Use of species delimitation approaches to tackle the cryptic diversity of an assemblage of high Andean butterflies (Lepidoptera: Papilionoidea)
Bibliographic record
Abstract
Cryptic biological diversity has generated ambiguity in taxonomic and evolutionary studies. Single-locus methods and other approaches for species delimitation are useful for addressing this challenge, enabling the practical processing of large numbers of samples for identification and inventory purposes. This study analyzed an assemblage of high Andean butterflies using DNA barcoding and compared the identifications based on the current morphological taxonomy with three methods of species delimitation (automatic barcode gap discovery, generalized mixed Yule coalescent model, and Poisson tree processes). Sixteen potential cryptic species were recognized using these three methods, representing a net richness increase of 11.3% in the assemblage. A well-studied taxon of the genus Vanessa, which has a wide geographical distribution, appeared with the potential cryptic species that had a higher genetic differentiation at the local level than at the continental level. The analyses were useful for identifying the potential cryptic species in Pedaliodes and Forsterinaria complexes, which also show differentiation along altitudinal and latitudinal gradients. This genetic assessment of an entire assemblage of high Andean butterflies (Papilionoidea) provides baseline information for future research in a region characterized by high rates of endemism and population isolation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".