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Table S1 All climate variables for which data were obtained.

Table S2 Flowering time experiments used as training data in SVM model.

Table S3 SNPs used as predictor variables in SVM models of flowering time (TAIR 10).

Table S4. Proportion of total SNP variation explained by climate and spatial variables.

Table S5 Bioclim abbreviations from the WorldClim data set.

Table S6 Climate variables and the percent of SNP variation among early-flowering accessions they explain in RDA (100*Px).

Table S7 Climate variables and the percent of SNP variation among late-flowering accessions they explain in RDA (100*Px).

Fig. S1 Correlation matrix between values of climatic variables at the 389 unique collection locations in Eurasia.

Fig. S2 Flowering times of accessions from 13 experiments used to train a genetic SVM model of early vs. late-flowering phenotype.

Fig. S3 The first two principal components of flowering time in the absence of vernalization.

Fig. S4 Histogram of the distribution of accessions along the first principal component of flowering time variation shown in Figure S3.

Fig. S5 Standardized flowering time for 27 accessions that were used to validate previous flowering time predictions. The first plant to flower was considered day 0.

Fig. S6 Portion of SNP variation explained (Px) by PCNM eigenvectors (only those with positive eigenvalues are shown).

Fig. S7 Portion of SNP variation explained by PCNM eigenvectors (Px) vs. Moran's I for each eigenvector.

Fig. S8 The first two RDA axes for all accessions combined. Climate variables with the strongest correlation to each quadrant are shown.

Fig. S9 The first two RDA axes for all accessions combined. Spatial structure variables were first removed in partial RDA.

Fig. S10 The first two RDA axes for early-flowering accessions. Climate variables with the strongest correlation to each quadrant are shown.

Fig. S11 The first two RDA axes for early-flowering accessions after removing spatial structure.

Fig. S12 The first two RDA axes for late-flowering accessions. Climate variables with the strongest correlation to each quadrant are shown.

Fig. S13 The first two RDA axes for late-flowering accessions. Spatial structure variables were first removed with partial RDA.

Fig. S14 Venn diagrams of variance partitioning results for early and late-flowering accessions.

Fig. S15 Comparison of the SNP variation explained by climate variables (Px) in early vs. late-flowering accessions.

Fig. S16 Distribution of flowering time groups across the Eurasian sample.

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