A systematic review on the behavioural responses of wild marine mammals to noise: the disparity between science and policy
Bibliographic record
Abstract
Noise can cause marine mammals to interrupt their feeding, alter their vocalizations, or leave important habitat, among other behavioural responses. The current North American paradigm for regulating activities that may result in behavioural responses identifies received levels (RL) of sound at which individuals are predicted to display significant behavioural responses (often termed harassment). The recurrent conclusion about the need for considering context of exposure, in addition to RL, when assessing probability and severity of behavioural responses led us to conduct a systematic literature review (370 papers) and analysis (79 studies, 195 data cases). The review summarized the critical and complex role of context of exposure. The analysis emphasized that behavioural responses in cetaceans (measured via a linear severity scale) were best explained by the interaction between sound source type (continuous, sonar, or seismic/explosion) and functional hearing group (a proxy for hearing capabilities). Importantly, more severe behavioural responses were not consistently associated with higher RL and vice versa. This indicates that monitoring and regulation of acoustic effects from activities on cetacean behaviour should not exclusively rely upon generic multispecies RL thresholds. We recommend replacing the behavioural response severity score with a response/no response dichotomous approach that can represent a measure of impact in terms of habitat loss and degradation.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".