Analytical framework
Analyses were performed in R (R Core Team 2018). Prior to model testing,
we performed transformations of continuous data to improve normality of
model residuals (details in Appendix S2). FST was
transformed using Tukey’s ladder of powers transformation (Tukey, 1970)
with the function transformTukey from the R package rcompanion
(Mangiafico, 2018). Continuous predictors were transformed using their
natural logarithm. We also estimated correlations (Plackett, 1983) and
evaluated multicollinearity issues (Acock & Stavig, 1979; Fox &
Monette, 1992) among predictor variables (Appendix S3). The
multicollinearity tests indicated that all predictors could be included
together in a multiple regression (Table S2 and Table S3).
In order to calculate and subsequently perform models that correct for
phylogenetic signal (Freckleton, Harvey, & Pagel, 2002), a
species-level phylogeny (Fig. S1) was produced with the R package
V.PhyloMaker (Jin & Qian, 2019). This package prunes a custom list of
species from the latest and most complete mega-tree of vascular plants
(Smith & Brown, 2018) (see Appendix S4 for details). We then assessed
phylogenetic signal in categorical predictors with Abouheif’s (1999)
method (Jombart, Balloux, & Dray, 2010; Pavoine, Ollier, Pontier, &
Chessel, 2008), and in FST values with Pagel’s (1999) λ
(Molina-Venegas & Rodríguez, 2017; Revell, 2012) (Appendix S5). We
found that closely related species tend to be more similar than expected
by chance in their mating system, growth form, pollination mode, seed
dispersal mode, latitudinal region and FST. The highest
observed Moran’s I was that of growth form, followed by
pollination mode, latitudinal region, seed dispersal mode, and lastly
mating system (Fig. S2). FST values were also
phylogenetically autocorrelated (Pagel’s λ=0.52, P<0.001 and
Pagel’s λ=0.53, P<0.001 for raw and transformed
FST values, respectively). Given the high levels of
phylogenetic signal, we implemented phylogenetically informed multiple
regressions (Symonds & Blomberg, 2014) with the function ‘phylolm’ from
the R package phylolm (Ho & Ané, 2014). For the fit of models, the
likelihood of the parameters was calculated with a Brownian motion model
of evolution (Ho & Ané, 2014) (Appendix S6).
Finally, for the categorical predictors with more than two levels we
chose reference levels based on exploratory analyses with phylogenetic
ANOVA and post-hoc tests (Garland, Dickerman, Janis, & Jones, 1993;
Revell, 2012). We selected the level which mean was most different from
that of other levels (Tables S4 and S5). Reference levels were as
follow: trees for growth form, small insects for pollination mode,
gravity for dispersal mode, and temperate for latitudinal region.