The sound of recovery: Integrating acoustics into fish status assessments and recovery strategies
Bibliographic record
Abstract
Many fish species use sound to communicate, attract mates, and acquire information about habitat quality—all of which can be negatively impacted by anthropogenic noise. The Canadian government is implementing an Ocean Noise Strategy to mitigate the impacts of this anthropogenic stressor, stating that actions will align with existing commitments to species protection and legislation. Here, we examine the extent to which acoustics have been considered in recommendations for listings by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) and subsequent action plans following listings under the Species at Risk Act (SARA). To do so, we collated 374 Assessments and Status Reports as well as Recovery and Action Plans, encompassing all assessed or at-risk marine and freshwater fishes in Canada. Of the 138 designatable taxa (i.e., species or specific populations) considered in these reports, 32 taxa (23 %) that span 11 families are known to be actively soniferous (i.e., produce sound for communication). Yet, no SARA documents mentioned sound production, soundscapes, or the potential for noise pollution to threaten population recovery. A single COSEWIC assessment acknowledged that a species is actively soniferous. Noise pollution was recognized as a threat to recovery in one COSEWIC report; in contrast, other aspects of vessel impacts (e.g., wave action) were considered in several documents. Therefore, we find that acoustics are rarely considered when developing strategies to safeguard at-risk fish populations from extinction. Integrating fish sonifery, soundscapes, and noise pollution considerations into Canadian policies will improve species management, conservation, and recovery efforts.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".