Statistical analysis
In all analyses, we identified and excluded outliers as explained in
Supplementary Methodology S3. We used multiple regression analyses to
test how phylogenetic isolation aboveground (PIA) and belowground (PIB)
conjointly affect i) the decomposition parameters (litter mass loss, C
loss, N loss and 13C isotope ratios) and ii) the
decomposer biota, i.e. Acari abundance, Collembola abundance, and
microbial biomass after 8- and 14-month decomposition, and fungal
abundance and fungal diversity after 8 month decomposition. The
regression results were illustrated in partial residual plots.
We then explored the role of different trajectories by which PIA or PIB
may affect decomposition (Table 1): (i) by changing the abundance or
diversity of a given group of decomposers, i.e. PIA or PIB
influences significantly a particular group of decomposers (abundance or
diversity) which in term influences decomposition (either mass loss or C
loss or N loss either at 8 or at 14 months); (ii) by changing the
efficiency of a given decomposer group (abundance or diversity),i.e. a significant interaction term between either of the
phylogenetic isolations and the decomposer group in their effect on
decomposition; or (iii) by affecting decomposition via abiotic
conditions, i.e. PIA or PIB maintain a significant effect on
decomposition after accounting for abundances and diversities of
decomposer groups and the corresponding interaction terms (assuming that
all pertinent decomposer groups have been accounted for). Testing the
influence of PIA and PIB on decomposers as part of relationship (i) was
explained in the before paragraph. The remaining relationships are
explained below.
We explained decomposition by including PIA and PIB and decomposer biota
as predictors, as well as the interaction terms of decomposers with both
PIA and PIB. Due to the large number of decomposer biota as predictors
we took a stepwise approach to select the most pertinent interaction
terms with decomposer variables. For each of the six decomposition
variables to be explained, we first carried out five distinct
statistical models corresponding to five biotic predictors: Acari
abundance, Collembola abundance, and microbial biomass. Each model
tested the effects of PIA, PIB, one biotic predictor and its
interactions with PIA and PIB. Finally, in order to decipher the
relative contributions of the five biotic predictors to explain litter
decomposition, we created a last model in which we included all the
significant predictors of the five previous models. The full models were
then simplified to determine the most parsimonious models using the
‘stepAIC’ function of the ‘BMASS’ package, an established model
selection procedure with both forward and backward selection algorithms,
which ranks all candidate models (all initial predictor variables
included in the full model) based on the lowest AICs (Crawley 2013). We
conducted a separate analysis for fungal abundances and diversities, as
these were only available for the 8-month sampling. The procedure of
including interaction terms was the same as before.
In the end, we further explored whether the observed direct or
interaction effects of PIA or PIB could be explained by litter traits
such as leaf phenolics, leaf phytophagy or leaf litter C/N ratios, or by
characteristics of the abiotic environment, as suggested by the
mechanisms hypothesized in the Introduction and in Table 1. We did so by
replacing either PIA or PIB by a given trait or environmental
characteristic (as listed in Supplementary Table S1) and repeated the
procedure for different traits/environmental characteristics.
Statistical analyses were performed with the R software (version 3.3.3).