Statistical analyses
We used linear mixed effects models to test for effects of plant
diversity and heterotroph removals on carbon fluxes using thenlme package (Pinheiro, Bates, DebRoy, Sarkar & R Core Team
2019) in R software (R Core Team 2019). We performed these analyses with
both total fluxes and mass-specific fluxes obtained by dividing total
fluxes by total plant biomass. Total and mass-specific fluxes were log
transformed. Plant diversity was considered a factor variable with three
distinct levels (1, 4 and 16 sp). In mixed model specification, plant
diversity and heterotroph removal treatments were included as fixed
effects. To account for the nested split plot design of the experiment,
we included subplots (heterotroph removal treatments) nested within
plots (plant diversity) as random intercepts in our mixed models. Full
model equations are provided with the model results (Tables 1-6). All
figures were generated using the ‘ggplot2’ package (Wickham 2009). To
examine percent nitrogen content and relative abundances of different
functional types we constructed generalized linear mixed effects models
with a logit link using ‘glmer’ function in lme4 package
(Bates et al. 2015). We performed path analysis with the most important
variables from bivariate analyses to determine the relative importance
of different pathways via which plant diversity and heterotroph removals
influenced carbon fluxes. To do so, we constructed a structural equation
model using the ‘piecewiseSEM’ package (Lefcheck et al. 2016).