Size and body condition drive the energetic cost of a baleen whale foraging in shallow habitat
Bibliographic record
Abstract
Energy expenditure strongly influences an animal's foraging decisions and activity budgets. Diving animals especially need to be energetically efficient because they exercise while oxygen is limited. By estimating the energetics of behavior, we can better understand the cascading effects of individual responses to disturbance and environmental change. Pacific Coast Feeding Group (PCFG) gray whales use a variety of foraging tactics in shallow habitats (<20 m), which present challenges associated with maneuverability and buoyancy. We use a seven-year dataset of concurrent individual behavior, morphology, and breath-by-breath respiration data collected via drone paired with two years of tri-axial accelerometry tag data to study patterns and correlates of respiration. We assess how several respiration metrics (acting as proxies for oxygen consumption) are associated with individual length, body condition and behavior (forage and travel), and test whether respiration reflects recovery from, or anticipation of, a foraging dive using Bayesian linear mixed effects models. Given model results, we simulated daily field metabolic rate (FMR) to explore how diving costs may affect energetics at a daily scale. We find that respiration reflects recovery from the preceding dive and that dives are more energetically expensive for longer, more buoyant whales. Longer dives and the most common foraging tactics also incur higher energetic costs. FMR simulations show that individual size and dive duration have the largest effects on energy expenditure. Thus, PCFG gray whale foraging success may be limited by the energetic costs associated with size and buoyancy, highlighting the costs of a shallow habitat foraging niche.
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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 | 0.002 |
| 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 | 0.001 | 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".