Social and maternal factors affecting duckling survival in eiders <i>Somateria mollissima</i>
Bibliographic record
Abstract
1. With the aid of a novel survivorship model, an 8-year field study of social and maternal factors affecting duckling survival in eiders (Somateria mollissima) revealed that duckling survival probability varies in accordance with maternal brood-rearing strategy. This variability in survival provides compelling evidence of different annual fitness consequences between females that share brood-rearing and those that tend their broods alone. Consequently, as prebreeding survival is often a major source of individual variation in lifetime reproductive success, a female's annual, state-dependent (e.g. condition) choice of a brood-rearing strategy can be a critical fitness decision. 2. Variance in duckling survival among lone tender broods was best explained by a model with significant interannual variability in survival, and survivorship tending to increase with increasing clutch size at hatch. Clutch size was correlated positively with female condition. Hatch date and female body condition together affected duckling survival, but their contributions are confounded. We were unable to identify a relationship between female age or experience and duckling survival. 3. Variance in duckling survival among multifemale brood-rearing coalitions was best explained by a model that included the number of tenders, the number of ducklings and interannual variation in how their ratio affected survivorship. Hatch date did not significantly influence survival. 4. Expected duckling survival is higher in early life for lone tenders when compared with multifemale brood-rearing coalitions. However, as ducklings approach 2-3 weeks of age, two or three females was the optimal number of tenders to maximize daily duckling survival. The survivorship advantage of multifemale brood-rearing coalitions was most evident in years of average survival. 5. The observed frequency distribution of female group sizes corresponds with the distribution of offspring survival probabilities for these groups. Evidence for optimal group sizes in nature is rare, but the most likely candidates may be groups of unrelated animals where entry is controlled by the group members, such as for female eiders. 6. Our study demonstrates that differences in social factors can lead to different predictions of lifetime reproductive success in species with shared parental care of self-feeding young.
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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".