Is There a Fly in My Soup? To What Extent Do Metabarcoding and Individual Barcoding Tell the Same Story?

Metabarcoding has become the method of choice for characterizing complex arthropod communities. The extent to which metabarcoded bulk samples will recover the same community composition as individual sequencing of all individuals in the sample remains poorly quantified. Biases such as unequal extraction of DNA from different taxa, primer mismatches and non-random PCR may cause the selective drop-out of species from metabarcoding data. At the same time, DNA metabarcoding may reveal arthropod taxa present not as individuals, but as DNA residues on the surface or in the gut of insects. To quantify the consistency in sample contents established by different means, we metabarcoded 45 bulk insect samples, then extracted all arthropods and sequenced them individually. Metabarcoding targeted 418 bp at the 3' end of the Folmer barcoding region, while individual barcodes captured the entire 658 bp Folmer region. The metabarcoding workflow, including PCR amplification, sequencing and bioinformatics, was performed in three replicates from three separate lysate aliquots per sample. For the main analyses, sequences were assigned to Barcode Index Numbers (BINs) as identical taxonomic categories across data types, thereby allowing the detection of even rare but biologically true taxa. Since such reference-based validation will be unavailable to any researcher dealing with metabarcoding data alone, we validated our key findings through an alternative workflow, i.e., de novo clustering of sequences. We found that metabarcoding is replicable, as different replicates of the same sample recover similar species richness and composition. Individual barcoding and metabarcoding provide similar impressions of relative differences in community structure: species-rich vs. species-poor samples rank similarly among data types (Spearman's ⍴ = 0.88-0.99) as do differences in relative dissimilarity between sample pairs (Spearman's ⍴ = 0.55-0.90). Dissimilarity between data types varies with BIN richness in the sample, but this relationship reflects nestedness rather than turnover: metabarcoding recovers the same set of core species as individual barcoding but adds hundreds of species on top. Any BIN recovered as an individual occurred with high probability in the metabarcoding data, and any BIN found in high read abundances by metabarcoding was likely found as an individual (p > 0.8). In terms of abundances, the number of individual insects per BIN was well predicted by the number of metabarcoding reads (R² > 0.68 for a model including taxonomy as a random effect). Our analysis suggests that metabarcoding data will be informative of the sample contents in terms of arthropod species richness, composition and taxon-specific abundances. Taxa recovered in low copy numbers in metabarcoding sequence data will likely represent DNA left as residues from past biotic interactions. Barring sequencing errors, both types of data yield biologically relevant insights into the taxa present in the source community.