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).