Data from: Metabarcoding of freshwater invertebrates to detect the effects of a pesticide spill
Andujar, Carmelo et al. (2017), Data from: Metabarcoding of freshwater invertebrates to detect the effects of a pesticide spill, Dryad, Dataset, https://doi.org/10.5061/dryad.104kg
Biomonitoring underpins the environmental assessment of freshwater ecosystems and guides management and conservation. Current methodology for surveys of (macro)invertebrates uses coarse taxonomic identification where species-level resolution is difficult to obtain. Next-generation sequencing of entire assemblages (metabarcoding) provides a new approach for species detection, but requires further validation. We used metabarcoding of invertebrate assemblages with two fragments of the cox1 "barcode" and partial nuclear ribosomal (SSU) genes, to assess the effects of a pesticide spill in the River Kennet (Southern England). Operational Taxonomic Unit (OTU) recovery was tested under72 parameters (read denoising, filtering, pair merging and clustering). Similar taxonomic profiles were obtained under a broad range of parameters. The SSU marker recovered Platyhelminthes and Nematoda, missed by cox1,while Rotifera were only amplified with cox1. A reference set was created from all available barcode entries for Arthropoda in the BOLD database and clustered into OTUs. The River Kennet metabarcoding produced matches to 207 of these reference OTUs, five times the number of species recognised with morphological monitoring. The increase was due to: greater taxonomic resolution (e.g. splitting a single morphotaxon ‘Chironomidae’ into 55 named OTUs); splitting of binomial species names into multiple molecular OTUs in species complexes; and the use of a filtration-flotation protocol for extraction of minute specimens (meiofauna). Community analyses revealed strong differences between "impacted" vs. "control" samples, detectable with each gene marker, for each major taxonomic group, and for meio- and macro-faunal samples separately. Thus, highly resolved taxonomic data can be extracted at a fraction of the time and cost of traditional non-molecular methods, opening new avenues for freshwater invertebrate biodiversity monitoring and molecular ecology.