Spatio-temporal Drivers of Nest Site Selection of the Northern Common Eider (Somateria mollissima borealis), East Bay Island, Nunavut.
Bibliographic record
Abstract
The Northern common eider (Somateria mollissima borealis) is an Arctic-breeding sea duck that balances trade-offs between thermal protection and predation avoidance in its nest-site selection. While the landscape features driving these preferences have been characterized, it remains unclear whether nest-site selection has changed in response to warming temperatures and increased risk from novel predators during the breeding season. This study examines long-term patterns of nest-site selection amongst five monitored nesting areas at East Bay Island, Nunavut, from 1999 to 2023, with a focus on habitat (climate and landscape) drivers and changes in nest-site preferences. Our results revealed heterogeneity exists among the five nesting areas on EBI, characterized mainly by differences in landscape attributes; and that over time, eiders switched nesting preferences amongst these sites. We furthermore revealed these shifts were driven by hens increasingly selecting for reduced visibility, denser vegetation, and improved wind cover, indicating a shift toward prioritizing protection against predation. These changes most likely coincide with increased polar-bear predation, suggesting an adaptive response to heightened predation pressures. Interestingly, climate variables were unable to describe site heterogeneity or explain preferences in site quality, suggesting that for nesting eiders, climate may not yet be a significant stressor. This study highlights the complexity of nest-site selection in Arctic-breeding eiders in face of multiple stressors, and underscores the importance of continual monitoring and protection critical habitat features that reflect increasing pressures due to climate change. Understanding these dynamics can inform conservation strategies to support the adaptive capacity of eiders in rapidly changing Arctic ecosystems.
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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 | 0.000 |
| 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".