Is there a fly in my soup? To what extent do metabarcoding and individual barcoding tell the same story?
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".