Small benthic fish strike at prey over distances that fall within theoretical predictions for active sensing using light
Bibliographic record
Abstract
Small, benthic, cryptic fishes represent a species-rich guild on marine substrates. Most of them are micropredators that feed on crustaceans that are often smaller than 1 mm. Typical examples are seahorses and pipefishes (Syngnathidae), most gobies (Gobiidae), dragonets (Callionymidae) and triplefins (Tripterygiidae). Previous work on the yellow black-headed triplefin Tripterygion delaisi demonstrated that it actively redirects downwelling sunlight sideways using its iris and can use this to locally illuminate objects of interest. We call this form of active sensing using light "diurnal active photolocation". Visual modelling predicted that light redirection can be sufficient to induce a perceivable change in luminance in the eyes of one of its prey species, a cryptic gammarid crustacean (Cheirocratus sp.), over distances of 1-2 cm. Empirical validation, however, was not possible because measurements of predation distances have not been quantified for free-ranging, small, benthic fishes before. Here, we present interaction distances measured from videos of T. delaisi approaching and striking at prey in the field. Out of 160 recordings, we were able to quantify 78 prey approaching distances and 100 striking distances. Approaching distances ranged from 2.1 to 4.1 cm (interquartile range, IQR) and involved one to five approaching steps before the actual strike occurred. The distance over which the final strike took place varied from 0.7 to 1.6 cm (IQR). Both approaching and striking distances increased with fish body size. We conclude that most approaching sequences started too far away to be explained by prey detection through light redirection. Striking distances, however, fell well with the distances predicted by the model. We conclude that if diurnal active photolocation plays a role in prey detection, it is during the final decision whether to strike or not.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".