Sound and movement data from two overlapping populations of killer whales reveal noise effects on foraging behavior
Bibliographic record
Abstract
Understanding ambient noise effects on foraging ecology of endangered killer whales is a necessary step toward predicting population-level consequences of acoustic disturbance. However, this has been limited by the difficulty of identifying prey capture which typically occurs out of sight, and by the challenge of obtaining a sufficient range of noise conditions. We addressed these problems using sound and movement data from suction cup-attached archival tags deployed on 52 Northern resident and endangered Southern resident killer whales sampled during overlapping field efforts. We measured broadband ambient noise levels above low-frequency cut-offs that minimized flow noise, and quantified foraging behavior using established acoustic and movement signatures to detect prey capture events. Statistical models revealed significant effects of noise level on searching, pursuit and capture outcomes, including the likelihood that a prey pursuit dive was aborted. We discuss the utility of broadening the range of noise level conditions available by sampling from multiple populations within overlapping geographic regions, and highlight the implications of noise-induced foraging interference on two killer whale populations with different population trajectories. Finally, we suggest future applications of this comparative approach to foster a greater understanding of the population consequences of acoustic disturbance in killer whales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".