Statistical analysis
We assessed the differences in the functional traits between active and
passive climbing species by using t-tests. We
log10-transformed the leaf area and seed mass before the
analysis to meet the requirements of normality checked by quantile
plots. In addition, we quantified the phylogenetic signal that indicates
to what extent phenotypic expression is explained by the lineage to
which a species belongs. We quantified the phylogenetic signal using the
Blomberg“s K (Blomberg, 2003). We first generated a phylogenetic
tree including all liana species for which we have functional trait
information using the V.PhyloMaker R package (Jin & Qian, 2019).
Consequently, we pruned separated phylogenies, one for each of the two
main climbing mechanisms, using the original tree with all species as a
base phylogeny. Subsequently, we pruned a tree for each group-trait
combination independently, removing taxa for which information trait
information was not available. Values of K = 1 imply that a trait
shows exactly the amount of phylogenetic signal expected under a null,
stochastic model of character evolution (Brownian motion evolution)
(Blomberg, 2003). If K does not differ from zero, then the trait
has no phylogenetic signal. The statistical significance of K was
assessed via permutation tests with 1000 randomizations. The
significance of the phylogenetic signal was based on the variance of
phylogenetically independent contrasts relative to tip shuffling
randomization implemented by the phylosignal function of thepicante package in R (Freckleton et al., 2002). The P-values were
determined by comparing the variance of the standardized independent
contrasts for the tip values against the randomized data.
We used piecewise structural equation modeling to examine the
hypothesized relationships between abundance and richness per climbing
mechanism and the climate, soil and forest structure variables
(Lefcheck, 2016). One of the main advantages of the structural equation
models (SEM) is that it allows partitioning the total effect of the
relationship between variables into direct and indirect effects (Grace,
2006). We evaluated the generality of the model across the tropics by
controlling the effect of biogeography (continent). We built the
theoretical SEM (Appendix S2, Fig. S1) based on hypothesized and
established relationships between environmental variables and the
abundance and species richness of lianas and plants in general (Table
1). We also evaluated if the ratio of active, relative to passive,
climbing species richness differed among biogeographic realms using a
generalized linear model. All analyses were performed in the R
environment (R Core Team 2018).