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, Nogués-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
(Rendoš et al. 2016, Jakšová 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), 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:
- 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)?
- What are the drivers for macrophyte DDG?
- 3.1. Has the DDG being stable over recent years?
3.2. Are temporal trends general or lake-specific?
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.