Indirect genetic effects and the evolution of aggression in a vertebrate system
Bibliographic record
Abstract
Aggressive behaviours are necessarily expressed in a social context, such that individuals may be influenced by the phenotypes, and potentially the genotypes, of their social partners. Consequently, it has been hypothesized that indirect genetic effects (IGEs) arising from the social environment will provide a major source of heritable variation on which selection can act. However, there has been little empirical scrutiny of this to date. Here we test this hypothesis in an experimental population of deer mice (Peromyscus maniculatus). Using quantitative genetic models of five aggression traits, we find repeatable and heritable differences in agonistic behaviours of focal individuals when presented with an opponent mouse. For three of the traits, there is also support for the presence of IGEs, and estimated correlations between direct and indirect genetic (rAO,F) effects were high. As a consequence, any selection for aggression in the focal individuals should cause evolution of the social environment as a correlated response. In two traits, strong positive rAO,F will cause the rapid evolution of aggression, while in a third case changes in the phenotypic mean will be constrained by negative covariance between direct and IGEs. Our results illustrate how classical analyses may miss important components of heritable variation, and show that a full understanding of evolutionary dynamics requires explicit consideration of the genetic component of the social environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 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".