Impacts of noise on the behavior and physiology of marine invertebrates: A meta-analysis
Bibliographic record
Abstract
Human-generated noise over the last 60 years has increased concerns regarding the implications for marine species. Many species have been documented to display behavioral and physiological responses to increased noise pollution in our oceans, but the majority of this research has focused on higher trophic organisms. Recently, investigations assessing the impacts of changing soundscapes on entire aquatic ecosystems have begun. To understand the impacts of underwater noise on invertebrate communities, a meta-analysis was conducted on the behavioral and physiological impacts of noise on invertebrates. A systematic review of the literature revealed 1,105 potential studies, which 25 were extracted for data analysis. The studies resulted in 473 data points evaluating the impacts of a plethora of acoustic stimuli on a wide range of marine invertebrate taxa. Here, two acoustic stimuli (ship noise and seismic surveys) were further broken down into behavioral and physiological parameters. Shipping noise had a negative effect size on the behavior and physiology of marine invertebrates. However, seismic surveys resulted in a positive effect size, which was not predicted. While further analysis is required to understand the impacts of these stimuli fully, this meta-analysis reveals the implications that elevated underwater noise levels may have on marine invertebrate communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.033 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".