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”.