Data analysis
Anatomic traits and their contrast between earlywood and latewood were
checked for normality using the ‘Sharpiro.test’ function in R (R Core
Team 2018). Phylogenetic independent contrast (Pics) of tracheid
characters and maximum plant height were calculated using the R package
‘ape’ (Paradis and Schliep 2018), and their correlations were tested
using the R package ‘psych’ (Revelle 2018). Phylogenetically paired
T-test was performed to test for a significant difference between
corresponding traits in earlywood and latewood using the R package
‘phytools’ (Revell 2012), which is similar to paired t-test but take
phylogeny into account.
As all of the five tracheid traits were positively correlated (Appendix
3), we applied a Phylogenetic principal component analysis (pPCA) to
aggregate traits into PCs and to detect non-independent values of
variables (traits or PCs) with the phylogenetic relationship
(phylogenetic autocorrelation) between species. Phylogenetic principal
components were analyzed using the R package ‘adephylo’ (Jombart,
Balloux, and Dray 2010), in which phylogenetic proximities were
calculated using Abouheif’s proximity, and the resulting matrix of
phylogenetic proximities was used to calculate phylogenetic
autocorrelation, i.e., Moran’s value (Jombart et al. 2010, Zheng and
Martínez-Cabrera 2013). Phylogenetic generalized least square regression
(PGLS) was used to build bi-variate and multi-variate models in a
phylogenetic context using the R package ‘nlme’ (Pinheiro et al. 2015),
in which cell traits were expressed as a function of environmental
variables, i.e., mid-point latitude, MAT, MAP, and interactions between
climatic variables. R2 for phylogeny and environmental
variables in each model were calculated using the R package ‘rr2’ (Ives
2019).