Foraging efficiency and prey selectivity in a visual predator: differential effects of turbid and humic water
Bibliographic record
Abstract
Predators exert strong regulating forces on lower trophic levels through predation. As most fish are visual foragers, visual conditions in the water may alter the strength of this regulation. We evaluated effects of turbidity and humic water on foraging efficiency and prey-size selectivity in Northern pike (Esox lucius) feeding on roach (Rutilus rutilus). Encounter rates decreased in both turbid and humic water but were not counteracted by increased searching activity. Capture success was unaffected by turbidity but was nonlinearly affected by humic water by being high in clear and highly humic water but low in less humic water. In highly humic water, the visual range approached pike’s strike distance and, together with its cryptic colours, pike may have initiated its attack before the prey detected it, limiting the possibility for prey evasive manoeuvres. Prey-size selectivity towards small prey in clear water disappeared in turbid water but was maintained in humic water. Owing to its optical properties, turbidity degrades the quality of the visual information more through scattering than humic water does through absorption. We show that the effect of visual degradation on foraging depends on the cause of visual degradation, which has not previously been acknowledged in the visual foraging literature.
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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".