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”.