Symbol Unit Description OLIGO- TROPHIC MESO- TROPHIC EU- TROPHIC Source
Min - Max Min - Max Min - Max
seedsStartAge dayno Age of the plants where seed formation starts 10-100 10-100 10-100 (h)
seedsEndAge dayno Age of the plants where SeedFraction is reached 30-120 30-120 30-120 (h)
cTuber fraction Fraction of tuber weight loss daily when sprouts starts growing 0.1 0.1 0.1 (f)
pMax h1 Maximal gross photosynthesis 0.001-0.01 0.001-0.02 0.001-0.03 (h)
q10 - Q10 for maintenance respiration 2 2 2 (f)
resp20 d−-1 Respiration at 20°C 0.002 0.002 0.002 (f)
heightMax m Maximal height 0.1-1 1-6 3-4 (c)
maxWeightLenRatio g m1 Weight of 1 m young sprout 0.01-0.1 0.4-0.8 0.1-0.4 (d)
rootShootRatio fraction Proportion of plant allocated to the roots 0.1 0.05-0.09 0.05-0.08 (d)
fracPeriphyton fraction Fraction of light reduced by periphyton 0.2 0.2 0.2 (f)
hPhotoDist m Distance from plant top at which the photosynthesis is reduced factor 2 1.0 1.0 1.0 (f)
hPhotoLight µE m2 s1 Half-saturation light intensity (PAR) for photosynthesis 15-60 30-60 40-60 (b)
hPhotoTemp °C Half-saturation temperature for photosynthesis 14 14-15 14-15 (b)
plantK m−2 g−1 Extinction coefficient of plant issue 0.02 0.02 0.02 (f)
pPhotoTemp - Exponent in temp. effect (Hill function) for photosynthesis 2-3 2-3 2-3 (a)
sPhotoTemp - Scaling of temperature effect for photosynthesis 1.35 1.35 1.35 (f)
cThinning - c factor of thinning function 5950 5950 5950 (f)
hWaveMort m Half-saturation depth for mortality 0-0.5 0-0.5 0-0.5 (f)
germinationDay dayno Day of germination of seeds 75-150 75-150 75-150 (h)
reproDay dayno Day of dispersal of seeds 227-289 227-289 227-289 (h)
maxAge day Maximal plant age 150-300 150-300 150-300 (h)
maxWaveMort g d−1 Maximum loss of weight in shallow areas 0.1-1 0.1-1 0.1-1 (h)
pWaveMort - Power of Hill function for wave mortality 0-8 0-8 0-8 (h)
hNutrient mg l1 Half-saturation nutrient concentration for photosynthesis 0.006-0.007 0.005-0.013 0.007-0.015 (e)
pNutrient - Power of Hill function for nutrient 4-8 3-6 1-2 (e)
seedBiomass g Individual weight of seeds 0.00002 0.001-0.007 0.005-0.007 (f) (g)
seedFraction g year−1 Fraction of plant weight allocated to seeds 0.13 0.13 0.13 (f)
seedGermination year−1 Fraction of seeds that germinate 0.8 0.8 0.8 (i)
seedInitialBiomass g Initial biomass of seeds 2 2 2 (f)
(a) Unpublished observations in climate chambers and in the field by Markus Hoffmann
(b) Unpublished observations in climate chambers
(c) Field observations
(d) Mean values from own growth experiments (M. Hoffmann et al., 2013; M. A. Hoffmann et al., 2014)
(e) Values adjusted to the observed values within the described datasets. Derived by means of Hill function of the real distribution (quantitative) of the reference species as a function of the total phosphate values. Assumption: direct correlation between photosynthesis rate and plant quantity.
(f) (van Nes et al., 2003)
(g) (Kleyer et al., 2008)
(h) Expert knowledge
(i) Arbitrary
    To study the effects of environmental change related to global warming and water quality change, we performed simulation experiments with the surviving virtual species under changed lake parameters in a full-factorial design. We chose two water temperature increase scenarios of +1.5°C and +3.0°C (reference period 2010 - 2020) and combined those scenarios with two further scenarios of correlated nutrient and water turbidity increase (+25%) or decrease (-25%). We coupled these water quality components because of the high correlation of both parameters within the data set and the well-known connection between nutrient content and turbidity in lakes via algae growth. This design resulted in a total of eight scenarios and allowed the investigation of interactive effects of environmental change drivers.

    Data analysis

    To answer question Q1.1, we calculate the number of oligotraphentic, mesotraphentic, and eutraphentic species in each lake type for observed species richness and for the potential species richness. To answer question Q1.2, we calculate the number of oligotraphentic, mesotraphentic, and eutraphentic species for each depth in each lake to obtain an observed species richness from the mapped data and to obtain the potential species from the modelled data. We plot them as box plots grouped by the lake groups as a proportion (on % scale) of the total species number. To compare lake-wise the observed species richness with the modelled one, we calculated the Pearson correlation between observed and potential species richness for each species group in each lake type.
    To answer question Q2.1, we analysed the individual effects of water temperature increase scenarios and water quality change scenarios by calculating per lake, depth, and species group the difference of species number between the selected scenario and the base scenario. We plotted the mean and the standard deviation between lakes to see the direction and intensity of change. Furthermore, we explored interactive scenarios of temperature increase and turbidity and nutrient increase by plotting the species richness changes after subtracting the single effects from the combined effects.
    To answer question Q2.2, we selected two scenarios, turbidity and nutrient decrease and turbidity and nutrient increase, and determined for each species if it loses (“loser”) or gains habitat (“winner”) by comparing the number of lakes the species is present between the base scenario and each selected scenario. Then, we performed a Generalised Linear Model (GLM) to explain if a species is a winner or a loser (binomial distribution) within the corresponding scenario by all available species-specific parameters, with traits as the explanatory variables. The explanatory variables are all species-specific parameters, the response variable is the winner-/loser-classification. Interactive effects are not considered.
    We plotted the odd ratio of all significant variables (p < 0.05) with the sjplot package (Lüdecke et al., 2021). The goodness of the model is determined with Tjur’sR2 within the performance package (Lüdecke et al., 2022). A value R2 ≥ 0.26 implies a substantial explanation of the model (Cohen, 1988). Traits that promote significantly (p < 0.05) that a species loses habitat will be called “loser traits” and traits that promote significantly (p < 0.05) an increase in habitat of the species are called “winner traits”.