Data from: Predation risk elicits a negative relationship between boldness and growth in Helisoma snails
Bibliographic record
Abstract
The relationship between risk-prone behavior and growth is central to tradeoff models that explain the existence and maintenance of among-individual variation in behavior (i.e., animal personality). These models posit positive relationships between among-individual variation in risk-prone behaviors and growth, yet how the strength and direction of such relationships depend on ecological conditions is unclear. We tested how different levels of predation risk from crayfish (Faxonius limosus) mediate the association between among-individual variation in snail (Helisoma trivolvis) boldness (emergence time) and growth in shell size. We found that crayfish predation risk reduced snail growth, but that the effect of snail boldness on individual growth was context-dependent – snail boldness was unrelated to growth in the absence of risk and under high risk, but shy snails grew faster than bold snails under low predation risk. Other traits (snail size, body condition and intrinsic growth rate under ad libitum food conditions) failed to explain snail growth variation under any risk level. Though opposite to the prediction of tradeoff models, enhanced growth of shy snails could function as a predator defense mechanism that protects their prospects for future reproduction consistent with the underlying premise of tradeoff models. Thus, our results highlight the importance of accounting for ecological conditions in understanding behavior-life history associations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.018 |
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".