Statistical analysis
To quantify the main axes of wood
trait variation across species, a principal component analysis (PCA) was
performed. The first axis (PC1), accounting for 55.9% of variance in
litter quality, was strongly related to the contents of wood nutrients
(nitrogen, phosphorus), cellulose and lignin, and wood density. We used
the PC1 scores for the respective tree species to represent their
position along the WES positions in the subsequent analyses. The second
axis (PC2) was related to wood water content, accounting for 17.1% of
variance (Fig. S1).
We also used PCA to quantify the community-level tree functional trait
variability for forest plots in TT and PT. The PC1 of PT and TT
accounted for 50.3% and 40.9% of trait
variance, respectively and were
strongly related to leaf resource economic traits (specific leaf area,
nitrogen, phosphorus, mean leaf area) and wood density (Fig. S4). We
used the community abundance-weighted mean (CWM) of WES, specific leaf
area and wood density to compare differences of community functional
identity between PT and TT sites by using Student’s t-tests.
To derive CWM of WES we multiplied
the PC1 scores of each species with its relative abundance for a given
community.
We used ANCOVA to determine the dependence of wood mass loss on specific
independent variables. Using the wood (cumulative or period) mass loss
% as the dependent variable, harvest time and termite presence/absence
as the independent variables, and the WES value as covariate, separate
ANCOVAs were performed for PT and TT respectively. To evaluate the
relationships between (cumulative) mass loss % (for termite access and
exclusion treatments) and position along the WES separately for the
different incubation periods, linear regression and non-linear
regressions were used to find the best-fit relationship between mass
loss % and the WES. To test the relationship between
(cumulative or period) mass loss %
in the termite access treatment and termite abundance at each harvest
time, linear and non-linear regressions were used to find the best-fit
relationship between mass loss % and termites abundance. We used
Student’s t-test to test the differences in cumulative mass loss % of
termite access and exclusion treatment, termite abundance and the
contribution of termites to wood mass loss between the sites.
To evaluate the relationship between termite abundance and WES in the
two sites at each harvest time, linear and non-linear regressions were
used to find the best-fit relationship. To evaluate the effect size of
the termites along the WES of the two sites at each harvest time, linear
and non-linear regressions were used to find the best-fit relationship.
For mass loss data, we used Levene’s test to examine the homogeneity of
variance and Shapiro-Wilk test for normality. Wood mass loss was
log-transformed as to best meet the
assumptions of normality and variance homogeneity. All statistical
analyses were performed in R language version 3.5.1.