Winner and loser effects: a meta-analysis
Bibliographic record
Abstract
Aggressive interactions can strongly influence an animal's performance in subsequent contests. Winners of aggressive contests are more likely to win successive contests and losers are more likely to lose successive contests. Such winner and loser effects can significantly influence an animal's dominance status, ability to acquire resources and reproductive success. Thus, quantifying the magnitudes of winner and loser effects across taxa is important for our understanding of hierarchy formation, life history trade-offs and reproductive tactics in different species. Furthermore, it is unclear whether the magnitude of winner effects differ from that of loser effects. Finally, experimenters often employ one of two distinct methods for quantifying the strength of winner and loser effects: self-selection and random assignment. Due to selection bias, it is possible that self-selection protocols overestimate the magnitude of winner and loser effects. We therefore systematically searched the literature to conduct a comprehensive meta-analysis of winner and loser effects. We analysed a total of 168 effect sizes from arachnids, crustaceans, fishes, insects, mammals and reptiles. We found that prior winners tend to win approximately two-thirds of their subsequent fights, while prior losers tend to lose approximately two-thirds of their subsequent fights. While we did not find that studies using self-selection generated effect size estimates that significantly differed from random assignment protocols, future studies should still avoid self-selection protocols. Overall, our study highlights the ubiquity of winner and loser effects across the animal kingdom and suggests several avenues for future research to unravel the evolutionary origins and mechanistic underpinnings of such experience effects.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.053 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".