Multi-marker DNA metabarcoding for precise species identification in ichthyoplankton samples.

Ichthyoplankton monitoring is crucial for stock assessments, offering insights into spawning grounds, stock size, seasons, recruitment, and changes in regional ichthyofauna. This study evaluates the efficiency of multi-marker DNA metabarcoding using mitochondrial cytochrome c oxidase subunit I (COI), 12S rRNA and 16S rRNA gene markers, in comparison to morphology-based methods for fish species identification in ichthyoplankton samples. Two transects with four coastal distance categories were sampled along the southern coast of Portugal, being each sample divided for molecular and morphological analyses. A total of 76 fish species were identified by both approaches, with DNA metabarcoding overperforming morphology-75 versus 11 species-level identifications. Linking species-level DNA identifications with higher taxonomic morphological identifications resolved several uncertainties associated with traditional methods. Multi-marker DNA metabarcoding improved fish species detection by 20-36% compared to using a single marker/amplicon, and identified 38 species in common, reinforcing the validity of our results. PERMANOVA analysis revealed significant differences in species communities based on the primer set employed, transect location, and distance from the coast. Our findings underscore the potential of DNA metabarcoding to assess ichthyoplankton diversity and suggest that its integration into routine surveys could enhance the accuracy and comprehensiveness of fish stock assessments.

BAGS: An automated Barcode, Audit & Grade System for DNA barcode reference libraries.

Biodiversity studies greatly benefit from molecular tools, such as DNA metabarcoding, which provides an effective identification tool in biomonitoring and conservation programmes. The accuracy of species-level assignment, and consequent taxonomic coverage, relies on comprehensive DNA barcode reference libraries. The role of these libraries is to support species identification, but accidental errors in the generation of the barcodes may compromise their accuracy. Here, we present an R-based application, Barcode, Audit & Grade System (BAGS) (https://github.com/tadeu95/BAGS), that performs automated auditing and annotation of cytochrome c oxidase subunit I (COI) sequences libraries, for a given taxonomic group of animals, available in the Barcode of Life Data System (BOLD). This is followed by implementing a qualitative ranking system that assigns one of five grades (A to E) to each species in the reference library, according to the attributes of the data and congruency of species names with sequences clustered in barcode index numbers (BINs). Our goal is to allow researchers to obtain the most useful and reliable data, highlighting and segregating records according to their congruency. Different tests were performed to perceive its usefulness and limitations. BAGS fulfils a significant gap in the current landscape of DNA barcoding research tools by quickly screening reference libraries to gauge the congruence status of data and facilitate the triage of ambiguous data for posterior review. Thereby, BAGS has the potential to become a valuable addition in forthcoming DNA metabarcoding studies, in the long term contributing to globally improve the quality and reliability of the public reference libraries.