Behavioural trade‐offs between growth and mortality explain evolution of submaximal growth rates
Bibliographic record
Abstract
1. The importance of body size and growth rate in ecological interactions is widely recognized, and both are frequently used as surrogates for fitness. However, if there are significant costs associated with rapid growth rates then its fitness benefits may be questioned. 2. In replicated whole-lake experiments, we show that a domestic strain of rainbow trout (artificially selected for maximum intrinsic growth rate) use productive but risky habitats more than wild trout. Consequently, domestic trout grow faster in all situations, experience greater survival in the absence of predators, but have lower survival in the presence of predators. Therefore, rapid growth rates are selected against due to increased foraging effort (or conversely, lower antipredator behaviour) that increases vulnerability to predators. In other words, there is a behaviourally mediated trade-off between growth and mortality rates. 3. Whereas rapid growth is beneficial in many ecological interactions, our results show the mortality costs of achieving it are large in the presence of predators, which can help explain the absence of an average phenotype with maximized growth rates in nature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".