Dissecting biodiversity: assessing the taxonomic, functional and phylogenetic structure of an insect metacommunity in a river network using morphological and metabarcoding data. Issue 1 (11th July 2023) - Laini, A.; Stubbington, R.; Beermann, A. J.; Burgazzi, G.; Datry, T.; Viaroli, P.; Wilkes, M.; Zizka, V. M. A.; Saccò, M.; Leese, F.
Abstract: Most empirical metacommunity studies rely solely on morphological identification of taxa, precluding the species-level identification of several biotic groups, which can influence the characterization of metacommunities. DNA metabarcoding enables inference of species and even intraspecific diversity from community samples but has rarely been used to infer metacommunity structure. Here, we combined morphology and metabarcoding to improve the characterization of an insect metacommunity at different identification levels. We included measures of taxonomic, functional and phylogenetic richness, and we evaluated drivers affecting metacommunity structure (i.e., environmental filtering and dispersal). Communities were sampled from an area that included nine perennial, two near-perennial and two intermittent sites in a river network characterized by high hydrological variability. We identified organisms to a mixed (family to species) taxonomic level using morphology, and to operational taxonomic unit (OTU) and haplotype levels using metabarcoding of the mitochondrial cytochrome c oxidase gene. Diptera and Ephemeroptera showed the greatest increases in taxonomic and phylogenetic richness but not biological trait richness with increasing taxonomic resolution. The joint effect of environmental filtering and dispersal was more important than their individual effects in shaping metacommunity structure at all identification levels. Mixed-level and OTU-level identification were more effective than family and haplotype in characterizing the drivers of metacommunity structure. We demonstrate that the greater taxonomic resolution enabled by metabarcoding could improve understanding of metacommunities within river networks, thus enhancing our capacity to predict ecological responses in ecosystems adapting to global change.