Individual optimization of reproduction in a long-lived migratory bird: a test of the condition-dependent model of laying date and clutch size
Bibliographic record
Abstract
1. Optimality theory predicts that both timing of arrival and arrival state on the breeding area will determine reproductive timing and investment in migratory organisms. We tested this idea using a condition-dependent individual optimization model (Ardea68, 1980, 225 and The American Naturalist143, 1994, 698) in common eider ducks through descriptive data, path analyses and experimental manipulation. 2. Our results support the causal pathways drawn from the optimization model indicating that individuals adjust their reproductive decisions as a function of their arrival date and body condition at arrival. 3. Independent of body condition, early-arriving females had a longer pre-laying period, but still initiated their nests earlier, and produced larger clutches than late-arriving birds. Independent of arrival date, females in good condition laid earlier than those in poor condition. Manipulation of pre-laying female body condition confirmed that the relationship between condition and laying date was causal. 4. Female common eiders appear to optimize reproductive decisions in response to both their external (i.e. environmental conditions affecting the egg-value) and internal (i.e. body condition) states. These adjustments seem to minimize the fitness costs of reproduction, in which higher clutch size is not associated with an apparent lower survival or future breeding probability. 5. Our study emphasizes the importance of (i) simultaneously considering the timing of migration, the state of individuals and the seasonal change in egg-value to understand clearly birds’ breeding decisions and (ii) appreciating the potential proximate and ultimate factors explaining why some individuals delay breeding and/or produce small clutches.
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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 | 0.003 |
| 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 | 0.001 |
| 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".