Song repertoire size, not territory location, predicts reproductive success and territory tenure in a migratory songbird
Bibliographic record
Abstract
In territorial animals occupying environments that vary in quality over the landscape, high-quality individuals are predicted to monopolize high-quality territories. Thus, in many cases it may be difficult to disentangle the relative effects of individual quality from those of territory quality on long-term fitness. We used a 9-year field data set from a migratory population of Eastern Song Sparrows (Melospiza melodia melodia (A. Wilson, 1810)) to evaluate the relative contributions of male song quality (as measured by song repertoire size) and territory location to fitness components including annual reproductive success, overwinter return rates, and between-year territory tenure. Song repertoire size did not predict territory location, allowing us to evaluate territory location and song quality separately. Song repertoire size, but not territory location, predicted annual reproductive success. Moreover, males with larger repertoires moved smaller distances between subsequent breeding seasons, suggesting more successful territory tenure. There was no effect of either repertoire size or territory location on overwinter return. We conclude that intrinsic male phenotype, indicated by song repertoire size, is an important predictor of male fitness, independent of breeding-territory location in this migratory population, and that the value of specific territories may depend largely on previous experience.
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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 | 0.000 | 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) | 0.001 | 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".