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.