Escape response kinematics in two species of tropical shark: short escape latencies and high turning performance
Bibliographic record
Abstract
Accelerative manoeuvres, such as fast-starts, are crucial for fish to avoid predation. Escape responses are fast-starts that include fundamental survival traits for prey that experience high predation pressure. However, no previous study has assessed escape performance in neonate tropical sharks. We quantitatively evaluated vulnerability traits of neonate tropical sharks by testing predictions on their fast-start escape performance. We predicted (1) high manoeuvrability, given their high flexibility, but (2) low propulsive locomotion owing to the drag costs associated with pectoral fin extension during escape responses. Further, based on previous work on dogfish, Squalus suckleyi, we predicted (3) long reaction times (as latencies longer than teleosts, >20 ms). We used two-dimensional, high-speed videography analysis of mechano-acoustically stimulated neonate blacktip reef shark, Carcharhinus melanopterus (n=12), and sicklefin lemon shark, Negaprion acutidens (n=8). Both species performed a characteristic C-start double-bend response (i.e. two body bends), but single-bend responses were only observed in N. acutidens. As predicted, neonate sharks showed high manoeuvrability with high turning rates and tight turning radii (3-11% of body length) but low propulsive performance (i.e. speed, acceleration and velocity) when compared with similar-sized teleosts and S. suckleyi. Contrary to expectations, escape latencies were <20 ms in both species, suggesting that the neurophysiological system of sharks when reacting to a predatory attack may not be limited to long response times. These results provide a quantitative assessment of survival traits in neonate tropical sharks that will be crucial for future studies that consider the vulnerability of these sharks to predation.
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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".