Motorboat noise disrupts co-operative interspecific interactions
Bibliographic record
Abstract
Human-made noise is contributing increasingly to ocean soundscapes. Its physical, physiological and behavioural effects on marine organisms are potentially widespread, but our understanding remains largely limited to intraspecific impacts. Here, we examine how motorboats affect an interspecific cleaning mutualism critical for coral reef fish health, abundance and diversity. We conducted in situ observations of cleaning interactions between bluestreak cleaner wrasses (Labroides dimidiatus) and their fish clients before, during and after repeated, standardised approaches with motorboats. Cleaners inspected clients for longer and were significantly less cooperative during exposure to boat noise, and while motorboat disturbance appeared to have little effect on client behaviour, as evidenced by consistency of visit rates, clientele composition, and use of cleaning incitation signals, clients did not retaliate as expected (i.e., by chasing) in response to increased cheating by cleaners. Our results are consistent with the idea of cognitive impairments due to distraction by both parties. Alternatively, cleaners might be taking advantage of distracted clients to reduce their service quality. To more fully understand the importance of these findings for conservation and management, further studies should elucidate whether the efficacy of ectoparasite removal by cleaners is affected and explore the potential for habituation to boat noise in busy areas.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".