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by claude@2026-07, 2026-07-16
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This paper investigates whether DNA metabarcoding of bulk insect samples reproduces the same arthropod community composition as individual barcoding of all arthropods extracted from the same samples, after potential biases from DNA extraction, primer mismatches, and PCR non-randomness. Using 45 insect bulk samples, the authors performed metabarcoding targeting a 418 bp fragment of the Folmer region and compared it with individual barcoding across the full 658 bp region, with replicate workflows across lysate aliquots and taxon assignment via Barcode Index Numbers. They found metabarcoding was replicable and that relative species richness and relative dissimilarity between sample pairs were congruent with individual barcoding, with most individual-identified BINs recovered in metabarcoding and many high-read-abundance BINs also present as individuals; they report that metabarcoding adds many additional taxa beyond core species, consistent with nestedness rather than turnover. The authors explicitly note that low-copy BINs in metabarcoding likely represent DNA residues rather than organisms. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Abstract
Metabarcoding has become the method of choice for characterising 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. Sequences were assigned to Barcode Index Numbers (BINs) as identical taxonomic categories across data types. We found that metabarcoding is replicable, as different replicates of the same sample recover similar species richness and composition. Individual barcoding and metabarcoding provide the same impression of relative differences in community structure: estimates of relative species richness and relative dissimilarity between sample pairs are congruent among data types. 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. Our analysis suggests that metabarcoding data will closely mimic the sample contents in terms of arthropod species richness and composition. Taxa recovered in low copy numbers in metabarcoding sequence data will typically 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.
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Abstract
Metabarcoding has become the method of choice for characterising 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. Sequences were assigned to Barcode Index Numbers (BINs) as identical taxonomic categories across data types.
We found that metabarcoding is replicable, as different replicates of the same sample recover similar species richness and composition. Individual barcoding and metabarcoding provide the same impression of relative differences in community structure: estimates of relative species richness and relative dissimilarity between sample pairs are congruent among data types. 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.
Our analysis suggests that metabarcoding data will closely mimic the sample contents in terms of arthropod species richness and composition. Taxa recovered in low copy numbers in metabarcoding sequence data will typically 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.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Data availability statement: The data behind this study were published as a data release paper by Orsholm et al. (2025).
Conflict of interest disclosure: The authors declare no conflicts of interest.
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