(ii) Factors affecting above- and belowground plant pathogens
We set various community-level indices (SR , Evenness ,Proneness , AGB and BGB ), and soil properties (Soil PCA1 ) as independent variables in a series of linear mixed-effects models to test their effects on PL , sfpOTUsand sfpRA , respectively. We using “MuMIn” package (v. 1.47.1; Bartoń, 2022) to conducted full model selections based on a series of linear mixed-effects models for PL , sfpOTUs andsfpRA , respectively. We then extracted the effect sizes with 95% confidence intervals from weighted average standardized coefficients from models with ΔAICc < 4 based on model selections, and compared these models with the null model (i.e. the intercept-only model) based on Akaike’s information criterion corrected for small sample sizes (AICc) using the “MuMIn” package. We also calculated the log-likelihood (LL) and AICc based parameters: change in AICc relative to the top-ranked model (ΔAICc), AICc weight (w AICc) and the percent deviance explained (De ) (Burnham et al., 2011), to estimate their possibilities of being the best predictor of PL and various soil pathogen indices. When the ratio ofw AICc of predictor to w AICc of null model more than 1.5, it indicates that the corresponding variable is associated withPL , sfpOTUs and sfpRA (Burnham et al., 2011).