DNA barcoding Central Asian butterflies: increasing geographical dimension does not significantly reduce the success of species identification
Bibliographic record
Abstract
DNA barcoding employs short, standardized gene regions (5' segment of mitochondrial cytochrome oxidase subunit I for animals) as an internal tag to enable species identification. Prior studies have indicated that it performs this task well, because interspecific variation at cytochrome oxidase subunit I is typically much greater than intraspecific variation. However, most previous studies have focused on local faunas only, and critics have suggested two reasons why barcoding should be less effective in species identification when the geographical coverage is expanded. They suggested that many recently diverged taxa will be excluded from local analyses because they are allopatric. Second, intraspecific variation may be seriously underestimated by local studies, because geographical variation in the barcode region is not considered. In this paper, we analyse how adding a geographical dimension affects barcode resolution, examining 353 butterfly species from Central Asia. Despite predictions, we found that geographically separated and recently diverged allopatric species did not show, on average, less sequence differentiation than recently diverged sympatric taxa. Although expanded geographical coverage did substantially increase intraspecific variation reducing the barcoding gap between species, this did not decrease species identification using neighbour-joining clustering. The inclusion of additional populations increased the number of paraphyletic entities, but did not impede species-level identification, because paraphyletic species were separated from their monophyletic relatives by substantial sequence divergence. Thus, this study demonstrates that DNA barcoding remains an effective identification tool even when taxa are sampled from a large geographical area.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".