2.5 Data analyses
All the data analyses were performed in R 4.1.3 (R Core Team, 2022), and
all the figures were created using SigmaPlot 14.0. Prior to data
analyzed, we natural log-transformed continuous variables to homogenize
variance, except for temperature data because they may contain negative
values. We used absolute values of P 50 for
transformation because the original values were negative (Liu et
al ., 2021). The differences in hydraulic and anatomical traits between
roots and branches were tested using Welch’s t -test (Delacreet al ., 2017). Pearson correlation and linear regression were
used to analyze the correlation between root and branch traits. One-way
analysis of variance (ANOVA) was used to test the differences in meanP 50 root - branch for species across different
biomes. Multiple regression and stepwise regression were used to select
the appropriate factors for analyses (Hector et al ., 1999).
Redundancy analysis (RDA; Braak, 1986) was used to determine the
relative importance of environmental factors explaining hydraulic
segmentation between roots and branches. Because of the
multicollinearity among the environmental factors, we used variation and
hierarchical partitioning (Chevan & Sutherland, 1991) and estimated
unique and shared contributions among the factors. RDA analysis was
performed using the rdacca.hp package (Lai et al ., 2022). The
‘rdacca.hp’ function was used in variation and hierarchical
partitioning. The ‘permu.hp’ function was implemented significance
testing for individual environmental factor contribution to explain
variation of P 50 root - branch. For all analyses,
the significant effects were inferred at P < 0.05.
Results