A high maximum growth rate (pMax) of the species is advantageous
for species under increased nutrient and turbidity and a disadvantage
under decreased nutrient and turbidity levels. If species are capable of
producing high amounts of biomass, they can reach shallower depth faster
to overcome light limitation. This is also observed in the shift from
slow-growing seagrass to fast-growing macroalgae in oceans under
increasing nutrient and light limitation conditions (Duarte, 1995). This
advantage of fast growth towards the water surface is also reflected in
the fact that a high maxWeightLengthRatio is a loser trait value
under decreased turbidity and nutrients. If a plant needs more biomass
to grow the same length as another plant, it has a physiological and
competitive disadvantage because it can be outgrown and shaded. A
species group that has slow growth rates and might have a disadvantage
under increased turbidity and nutrients are charophytes (Blindow, 1992;
Henricson et al., 2006). In fact, a high maximum growth rate is a trait
commonly found in invasive species (e.g. Elodea canadensis)
because a high biomass production in general helps to outcompete slower
growing species (Dawson et al., 2011; Hussner et al., 2021; Schultz &
Dibble, 2012).
Several traits within the model determine the wave mortality within the
model which can be seen as any depth-dependent reduction of biomass due
to disturbance (e.g. also herbivory by water birds). Within the scenario
with decreased turbidity and nutrients, a high wave mortality rate
(maxWaveMort) and a depth effect (hWaveMort, pWaveMort)
bring advantages for species that are susceptible to disturbance as
enough light reaches deep water, where they can exploit as newly
available habitat. Under more turbid and nutrient-rich conditions, it is
the other way around. In fact, the effect of waves on macrophytes in
shallow areas of lakes is not negligible (Schutten et al., 2004, 2005).
However, our results stress the relevance of a combined effect of wave
mortality and light limitation. Light limitation in deeper areas of the
lake puts pressure on disturbance-sensitive species.
Several traits that determine the life cycle have significant influence
on whether a species becomes a winner or a loser. A later germination
during the year makes the species a “loser” in the more turbid and
nutrient rich lake, as it has to deal with higher temperature (higher
respiration) (Milbau et al., 2017). Furthermore, in the scenario of
decreased nutrient and turbidity conditions a later reproduction is a
winner trait probably because it implies a longer growth period. With a
longer growth period it is more likely that sufficient seed biomass can
be produced for regrowth during the next year.
In summary, the change of species richness depends on their
eco-physiological traits and for each scenario there are specific traits
that determine the success of submerged macrophytes. The learnings about
the traits are highly informative especially in the light of the fact
that few of these traits are studied under environmental change.
Therefore, the approach is a useful tool to study loser and winner
traits in the absence of empirical data and for making risk assessments
under environmental change.
Conservation implications
Knowledge about the loss of potential species richness can warn against
upcoming threats under different scenarios. In the presented shiny app
(Appendix S1) conservation practitioners can see which lakes and depths
are hotspots of change under different scenarios. We confirmed that the
main threatened areas within a scenario of turbidity and nutrient
increase are the deep areas of turbid lakes (Figure 3). They not just
lose species richness, some of them become even uninhabitable for
submerged macrophytes.
It is the aim of lake management and conservation within the European
water framework directive monitoring is not to maximise species richness
within lakes in general. The aim is to promote a species composition
that represents and corresponds to the lake type (Poikane et al., 2018).
Although we show that clear lakes in particular would have higher
potential species diversity due to increased nutrient levels, we have to
consider that they host under recent conditions a high share of the
oligotrophic species that would be lost under increased nutrient and
turbidity conditions (Table 2 and Figure 3). Therefore, to protect
freshwater biodiversity under the multiple stressors of land-use change,
eutrophication, and climate change, the restoration and conservation of
suitable refugia for vulnerable species is crucial (Hofstra et al.,
2020; Sarmento Cabral et al., 2013).
Limitations and
perspectives
One typical limitation of eco-physiological models is complexity in
terms of parameter numbers. Whereas more complex models (like Charisma
2.0) are useful to answer multiple complex questions (e.g. alternative
stable states, spatial processes, and competition), modellers must be
able to interpret process interactions and overcome equifinality. We
simplified Charisma 2.0 down to 28 parameters by reducing the processes
to include mostly eco-physiological ones. However, this means that
spatial processes and competition are no longer considered. Spatially
explicit modelling of the dispersal of seeds or other reproductive
organs might bring findings about the speed of the dispersal of
macrophytes from lake to lake. A reason for a higher potential then
observed species richness might be that environmental conditions are
already constantly changing. It might be that the observed species
simply have not yet reached their full potential habitat (García-Girón
et al., 2019; Padial et al., 2014). Another factor which increased the
mismatch might be inter-specific competition. In freshwater lakes, not
only submerged macrophytes compete with each other for resources, they
also compete with emergent species or species with floating leaves,
mainly in shallow water. Emergent species and floating-leave species
have competitive advantages like a higher light availability and carbon
use from air. Moreover, submerged species compete with each other for
resources above and below ground mainly by different biomass-allocation
strategies (J.-W. Wang et al., 2008). Despite the fact that these not
considered processes would bring further insights into spatial processes
and communities, the modelled distribution patterns of species richness
already bring valuable insights.
Our experimental design provides starting points for further approaches
that were not yet realised due to limited data availability. A likely
reason for the underestimation of species richness within a lake might
be the missing implementation of environmental heterogeneity within a
lake. We use per lake the measurements of environmental parameters at
one point in the middle of the lake, which just represents the general
state of a lake, but not its internal heterogeneity. To depict the
environmental heterogeneity within a lake a denser net of measurements
would be necessary. Moreover, data from public monitoring is in the
studied region just available for the bigger lakes (>50ha).
However, large lakes only constitute a part of all natural water bodies
(Downing et al., 2006). More information about macrophytes distribution
and environmental parameters in small lakes and ponds would help to
close a knowledge gap and to integrate those in analysis of future
scenarios, as those lentic systems will change at the most extreme
level. With a broader data base on small lakes, MGM could be applied to
include those ecosystems within studies about future changes of
macrophyte species richness.
This framework of applying a process-based model in combination with
random, theoretical species (Webb et al., 2010; Zakharova et al., 2019)
to identify hotspots of change can be a template also for other lake
regions or even other species groups also within terrestrial systems,
and is already applied e.g. for epiphytes (Petter et al., 2021) or
invasive species on islands (Vedder et al., 2021). Overall, MGM can
generate species richness patterns across different environmental
gradients of nutrient availability, latitudes (by varying light
intensity and seasonality), turbidity, water temperature, and depth.