The evolutionary history of Great Horned Owls (Bubo virginianus) at multiple scales
Bibliographic record
Abstract
This dissertation investigates how different mechanisms, such as biogeographic barriers, historical isolation and environmental conditions cause and maintain population structure at multiple geographic scales. I used genomic data to investigate the evolutionary history of Bubo virginianus (Great Horned Owl) at three scales: range wide, throughout the United States and Canada, and B. virginianus and their lice throughout a population and plumage contact zone from Illinois to Colorado. I first assess disputed species boundaries in B. virginianus at the range wide scale using probe capture and mitochondrial data and find support for a deep species level split between populations north (B. virginianus) and south (B. v. magellanicus) of the Marañón Valley. I also identified shallow population structure in North America despite high amounts of described phenotypic variation. Using reduced-representation genomic data, I investigated this pattern in more detail and found that despite many described subspecies, only three shallowly diverged populations existed in the United States and Canada that likely formed due to isolation during the Pleistocene glacial cycles. Finally, I used reduced representation and genomic sequencing to investigate how parasitic feather lice (Strigiphilus syrnii) are evolving with their hosts across a secondary contact zone between two of the three identified populations. I identified that the lice are surprisingly not as limited by host connectivity as the literature suggests they should be. Their population structure is more strongly influenced by environmental variation such as humidity and temperature than host characteristics or geographic distance. At each level, I identified different causes of population structure.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".