Behavioural compensation reduces energy expenditure during migration hyperphagia in a large bird
Bibliographic record
Abstract
Summary 1. Bird migration is often seen as the summit of animal performance. Because higher foraging effort associated with fuelling and increased flight activity should generate maximum level of energy turnover, we expect the migratory process to be constrained by energy, whether it is energy expenditure or energy intake. 2. We used implanted data loggers to continuously monitor foraging effort, flight activity and heart rate (as a proxy for rate of energy expenditure), in a wild migratory sea duck before and after moult migration to test the energy budget limitation hypothesis. 3. We show that despite the fact that departing birds forage at three times the level observed during post‐migration, daily energy expenditure (DEE) remains the same when periods before and after migration are compared. 4. In an attempt to determine how the birds achieve such a feat, we compared heart rate when active (feeding and flying) for the periods before and after migration and found no significant difference for feeding and flight heart rate. However, heart rate during periods of inactivity was significantly lower during fuelling which together with a reduced time spent flying completely counteracted the observed high energy costs of foraging. 5. The time spent active represented a tiny proportion (8–20%) of the 24‐h time budget suggesting that energy minimization and an optimization process may be at work here, confounding any apparent support for the energy budget limitation hypothesis. We thus concluded that similar DEE before and after migration could be owing to the bird’s inability to increase energy expenditure over a certain limit or to the survival costs associated with a certain level of physical exertion. Nevertheless, our results indicate that behavioural, and possibly physiological, compensation may be used as a mechanism to reduce the overall energy cost of fuelling in migrating birds.
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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".