Assessing the Value of DNA Barcodes for Molecular Phylogenetics: Effect of Increased Taxon Sampling in Lepidoptera
Bibliographic record
Abstract
BACKGROUND: A common perception is that DNA barcode datamatrices have limited phylogenetic signal due to the small number of characters available per taxon. However, another school of thought suggests that the massively increased taxon sampling afforded through the use of DNA barcodes may considerably increase the phylogenetic signal present in a datamatrix. Here I test this hypothesis using a large dataset of macrolepidopteran DNA barcodes. METHODOLOGY/PRINCIPAL FINDINGS: Taxon sampling was systematically increased in datamatrices containing macrolepidopteran DNA barcodes. Sixteen family groups were designated as concordance groups and two quantitative measures; the taxon consistency index and the taxon retention index, were used to assess any changes in phylogenetic signal as a result of the increase in taxon sampling. DNA barcodes alone, even with maximal taxon sampling (500 species per family), were not sufficient to reconstruct monophyly of families and increased taxon sampling generally increased the number of clades formed per family. However, the scores indicated a similar level of taxon retention (species from a family clustering together) in the cladograms as the number of species included in the datamatrix was increased, suggesting substantial phylogenetic signal below the 'family' branch. CONCLUSIONS/SIGNIFICANCE: The development of supermatrix, supertree or constrained tree approaches could enable the exploitation of the massive taxon sampling afforded through DNA barcodes for phylogenetics, connecting the twigs resolved by barcodes to the deep branches resolved through phylogenomics.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".