Linking the wintering and breeding grounds of warblers along the Pacific Flyway
Bibliographic record
Abstract
Abstract Long‐distance migration is a behavior that is exhibited by many animal groups. The evolution of novel migration routes can play an important role in range expansions, ecological interactions, and speciation. New migration routes may evolve in response to selection in favor of reducing distance between breeding and wintering areas, or avoiding navigational barriers. Many migratory changes are likely to evolve gradually and are therefore difficult to study. Here, we attempt to connect breeding and wintering populations of myrtle warblers (Setophaga coronata coronata) to better understand the possible evolution of distinct migration routes within this species. Myrtle warblers, unlike most other warblers with breeding ranges primarily in eastern North America, have two disjunct overwintering concentrations—one in the southeasternUSAand one along the Pacific Coast—and presumably distinct routes to‐and‐from these locations. We studied both myrtle and Audubon's warblers (S. c. auduboni) captured during their spring migration along the Pacific Coast, south of the narrow region where these two taxa hybridize. Using stable hydrogen isotopes and biometric data, we show that those myrtle warblers wintering along the southern Pacific Coast of North America are likely to breed at high latitudes in Alaska and the Yukon rather than in Alberta or further east. Our interpretation is that the evolution of this wintering range and migration route along the Pacific Coast may have facilitated the breeding expansion of myrtle warblers into northwestern North America. Moreover, these data suggest that there may be a migratory divide within genetically similar populations of myrtle warblers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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".