Analyses across plant families
First, we used phylogenetic principal components analysis (pPCA) with
scaling to ordinate and derive two orthogonal variables, accounting for
the most variation among them (Fig. 2, see Supporting information). We
then fit linear models of ERR slopes as a function of each variable
(Table 1), as well as the first two principal components at the family
level, using phylogenetic independent contrasts (PIC). For across-family
analyses, all variables were transformed into z-scores to facilitate
comparisons of factors at different scales (reported in Table 1),
meaning results remain consistent (adj. R2,F -statistic, and P -values), but the intercept becomes zero
and slopes of said relationships can be described as positive (slope of
1), negative (slope of -1), or neutral (0). Note, we did not use scaling
for reporting individual family ERR slopes, or for visual representation
of scatterplot axes. For extended methods, see Supporting information.