One species in eight: <scp>DNA</scp> barcodes from type specimens resolve a taxonomic quagmire
Bibliographic record
Abstract
Each holotype specimen provides the only objective link to a particular Linnean binomen. Sequence information from them is increasingly valuable due to the growing usage of DNA barcodes in taxonomy. As type specimens are often old, it may only be possible to recover fragmentary sequence information from them. We tested the efficacy of short sequences from type specimens in the resolution of a challenging taxonomic puzzle: the Elachista dispunctella complex which includes 64 described species with minuscule morphological differences. We applied a multistep procedure to resolve the taxonomy of this species complex. First, we sequenced a large number of newly collected specimens and as many holotypes as possible. Second, we used all >400 bp examine species boundaries. We employed three unsupervised methods (BIN, ABGD, GMYC) with specified criteria on how to handle discordant results and examined diagnostic bases from each delineated putative species (operational taxonomic units, OTUs). Third, we evaluated the morphological characters of each OTU. Finally, we associated short barcodes from types with the delineated OTUs. In this step, we employed various supervised methods, including distance-based, tree-based and character-based. We recovered 658 bp barcode sequences from 194 of 215 fresh specimens and recovered an average of 141 bp from 33 of 42 holotypes. We observed strong congruence among all methods and good correspondence with morphology. We demonstrate potential pitfalls with tree-, distance- and character-based approaches when associating sequences of varied length. Our results suggest that sequences as short as 56 bp can often provide valuable taxonomic information. The results support significant taxonomic oversplitting of species in the Elachista dispunctella complex.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".