Boat noise alters behaviour of two coral reef macroinvertebrates, Lambis lambis and Tridacna maxima
Bibliographic record
Abstract
Boat noise has been shown to distract and cause harm to many marine organisms. Most of the study effort has focused on fish & marine mammals, even though invertebrates represent over 92 % of all marine life. The few studies conducted on invertebrates have demonstrated clear negative effects of anthropogenic noise pollution. The small giant clam Tridacna maxima and the spider conch Lambis lambis are two invertebrate species which play key roles in coral reef ecosystems, and are little studied for the effects of noise disturbance. T. maxima functions as prey for many fish species, contributes up to 9 % of the reef's calcium carbonate budget, and plays a role in nutrient cycling. The herbivorous strombid L. lambis can occur in large numbers on reef flats and is prey for other snails and several elasmobranchs. Using two case study reefs, we show that both boat noise and biotic sounds are prominent sound sources in Red Sea reef habitats. In-situ controlled exposure experiments were conducted on two shallow central Red Sea reefs, where Daily Diary smart tags were used to measure the reactions of T. maxima and L. lambis during underwater playback of boat noise and ambient reef sound. Both macroinvertebrates exhibited behavioral changes during the boat noise treatment. Our results suggest that L. lambis and T. maxima individuals may spend energy averting the invisible "threat" of boat noise, rather than feeding or staying open for symbiotic algae to perform photosynthesis, in the case of T. maxima. As boat noise is prevalent on Red Sea reefs, invertebrates may be affected on a large scale in the Red Sea.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".