Sexual Conflict Arising from Extrapair Matings in Birds
Bibliographic record
Abstract
The discovery that extrapair copulation (EPC) and extrapair paternity (EPP) are common in birds led to a paradigm shift in our understanding of the evolution of mating systems.The prevalence of extrapair matings in pair-bonded species sets the stage for sexual conflict, and a recent focus has been to consider how this conflict can shape variation in extrapair mating rates.Here, we invert the causal arrow and consider the consequences of extrapair matings for sexual conflict.Extrapair matings shift sexual conflict from a simple two-player (male vs. female) game to a game with three or more players, the nature of which we illustrate with simple diagrams that highlight the net costs and benefits of extrapair matings to each player.This approach helps identify the sorts of traits that might be under selection because of sexual conflict.Whether EPP is driven primarily by the extrapair male or the within-pair female profoundly influences which players are in conflict, but the overall pattern of conflict varies little among different mating systems.Different aspects of conflict are manifest at different stages of the breeding cycle and can be profitably considered as distinct episodes of selection caused by conflict.This perspective is illuminating both because conflict between specific players can change across episodes and because the traits that evolve to mediate conflict likely differ between episodes.Although EPP clearly leads to sexual conflict, we suggest that the link between sexual conflict and multiple paternity might be usefully understood by examining how deviations from lifetime sexual monogamy influence sexual conflict.T he development of genetic tools for deter- mining parentage fundamentally altered our understanding of animal mating systems (Jeffreys et al. 1985;Avise 1996;Reynolds 1996) and provided invaluable insights into the consequences and causes of females mating with more than one male.Particularly for the study of birds, these methods revealed that social pair bonds often fail to match the actual patterns of copulations that produced offspring (Gowaty and Karlin 1984;Birkhead and Møller 1992;Reynolds 1996;Petrie and Kempenaers 1998), revolutionizing the study of avian mating systems.Extensive research and two recent reviews point out the progress we have made in this field and show how little we still understand extrapair behavior (Griffith et al. 2002;Westneat and Stewart 2003).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".