Cost of Living Dictates what Whales, Dolphins and Porpoises Eat: The Importance of Prey Quality on Predator Foraging Strategies
Bibliographic record
Abstract
Understanding the mechanisms that drive prey selection is a major challenge in foraging ecology. Most studies of foraging strategies have focused on behavioural costs, and have generally failed to recognize that differences in the quality of prey may be as important to predators as the costs of acquisition. Here, we tested whether there is a relationship between the quality of diets (kJ · g(-1)) consumed by cetaceans in the North Atlantic and their metabolic costs of living as estimated by indicators of muscle performance (mitochondrial density, n = 60, and lipid content, n = 37). We found that the cost of living of 11 cetacean species is tightly coupled with the quality of prey they consume. This relationship between diet quality and cost of living appears to be independent of phylogeny and body size, and runs counter to predictions that stem from the well-known scaling relationships between mass and metabolic rates. Our finding suggests that the quality of prey rather than the sheer quantity of food is a major determinant of foraging strategies employed by predators to meet their specific energy requirements. This predator-specific dependence on food quality appears to reflect the evolution of ecological strategies at a species level, and has implications for risk assessment associated with the consequences of changing the quality and quantities of prey available to top predators in marine ecosystems.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".