Statistical analysis
Statistical analyses were done within R version 4.2.2 (Team R Development Core, 2022). First, we performed a principal component analysis (PCA) on above- and below-ground plant traits where values of individual traits were standardized using z-transformation by using the function PCA from the FactoMinerR package(Csardi & Nepusz 2006). The explained variance from the first two PCs and individual plant trait loading weightage on them were extracted. To test the contribution of PCA loadings, a combination of a threshold selected using the number of dimensions (Richman 1988) and a bootstrapped eigenvector method (Peres-Neto, Jackson & Somers 2003) were used. For plant traits and bacterial diversity indices (ASV Richness, Shannon diversity, and Pielou’s evenness), we performed linear models to test the main effects of domestication (wild versus modern barley), soil microbiome (DSM versus NSM), and their interactions. The step-wise data exploration protocol from (Zuur, Ieno & Elphick 2010) was followed to avoid common statistical errors in which, the mean-variance relationship from residual plots was visually checked. Measured plant and bacterial variables are presented as means with 95% confidence intervals that were computed by using a non-parametric bootstrap resampling with 10,000 iterations. The 95% confidence intervals are referred to as compatibility intervals (95% CI), henceforth (Amrhein & Greenland 2018; Berner & Amrhein 2022). Given the practice of usingp -values (α = 0.05) as dichotomous to test the null hypothesis and to favor ‘statistically significant’ over ‘non-significant’ results, we refrain from using the above-mentioned terms and mostly mentioned the mean differences between treatments and effect sizes wherever possible while interpreting our results (Halsey 2019; Berner & Amrhein 2022).
We performed network analysis to investigate how multiple plant traits are interacting. In the network analysis, traits are assigned as nodes and their connections as edges. We extracted network parameters such as edge density, diameter, distance, and modularity which have clear ecological significance (Flores-Moreno et al. 2019). For instance, edge density is the ratio of present to total possible connections, ranging from 0 to 1 and traits with higher edge density are considered more efficient. Modularity determines connectivity among trait modules where trait networks with higher modularity have tighter traits within than between modules. Trait networks with shorter diameter and mean distance imply stronger coordination among various traits. For the trait-trait relationship, data was log-transformed followed by the calculation of correlation coefficients for both wild and modern barley separately for DSM and NSM. Trait network analysis was described by significant correlation coefficients and illustrated by using the igraph package (Csardi & Nepusz 2006). Network properties such as edge density, network diameter, mean path length, and modularity were extracted (He et al. 2020; Xie, Wang & Li 2022). We excluded root AMF colonization data from correlation and trait networks as this dataset was comprised of many zero values.
The effect of DSM verus NSM and wild versus modern barley on overall bacterial community composition was analyzed via NMDS ordination of weighted uniFrac distance and PERMANOVA using the ordinate and adonis2 function of the vegan package (Oksanen et al. 2022), whereas, for the identification of biomarker taxa, a generalized linear model was used from MASS package (Ripley 2023). Additionally, differences between log2fold changes were calculated and both LEfSe (Linear discriminant analysis Effect Size) and ANCOMBC (Analysis of Compositions of Microbiomes with Bias Correction) were used to validate the results. Multiple test correction was performed by p-value adjustment via the Benjamini-Hochberg method. The core microbiome of barley species under DSM and NSM was defined as genera occurring with 0.1% in at least 75% of replicates. Plots were created in R using ggplot2 (Wickham et al. 2023) and metacoder (Zachary, Grunwald & Gilmore 2023) packages.