Data analyses
Before analysis, data was subjected to Shapiro-Wilk test for checking its normal distribution. Two-way analysis of variance (ANOVA) followed by Least Significant Difference (LSD) post hoc tests with p-value adjusted using ”BH” method were applied to analyze the difference in microbial communities and soil physicochemical properties between the plots and among sites. Data was also subjected to principal component analysis (PCA) using the FactoMineR (https://CRAN.R-project.org/package= FactoMineR) and factoextra (https://CRAN.R-project.org/package= factoextra) packages, to determine whether the overall physicochemical properties of the invaded plots were different from those of uninvaded plots across different sites along the altitudinal gradient.
We also used the community ecology R package‘vegan_2.5.6’ (https://github.com/vegandevs/vegan) for plotting the rarefaction curves for assessing the sequencing depth of individual samples both in bacteria and fungi at species level. We measured the α-diversity of bacterial and fungal communities within the samples. Both richness (i.e. number of different species present in a sample) and evenness (i.e. how common in number different species in a sample are) measures were computed for the invaded and uninvaded plots across all the sampling sites along an altitudinal gradient using QIIME v1.9.1. Total observed OTUs and Chao-1 estimator were employed to measure the observed and real richness (i.e. observed plus less frequent species) of the samples. For measurement of species diversity in each sample, we used Shannon index and Simpson index – the two commonly employed diversity indices in community ecology.
We determined the β-diversity (the differences of bacterial and fungal diversity between the samples) using non-metric multidimensional scaling (NMDS) plots based on Bray-Curtis dissimilarity index in vegan_2.5.6. This index measures the relatedness of the species composition of soil microbial communities across different sites and is a distance-based method that maximizes rank dependent correlation between the original distances and the distances between samples from multidimensional space into new reduced 2D ordination space. Analysis of similarities (ANOSIM), a non-parametric statistical test, was carried out to determine the variation in the community composition.
Similarly, the most significant soil nutrient parameters governing the composition of microbial communities at different sites were determined by Canonical Correspondence Analysis (CCA). Forward selection method of explanatory variables was carried out to find out the model that includes only those parameters which contributed significantly to the overall microbial diversity at different sites using ‘vegan_2.5.6’. Chart.Correlation() function from the “PerformanceAnalytics” package was used to estimate Spearman’s rho (ρ)  statistic, which is a rank-based measure of association between environmental variables (https://github.com/braverock/PerformanceAnalytics). For data visualization, all plots were created using the package ggplot 2_3.3.2 (https://ggplot2.tidyverse.org) in R software (R Core Team 2020).