Higher rates of prebreeding condition gain positively impacts clutch size: A mechanistic test of the condition‐dependent individual optimization model
Bibliographic record
Abstract
Abstract A combination of timing of and body condition (i.e., mass) at arrival on the breeding grounds interact to influence the optimal combination of the timing of reproduction and clutch size in migratory species. This relationship has been formalized by Rowe et al. in a condition‐dependent individual optimization model (American Naturalist, 1994, 143, 689‐722), which has been empirically tested and validated in avian species with a capital‐based breeding strategy. This model makes a key, but currently untested prediction; that variation in the rate of body condition gain will shift the optimal combination of laying date and clutch size. This prediction is essential because it implies that individuals can compensate for the challenges associated with late timing of arrival or poor body condition at arrival on the breeding grounds through adjustment of their life history investment decisions, in an attempt to maximize fitness. Using an 11‐year data set in arctic‐nesting common eiders (Somateria mollissima), quantification of fattening rates using plasma triglycerides (an energetic metabolite), and a path analysis approach, we test this prediction of this optimization model; controlling for arrival date and body condition, females that fatten more quickly will adjust the optimal combination of lay date and clutch size, in favour of a larger clutch size. As predicted, females fattening at higher rates initiated clutches earlier and produced larger clutch sizes, indicating that fattening rate is an important factor in addition to arrival date and body condition in predicting individual variation in reproductive investment. However, there was no direct effect of fattening rate on clutch size (i.e., birds laying on the same date had similar clutch sizes, independent of their fattening rate). Instead, fattening rate indirectly affected clutch size via earlier lay dates, thus not supporting the original predictions of the optimization model. Our results demonstrate that variation in the rate of condition gain allows individuals to shift flexibly along the seasonal decline in clutch size to presumably optimize the combination of laying date and clutch size. A plain language summary is available for this article.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".