Temperature and stopover duration carry-over to affect Arctic arrival timing and breeding success in the cackling goose (Branta hutchinsii)
Bibliographic record
Abstract
Arrival timing in spring may be mediated by conditions experienced during migratory stopovers or staging areas, but our knowledge about their impact on migration timing and reproduction is limited. We explored the role of stopover duration on spring migration timing and successful incubation in cackling geese (Branta hutchinsii), which breed at high arctic latitudes where climate change effects are more pronounced. To track migration phenology and incubation duration, 236 light-level geolocators were deployed on cackling geese during the breeding period at Baffin Island, Nunavut, Canada between 2016 and 2018. Using data available for spring migration in the year following tag deployment (25 tags retrieved), we found that most geese had long, coastal stopovers (8–39 days) before crossing Hudson Bay on the last leg of their spring migration to their Baffin Island breeding area. We show that longer stopover durations at these Hudson Bay Lowland sites were associated with successful completion of incubation (a proxy for breeding success). Although spending more time at the stopover led to a later arrival date at the breeding ground, longer stopovers may increase the energy stores necessary for reproduction in these capital breeders. Stopover duration did not influence the incubation interval (number of days between arrival date at the breeding ground and start of incubation). Lastly, we found that the temperature at stopovers influenced migration timing, with higher temperatures resulting in earlier arrival at the breeding ground. Overall, our results demonstrate that conditions and behaviour at distant stopovers (1700–3000 km away) have important influence on timing and breeding success once birds arrive at their Arctic breeding sites. Therefore, our understanding of climate change impacts on these Arctic-breeding geese must also include the influence of en route conditions
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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 | 0.000 | 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".