Effects of wave-driven water flow on the fast-start escape response of juvenile coral reef damselfishes
Bibliographic record
Abstract
Fish often evade predators with a fast-start escape response. Studies typically examine this behaviour in still water despite water motion being an inherent feature of aquatic ecosystems. In shallow habitats, waves create complex flows that likely influence escape performance, particularly in small fishes with low absolute swimming speeds relative to environmental flows. I examined how wave-driven water flow affects the behaviour and kinematics of escape responses in juveniles of three coral reef damselfishes (Pomacentridae) with different body morphologies. Tropical damselfishes have similar fin and body shapes during early development, with the exception of body depth, a trait deemed important for postural control and stability. Wave-driven flow increased response latency in two of the three species tested: fish with a fusiform body responded 2.9 times slower in wave-driven flow than in still water, whereas this difference was less pronounced in fish with an intermediate body depth (1.9 times slower response) and absent in fish with a laterally compressed body. The effect of wave-driven flow on swimming performance (cumulative escape distance and turning rate) was variable and depended on the timing and trajectory of escape responses in relation to the wave phase. Given intense predation pressure on juvenile coral reef fishes during settlement, interspecific differences in how wave-driven flow affects their ability to escape predators could influence the distribution and abundance of species across spatial and temporal scales.
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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".