Statistical analyses
All statistical analyses were performed in the R statistical software
package, version 3.3.2 (R Core Team, 2019). We used scatterplot to
explore the bivariate relationship between four taxonomic diversity
metrics (species richness, Shannon Index, Pielou Evenness and Simpson
index) and AGB, and tested for their significant effects on AGB using
separate simple linear models. To determine how multiple
trait-functional diversity, single trait-functional diversity,
functional dominance, and structural diversity influence AGB, we ran a
multiple linear model incorporating all the 11 metrics. These metrics
were functional richness (FRic), functional evenness (FEve), functional
divergence (FDiv) and functional dispersion (FDis) for the multiple
trait-functional diversity; functional divergence of wood density (FDvar
WD) and of maximum plant height (FDvar Hmax) for single trait-functional
diversity; community weight mean of wood density (CWM WD) and of maximum
plant height (CWM Hmax) for functional dominance; and structural
diversity [CV DBH: tree diameter variation; CV Ht: tree height
variation; CV Nbp: variation of number of primary branches]. Due to
this high number of independent variables and the likelihood of
autocorrelation, a Multi-model Inference followed by a full averaging
procedure was performed using the package “MuMIn” (Barton, 2018) to
determine the optimal best model, which was selected based on the Akaike
Information Criterion (AICc). Between two subset models that are equally
supported (ΔAICc <2), the most parsimonious was the one with
more independent variables. In addition to the significance fits of the
predictors retained in the finally selected models, we computed for all
predictors their relative importance. For easier interpretation of the
results, bivariate relationships were also constructed.
Finally, we used Structural Equation Modelling (SEM) to assess the
direct and indirect response of AGB to diversity metrics. To simplify
the analytical framework of the SEM, we only considered the independent
variables that were retained in the previous selected models. These were
species richness, functional evenness, coefficient of variation of tree
diameter and of the number of branches. We tested the a-priori model
that AGB increased with increasing species richness, as result of
positive mediation of functional evenness, and structural diversity.
To determine the individual mediation role of functional evenness,
coefficient of variation of tree diameter and number of branches, we
fitted three separate SEMs, and a fourth integrative SEM incorporating
both direct and indirect paths between species richness and AGB via
functional evenness, coefficient of variation of tree diameter and
number of branches. The SEMs were fitted using “lavaan” package
(Rosseel, 2012) in the R statistical software. The goodness of fit of
the SEM was assessed using the Chi square statistic, comparative fit
index (cfi), and root mean square error of approximation (rmsea) (Grace
& Bollen, 2005; S. Mensah, du Toit, et al., 2018; S. Mensah, Veldtman,
Assogbadjo, et al., 2016).