Canoco for windows version 4

Fungal distribution of abundance and diversity were drawn by Origin 8.0 software. A multivariate redundancy analysis (RDA) was performed by Canoco software (Canoco for Windows version 4.5, Microcomputer Power, Ithaca, USA) to illustrate the confounding changes among fungal abundance, communities structure and environmental factors (the content of organic matter, total phosphorus, quickly available phosphorus, total nitrogen, ammonia nitrogen, nitrate nitrogen, redox potential, pH, and samples location, including longitude, latitude and altitude). The diversity values of fungi were centered and standardized in the redundancy analysis, and environmental factors were also standardized as variables before performing RDA analysis. Meanwhile, the Monte Carlo test was used to examine the significance of analysis. A generalized linear model (GLM) and generalized additive model (GAM) was performed in a stepwise manner to predict the multiple response of fungal community to each variable factor, and the visualization formula was given in terms of linear, quadratic and cubic degree of GLM, and F statistics were conducted to test the significance of both GLM and GAM. ANOVAs analysis was used to assess the significance of differences in measured or calculated parameters. ANOVA analysis was performed using R statistical software (http://www.r-project.org/).

The statistical analyses of data were conducted in the SPSS software program (ver. 17.0 for Windows, Chicago, IL, USA). Variation among samples for different treatments and rice growth stages were analyzed using ANOVA. Levene’s test was used to assess the equality of variances before performing ANOVA, and significant differences between the treatments and growth stages were determined by the SNK test. The differences were considered statistically significant when P < 0.05. PCA was used to analyze the substrate utilization pattern based on the Eco-plate data. The diversity indexes variation were also analyzed by ANOVA. PLFA profiles and enzyme activities were compared using redundancy discriminate analysis (RDA) with Monte Carlo permutation test (CANOCO, for windows version 4.5, Microcomputer Power, Ithaca, USA). The Monte Carlo tests were based on 9999 random permutations of the data to explore significance of the environmental variables63 . Soil chemical factors potentially affecting community structure and enzyme activities were used as the restricted variables. Microbial community data potentially affecting the normalized enzymatic activities were also used as the restricted variables where necessary.