COI barcoding provides reliable species identification and pinpoints cryptic diversity in Western Palearctic amphibians
Bibliographic record
Abstract
Abstract Assembling DNA barcode reference libraries for various taxonomic groups allows researchers to use metabarcoding or environmental DNA approaches to gain a rapid understanding of diversity in given environments. However, our ability to use reference libraries depends on how accurately DNA barcodes are able to recover taxonomic boundaries and identify species, which is rarely considered. We constructed an extensive COI barcoding library for amphibians of the Western Palearctic and successfully recovered barcodes from 60 urodele and 73 anuran species (representing 94% and 98% of the nominal anuran and urodele species in the Western Palearctic, respectively), covering the intraspecific diversity of the majority of species in this region. We tested the effectiveness of our assembled DNA barcode dataset for species identification using barcoding gap, efficiency analyses, and two phylogenetic species delimitation methods. We obtained DNA barcodes for 1251 specimens (691 anurans and 560 urodeles) with a high success rate (92-96%) of species identification. The absence of a barcoding gap in a number of samples was linked to species misidentifications, which suggest incipient speciation or cryptic diversity, or previously described mitochondrial introgression events. The phylogenetic species delimitation methods resulted in substantial oversplitting of currently accepted taxonomy. This COI barcoding library provides an almost complete and reliable reference library for Western Palearctic amphibians. We highlight the importance of generating comprehensive and well curated reference libraries that include intra- and interspecific genetic variability and the need of detailed taxonomic revision when ambiguous or incorrect DNA barcodes exist.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".