Date and parental quality effects in the seasonal decline in reproductive performance of the Tree Swallow <i>Tachycineta bicolor</i>: interpreting results in light of potential experimental bias
Bibliographic record
Abstract
In many bird species early breeders have higher reproductive performance than late breeders from the same population. This could be caused by a reduction in environmental factors related to date per se (Date Hypothesis), or because poorer performers nest later (Parent Quality Hypothesis). We manipulated hatch date of Tree Swallows Tachycineta bicolor by switching clutches with different lay dates, generating broods with advanced or delayed timing, and assessed the impact of the experiment on nestling mass. The Date Hypothesis better explained the decline in nestling mass in the first half of the season, while the Parent Quality Hypothesis was supported in the second half. We also found that female mass loss was unintentionally reduced in advanced females and suggest that such impacts of the experiment on parent quality, or correlations between nestlings and their actual parents via heritability or maternal effects, could bias hatch‐date manipulation experiments towards supporting the Date Hypothesis. Differential costs of incubation, either due to naturally low temperatures early in the season, or due to the unintentional manipulation of female incubation costs, appear to have driven support for the Date Hypothesis early in the season.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".