Migration strategies of a high-latitude breeding songbird ( <i>Setophaga coronata coronata</i> ) revealed using multi-sensor geolocators and stable isotopes
Bibliographic record
Abstract
ABSTRACT Seasonal migration allows animals to use habitat where conditions are unfavorable for part of the year but may constrain breeding ranges due to the costs of longer migrations as ranges expand poleward. In species with large ranges, high latitude breeding populations may employ different migratory strategies allowing them to persist far from other core non-breeding areas. The myrtle warbler ( Setophaga coronata coronata ) has two disjunct non-breeding ranges in North and Central America–one along the Gulf Coast and the other on the Pacific. Previous work indirectly linked birds breeding in Alaska with the Pacific non-breeding area, suggesting that high latitude populations evolved a shorter migration route. We directly tested this hypothesis using geolocators measuring both light and atmospheric pressure to track Alaskan myrtle warbler migration in fine detail and inferred non-breeding areas using hydrogen isotopes for a larger sample of birds breeding in Alaska, British Columbia, and Alberta. We found, contrary to expectations, that all geolocator-tracked birds and most birds with stable isotope data migrated to the southeastern United States, while only a small subset of birds (∼5%) likely wintered on the Pacific Coast. We additionally demonstrate the advantages of pressure geolocation for characterizing migratory behavior at a fine scale.
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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 | 0.000 |
| 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 | 0.000 |
| 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".