Wind patterns as a potential driver in the evolution and maintenance of a North American migratory suture zone
Bibliographic record
Abstract
Suture zones are areas where range contact zones and hybrid zones of multiple taxa are clustered. Migratory divides, contact zones between divergent populations that breed adjacent to one another but use different migratory routes, are a particular case of suture zones. Although multiple hypotheses for both the formation and maintenance of migratory divides have been suggested, quantitative tests are scarce. Here, we tested whether a novel factor, prevailing winds, was sufficient to explain both the evolution and maintenance of the Cordilleran migratory divide using individual-based models. Empirical observations of eastern birds suggest a circuitous migratory route across Canada before heading south. Western breeders, however, travel south along the Pacific coast to their wintering grounds. We modeled the effect of wind on bird migratory flights by allowing them to float at elevation using spatially explicit modeled wind data. Modeled eastern birds had easterly mean trajectories, whereas western breeders showed significantly more southern trajectories. We also determined that a mean airspeed of 18.5 m s(-1) would be necessary to eliminate this difference in trajectory, a speed that is achieved by waterfowl and shorebirds, but is faster than songbird flight speeds. These results lend support for the potential importance of wind in shaping the phylogeographic history of North American songbirds.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".