Barcode-based species delimitation in the marine realm: a test using Hexanauplia (Multicrustacea: Thecostraca and Copepoda)
Bibliographic record
Abstract
DNA barcoding has been used successfully for identifying specimens belonging to marine planktonic groups. However, the ability to delineate species within taxonomically diverse and widely distributed marine groups, such as the Copepoda and Thecostraca, remains largely untested. We investigate whether a cytochrome c oxidase subunit I (COI-5P) global pairwise sequence divergence threshold exists between intraspecific and interspecific divergences in the copepods plus the thecostracans (barnacles and allies). Using publicly accessible sequence data, we applied a graphical method to determine an optimal threshold value. With these thresholds, and using a newly generated planktonic marine data set, we quantify the degree of concordance using a bidirectional analysis and discuss different analytical methods for sequence-based species delimitation (e.g., BIN, ABGD, jMOTU, UPARSE, Mothur, PTP, and GMYC). Our results support a COI-5P threshold between 2.1% and 2.6% p-distance across methods for these crustacean taxa, yielding molecular groupings largely concordant with traditional, morphologically defined species. The adoption of internal methods for clustering verification enables rapid biodiversity studies and the exploration of unknown faunas using DNA barcoding. The approaches taken here for concordance assessment also provide a more quantitative comparison of clustering results (as contrasted with "success/failure" of barcoding), and we recommend their further consideration for barcoding studies.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".