DNA barcoding Central Asian butterflies: increasing geographical dimension does not significantly reduce the success of species identification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it