Additional file 2 of Grapevine bacterial communities display compartment-specific dynamics over space and time within the Central Valley of California

Additional file 2. Table S1 Characteristics of the vineyard blocks used within the study. Table S2 Soil elemental composition of the vineyard blocks used within the study. Values are reported in mg/kg (ppm) with the exception of pH which is reported in the standard scale. Table S3 Optimal hyperparameters for training machine learning models. Table S4 Linear model results for soil texture. Type-2 ANOVA table. Table S5 Linear model results for soil elemental composition principal components (PC). Type-2 ANOVA table. Table S6 Linear model results for Bray-Curtis Dissimilarity for soil samples. Type-2 ANOVA table. Table S7 Linear model results for alpha diversity statistics for soil samples, Chao1 index and Faith’s phylogenetic diversity. Type-2 ANOVA table. Table S8 Linear model results for sugar content of the berries. Type-3 ANOVA table. Table S9 Linear mixed model results for Faith’s Diversity index. Type-3 ANOVA table. Table S10 Linear mixed model results for Chao1 index. Type-3 ANOVA table. Table S11 Linear model results for root compartment taxa across taxonomic levels for each experimental factor. Table S12 Linear model results for leaf compartment taxa across taxonomic levels for each experimental factor. Table S13 Linear model results for berry compartment taxa across taxonomic levels for each experimental factor. Table S14 Output statistics for three-class machine learning models predicting rootstock genotype, collection site, and plant compartment. Table S15 Output statistics for binary class machine learning models predicting scion genotype and collection year. In these models Cabernet Sauvignon and 2018 represented the positive class.