Canoco for windows 4

Statistical analyses were performed mainly using the R packages, including ade4 ggplot2, (http://www.r‐project.org/), together with Python (Sanner, 1999 ), Canoco for Windows 4.5 (Microcomputer Power), and PAST (Hammer et al., 2001 ). Differences in the relative abundances of taxonomic groups between samples at gene level were evaluated using a Mann–Whitney test. False discovery rate (FDR) values were estimated using the Benjamini–Yekutieli method to control for multiple testing (Benjamini & Yekutieli, 2001 (link)). p‐values <.05 were considered statistically significant. Differentially expressed genes were identified using DESeq2 (Love et al., 2014 (link)).

Both the forward and the reverse ends were cut off from the same reads at the first base for which the Q value was less than 2. All of the reads were kept when the length was more than 399 bp and the expected error was less than 0.5 (57 (link)).
High-quality sequences were clustered into OTUs (operational taxonomic units) at 97% identity by Usearch and the representative nonchimeric OTU sequences were picked by Uparse’s default (58 (link)). The number of high-quality reads was more than 10,000 for each sample. The representative sequences of each OTU were classified by the RDP classifier online, and the RDP-classified sequences were used for taxonomical assignments at 80% confidence level (59 (link)).
The tree, together with sequence abundance data, was then used for beta-diversity analysis based on weighted metric by QIIME 1.6 (60 (link)). The relative abundances of OTUs were used for principal-component analysis, multivariate analysis of variance, and redundancy analysis via Matlab R2015a (The MathWorks, Natick, MA, USA) and Canoco for Windows 4.5 (Microcomputer Power, NY, USA).
Two-way ANOVA test and Mann-Whitney test were used to test the statistical significance of the physiological and biochemical data via software SPSS 19.0 (SPSS Inc, Chicago, IL, USA). P values were adjusted by the method of Benjamini and Hochberg (61 (link)).

Differences in soil chemical properties, read numbers of dominant OTUs, and α-diversity indices (all fungi, ECM, and saprotrophic fungi) were assessed using ANOVA in SPSS 19.0 (SPSS Inc., Chicago, IL, United States), and a p-value of 0.05 was considered statistically significant (Shen et al., 2016 (link)). Redundancy analysis (RDA) was performed using the CANOCO software (Canoco for Windows 4.5, Microcomputer Power Inc., Willis, TX, United States) to test the relationships among genera and soil chemical properties according to the method described by Van Den Wollenberg (1977) (link). Statistical significance of the relative abundance data at the phylum and class levels was analyzed using the STAMP software (Parks et al., 2014 (link)). Co-occurrence network analysis was performed using relative abundance values of OTUs with a Spearman correlation coefficient (r) >0.7 and p< 0.05, and the data were visualized using Cytoscape (version 3.4.0), according to a previous study (Shannon et al., 2003 (link)).