Ruiz et al. (2025) - Fish eDNA metabarcodes' taxonomic resolution

Even though environmental DNA (eDNA) metabarcoding is revolutionizing biomonitoring, many critical steps remain unstandardized, leading to arbitrary choices, which is particularly critical for metabarcodes as well as clustering methods and similarity thresholds choices. For fishes, current in silico comparisons have generally focused on primer sets properties, but few have compared the taxonomic resolution for a comprehensive set of metabarcodes while more than 20 exist. In addition, these studies were hampered by genetic database biases and unstandardized taxonomic resolution definitions. To overcome these issues, we developed a robust framework to compare metabarcodes extracted from the same mitogenomes (all available for Actinopterygians in NCBI) to a standardized taxonomic reference baseline based on COI Barcode Index Numbers (BINs), allowing to quantify separately false-positive (same taxon splitting) and false-negative (different taxa merging). Although each metabarcode exhibited various sensitivities to false-negative or false-positive errors and there were differences between clustering methods, the clustering threshold appears as the most important factor influencing biodiversity estimates, leading us to propose optimal thresholds per metabarcode for taxonomic levels delineation (metabarcode gaps). In addition, we also found that taxonomic resolution significantly varied among genes, orders, and community properties (diversity/redundancy), but not with metabarcodes lengths as previously thought. Overall, our study suggests that the metabarcode and clustering threshold must be chosen to minimize false-negatives and/or false-positives along with providing accurate lower taxonomic delineations. A set of commented R functions makes this taxonomic resolution evaluation framework easy to apply to any other taxonomic group and full genes or mitogenomes reference database.