Countering criticisms of single mitochondrial DNA gene barcoding in birds
Bibliographic record
Abstract
General criticisms of a single mtDNA gene barcodes include failure to identify newly evolved species, use of species-delimitation thresholds, effects of selective sweeps and chance occurrence of reciprocal monophyly within species, inability to deal with hybridization and incomplete lineage sorting, and superiority of multiple genes in species identification. We address these criticisms in birds because most species are known and thus provide an ideal test data set, and we argue with selected examples that with the exception of thresholds these criticisms are not problematic for avian taxonomy. Even closely related sister species of birds have distinctive COI barcodes, but it is not possible to universally apply distance thresholds based on ratios of within-species and among-species variation. Instead, more rigorous methods of species delimitation should be favoured using coalescent-based techniques that include tests of chance reciprocal monophyly, and times of lineage separation and sequence divergence. Incomplete lineage sorting is also easily detected with DNA barcodes, and usually at a younger time frame than a more slowly evolving nuclear gene. Where DNA barcodes detect divergent reciprocally monophyletic lineages, the COI sequences can be combined with multiple nuclear genes to distinguish between speciation or population subdivision arising from high female philopatry or regional selective sweeps. Although selective sweeps are increasingly invoked to explain patterns of shallow within-species coalescences in COI gene trees, caution is warranted in this conjecture because of limited sampling of individuals and the reduced power to detect additional mtDNA haplotypes with one gene.
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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 | 0.111 | 0.305 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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".