(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).