Fish responses to underwater sounds depend on auditory adaptations: An experimental test of the effect of motorboat sounds on the fish community of a large fluvial lake
Bibliographic record
Abstract
Freshwater fishes exhibit a wide range of auditory adaptations and capabilities, which are assumed to help them navigate their environment, avoid predators, and find potential mates. Yet, we know very little about how freshwater environments sound to fish, or how fish with different auditory adaptations respond to different soundscapes. We first compiled data on fish hearing acuity and adaptations and provided a portrait of how anthropogenic sounds compare to natural sounds in different freshwater soundscapes. We then conducted a sound-enrichment field experiment at Lake Saint Pierre, a large fluvial lake in Canada, to evaluate the effect of motorboat sound exposure on the fish community by looking at the extent to which changes in species abundances were linked to auditory adaptations. Data compilation showed that the hearing acuity of most species overlaps with a wide range of ambient and anthropogenic underwater sounds while the field experiment showed that species with more specialized auditory structures were captured less often in sound-enriched traps, indicating avoidance behavior. Our findings highlight the importance of considering species' sensorial adaptations when evaluating the community-scale effects of anthropogenic sounds on the fish community, especially at low levels of anthropogenic activity.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".