Delineating Species with DNA Barcodes: A Case of Taxon Dependent Method Performance in Moths
Bibliographic record
Abstract
The accelerating loss of biodiversity has created a need for more effective ways to discover species. Novel algorithmic approaches for analyzing sequence data combined with rapidly expanding DNA barcode libraries provide a potential solution. While several analytical methods are available for the delineation of operational taxonomic units (OTUs), few studies have compared their performance. This study compares the performance of one morphology-based and four DNA-based (BIN, parsimony networks, ABGD, GMYC) methods on two groups of gelechioid moths. It examines 92 species of Finnish Gelechiinae and 103 species of Australian Elachistinae which were delineated by traditional taxonomy. The results reveal a striking difference in performance between the two taxa with all four DNA-based methods. OTU counts in the Elachistinae showed a wider range and a relatively low (ca. 65%) OTU match with reference species while OTU counts were more congruent and performance was higher (ca. 90%) in the Gelechiinae. Performance rose when only monophyletic species were compared, but the taxon-dependence remained. None of the DNA-based methods produced a correct match with non-monophyletic species, but singletons were handled well. A simulated test of morphospecies-grouping performed very poorly in revealing taxon diversity in these small, dull-colored moths. Despite the strong performance of analyses based on DNA barcodes, species delineated using single-locus mtDNA data are best viewed as OTUs that require validation by subsequent integrative taxonomic work.
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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 | 0.076 | 0.225 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".