How do misassigned paternities affect the estimation of heritability in the wild?
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Bibliographic record
Abstract
Studies of birds have recently played an important role in the increasing success of quantitative genetics applied to natural populations. However, these studies mostly base their pedigree relationships on social information, despite the known widespread genetic polygamy in avian species. Here, we study the influence of misassigned paternities, combined with the effect of pedigree size and depth, on the estimation of heritability. First, we compute simulations of a polygenic trait for two levels of heritability (0.1 and 0.4), several extra-pair paternity rates (ranging from 5% to 40%), and varying sample sizes (20, 50 and 100 broods) or pedigree depth (2 or 4 generations). We compare heritability estimates from the social and the genetic pedigree, running a restricted maximum-likelihood 'animal model'. Social pedigree underestimates heritability by an average of 0-17% for 5-20% extra-pair paternities and by up to 18% for 40% extra-pair paternities and a heritability of 0.4. Second, we identifyied extra-pair offspring using microsatellite loci in two populations of blue tits (Parus caeruleus) showing high levels of extra-pair paternities (15% and 25% of extra-pair offspring). We compare heritabilities of tarsus length and body mass estimated with pedigrees of increasing accuracy. These analyses suggest that the bias induced by misassigned paternities on heritability estimation depends on the level of heritability and the rate of paternity error. Typical rates of extra-pair paternities in birds (around 20% of offspring) should result in an underestimation of heritability of less than 15% when estimated over a minimum of 100 broods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it