Quantification of taxonomic, functional and structural diversity
metrics
We computed plot-level taxonomic, functional and structural diversity
metrics. Taxonomic diversity was quantified using species richness,
Shannon Index, Pielou Evenness and Simpson index. Species richness was
defined at plot level, as the number of distinct species enumerated
inside each plot.
To quantify functional trait-based diversity, we computed multiple- and
single-trait diversity metrics and functional dominance. Multiple-trait
diversity metrics include functional richness (FRic), functional
evenness (FEve), functional divergence (FDiv) and functional dispersion
(FDis) (Mouchet, Villéger, Mason, & Mouillot, 2010; Villéger, Mason, &
Mouillot, 2008). They were calculated at plot level, using the relative
abundance of each species in each plot and the values of functional
traits. We quantified the single trait functional divergence (FDvar), as
the variance of each trait value weighted by the species’ relative
density in each plot (Mason, Mouillot, Lee, & Wilson, 2005). For
functional dominance, we calculated the plot-level community weight mean
(CWM) for each functional trait based on the relative density of the
species. We used CWM because it reflects functional shifts in mean trait
values and species dominance in a community (S. Mensah, Veldtman,
Assogbadjo, et al., 2016; Ricotta & Moretti, 2011). Both the
multiple-trait functional diversity and dominance were calculated with
“FD” package in R (Laliberté, Legendre, & Shipley, 2015). FDvar was
calculated for each trait (i.e. wood density and plant maximum height)
using the FDiversity software.
Structural diversity can be expressed with measures that relate to the
horizontal and vertical extent as well as to the internal branching
pattern of the trees. Thus, we focused on tree height and diameter
differentiation across individual trees inside each plot, as well as on
number of primary branches at crown base level, and assessed structural
diversity as coefficient of variation (standard deviation to mean) of
each of these three variables at plot level (S. Mensah, Pienaar, et al.,
2018).