Age-Related Interactions with Wind During Migration Support the Hypothesis of Developmental Learning in a Migrating Long-Lived Seabird
Bibliographic record
Abstract
Wind patterns shape migratory pathways and detours of many procellariiform bird species that seasonally migrate between hemispheres. These seabirds are long-lived, and the period of immaturity is presumed to be a time of development and learning the environment, specifically how to use wind to their advantage. We assess how wind encountered by individual Great Shearwaters (Ardenna gravis) varies along the migration journey and compare responses between presumed mature and immature birds (early and late, respectively) in southbound migration and mature birds in northbound migration. We analyze modeled Argos locations from 71 individual tracks of migratory Great Shearwaters with concurrent U (East/West) and V (North/South) wind components. Migration in seabirds is well studied, but there is limited quantitative work measuring individual birds directly interacting with wind and their associated changes in flight behavior during migration. We show that Great Shearwaters made optimal use of winds, and that different age groups made decisions that exposed them to different wind constraints. Overall, Great Shearwaters derived positive responses from wind under most conditions and did not rely on a drifting strategy, which would be suggested if wind effect (difference between ground and airspeed) was predominantly positive during migrations. Instead, they appeared to use a compensating strategy to achieve an acceptable course and speed. The difference we observed in migration phenologies suggests that by migrating later, immature birds might travel the path of least resistance and experience flight conditions that are less risky furthering their ability to withstand a variety of wind conditions encountered later in life as done by adults, which migrate earlier and are subject to more variable flight conditions. We conclude that like other procellariiforms, a longer period of sexual maturity is required to enhance flight performance and mediate energy expenditure through experiential learning and increased fitness.
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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) | 0.002 | 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".