Prey life‐history and bioenergetic responses across a predation gradient
Bibliographic record
Abstract
To evaluate the importance of non-consumptive effects of predators on prey life histories under natural conditions, an index of predator abundance was developed for naturally occurring populations of a common prey fish, the yellow perch Perca flavescens, and compared to life-history variables and rates of prey energy acquisition and allocation as estimated from mass balance models. The predation index was positively related to maximum size and size at maturity in both male and female P. flavescens, but not with life span or reproductive investment. The predation index was positively related to size-adjusted specific growth rates and growth efficiencies but negatively related to model estimates of size-adjusted specific consumption and activity rates in both vulnerable (small) and invulnerable (large) size classes of P. flavescens. These observations suggest a trade-off between growth and activity rates, mediated by reduced activity in response to increasing predator densities. Lower growth rates and growth efficiencies in populations with fewer predators, despite increased consumption suggests either 1) a reduction in prey resources at lower predator densities or 2) an intrinsic cost of rapid prey growth that makes it unfavourable unless offset by a perceived threat of predation. This study provides evidence of trade-offs between growth and activity rates induced by predation risk in natural prey fish populations and illustrates how behavioural modification induced through predation can shape the life histories of prey fish species.
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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".