DNA barcode reference library for Iberian butterflies enables a continental-scale preview of potential cryptic diversity
Bibliographic record
Abstract
How common are cryptic species--those overlooked because of their morphological similarity? Despite its wide-ranging implications for biology and conservation, the answer remains open to debate. Butterflies constitute the best-studied invertebrates, playing a similar role as birds do in providing models for vertebrate biology. An accurate assessment of cryptic diversity in this emblematic group requires meticulous case-by-case assessments, but a preview to highlight cases of particular interest will help to direct future studies. We present a survey of mitochondrial genetic diversity for the butterfly fauna of the Iberian Peninsula with unprecedented resolution (3502 DNA barcodes for all 228 species), creating a reliable system for DNA-based identification and for the detection of overlooked diversity. After compiling available data for European butterflies (5782 sequences, 299 species), we applied the Generalized Mixed Yule-Coalescent model to explore potential cryptic diversity at a continental scale. The results indicate that 27.7% of these species include from two to four evolutionary significant units (ESUs), suggesting that cryptic biodiversity may be higher than expected for one of the best-studied invertebrate groups and regions. The ESUs represent important units for conservation, models for studies of evolutionary and speciation processes, and sentinels for future research to unveil hidden diversity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".