Use of acoustic refuges by freshwater fish: Theoretical framework and empirical data in a three‐species trophic system
Bibliographic record
Abstract
Abstract Sounds are more easily transmitted underwater than through air and many freshwater fish species can hear them, particularly over the low frequencies. Recent studies on freshwater fish evidenced that hearing sensitivities can be limited by the level of ambient noise, a phenomenon also known as acoustic masking. However, it is still unclear whether variations in ambient noises, such as those produced by human activities, may alter fish trophic interactions. The general objective of this study was to propose and evaluate a theoretical framework explicitly linking fish attack rates to species hearing sensitivities and ambient noise levels in freshwater ecosystems. The proposed model shows that the feeding activity of fish at an intermediate position in the food web is conditional on the probability of being acoustically detected by their predators, or of encountering an acoustically distressed resource. Model simulations and preliminary field results suggest that yellow perch (Perca flavescens) could feed more actively in the presence of augmented ambient noise levels. Yellow perch captures per unit effort were higher by a factor of 2.7 in noisy versus quiet days. We argue that fish exposed to intermediate levels of noise could maximise the probability of detecting food patches while minimising their predation risk. Acoustic monitoring programmes for freshwater ecosystems require a fundamental knowledge of underwater noise levels, species hearing sensitivities and features affecting sound propagation. The approach proposed in this paper is seminal in linking the above descriptors in a coherent mathematical framework to understand the effect of underwater sounds on trophic interactions. Such framework is needed to make testable predictions and generalise to other taxa and ecological contexts.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| 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".