Predators, alternative prey and climate influence annual breeding success of a long‐lived sea duck
Bibliographic record
Abstract
1. Perturbations to ecosystems have the potential to directly and indirectly affect species interactions, with subsequent impacts on population dynamics and the vital rates that regulate them. 2. The few long-term studies of common eider breeding ecology indicate that reproductive success is low in most years, interrupted by occasional boom years. However, no study has explicitly examined the drivers of long-term variation in reproductive success. 3. Here, we use encounter history data collected across 41 years to examine the effects of arctic foxes (a terrestrial nest predator), local abundance and spatial distribution of lesser snow geese (an alternative prey source), and spring climate on common eider nest success. 4. Eider nest success declined over the course of the study, but was also highly variable across years. Our results supported the hypothesis that the long-term decline in eider nest success was caused by apparent competition with lesser snow geese, mediated by shared predators. This effect persisted even following a large-scale exodus of nesting geese from the eider colony. Nest success was also lowest in years of low arctic fox index, presumably driven by prey switching in years of low small mammal availability. However, increased snow goose abundance appeared to buffer this effect through prey swamping. The effect of spring climate depended on the stage of the breeding season; cold and wet and warm and dry conditions in early spring were correlated with decreased nest success, whereas warm and wet conditions in late spring increased eider nest success. 5. These results underscore the significance of both trophic interactions and climate in regulating highly variable vital rates, which likely have important consequences for population dynamics and the conservation of long-lived iteroparous species.
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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 | 0.001 | 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".