Asynchronous movement patterns between breeding and stopover locations in a long-distance migratory songbird
Bibliographic record
Abstract
The species-specific migratory patterns and strategies of many songbirds remain unknown or understudied, as research in animal ecology is biased toward the breeding period, with the fewest studies on the migratory period across taxa. Identifying large-scale spatiotemporal migratory patterns is challenging, as individuals within a species may vary in their migratory behavior and strategies. The Yellow Warbler (Setophaga petechia) is a Nearctic-Neotropical migrant that is relatively well studied during the breeding season, but its species-wide migratory patterns remain understudied. Our aim in studying Yellow Warbler movement ecology was to characterize temporal migration patterns during fall migration. We sought to determine the temporal migration pattern among breeding locations, as determined by the hydrogen stable isotope values in feather samples collected at disjunct (~2000 km) stopover sites in the Gulf of Maine (n = 50) and the Gulf of Mexico (n = 150). We used a similarity matrix to group individuals into a geographic cluster by breeding location, which was then used as the response variable in a modeling analysis. Our results provide evidence that Yellow Warblers exhibit an asynchronous, type 1 temporal migration pattern with southern breeding populations initiating migration prior to northern populations. Using hydrogen isotopes, we identified the temporal migration patterns between geographic clusters, representing an individual’s breeding location, and stopover sites along the Gulf of Maine and Gulf of Mexico, which fills a gap in understanding Yellow Warbler migration ecology.
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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 | 0.000 | 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) | 0.000 | 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".