Marine and Freshwater Sounds Impact Invertebrate Behavior and Physiology: A Meta‐Analysis
Bibliographic record
Abstract
The diversity of biotic and abiotic sounds that fill underwater ecosystems has become polluted by anthropogenic noise in recent decades. Yet, there is still great uncertainty surrounding how different acoustic stimuli influence marine and freshwater (i.e., aquatic) communities. Despite capabilities to detect and produce sounds, aquatic invertebrates are among the most understudied taxa within the field of soundscape ecology. We conducted a meta-analysis to understand how sounds from various sources influence the behavior and physiology of aquatic invertebrates. We extracted 835 data points from 46 studies conducted in 15 countries. The resulting data included 50 species, a range of experimental conditions, and four sound categories: anthropogenic, environmental, synthetic, and music. We used meta-analytic multivariate mixed-effect models to determine how each sound category influenced aquatic invertebrates and if responses were homogeneous across taxa. Our analyses illustrate that anthropogenic noise and synthetic sounds have detrimental impacts on aquatic invertebrate behavior and physiology, and that environmental sounds have slightly beneficial effects on their behavior. Defence responses were the most impacted behaviors, while the most prominent physiological responses were related to biochemistry, genetics, and morphology. Additionally, arthropods and molluscs exhibited the most pronounced physiological responses to anthropogenic and synthetic noise. These findings support the conclusion that many invertebrate species are sensitive to changes in aquatic soundscapes, which can cause adverse or favorable consequences to individuals and populations, dependent on the sound source. This quantitative synthesis highlights the necessity of including marine and freshwater invertebrates in acoustic exposure studies, aquatic ecosystem assessments, and emerging noise pollution policies.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.030 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".