Corticosterone predicts double-brooding in female savannah sparrows (Passerculus sandwichensis)
Bibliographic record
Abstract
Given that double-brooding (rearing two broods within a season) can increase annual fecundity, it is unclear why some females in multi-brooded populations rear only one brood per season. The Quality Hypothesis proposes that double-brooded females are high quality and, thus, have sufficient energetic resources available to bear the costs of rearing two broods per season. Glucocorticoids - endocrine hormones that have a critical role in energy regulation - could reflect female quality, and, therefore, also have the potential to indicate whether a female will rear a second brood. Using 12 years of reproductive data on migratory Savannah sparrows (Passerculus sandwichensis) from a population in eastern Canada, we explored whether baseline corticosterone concentrations were correlated with measures of female quality (body condition and fat score) and whether a female's baseline corticosterone concentrations during her first brood would predict whether she attempted a second. We found weak evidence that baseline corticosterone was negatively correlated with female body condition and found strong evidence that baseline corticosterone was negatively correlated with fat score. There was weak evidence for a positive relationship between double-brooding and baseline corticosterone in females sampled during the first brood incubation stage. Additionally, there was moderate evidence to suggest that the probability of double-brooding was negatively related to baseline corticosterone in females sampled during the first brood nestling stage. Our results provide evidence that corticosterone can reflect female condition in the context of double-brooding and demonstrate the importance of considering breeding stage when assessing corticosterone concentrations in parents.
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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".