Statistical analysis
MAT and mean annual precipitation (MAP) of each plot were extracted from
the relevant latitude and longitude of the global climate layers of
WorldClim (1 km2 spatial
resolution; http://www.worldclim.org/) using the extract function
in the “raster” R package (v. 2.6.7), and the aspect of each plot was
obtained by analyzing
the digital
elevation model data using the “raster” R package (Hijmans, 2020). We
calculated the above-ground biomass (AGB) using
a pantropical model
(Chave et al. , 2014) in the “BIOMASS” R package (v. 2.1.1)
(Réjou-Méchain et al. , 2017).
In this study, we totally collected 54 abiotic and biotic factors
relative to the MR and Q 10. Regression analyses
were used to examine the elevational trends of all response variables.
Then, the 54 factors were clarified into seven groups: topography
(elevation, aspect) and climate (MAT and MAP), soil environment (WHC,
pH), soil texture (bulk density, sand content, silt content and clay
content), plant community structure (AGB, species richness, Shannon
diversity index (H’), Simpson index), plant carbon input (litter C, N, P
concentration and C:N ratio (litter CNR), C:P ratio (litter CPR), N:P
ratio (litter NPR), fine root biomass, fine root C, N, P concentration
and C:N ratio (root CNR), C:P ratio (root CPR), N:P ratio (root NPR)),
soil organic matter (soil C, N, P, C:N ratio (soil CNR), C:P ratio (soil
CPR) and N:P ratio (soil NPR), soil available N and P), soil microbial
biomass (soil microbial biomass C, N, P, C:N ratio (microbial CNR), C:P
ratio (microbial CPR) and N:P ratio (microbial NPR)), soil microbial
community structure (total phospholipid fatty acids, and its components
of bacteria, gram-positive bacteria, gram-negative bacteria,
actinomycetes, fungi, and gram-positive: negative bacteria ratio (GNR),
actinomycetes: bacteria ratio (ABR), actinomycetes: fungi ratio (AFR),
fungi: bacteria ratio (FBR)).
For each group of factors, we performed all subsets regression analysis
to select the best model that had the lowest Bayesian information
criterion (BIC) in predicting AccMR_MAT and Q 10,
respectively (Table S1 and S2). If the difference of BIC was <
2 units (Burnham and Anderson, 2002), we obtained the model with the
highest adjust R 2. Using this approach, we
selected 14 and 12 variables from the best models for AccMR_MAT andQ 10, respectively. The unselected variables
either had no significant influence on AccMR_MAT orQ 10, or were highly collinear with the selected
variables. Then, the selected variables were used in structural equation
modelling (SEM) to explain the variation of AccMR_MAT andQ 10 along the elevation. We dropped the
non-significant path and variables in the SEM to simplify the initial
model and improve the model fit. The indirect effect of each predictor
was calculated by multiplying the standardized direct effects of a given
predictor on AccMR_MAT or Q 10 via mediator in
one route, and then we summed the multiple indirect effects and direct
effect of the given predictor to quantify its total effect (Lefcheck,
2016). All the analyses were conducted in R 3.3.4. We used packages
corrplot (Wei and Simko, 2013), leaps, piecewiseSEM (Lefcheck, 2016).