How Veeries vary: Whole genome sequencing resolves fine-scale genetic structure in a long-distance migratory bird, <i>Catharus fuscescens</i>
Bibliographic record
Abstract
Abstract Fine-scale resolution of spatial genetic structure is important for understanding a species’ evolutionary history and contemporary genetic diversity. For high-latitude species with high dispersal ability, such as long-distance migratory birds, populations typically exhibit little genetic structure due to high gene flow and recent postglacial expansion. Some migratory birds, however, show high breeding site fidelity, which might reduce gene flow such that population genetic structure could be detectable with sufficient genomic data. We sequenced over 120 low-coverage whole genomes from across the breeding range of a long-distance migratory bird, the Veery ( Catharus fuscescens ). As this species’ breeding range extends across both historically glaciated and unglaciated regions in North America, we evaluated whether contemporary patterns of structure and genetic diversity are consistent with historical population isolation in glacial refugia. We found strong evidence for isolation by distance across the breeding range, as well as significant population structure between southern Appalachian and northern populations. However, patterns of genetic diversity did not support southern Appalachia as a glacial refugium. Resolution of isolation by distance across the breeding range was sufficient to assign likely breeding origins of individuals sampled in this species’ poorly understood South American nonbreeding range, demonstrating the potential to assess migratory connectivity in this species using genomic data. Overall, our findings suggest that isolation by distance yields subtle associations between genetic structure and geography across the breeding range even in the absence of obvious historical vicariance or contemporary barriers to dispersal.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".