Foraging behavior of humpback whales: kinematic and respiratory patterns suggest a high cost for a lunge
Bibliographic record
Abstract
Lunge feeding in rorqual whales is a drag-based feeding mechanism that is thought to entail a high energetic cost and consequently limit the maximum dive time of these extraordinarily large predators. Although the kinematics of lunge feeding in fin whales supports this hypothesis, it is unclear whether respiratory compensation occurs as a consequence of lunge-feeding activity. We used high-resolution digital tags on foraging humpback whales (Megaptera novaengliae) to determine the number of lunges executed per dive as well as respiratory frequency between dives. Data from two whales are reported, which together performed 58 foraging dives and 451 lunges. During one study, we tracked one tagged whale for approximately 2 h and examined the spatial distribution of prey using a digital echosounder. These data were integrated with the dive profile to reveal that lunges are directed toward the upper boundary of dense krill aggregations. Foraging dives were characterized by a gliding descent, up to 15 lunges at depth, and an ascent powered by steady swimming. Longer dives were required to perform more lunges at depth and these extended apneas were followed by an increase in the number of breaths taken after a dive. Maximum dive durations during foraging were approximately half of those previously reported for singing (i.e. non-feeding) humpback whales. At the highest lunge frequencies (10 to 15 lunges per dive), respiratory rate was at least threefold higher than that of singing humpback whales that underwent a similar degree of apnea. These data suggest that the high energetic cost associated with lunge feeding in blue and fin whales also occurs in intermediate sized rorquals.
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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 | 0.000 | 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".