gcb2480-sup-0001-appendix1-AA.docWord document576KAppendix S1.  Sensitivity analysis
gcb2480-sup-0002-appendix2-AA.docWord document280KAppendix S2.  RLQ analysis
gcb2480-sup-0003-fS1-AA.tifimage/tif790KFigure S1.  Prediction for the proportion of species that are native based on various traits, and scaled by assuming knowledge of the true proportion native at five randomly selected sites (black dots). For all traits except for seed mass, the modeling approach provides a good prediction for the proportion native [good fit of the regression (solid line) to the one-to-one line (dashed)], with high r2 and unity-line r2 values.
gcb2480-sup-0004-fS2-AA.tifimage/tif209KFigure S2.  Weed species richness and the proportion of species that are weeds increases with increasing mean annual temperature. This relationship is similar to that observed for exotic species in general.
gcb2480-sup-0005-fS3-AA.tifimage/tif192KFigure S3.  Comparison of trait means for native (N), non-weed exotic (E) and weed (W) species. Different letters indicate significant pairwise differences (post hoc Tukey HSD test). For most traits, weeds differed from other exotic species in the same manner as nonweed exotic species differed from native species. This suggests that the trait states possessed by exotic species, expressed in extremes, tend to promote the species becoming invasive.
gcb2480-sup-0006-fS4-AA.tifimage/tif123KFigure S4.  Prediction for the proportion of species that are invasive weeds based on leaf width. The fit of prediction to observed data is similar to but weaker than that obtained for exotic species in general.
gcb2480-sup-0007-tS1-AA.docWord document38KTable S1  OLS and SAR parameter estimate comparison for the relationship between mean annual temperature and the named variable across 800 zones in California.

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