Data analysis
The raw fastq files were demultiplexed and subjected to quality filtering using QIIME (Caporaso et al., 2010) (version 1.17). Operational Taxonomic Units (OTUs) were clustered at a 97% similarity cutoff using UPARSE version 7.1 (Edgar, 2013). Chimeric sequences were identified and removed using UCHIME. The taxonomy of each representative sequence of the OTUs was determined using the RDP Classifier version 2.2 (Wang et al., 2007) against the ITS database with a confidence threshold of 0.7.
Diversity indices were based on resampled sequences using the MOTHUR program (Schloss et al., 2009). Alpha diversity (observed OTU richness) index was calculated for each sample using the vegan package (Oksanen et al., 2022) in the R environment (version 4.3.1). We modeled the alpha diversity as a response variable and site location and seasonal change as fixed effects. The relative importance of the location and season for alpha diversity was evaluated by analysis of variance (ANOVA) (Way and Klasson, 2020), with the p values corrected using the false discovery rate (FDR) method. Principal coordinate analysis (PCoA) was conducted using Bray–Curtis distances for the 216 samples to explore fungal community compositional differences (beta diversity) in different compartments, seasons and locations, and then was visualized by using ggplot2 (Wickham, 2009). The relative importance of the locations/sites and two seasons for explaining the variation in environmental variables and the alpha diversity of fungi were evaluated by two-way ANOVA. The permutational multivariate analysis of variance (PERMANOVA) in the R vegan package was used to test the variations in fungal beta diversity as explained by locations and seasons (Jiao et al., 2022).
Random forest (RF) analysis (rfPermute function in rfPermute package in R) (Archer, 2013) was used to identify the main environmental drivers for soil fungal alpha diversity (Delgado-Baquerizo et al., 2016; Trivedi et al., 2016). To reveal the relationship between alpha diversity and environmental factors, a linear (linear least-squares regression analysis) or nonlinear regression was used based on RF results. The Mantel test was performed to evaluate the influence of soil properties, climate factors and leaf properties upon fungal community of different compartments, using the mantel function of the ecodist package (Goslee and Urban, 2007) and vegan package for R (Oksanen et al., 2022) and visualized by using the linkET packge (Huang, 2021). We used variation partitioning to quantify the relative importance of seasonal change (dry and rainy season), climatic factors (temperature and precipitation), physicochemical properties (soil including soil pH, WC, SOM, TK, TN, TP, AN, NN, AK, AP; leaf including pH, WC, LOM, K, N, P) and geographic variables (Yeh et al., 2015). Latitudinal and longitudinal data for each site was transferred to rectangular data to represent spatial distance by function pcnm ofVegan package, and variation partitioning analyses were conducted with function varpart in the vegan package for R (Oksanen et al., 2022).