Body Condition, Migration, and Timing of Reproduction in Snow Geese: A Test of the Condition‐Dependent Model of Optimal Clutch Size
Bibliographic record
Abstract
The seasonal decline of avian clutch size may result from the conflict between the advantage of early breeding (greater offspring value) and the advantage of a delay in lay date (improved body condition and hence clutch size). We tested predictions of a condition-dependent individual optimization model based on this trade-off (Rowe et al. 1994) in a long-distance migrant, the greater snow goose (Chen caerulescens atlantica), using data on condition, migration, and reproductive decisions of individuals. We closely tracked radio-marked females at their main spring staging area and on their breeding grounds. Our results were consistent with predictions of the model. Early-arriving females had a longer prelaying period and initiated their nests earlier than late arrivals. After controlling statistically for arrival date, we determined that females with high premigration condition had an earlier lay date than those in low condition. After controlling for the seasonal decline (i.e., lay date), we observed that clutch size was not related to premigration condition. Moreover, we took advantage of an unplanned manipulation of the prebreeding condition that occurred during our long-term study. We found that a reduction in condition caused a delay in lay date. However, after controlling for the seasonal decline, it did not affect clutch size. Our study indicates that geese simultaneously adjust their lay date and clutch size according to their premigration condition and migratory behavior as predicted by the condition-dependent optimization model.
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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 | 0.000 |
| 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".