Arrival‐breeding interval is flexible in a songbird and is not constrained by migration carry‐over effects
Bibliographic record
Abstract
As spring phenology advances with climate change, so too must the timing of life cycle events. Breeding at the right time is critical in many species as it maximizes fitness. For long-distance migratory birds, flexibility in the duration of the arrival-breeding interval (pre-breeding period) may allow populations to adjust their timing of breeding. However, whether first egg-lay dates are flexible to local environmental conditions after arrival, and if they are constrained by the time needed to replenish energy lost during migration, remains unclear. We investigated the regional flexibility of the arrival-breeding interval in an avian migrant, the purple martin, Progne subis, across their breeding range. We evaluated whether the duration of the arrival-breeding interval was flexible to temperature and precipitation at breeding sites, and if timing was limited by migration rate and stopover duration. We also tested if longer interval durations were associated with higher fledging success. To address our hypotheses, we used a combination of migration tracking, weather and breeding data collected from four regions across eastern North America (26.1° N to 52.4° N latitude). We found the arrival-breeding interval to be shortest in the north and longest in the south. Across all regions, warmer temperatures encountered at breeding grounds were associated with shorter intervals, and faster migration rates led to longer intervals. The length of the interval was not influenced by precipitation or stopover duration. Finally, longer intervals were not associated with higher fledge success. Currently, the longer arrival-breeding intervals in this study system, on average 28.3 days, may provide both early and late-arriving birds with ample time for recovery so birds can lay eggs according to temperature. Any negative effects of faster migration may have been buffered by longer arrival-breeding intervals, as interval length did not determine fledge success. With ongoing climate change, further research is needed to examine if arrival-breeding intervals become constrained by migration timing, which may limit opportunities for migrants to match the timing of breeding with key resources.
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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 | 0.001 | 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".