Genetic variation in nuclear and mitochondrial markers supports a large sex difference in lifetime reproductive skew in a lekking species
Bibliographic record
Abstract
Sex differences in skews of vertebrate lifetime reproductive success are difficult to measure directly. Evolutionary histories of differential skew should be detectable in the genome. For example, male-biased skew should reduce variation in the biparentally inherited genome relative to the maternally inherited genome. We tested this approach in lek-breeding ruff (Class Aves, Philomachus pugnax) by comparing genetic variation of nuclear microsatellites (θ n ; biparental) versus mitochondrial D-loop sequences (θ m ; maternal), and conversion to comparable nuclear (N e ) and female (N ef ) effective population size using published ranges of mutation rates for each marker (μ). We provide a Bayesian method to calculate N e (θ n = 4N e μ n ) and N ef (θ m = 2N ef μ m ) using 95% credible intervals (CI) of θ n and θ m as informative priors, and accounting for uncertainty in μ. In 96 male ruffs from one population, N e was 97% (79-100%) lower than expected under random mating in an ideal population, where N e :N ef = 2. This substantially lower autosomal variation represents the first genomic support of strong male reproductive skew in a lekking species.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".