Taxonomic functional and phylogenetic indicators from eDNA
Using the fish identification outputs from the ObiTools pipeline, we
computed taxonomic, functional and phylogenetic indices of the structure
of the fish assemblages for the two coastal areas. We collected
functional traits using online databases (Fishbase.org; Froese
and Pauly 2021, Robertson and Van Tassell 2015). We compiled five traits
linked to diverse ecological functions: the minimum and maximum depth
(m), the position in the water column divided in six groups (”pelagic”,
”bathypelagic”, ”benthopelagic”, ”demersal”, ”benthic”, ”bathydemersal”)
indicating habitat, the trophic level and the maximum body size
associated with food acquisition, mobility and predation functions.
Sequences attributed to the species were directly associated with the
corresponding functional traits. For sequences assigned at the genus or
family level by ObiTools, we randomly selected from the list of the
regional fish species, one species belonging to the same genus or family
along with its associated traits. The random selection was performed 100
times resulting in 100 traits matrices. For each trait matrix and each
coastal area, we computed the community mean of continuous trait values
and the proportion for categorical traits repeated across all 100
matrices. We also computed the standard deviation of those measures.
Moreover, we computed 100 distance matrices using Gower’s distance which
allows continuous and categorical traits (Gower 1971). We applied a
Principal Coordinates Analysis (PCoA) on each of the 100 distance
matrices and computed the corresponding multivariate functional spaces
(Mouchet et al. 2010). We selected the most appropriate number of axes
following the framework proposed by Maire et al (2015) that evaluates
the quality of the functional space based on the deviation between the
original trait-based distance and the final Euclidean distance. From the
PCoA, we computed the functional richness (FRic) that represents the
volume of functional space defined by the convex envelope of all species
in a given community (Villeger et al. 2008, Mouillot et al. 2013), the
functional evenness (Feve) that represent the regularity of the
distribution and relative abundance of species in functional space for a
given community. We also characterized the functional divergence (Fdis)
that quantifies how species diverge in their distance from the center of
gravity of the functional space. As a measure of functional regularity,
we computed the functional specialisation (FSpe) as the average distance
of species from the barycentre of the functional space and characterised
the functional distance of species from the rest of the community as a
proportion of the maximum distance (Mouillot et al. 2013). We further
computed the functional originality (Fori) that was calculated as the
average pairwise distance between a species and its nearest-neighbor
into the functional space. We produced species and functional richness
accumulation curves across filtration samples by randomly selecting the
samples among all possible permutations and we measured the species
richness and the FRic index. To investigate the relationship between the
functional richness or the species richness and the considered number of
samples, we fitted a Generalized additive model.
We assessed the phylogenetic diversity components, based on a list of
100 randomised phylogenetic trees previously extracted from the
phylogeny of Rabosky et al. (2018) and the taxonomic list obtained from
the ObiTools assignment. For ɑ- diversity at both the
Valentijnsbaai and Willemstad areas, we computed five indices to
characterize the phylogenetic, richness, divergence and regularity
facets (Tucker et al. 2017). We quantified the richness dimension by
calculating Faith’s phylogenetic diversity index (PD, Faith 1992) that
corresponds to the overall amount of evolutionary history in a sampled
community (Faith 1992). We computed the divergence facet using two
indices, the phylogenetic Mean Pairwise Distance (MPD) corresponding to
the average phylogenetic distance among species and the phylogenetic
Mean Nearest Taxonomic Distance (MNTD) that measures the average
phylogenetic distance among the closest relatives species within a
community (Tucker et al. 2017). Then we assessed the regularity facet by
calculating the variance of the phylogenetic distance among species (VPD
index) and the variance of the phylogenetic distance among the closest
relatives species within a community (VNTD; Tucker et al. 2017). We
produced phylogenetic richness accumulation curves across filtration
samples by randomly selecting the samples among all possible
permutations and we measured the PD. To investigate the relationship
between the phylogenetic richness and the considered number of samples,
we fitted a Generalized additive model.