Genomics-informed species delimitation to support morphological identification of anglewing butterflies (Lepidoptera: Nymphalidae: Polygonia)
Bibliographic record
Abstract
Species delimitation and identification are integral to virtually all biological disciplines, but are far from straightforward tasks. Taxonomy has recently focused on integrative approaches that consider multiple types of data to resolve species boundaries, yet methodologies to that end are still being developed. Here, we assess species limits in an area of wide distributional overlap between several species of anglewing butterflies in western Canada. Focusing on an area of sympatry provides a rich system to test species boundaries in the face of potential gene flow between morphologically variable yet similar species. Mitochondrial DNA and genome-wide single nucleotide polymorphisms provided clear species delimitation, although previously identified cytonuclear discordance was also apparent. Analysis of two morphological data sets, based on characters commonly used as diagnostic characters in the literature and field guides, was variably successful at separating species. Using analyses that were guided by the results of the genetic data increased successful species identification for traditionally used characters, but not for the morphological data set based on digital colour analysis. Our application of genetics to guide morphological analysis demonstrates a useful approach for implementing iterative methodologies as part of integrative taxonomy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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".