Effects of known age on male paternity in a migratory songbird
Bibliographic record
Abstract
Many avian studies have shown that reproductive performance improves with age, but little is known about how key components of male fitness, extrapair and within pair paternity, vary across life spans. We tested for age effects on male paternity in purple martins (Progne subis) using cross-sectional analyses of known-aged males (1–9 years old) and longitudinal analyses of individuals sampled in 2 successive years. Microsatellite analyses found that 137 of 297 (46%) nests contained extrapair offspring and 273 of 1235 (22%) offspring were extrapair. Using a subsample of unique known-aged males (n = 160), we found significant linear and nonlinear effects of male age on the number of within pair offspring and, to a lesser extent, on the number of extrapair offspring sired. Male genetic reproductive success increased with age to 3 years and then leveled off. In longitudinal comparisons of known age males sampled in successive years (n = 41), within pair offspring increased with age, even for males ≥2 years old. Paired comparisons (n = 74) found that extrapair sires were older than the males they cuckolded, and that first-year males were significantly underrepresented as extrapair sires given the known age distribution in the population. Poor genetic reproductive performance in younger males is likely constrained through male–male competition during mate guarding and female choice for older males.
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How this classification was reachedexpand
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) | 0.007 | 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; both teacher heads agree on what is shown here.
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".