Year-class variation drives interactions between warm-water predators and yellow perch
Bibliographic record
Abstract
Walleye (Sander vitreus), smallmouth bass (Micropterus dolomieu), and largemouth bass (Micropterus salmoides) are common top predators across many north temperate lakes, but no previous analyses have assessed factors driving their combined impact on mortality of a shared prey, yellow perch (Perca flavescens). We estimated consumption dynamics of walleye, smallmouth bass, and largemouth bass during 3 years that differed in age-0 yellow perch year-class strength and evaluated the relative contribution of each predator to age-0 yellow perch mortality, in Oneida Lake, New York, USA. Habitat-specific diet composition and population densities were integrated with temperature and growth rates to parameterize a bioenergetics model and estimate annual consumption of major diet items. Walleye were the dominant predator in both offshore and inshore habitats, while smallmouth bass and largemouth bass were also important inshore predators. Consumption of age-0 yellow perch by all three predators was positively correlated to age-0 yellow perch year-class strength, but our ability to account for age-0 yellow perch mortality decreased during years when year-class strength was strong. Within each year, predation by the three species accounted for all observed age-0 yellow perch mortality in late summer and fall, but not in the early summer, suggesting other predators in the lake likely predate on the youngest, most vulnerable yellow perch. These results are important for understanding how diverse predator communities can alter the spatial and temporal availability of prey refuges and influence mortality of a shared 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 | 0.000 | 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".