ASV tables were classified according to climatic zones (tropical, subtropical, and subalpine). ASV tables were used to determine alpha diversity and community composition of host plants, fungi (including total fungi, ectomycorrhiza, arbuscular mycorrhiza and pathogen) and bacteria (including total bacteria, nitrifier, nitrogen-fixing bacteria and pathogen). Species richness (SR), Shannon–Wiener diversity index (Shannon), and phylogenetic diversity (PD) were calculated to measure the alpha diversity of plant and soil microorganisms. SR was calculated as the total number of species in a plot (Zhang et al., 2021 (link)). Shannon-wiener index is based on relative abundance data, which is affected by both richness and evenness (Shannon, 1948 (link)). PD estimates phylogenetic alpha diversity (Faith, 1992 (link)), defined as the sum of branch lengths (from the terminal to the base of the phylogeny) of all species in a plot (Zhang et al., 2021 (link)). These diversity indexes were calculated using the picante package (Kembel et al., 2010 (link)) and vegan package (Oksanen et al., 2013 ) in R software.
The relationship between alpha diversity and elevation in the three climatic zones was evaluated by linear regression. We compared the Akaike information criterion (AIC) values of simple and multinomial linear regressions and selected models with smaller AIC values for visualization (Supplementary Table S3 in Supplementary material ). The models were visualized using the ggplot2 package in R (Wickham, 2016 ).
The association between alpha diversity and environmental variables (elevation, OM, TC, TN, TK, AK, TP, HN, humidity, temperature, and soil water content) was assessed by Pearson correlation analysis. The importance of environmental variables to diversity was evaluated by random forest. The random forest method accommodates collinear predictors by distributing the relevance of a variable across all variables (Yang et al., 2021 (link)). The explanatory power of environmental variables for three alpha diversity metrics was estimated using the linkET package (Huang, 2021 ) and randomForest package (Liaw and Wiener, 2007 ) in R software.
Plant and microbial community compositions were ordinated using nonmetric multidimensional scaling (NMDS) with Bray–Curtis dissimilarity matrices using the metaMDS function in the Vegan package (Oksanen et al., 2013 ). The association of species community composition with environmental factors was evaluated by distance-based redundancy analysis (dbRDA). In this analysis, we performed Hellinger transformation on microbial ASV tables. Diversity indices were correlated with environmental variables using the rdacca.hp function in R (Lai et al., 2022 (link)). Canonical analyzes (RDA, canonical correspondence analysis, and dbRDA) are the best multivariate statistical approaches for investigating explanatory factors for the matrix of response variables. The overall explanatory power of environmental variables can be calculated using R packages; however, it is challenging to accurately calculate the explanatory power of individual variables because of covariance across variables. The rdacca.hp function reduces collinearity among environmental factors.
The impact of environmental factors on microbial diversity was assessed using pathway analysis via a piecewise structural equation model approach (Grace et al., 2012 (link)). First, we performed principal component analysis of environmental factors (climatic and soil properties), plant diversity, and microbial diversity to extract data on the first axis (PC1, explained variance >70%). Then, two models (A and B) were fitted to the data to examine whether the effects of environmental factors on microbial diversity were direct (model A, in which environmental factors directly affected microbial and plant diversity) or indirect (model B, in which environmental factors directly affected plant diversity, which in turn impacted microbial diversity). These analyzes were performed using the FactoMineR package (Lê et al., 2008 (link)) and the piecewiseSEM package (Lefcheck, 2015 (link)) in R.
All statistical analyzes were conducted in R version 4.1.2 (R Core Team, 2021 ).
The relationship between alpha diversity and elevation in the three climatic zones was evaluated by linear regression. We compared the Akaike information criterion (AIC) values of simple and multinomial linear regressions and selected models with smaller AIC values for visualization (
The association between alpha diversity and environmental variables (elevation, OM, TC, TN, TK, AK, TP, HN, humidity, temperature, and soil water content) was assessed by Pearson correlation analysis. The importance of environmental variables to diversity was evaluated by random forest. The random forest method accommodates collinear predictors by distributing the relevance of a variable across all variables (Yang et al., 2021 (link)). The explanatory power of environmental variables for three alpha diversity metrics was estimated using the linkET package (Huang, 2021 ) and randomForest package (Liaw and Wiener, 2007 ) in R software.
Plant and microbial community compositions were ordinated using nonmetric multidimensional scaling (NMDS) with Bray–Curtis dissimilarity matrices using the metaMDS function in the Vegan package (Oksanen et al., 2013 ). The association of species community composition with environmental factors was evaluated by distance-based redundancy analysis (dbRDA). In this analysis, we performed Hellinger transformation on microbial ASV tables. Diversity indices were correlated with environmental variables using the rdacca.hp function in R (Lai et al., 2022 (link)). Canonical analyzes (RDA, canonical correspondence analysis, and dbRDA) are the best multivariate statistical approaches for investigating explanatory factors for the matrix of response variables. The overall explanatory power of environmental variables can be calculated using R packages; however, it is challenging to accurately calculate the explanatory power of individual variables because of covariance across variables. The rdacca.hp function reduces collinearity among environmental factors.
The impact of environmental factors on microbial diversity was assessed using pathway analysis via a piecewise structural equation model approach (Grace et al., 2012 (link)). First, we performed principal component analysis of environmental factors (climatic and soil properties), plant diversity, and microbial diversity to extract data on the first axis (PC1, explained variance >70%). Then, two models (A and B) were fitted to the data to examine whether the effects of environmental factors on microbial diversity were direct (model A, in which environmental factors directly affected microbial and plant diversity) or indirect (model B, in which environmental factors directly affected plant diversity, which in turn impacted microbial diversity). These analyzes were performed using the FactoMineR package (Lê et al., 2008 (link)) and the piecewiseSEM package (Lefcheck, 2015 (link)) in R.
All statistical analyzes were conducted in R version 4.1.2 (R Core Team, 2021 ).
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