Foraging behavior, morphology, and life history variation determine the ontogeny of piscivory in two closely related predators
Bibliographic record
Abstract
The ontogeny of piscivory is an important process during the early life history of many fishes, and why ontogenetic patterns of closely related species vary is unclear. We experimentally evaluated the importance of several factors that can determine the switch to piscivory in two predators with different dietary ontogenies: walleye (Sander vitreus), a specialist piscivore, and yellow perch (Perca flavescens), a dietary generalist. We conducted growth and prey selection experiments across several sizes of both predators using fish, zooplankton, and benthic invertebrates as prey. Walleye exhibited piscivorous feeding behavior throughout all size classes (2080 mm), whereas yellow perch were generalist predators, showing negative to neutral selection for fish prey and lower growth than walleye when feeding on fish. Walleye foraged more efficiently than yellow perch on all prey types, in part because gape widths of walleye increased more quickly with size. Bioenergetic model simulations showed that walleye grew slower than yellow perch when the proportion of fish was low in diets, but walleye growth was faster than yellow perch on a diet dominated by fish. Feeding behavior, morphology, and life history patterns likely work in combination, allowing walleye to switch to piscivory earlier than yellow perch.
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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 | 0.000 | 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".