Individual experience and evolutionary history of predation affect expression of heritable variation in fish personality and morphology
Bibliographic record
Abstract
Predation plays a central role in evolutionary processes, but little is known about how predators affect the expression of heritable variation, restricting our ability to predict evolutionary effects of predation. We reared families of three-spined stickleback Gasterosteus aculeatus from two populations-one with a history of fish predation (predator sympatric) and one without (predator naive)-and experimentally manipulated experience of predators during ontogeny. For a suite of ecologically relevant behavioural ('personality') and morphological traits, we then estimated two key variance components, additive genetic variance (VA) and residual variance (VR), that jointly shape narrow-sense heritability (h2=VA/(VA+VR)). Both population and treatment differentially affected VA versus VR, hence h2, but only for certain traits. The predator-naive population generally had lower VA and h2 values than the predator-sympatric population for personality behaviours, but not morphological traits. Values of VR and h2 were increased for some, but decreased for other personality traits in the predator-exposed treatment. For some personality traits, VA and h2 values were affected by treatment in the predator-naive population, but not in the predator-sympatric population, implying that the latter harboured less genetic variation for behavioural plasticity. Replication and experimental manipulation of predation regime are now needed to confirm that these population differences were related to variation in predator-induced selection. Cross-environment genetic correlations (rA) were tight for most traits, suggesting that predator-induced selection can affect the evolution of the same trait expressed in the absence of predators. The treatment effects on variance components imply that predators can affect evolution, not only by acting directly as selective agents, but also by influencing the expression of heritable variation.
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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 | 0.001 |
| 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".