Statistical analyses
All statistical analyses were done in R version 3.6.1 (R Core Team
2019).
The homeostatic coefficient 1/H was calculated to analyze the response
of fungal stoichiometric ratios to varying nutrient supply in glucose
and cellulose media (Persson et al. 2010). For example,
1/HCN represents the regression slope of fungal log10
(C:N) correlated with log10 (C:N) in media, and takes values between 0
(strictly homeostatic) and 1 (non-homeostatic) (for more details see
also Sterner and Elser (2002)). The same was calculated for
1/HCP and 1/HNP values, for the latter
using the whole gradient of N and P manipulation in glucose media.
Differences in isolate-specific 1/H values among different element
gradients were calculated by paired t-tests. Variations in fungal
stoichiometric ratios in fertilized SEA media were analyzed by linear
mixed-effects models, taking isolate as random effect into account
(lme(); package nlme (Pinheiro et al. 2020)). Here, in
case of non-normality, data were log-transformed.
Details of the results and analyses of fungal growth responses to
varying nutrient supply in glucose, cellulose and SEA medium are shown
in Fig. S2, S3 and S4, respectively.
Total fungal element masses [mg] were correlated with fungal biomass
[mg] in each medium type, in order to understand the stability of
different elements in fungal mycelia under varying resource conditions.
In this case, data were normalized within isolates for each element and
medium type, respectively, by standardizations between 0 and 1. Model
outputs are based on linear random slope models, using isolate as random
factor. Explanatory power of biomass versus nutrient manipulation on
total fungal element masses were assessed by the comparison of sums of
squares, evaluated by type III analyses of variances of model outputs
(package lmerTest (Kuznetsova et al. 2017)).
To assess simultaneous decreases in fungal N and P concentrations
[%] in both, N and P manipulations, linear correlations were done
by linear-mixed effects models with isolate as random factor for each
medium and nutrient manipulation, respectively. Relative deviations in
element concentrations [% deviation from maximum values of each
isolate] were used to compare effect sizes of element reductions among
isolates. In SEA media, added carbon sources were included as fixed
effect, since element concentrations were significantly affected by
cellulose and glucose additions. Marginal R² values were calculated
using function r.squaredGLMM() (package MuMIn (Barton 2019)).
Differences in fungal C:N values of inner versus outer mycelium in SEA
medium, as well as of fungi growing 12 or 26 days, respectively, were
determined by paired t-tests (one-sided). To evaluate the effects of
treatment (low vs. high N), position (in vs. outer mycelium) and isolate
on fungal C:N and C:P values in low and high N glucose media, three-way
type III analyses of variances were applied.
The predictive power of stoichiometric ratios for fungal growth and
activity was determined by linear correlations of growth responses
(namely fungal biomass, density, enzymatic activity and the ratio of
leucine-aminopeptidase and phosphatase activity) with stoichiometric
ratios, applying linear mixed-effects models (lmer(); packagelme4 (Bates et al. 2012)). Marginal (variance explained by
fixed effects only) and conditional (variance explained by fixed effects
and random effects due to varying intercepts among isolates) R² values
were calculated with function r.squaredGLMM() .