Speciation and hybridization in the Old World swallowtail butterfly (Papilio machaon) species complex
Bibliographic record
Abstract
Species delimitation is fundamental to evolutionary biology. However the process is far from straightforward in systems with complex evolutionary histories, and the concept of species as taxonomic hypotheses is often overlooked in many biological disciplines. Here I investigate species delimitation operationally, with a review and meta-analysis of the literature, and empirically, by investigating hybridization in swallowtail butterflies. First, I conducted a literature review on studies that used multiple molecular markers to delimit closely related species of animals and fungi. I evaluated the relative success of different types of molecular markers (mitochondrial, ribosomal, nuclear, and sex-linked genes) in delimiting closely related species and asked whether increased geographic or population-level sampling and the number of markers affected identification success. With this foundation, I then investigated hybridization in the Old World swallowtail butterfly (Papilio machaon Linnaeus, 1758) species complex. At a North America-wide scale, I assessed the putative hybrid origins of multiple lineages in the group, using morphology, mitochondrial DNA, microsatellites, and ecological characteristics. I then focused on a hybrid zone in southwestern Alberta and tested whether population genetic structure of the area (using mitochondrial DNA and microsatellites) was similar to an assessment done 30 years ago using morphology and allozymes. I also compared multiple hybrid identification and classification (F1, F2, backcross) methods for microsatellites and a genome-wide single nucleotide polymorphism dataset for a subset of individuals. Finally, I asked whether environmental or landscape variables could explain variation in genetic differentiation and interspecific hybridization in this hybrid zone, using spatial ecology and landscape genetics methods. This is the first application of raster-based landscape genetics methods to interspecific hybridization. Together, the progression of studies in this thesis provide important insight into species delimitation and add to a growing body of research documenting the complexity of hybridization, as well as its potential for generating biodiversity.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".