IntroductionSubmerged 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.

Juliano Sarmento Cabral1, Alma Mendoza-Ponce2,3, André Pinto da Silva4,5, Johannes Oberpriller6, Anne Mimet7, Julia Kieslinger8, Thomas Berger9, Jana Blechschmidt1, Maximilian Brönner8, Alice Classen10, Stefan Fallert1, Florian Hartig6, Christian Hof7, Markus Hoffmann11, Thomas Knoke12, Andreas Krause13, Anne Lewerentz1, Perdita Pohle8, Uta Raeder11, Anja Rammig13, Sarah Redlich10, Sven Rubanschi7, Christian Stetter14, Wolfgang Weisser7, Daniel Vedder1,15,16,17 , Peter H. Verburg18, Damaris Zurell191 Ecosystem Modelling, Center for Computational and Theoretical Biology (CCTB), University of Würzburg, Klara-Oppenheimer-Weg 32, 37074, Würzburg, Germany2 Research Program on Climate Change, Universidad Nacional Autónoma de México, Mexico City, Mexico3 International Institute for Applied Systems Analysis, Laxenburg, Austria4 Department of Ecology and Genetics, Animal Ecology, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden5 Centre for Ecology, Evolution and Environmental Changes (cE3c), Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal6 Theoretical Ecology Lab, University of Regensburg, Universitätsstraße 31, 93053 Regensburg, Germany7 Technical University of Munich, Terrestrial Ecology Research Group, Department of Life Science Systems, School of Life Sciences, 84354 Freising, Germany8 Institute of Geography, Friedrich-Alexander University Erlangen-Nuernberg, Wetterkreuz 15, 91058 Erlangen, Germany9 Land-Use Economics in the Tropics and Subtropics, Hans-Ruthenberg Institute, Hohenheim University, Hohenheim, Germany10 Department of Animal Ecology and Tropical Biology, Biocentre, University of Würzburg, Am Hubland, 97074 Würzburg, Germany11 Technical University of Munich, Limnologische Station Iffeldorf, Chair of Aquatic Systems Biology, Department of Life Science Systems, School of Life Science,Hofmark 1-3, 82393 Iffeldorf, Germany12 Technical University of Munich, Institute of Forest Management, Department of Life Science Systems, School of Life Sciences, 58354 Freising, Germany13 Technical University of Munich, Land Surface-Atmosphere Interactions, Department of Life Science Systems, School of Life Sciences, 85354 Freising, Germany14 Agricultural Production and Resource Economics, School of Life Sciences, Technical University of Munich, 84354 Freising, Germany15 Helmholtz Center for Environmental Research - UFZ, Department of Ecosystem Services, Permoserstr. 15, 04318 Leipzig, Germany16 Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger Straße 159, 07743 Jena, Germany17 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstr. 4, 04103 Leipzig, Germany18 Institute for Environmental Studies, VU University Amsterdam, De Boelelaan 1111, 1081 HV Amsterdam, The Netherlands19 Ecology & Macroecology, Inst. for Biochemistry and Biology, University of Potsdam, Am Neuen Palais 10, 14469 Potsdam, GermanyArticle type: review/perspective

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 (Rmax) is significantly influenced by lake area only, whereas for the corresponding depth (Dmax) 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.