The trade‐off between clutch size and egg mass in tree swallows <i>Tachycineta bicolor</i> is modulated by female body mass
Bibliographic record
Abstract
Egg production is a costly component of reproduction for female birds in terms of energy expenditure and maternal investment. Because resources are typically limited, clutch size and egg mass are expected to be constrained, and this putative trade‐off between offspring number and size is at the core of life history theory. Nevertheless, empirical evidence for this trade‐off is equivocal at best, as individual heterogeneity in resource acquisition and allocation may hamper the detection of the negative correlation between egg number and mass within populations. Here, we investigated how female body mass and landscape composition influences clutch size, egg mass, and the relationship between these two traits. To do so, we fitted linear mixed models using data from tree swallows Tachycineta bicolor breeding in a network of 400 nestboxes located along a gradient of agricultural intensity between 2004 and 2011. Our dataset comprised 1463 broods for clutch size analyses and 4371 eggs (from 847 broods laid between 2005–2008) for egg mass analyses. Our results showed that agricultural intensity negatively impacted clutch size, but not egg mass nor the relationship between these two traits. Female mass, on the other hand, modulated the trade‐off between clutch size and egg mass. For heavier females, both traits increased jointly, without evidence of a trade‐off. However, for lighter females, there was a clear negative relationship between clutch size and egg mass. This work shows that accounting for individual heterogeneity in body mass allows the detection of a clutch size/egg mass trade‐off that would have remained undetected otherwise. Identifying habitat and individual effects on resource allocation towards reproductive traits may help bridging the gap between predictions from theory and empirical evidence on life history trade‐offs.
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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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".