max}: becomes deeper; Meaning for
R\textsubscript{max} : less species); significance levels of p-values: 0
`***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Lake & Gamma richness & \selectlanguage{greek}\emph{D\textsubscript{(α,max)}}\selectlanguage{english} &
\selectlanguage{greek}\emph{R\textsubscript{(α,max)}}\selectlanguage{english} & \selectlanguage{greek}\emph{D\textsubscript{(β,max)}}\selectlanguage{english} &
\selectlanguage{greek}\emph{R\textsubscript{(β,max)}}\selectlanguage{english} &
\selectlanguage{greek}\emph{D\textsubscript{(γ,max)}}\selectlanguage{english}\textsubscript{.} &
\selectlanguage{greek}\emph{R\textsubscript{(γ,max)}}\selectlanguage{english}\textsubscript{.}\tabularnewline
\midrule
\endhead
Lake Abtsdorf & - & - & - & - & + & - & -\tabularnewline
Ammersee & - & + & - & + & - & + & -\tabularnewline
Chiemsee & \textbf{+**} & + & - & - & + & + & \textbf{+.}\tabularnewline
Gr. Alpsee & + & \textbf{-*} & + & - & - & - & +\tabularnewline
Grosser Ostersee & - & + & - & + & - & + & -\tabularnewline
Hopfensee & + & - & + & & \textbf{+*} & & \textbf{+.}\tabularnewline
Lake Niedersonthofen & - & + & - & + & + & + & -\tabularnewline
Lake Pelham & + & - & + & + & - & - & +\tabularnewline
Riegsee & + & + & + & \textbf{+*} & \textbf{+.} & \textbf{+.} &
+\tabularnewline
Schliersee & - & + & - & + & - & - & -\tabularnewline
Simssee & + & + & + & + & + & + & +\tabularnewline
Staffelsee - Nord & \textbf{+*} & + & + & + & \textbf{+.} & \textbf{+.}
& +\tabularnewline
Staffelsee - Sued & \textbf{+*} & + & \textbf{+.} & - & \textbf{+.} & +
& \textbf{+.}\tabularnewline
Lake Starnberg & \textbf{+.} & \textbf{+.} & \textbf{+***} & + &
\textbf{+*} & + & \textbf{+***}\tabularnewline
Tegernsee & + & \textbf{+*} & + & + & + & \textbf{+.} & +\tabularnewline
Lake Waging & + & - & + & + & + & + & +\tabularnewline
Woerthsee & - & \textbf{-*} & + & + & + & + & +\tabularnewline
Sign (p\textless{}0.1) pos. slope {[}N lakes) & \textbf{4} & \textbf{2}
& \textbf{2} & \textbf{1} & \textbf{5} & \textbf{3} &
\textbf{4}\tabularnewline
Sign (p\textless{}0.1) neg. slope (N lakes) & \textbf{0} & \textbf{2} &
\textbf{0} & \textbf{0} & \textbf{0} & \textbf{0} &
\textbf{0}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/DDG-Fig1/DDG-Fig1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/DDG-Fig2/DDG-Fig2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/DDG-Fig3/DDG-Fig3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/DDG-Fig4/DDG-Fig4}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

