2.5 Statistical analyses
A one-way analysis of variance (ANOVA) followed by a Duncan’s test
(p <0.05 ) was applied to test the differences in soil
properties, fungal diversity and abundance across five sites. PCA and
Pearson’s correlation was used to evaluate the SQI using the IBM SPSS
software (SPSS Inc., Chicago, USA). For the sequence data, the α
diversity indices including
Observed species, Shannon-Wiener’s
diversity, Chao1 and species accumulation curves were calculated using
Mothur and visualized in Origin 2018 software (OriginLab, USA). The β
diversity in the fungal community was visualized in CANOCO 5.0 software
(Microcomputer Power, Ithaca, New York) by using non-metric
multidimensional scaling (NMDS) ordinations based on
Bray–Curtis distance. Network
visualized by Cytoscape (Vailaya et al., 2007) was used to analyze the
number of OTU which was cosmopolitan (present in all forest types),
specific to forest or plantations (i.e., only detected in one forest
type) or broadly dispersed (found in two or four sites). Linear
discriminant analysis (LDA) effect size (LEfse) was conducted to
identify the biomarkers between forest and plantations using an LDA
score > 4 and p < 0.05 for the
nonparametric Kruskal–Wallis test. A heatmap was conducted in R with
the “pheatmap” package to compare the variation in dominant
“funguilds” (top 35 in abundance). Relationships between soil
parameters and the fungal community were performed with redundancy
analysis (RDA) by using the CANOCO 5.0 software. Structural equation
model (SEM) was further used to determine indirect or direct
contributions of soil properties and fungal community with a
multivariate approach using AMOS software (IBM SPSS AMOS 20.0.0). In
this model, the Shannon index was selected as an informative estimate of
taxonomic diversity for the fungal communities (Delgado-Baquerizo et
al., 2016). The SEM fitness was examined on the basis of a
non-significant chi-square test (χ2), p value
(>0.05), the goodness-of-fit index (GFI, good fit when GFI>0.9) and
the root mean square error of approximation (RMSEA, good fit when
RMSEA<0.05) (Byrne & Erlbaums, 2009). The fit of additional indices of
SEM is shown in Table S6.