Males miss and females forgo: Auditory masking from vessel noise impairs foraging efficiency and success in killer whales
Bibliographic record
Abstract
Understanding how the environment mediates an organism's ability to meet basic survival requirements is a fundamental goal of ecology. Vessel noise is a global threat to marine ecosystems and is increasing in intensity and spatiotemporal extent due to growth in shipping coupled with physical changes to ocean soundscapes from ocean warming and acidification. Odontocetes rely on biosonar to forage, yet determining the consequences of vessel noise on foraging has been limited by the challenges of observing underwater foraging outcomes and measuring noise levels received by individuals. To address these challenges, we leveraged a unique acoustic and movement dataset from 25 animal-borne biologging tags temporarily attached to individuals from two populations of fish-eating killer whales (Orcinus orca) in highly transited coastal waters to (1) test for the effects of vessel noise on foraging behaviors-searching (slow-click echolocation), pursuit (buzzes), and capture and (2) investigate the mechanism of interference. For every 1 dB increase in maximum noise level, there was a 4% increase in the odds of searching for prey by both sexes, a 58% decrease in the odds of pursuit by females and a 12.5% decrease in the odds of prey capture by both sexes. Moreover, all but one deep (≥75 m) foraging attempt with noise ≥110 dB re 1 μPa (15-45 kHz band; n = 6 dives by n = 4 whales) resulted in failed prey capture. These responses are consistent with an auditory masking mechanism. Our findings demonstrate the effects of vessel noise across multiple phases of odontocete foraging, underscoring the importance of managing anthropogenic inputs into soundscapes to achieve conservation objectives for acoustically sensitive species. While the timescales for recovering depleted prey species may span decades, these findings suggest that complementary actions to reduce ocean noise in the short term offer a critical pathway for recovering odontocete foraging opportunities.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".