Statistical analysis
The Kolmogorov–Smirnov test was performed to determine whether all data
were normal. Non-normal variables were log-transformed before the
analyses. The significant differences of Rm among different biome zones
and forest types were tested by the least significant difference (LSD)
multiple-comparison post hoc test after the one-way analysis of
variance. Spearman’s correlation analyses were performed to assess the
relationships between important variables of soil properties and Rm. The
above statistical analyses were performed using the SPSS software
(version 19.0; SPSS Inc., Chicago, Illinois, USA), and significant
differences were accepted at the p < 0.05 level of
probability. Variation partitioning modeling was performed to assess the
relative importance of three groups, namely, climate, soil
physicochemical properties and microbial properties, in driving
continental variations in soil Rm. We used the “forward.sel” function
to avoid redundancy and multicollinearity in variation partitioning
analysis that was conducted using the “Vegan” package.
First, we used “selected.forward” procedure to select most important
predictors of Rm and then included these variables in structural
equation modelling (SEM). SEM was performed to determine the pathways
underlying the observed effects of environmental predictors on microbial
respiration. SEM was conducted using “piecewiseSEM”, “nlme” and
“lme4” packages. The piecewiseSEM could also account for random
effects of sampling sites (to account for having more than one sample
per site), by providing ”marginal” and ”conditional” contribution of
environmental predictors in driving microbial diversity. We used the
Fisher’s C test (when 0 ≤ Fisher’s C/df ≤ 2 and 0.05 <p ≤ 1.00) to confirm the goodness of the modelling results. We
then modified our models according to the significance (p< 0.05) and the goodness of the model. We included the same
predictors in SEM for Rm at different scales, which could disentangle
the best predictors for Rm according to their differences in total
standardized effects. We further conducted boosted regression tree
analysis to quantify the relative importance of the moderator variables
in regulating the spatial variation in soil Rm. A gaussian error
structure was assumed during a 10-fold cross-validation to estimate the
optimal number of trees. Tree complexity was set to 4, whilst the
learning rate was kept at 0.01 and bagging fraction at 0.6. The BRT
model was performed using the R package “gbm” (Ridgeway, 2013)
combined with the “dismo” package in R 3.6.4.