Low-frequency noise pollution impairs burrowing activities of marine benthic invertebrates
Bibliographic record
Abstract
Sounds from human activities such as shipping and seismic surveys have been progressively invading natural soundscapes and pervading oceanic ambient sounds for decades. Benthic invertebrates are important ecosystem engineers that continually rework the sediment they live in. Here, we tested how low-frequency noise (LFN), a significant component of noise pollution, affects the sediment reworking activities of selected macrobenthic invertebrates. In a controlled laboratory setup, the effects of acute LFN exposure on the behavior of three abundant bioturbators on the North Atlantic coasts were explored for the first time by tracking their sediment reworking and bioirrigation activities in noisy and control environments via luminophore and sodium bromide (NaBr) tracers, respectively. The amphipod crustacean Corophium volutator was negatively affected by LFN, exhibiting lower bioturbation rates and shallower luminophore burial depths compared to controls. The effect of LFN on the polychaete Arenicola marina and the bivalve Limecola balthica remained inconclusive, although A. marina displayed greater variability in bioirrigation rates when exposed to LFN. Furthermore, a potential stress response was observed in L. balthica that could reduce bioturbation potential. Benthic macroinvertebrates may be in jeopardy along with the crucial ecosystem-maintaining services they provide. More research is urgently needed to understand, predict, and manage the impacts of anthropogenic noise pollution on marine fauna and their associated ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 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 teacher head, 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".