Additive Genetic Variance, Heritability, and Inbreeding Depression in Male Extra-Pair Reproductive Success
Bibliographic record
Abstract
The hypothesis that female extra-pair reproduction in socially monogamous animals reflects indirect genetic benefits requires that there be additive and/or nonadditive genetic variance in fitness. However, the specific hypotheses that male extra-pair reproductive success (EPRS) shows additive genetic variance (V(A)), heritability (h2), or inbreeding depression, and hence that females could acquire indirect genetic benefits through increased EPRS of sons, have not been explicitly tested. We used comprehensive genetic pedigree data from song sparrows (Melospiza melodia) to estimate V(A), h2, and inbreeding depression in the number of extra-pair offspring a male sired per year and the probability that a male would sire any extra-pair offspring per year. Inbreeding depression was substantial: more inbred males sired fewer extra-pair offspring and were less likely to sire any extra-pair offspring. In contrast, estimates of V(A) and h2 were close to 0, although 95% credible intervals were relatively wide. These data suggest that females could accrue indirect genetic benefits, in terms of increased EPRS of outbred sons, by mating with unrelated social or extra-pair mates. In contrast, any indirect benefit of extra-pair reproduction in terms of producing sons with high additive genetic value for EPRS is most likely to be small.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".