Evaluating DNA Barcoding for Species Identification and Discovery in European Gracillariid Moths
Bibliographic record
Abstract
Gracillariidae is the most species-rich leaf-mining moth family with over 2,000 described species worldwide. In Europe, there are 263 valid named species recognized, many of which are difficult to identify using morphology only. Here we explore the use of DNA barcodes as a tool for identification and species discovery in European gracillariids. We present a barcode library including 6,791 COI sequences representing 242 of the 263 (92%) resident species. Our results indicate high congruence between morphology and barcodes with 91.3% (221/242) of European species forming monophyletic clades that can be identified accurately using barcodes alone. The remaining 8.7% represent cases of non-monophyly making their identification uncertain using barcodes. Species discrimination based on the Barcode Index Number system (BIN) was successful for 93% of species with 7% of species sharing BINs. We discovered as many as 21 undescribed candidate species, of which six were confirmed from an integrative approach; the other 15 require additional material and study to confirm preliminary evidence. Most of these new candidate species are found in mountainous regions of Mediterranean countries, the South-Eastern Alps and the Balkans, with nine candidate species found only on islands. In addition, 13 species were classified as deep conspecific lineages, comprising a total of 27 BINs with no intraspecific morphological differences found, and no known ecological differentiation. Double-digest restriction-site associated DNA sequencing (ddRAD) analysis showed strong mitonuclear discrepancy in four out of five species studied. This discordance is not explained by Wolbachia -mediated genetic sweeps. Finally, 26 species were classified as “unassessed species splits” containing 71 BINs and some involving geographical isolation or ecological specialization that will require further study to test whether they represent new cryptic 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".