Hybrid dynamics in a species group of swallowtail butterflies
Bibliographic record
Abstract
Hybrid zones provide unique natural laboratories for studying mechanisms of evolution. But identification and classification of hybrid individuals (F1, F2, backcross, etc.) can be complicated by real population changes over time as well as by use of different marker types, both of which challenge documentation of hybrid dynamics. Here, we use multiple genetic markers (mitochondrial DNA, microsatellites and genomewide single nucleotide polymorphisms) to re-examine population structure in a hybrid zone between two species of swallowtail butterflies in western Canada, Papilio machaon and P. zelicaon. Our aim was to test whether their hybrid dynamics remain the same as found 30 years ago using morphology and allozymes, and we compared different genetic data sets as well as alternative hybrid identification and classification methods. Overall, we found high differentiation between the two parental species, corroborating previous research from the 1980s. We identified fewer hybrid individuals in the main zone of hybridization in recent years, but this finding depended on the genetic markers considered. Comparison of methods with simulated data sets generated from our data showed that single nucleotide polymorphisms were more powerful than microsatellites for both hybrid identification and classification. Moreover, substantial variation among comparisons underlined the value of multiple markers and methods for documenting evolutionarily dynamic systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".