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.