Metabolic Expenditures of Lunge Feeding Rorquals Across Scale: Implications for the Evolution of Filter Feeding and the Limits to Maximum Body Size
Bibliographic record
Abstract
Bulk-filter feeding is an energetically efficient strategy for resource acquisition and assimilation, and facilitates the maintenance of extreme body size as exemplified by baleen whales (Mysticeti) and multiple lineages of bony and cartilaginous fishes. Among mysticetes, rorqual whales (Balaenopteridae) exhibit an intermittent ram filter feeding mode, lunge feeding, which requires the abandonment of body-streamlining in favor of a high-drag, mouth-open configuration aimed at engulfing a very large amount of prey-laden water. Particularly while lunge feeding on krill (the most widespread prey preference among rorquals), the effort required during engulfment involve short bouts of high-intensity muscle activity that demand high metabolic output. We used computational modeling together with morphological and kinematic data on humpback (Megaptera noveaangliae), fin (Balaenoptera physalus), blue (Balaenoptera musculus) and minke (Balaenoptera acutorostrata) whales to estimate engulfment power output in comparison with standard metrics of metabolic rate. The simulations reveal that engulfment metabolism increases across the full body size of the larger rorqual species to nearly 50 times the basal metabolic rate of terrestrial mammals of the same body mass. Moreover, they suggest that the metabolism of the largest body sizes runs with significant oxygen deficits during mouth opening, namely, 20% over maximum VO2 at the size of the largest blue whales, thus requiring significant contributions from anaerobic catabolism during a lunge and significant recovery after a lunge. Our analyses show that engulfment metabolism is also significantly lower for smaller adults, typically one-tenth to one-half VO2|max. These results not only point to a physiological limit on maximum body size in this lineage, but also have major implications for the ontogeny of extant rorquals as well as the evolutionary pathways used by ancestral toothed whales to transition from hunting individual prey items to filter feeding on prey aggregations.
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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 | 0.001 |
| 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 | 0.001 | 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".