To fear or to feed: the effects of turbidity on perception of risk by a marine fish
Bibliographic record
Abstract
Coral reefs are currently experiencing a number of worsening anthropogenic stressors, with nearshore reefs suffering from increasing sedimentation because of growing human populations and development in coastal regions. In habitats where vision and olfaction serve as the primary sources of information, reduced visual input from suspended sediment may lead to significant alterations in prey fish behaviour. Here, we test whether prey compensate for reduced visual information by increasing their antipredator responses to chemically mediated risk cues in turbid conditions. Experiments with the spiny damselfish, Acanthochromis polyacanthus, found that baseline activity levels were reduced by 23 per cent in high turbidity conditions relative to low turbidity conditions. Furthermore, risk cues elicited strong antipredator responses at all turbidity levels; the strongest antipredator responses were observed in high turbidity conditions, with fish reducing their foraging by almost 40 per cent, as compared with 17 per cent for fish in clear conditions. This provides unambiguous evidence of sensory compensation in a predation context for a tropical marine fish, and suggests that prey fish may be able to behaviourally offset some of the fitness reductions resulting from anthropogenic sedimentation of their habitats.
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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 | 0.000 |
| 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".