Barcoding Beetles: A Regional Survey of 1872 Species Reveals High Identification Success and Unusually Deep Interspecific Divergences
Bibliographic record
Abstract
With 400 K described species, beetles (Insecta: Coleoptera) represent the most diverse order in the animal kingdom. Although the study of their diversity currently represents a major challenge, DNA barcodes may provide a functional, standardized tool for their identification. To evaluate this possibility, we performed the first comprehensive test of the effectiveness of DNA barcodes as a tool for beetle identification by sequencing the COI barcode region from 1872 North European species. We examined intraspecific divergences, identification success and the effects of sample size on variation observed within and between species. A high proportion (98.3%) of these species possessed distinctive barcode sequence arrays. Moreover, the sequence divergences between nearest neighbor species were considerably higher than those reported for the only other insect order, Lepidoptera, which has seen intensive analysis (11.99% vs up to 5.80% mean NN divergence). Although maximum intraspecific divergence increased and average divergence between nearest neighbors decreased with increasing sampling effort, these trends rarely hampered identification by DNA barcodes due to deep sequence divergences between most species. The Barcode Index Number system in BOLD coincided strongly with known species boundaries with perfect matches between species and BINs in 92.1% of all cases. In addition, DNA barcode analysis revealed the likely occurrence of about 20 overlooked species. The current results indicate that DNA barcodes distinguish species of beetles remarkably well, establishing their potential to provide an effective identification tool for this order and to accelerate the discovery of new beetle species.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".