Not So Fast: Swimming Behavior of Sailfish during Predator–Prey Interactions using High-Speed Video and Accelerometry
Bibliographic record
Abstract
Billfishes are considered among the fastest swimmers in the oceans. Despite early estimates of extremely high speeds, more recent work showed that these predators (e.g., blue marlin) spend most of their time swimming slowly, rarely exceeding 2 m s(-1). Predator-prey interactions provide a context within which one may expect maximal speeds both by predators and prey. Beyond speed, however, an important component determining the outcome of predator-prey encounters is unsteady swimming (i.e., turning and accelerating). Although large predators are faster than their small prey, the latter show higher performance in unsteady swimming. To contrast the evading behaviors of their highly maneuverable prey, sailfish and other large aquatic predators possess morphological adaptations, such as elongated bills, which can be moved more rapidly than the whole body itself, facilitating capture of the prey. Therefore, it is an open question whether such supposedly very fast swimmers do use high-speed bursts when feeding on evasive prey, in addition to using their bill for slashing prey. Here, we measured the swimming behavior of sailfish by using high-frequency accelerometry and high-speed video observations during predator-prey interactions. These measurements allowed analyses of tail beat frequencies to estimate swimming speeds. Our results suggest that sailfish burst at speeds of about 7 m s(-1) and do not exceed swimming speeds of 10 m s(-1) during predator-prey interactions. These speeds are much lower than previous estimates. In addition, the oscillations of the bill during swimming with, and without, extension of the dorsal fin (i.e., the sail) were measured. We suggest that extension of the dorsal fin may allow sailfish to improve the control of the bill and minimize its yaw, hence preventing disturbance of the prey. Therefore, sailfish, like other large predators, may rely mainly on accuracy of movement and the use of the extensions of their bodies, rather than resorting to top speeds when hunting evasive prey.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".