(i) Changes of abiotic and biotic factors along elevational
gradients
We conducted principal component analysis (PCA) to summarize soil
properties (W, pH, C, NO3- and
NH4+) using the “vegan” package (v.
2.5.7; Oksanen et al., 2020) (Fig. S1.2). We then
calculated the Spearman rank-order
correlation between Soil PCA1 and each of the soil properties (W,
pH, C, NO3- and
NH4+) (Fig. S1.2). We also calculated
the plant species richness (SR ) and Pielou’s evenness index
(Evenness ) using the “vegan” package for each plot. To test
correlations among various variables, we plotted the correlation matrix
for all biotic or abiotic variables (Elevation , SR ,Evenness , Proneness , AGB, BGB, MDT, MDH andSoil PCA1 ), and we then calculated the Pearson’s correlation
between these variables and PL /various soil pathogen indices
(Fig. S1.3a). We also conducted Mantel tests based on “Bray-Curtis”
distance between sfpOTUs and biotic or abiotic variables using
the “ggcor” package (v. 0.9.8.1; Huang et al., 2020) (Fig. S1.3b).
To minimize the influence of the potential spatial autocorrelation on
the results, we used the respective coordinates of each sample plot to
generate a spatial matrix. Specifically, given the sample plots were
distributed along a cambered mountain slope which approximated to the
spherical surface, we calculated the spatial matrix based on spherical
correlation structure (i.e. “corSpher” class) using the “nlme”
package (v. 3.1-152; Pinheiro et al., 2021). We then introduced the
spatial matrix into a series of linear mixed-effects models with five
elevations as a random effect in following analyses, using the “nlme”
package. We set Elevation as independent variables in a series of
linear mixed-effects models to test its associations with various
community-level indices (SR , Evenness , Proneness ,AGB and BGB ) and soil properties (SoilPCA1 ), respectively. At the
plant community level, we set Elevation as the independent
variable and PL and soil pathogen indices (sfpOTUs andsfpRA ) as response variables in a series of linear mixed-effects
models to test the direct correlations between Elevation and
above- and belowground plant pathogens.
For the soil fungal pathogen community, we conducted permutational
multivariate analysis of variance (PERMANOVA) to test the compositional
difference of soil fungal pathogens along the elevational gradient. A
significant result of PERMANOVA supports the hypothesis that pathogen
communities change along elevational gradients.