Fitness Correlates of Song Repertoire Size in Free‐Living Song Sparrows (<i>Melospiza melodia</i>)
Bibliographic record
Abstract
Models of sexual selection propose that exaggerated secondary sexual ornaments indicate a male's own fitness and the fitness of his offspring. These hypotheses have rarely been thoroughly tested in free-living individuals because overall fitness, as opposed to fitness components, is difficult to measure. We used 20 years of data from song sparrows (Melospiza melodia) inhabiting Mandarte Island, British Columbia, Canada, to test whether a male's song repertoire size, a secondary sexual trait, predicted overall measures of male or offspring fitness. Males with larger song repertoires contributed more independent and recruited offspring, and independent and recruited grandoffspring, to Mandarte's population. This was because these males lived longer and reared a greater proportion of hatched chicks to independence from parental care, not because females mated to males with larger repertoires laid or hatched more eggs. Furthermore, independent offspring of males with larger repertoires were more likely to recruit and then to leave more grandoffspring than were offspring of males with small repertoires. Although we cannot distinguish whether observed fitness variation reflected genetic or environmental effects on males or their offspring, these data suggest that female song sparrows would gain immediate and intergenerational fitness benefits by pairing with males with large song repertoires.
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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 | 0.001 |
| 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) | 0.001 | 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".