Introduction
Submerged macrophytes provide a broad range of ecosystem services in
lakes (Thomaz, 2021). They create habitat for many other species and
change the lake environment by binding nutrients or stabilising the
sediment. Most importantly, the eco-physiological processes controlling
their growth and survival are strongly affected by environmental
conditions, as they depend on light availability, nutrient availability,
and temperature. However, the accelerating global biodiversity loss,
especially of submerged macrophytes, is well documented (Körner, 2002;
Phillips et al., 2016; Sand‐Jensen et al., 2000; Zhang et al., 2017)
despite evidence of increasing species richness in some lakes (Murphy et
al., 2018). The main influencing factors on the change of species
richness seem to be global climate and regional land-use changes
(Hofstra et al., 2020; Zhang et al., 2017) resulting in changes of light
availability (due to changes in water turbidity), nutrient availability,
or water temperature. The ways in which climate change influences water
temperature, nutrients, and turbidity in lakes are highly complex
including direct and indirect effects (Lind et al., 2022). For example,
lake water temperature rises due to climate change which can also have
indirect impacts on turbidity and nutrients in lakes, as higher
temperature can promote algae growth (lower light availability).
Therefore, nutrients and turbidity seem to co-vary, while temperature
could change independently. However, nutrient content and turbidity can
also be influenced by land-use practices (e.g. content and timing of
fertiliser on fields), and wastewater treatment management. Overall, the
direction and impact of future climate change on submerged macrophytes
seems to be more obvious than changes in land use.
All these stressors have an effect on the eco-physiological processes of
macrophytes (Cao & Ruan, 2015; Reitsema et al., 2018). Hence, changes
in these stressors likely affect the geographical distribution of
individual species and of species richness of submerged macrophytes.
Because of their response to their physio-chemical, geomorphological,
hydrological, and biotic surroundings (O’Hare et al., 2018), species can
be differentiated into oligo-, meso-, and eutraphentic species by their
preference for nutrient conditions (Melzer, 1999). The presence of
distinct species can then be an indicator of the water quality and
ecological state of the lake (Schaumburg et al., 2004). However, studies
on macrophyte species richness distribution remain largely based on
observational and correlative studies, while there is a need to
understand how simultaneous stressors combine and may result in
synergistic effects on biodiversity (Lind et al., 2022). Therefore, it
is paramount to assess these potential effects on macrophytes.
Process-based models based on first principles and known ecophysiology
are better suited to assess biodiversity response from changing
conditions than correlative models (Cabral et al., 2017; Dormann et al.,
2012; Higgins et al., 2020; Schouten et al., 2020). To predict the
potential distribution of species based on environmental factors,
process-based models incorporating critical eco-physiological processes
are necessary. The application of process-based models describing the
growth of submerged macrophytes have a long tradition, already stemming
as early as the late 1980s (Best et al., 2001; Collins & Wlosinski,
1989; Herb & Stefan, 2003; Hootsmans, 1994; Scheffer et al., 1993;
Wortelboer, 1990). The majority of those models were developed to answer
different study questions, however, like the effect of macrophytes on
algal blooms (Asaeda & Van Bon, 1997), the effect of varying light
regimes (Herb & Stefan, 2003), or their impact on water quality (Sachse
et al., 2014). Furthermore, most models were only used and calibrated
for one or a few species (Gao et al., 2017; van Nes et al., 2003) and
under very specific environmental conditions. None of these models was
used to study the macroecological distribution patterns of macrophytes
or its response to environmental change. Among the reasons for the delay
in applying any of these models to multiple species and under different
environmental conditions is the lack of computationally efficient models
and empirical data to constrain both eco-physiological and environmental
parameters. Hence, applying eco-physiological models to assess the
species and richness distribution in both current and future conditions
deserves further attention for this neglected group of species.
Macrophytes are still underrepresented in trait-based research and in
environmental change assessments (Dalla Vecchia et al., 2020; Iversen et
al., 2022). Consequently, the determination of a broad range of
eco-physiological parameters has yet to be established for most
macrophytes. In the case of low trait-based information, applying
eco-physiological models to a virtual species pool remains the best
alternative to assess impacts of environmental change on macrophytes
(Cabral et al., 2017; Dormann et al., 2012). As computational power and
methods are evolving (Peréz-Sánchez et al., 2015), experiments with a
broad range of randomly selected species within defined functional types
can be a way of determining trait combinations (potential species) that
allow species to survive and reproduce, as already done for terrestrial
plants (Webb et al., 2010; Zakharova et al., 2019). In such
applications, the process-based model acts as a performance filter, with
the surviving virtual species representing those trait combinations able
to cope with the environmental conditions given the considered
mechanisms. However, this approach was not yet used for macrophytes.
In this study, we tackle two main objectives. First, we address the
potential species richness of oligotraphentic, mesotraphentic, and
eutraphentic submerged macrophytes under recent environmental
conditions. We ask: How many observed and potential species of the
species pool can grow in clear, intermediate, and turbid lakes (Q1.1)?
Do the potential species richness patterns across the depth follow the
observed distribution in all lake types (Q1.2)? Second, we assess
scenarios of water temperature increase and water quality change
(increase or decrease in both nutrients and turbidity). Here, we ask: In
which depth and lake types do we lose or gain oligo-, meso-, and
eutraphentic species (Q2.1)? Is this change dependent on
eco-physiological traits (Q2.2)?
To answer questions Q1.1 and Q1.2, we run random species parameter
combinations within the three defined parameter spaces of
oligotraphentic, mesotraphentic, and eutraphentic species and analyse
the resulting distribution patterns of the growing species by comparing
them with the corresponding observed pattern. We expect to find the
highest species richness in moderately nutrient rich lakes (Q1.1)
(Lewerentz & Cabral, 2021). We hypothesise to find hump-shaped patterns
(Q1.2) of species richness along depths (Lewerentz et al., 2021). In
general, we hypothesise that we see a higher potential species richness
than observed species richness because limiting processes like herbivory
have not been modelled (Q1.1 and Q1.2). To answer question Q2.1 and
Q2.2, we run scenarios of water temperature increase and scenarios of
water quality change for the recent potential species pool. We
hypothesise that the studied lakes will lose species under increased
turbidity and nutrient conditions but gain species under decreased
turbidity and nutrient conditions and under increased water temperature
(Q2.1) (Lewerentz & Cabral, 2021). To answer question Q2.2, we
determine the plant traits that significantly influence if a species
will win or lose habitat within two selected scenarios of turbidity and
nutrient decrease or increase. We hypothesise that under increased
conditions, high biomass production is the main advantage, as species
can grow at high rates even under limited conditions.