Compensation for wind drift prevails for a shorebird on a long-distance, transoceanic flight
Bibliographic record
Abstract
BACKGROUND: Conditions encountered en route can dramatically impact the energy that migratory species spend on movement. Migratory birds often manage energetic costs by adjusting their behavior in relation to wind conditions as they fly. Wind-influenced behaviors can offer insight into the relative importance of risk and resistance during migration, but to date, they have only been studied in a limited subset of avian species and flight types. We add to this understanding by examining in-flight behaviors over a days-long, barrier-crossing flight in a migratory shorebird. METHODS: Using satellite tracking devices, we followed 25 Hudsonian godwits (Limosa haemastica) from 2019-2021 as they migrated northward across a largely transoceanic landscape extending > 7000 km from Chiloé Island, Chile to the northern coast of the Gulf of Mexico. We identified in-flight behaviors during this crossing by comparing directions of critical movement vectors and used mixed models to test whether the resulting patterns supported three classical predictions about wind and migration. RESULTS: Contrary to our predictions, compensation did not increase linearly with distance traveled, was not constrained during flight over open ocean, and did not influence where an individual ultimately crossed over the northern coast of the Gulf of Mexico at the end of this flight. Instead, we found a strong preference for full compensation throughout godwit flight paths. CONCLUSIONS: Our results indicate that compensation is crucial to godwits, emphasizing the role of risk in shaping migratory behavior and raising questions about the consequences of changing wind regimes for other barrier-crossing aerial migrants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".