Resting costs too: the relative importance of active and resting energy expenditure in a sub-arctic seabird
Bibliographic record
Abstract
Breeding is costly for many animals, including birds that must deliver food to a central place (i.e. nest). Measuring energy expenditure throughout the breeding season can provide valuable insight into physiological limitations by highlighting periods of high demand, and ultimately allows improvement of conservation strategies. However, quantifying energy expenditure in wildlife can be challenging, as existing methods do not measure both active (e.g. foraging) and resting energy costs across short and long time scales. Here, we developed a novel method for comparing active and resting costs in 66 pre-breeding and breeding seabirds (black-legged kittiwakes, Rissa tridactyla) by combining accelerometry and triiodothyronine (T3) as proxies for active and resting costs, respectively. Active energy costs were higher during incubation (P=0.0004) and chick rearing (P<0.0001) than during pre-laying, because of an increase in the time spent in flight of 11% (P=0.0005) and 15% (P<0.0001), respectively. Levels of T3, reflecting resting costs, peaked marginally during incubation with a mean (±s.d.) concentration of 4.71±1.97 pg ml-1 in comparison to 2.66±1.30 pg ml-1 during pre-laying (P=0.05) and 3.16±2.85 pg ml-1 during chick rearing (P=0.11). Thus, although chick rearing is often assumed to be the costliest breeding stage by multiple studies, our results suggest that incubation could be more costly as a result of high resting costs. We highlight the importance of accounting for both active and resting costs when assessing energy expenditure.
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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".