Taxonomic, phylogenetic and functional diversity estimation at
alpha scale
We computed alpha-diversity metrics for each plot using the framework of
Chao and colleagues (Chao et al., 2014), which uses Hill numbers that
differentially weigh common and rare species for each of the following
biodiversity facets–taxonomic, functional and phylogenetic. We used the
Hill number q =0, where species are weighted equally (i.e., species
richness for taxonomic diversity), and the Hill number q = 2 (equivalent
to Simpson’s diversity in taxonomic analyses), where abundant species
are strongly influential and rare species largely ignored. By comparing
Hill numbers when q=0 versus q=2, we can detect if diversity patterns
are driven by rare or common species (we also used q=1, which is
equivalent to Shannon diversity, but the results were largely
intermediate between these extremes; Figure S1). These metrics express
diversity in multiple facets in comparable units, where taxonomic
diversity quantifies the effective number of equally abundant species,
phylogenetic diversity (PD) quantifies the effective total branch
lengths and functional diversity (FD) quantifies the effective number of
equally distinct virtual functional groups. This allows a meaningful and
direct comparison across the three diversity facets (Chao et al., 2021).
The framework also controls for plots with different sample sizes; here
we used sample-coverage-based rarefaction (sensu Chao et al.,
2021), which standardizes to a level of sample completeness. We compared
taxonomic, functional and phylogenetic diversity estimation of plots
using a sample completeness of 80% (i.e., sample coverage), for both
analyses that included only native species and for both native and alien
species. The standardization of diversity metrics via controlling for
sample coverage, allow for a direct comparison among plots and diversity
facets (Chao et al., 2021).
For taxonomic diversity, we used the taxonomic names (and abundances) of
each species to calculate diversity metrics while controlling for
differences in sample effort using coverage. For phylogenetic diversity,
species abundances and species evolutionary histories (based on Faith’s
metric (Faith, 1992)) are included to quantify the number of equally
divergent lineages. To compute Faith’s metric, we first built the
phylogenetic tree with native species (n=79) and with all species
(n=142) using data of the mega-tree for vascular plants as the backbone
(GBOTB.extended.tre; Jin & Qian, 2019). We resolved missing species by
randomly placing them within genera or families (“scenario 2”) using
the phylo.maker function in the R package ‘V.PhyloMaker’ (Jin & Qian,
2019). For functional diversity, we used four traits related to the
plant’s ecological responses to precipitation. Specific leaf area
(mm2/mg) because smaller leaves with higher SLA
prevent water loss in dry environments (Westoby et al., 2000).
Stem-specific density (g/cm3) relates to drought
tolerance, as it tends to increase in dry environments (Barajas Barbosa
et al., 2023). Maximum plant height (m); plants can reach higher heights
in wet environments because substantial water is required to maintain
their hydraulic safety (Liu et al., 2019). Leaf Nitrogen content (mg/g)
relates to plant ecological strategies (i.e., fast-growing versus
slow-growing) to tolerate and recover from environmental stressors, such
as drought (Díaz et al., 2016). We sourced trait information from the
Flora of the Hawaiian islands (Wagner and Khan et al 2023) and the TRY
database (Kattge et al., 2020). In all, for the 142 species in Open
Nahele, we found data on these four traits for 70% of the species
(species by trait matrix; Figure S2a). We filled the 30% of missing
trait values in the species by trait matrix using phylogenetic
imputation (Penone et al., 2014). For this, we used the previously built
phylogenetic tree (sensu Jin & Qian, 2019) including native and
alien species from OpenNahele (n = 142; Figure S3). We then used a
random forest algorithm using the ´missForest‘ function from the
MissForest R package (Stekhoven, 2013) to predict the 30% of missing
trait values. We measured the prediction error of the random forest
algorithm that included phylogenetic relationships using out-of-bag
error (OOB; Figure S2b). We checked for the differences between
empirical and predicted trait values using Kruskal-Wallis-Test (Figure
S2b). Functional diversity is computed from species pairwise distances
based on the Gower distance. The measure qFD quantifies the effective
number of equally distinct functional groups (or functional ‘species’)
at a given threshold level termed tau. We used tau = dmean , i.e.,
mean distance between any two individuals randomly selected from the
pooled assemblage (Chao et al., 2019). We calculated taxonomic,
phylogenetic and functional diversity metrics using the ‘estimate3D’
function from the iNEXT.3D
package
(Chao et al., 2021).