Phenotypic divergence during speciation is inversely associated with differences in seasonal migration
Bibliographic record
Abstract
Differences in seasonal migration might promote reproductive isolation and differentiation by causing populations in migratory divides to arrive on the breeding grounds at different times and/or produce hybrids that take inferior migratory routes. We examined this question by quantifying divergence in song, colour, and morphology between sister pairs of North American migratory birds. We predicted that apparent rates of phenotypic differentiation would differ between pairs that do and do not form migratory divides. Consistent with this prediction, results from mixed effects models and Ornstein-Uhlenbeck models of evolution showed different rates of divergence between these groups; surprisingly, differentiation was greater among non-divide pairs. We interpret this finding as a result of variable rates of population blending and fusion between partially diverged forms. Ancient pairs of populations that subsequently fused are now observed as a single form, whereas those that did not fuse are observable as pairs and included in our study. We propose that fusion of two populations is more likely to occur when they have similar migratory routes and little other phenotypic differentiation that would cause reproductive isolation. By contrast, pairs with migratory divides are more likely to remain reproductively isolated, even when differing little in other phenotypic traits. These findings suggest that migratory differences may be one among several isolating barriers that prevent divergent populations from fusing and thereby increase the likelihood that they will continue differentiating as distinct species.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".