Does polar bear (Ursus maritimus) foraging on Common eiders (Somateria mollissima) facilitate predation risk from Herring gulls (Larus argentatus)?
Bibliographic record
Abstract
Due to climate-induced rapid decline in sea-ice, polar bears (Ursus maritimus) are being forced onto land sooner. As a consequence, bears are foraging on Arctic nesting seabird eggs, since their earlier arrival ashore coincides with the seabird's incubation period. Increasing predation risk by bears may also facilitate traditional egg predators of seabirds. I study predator-prey interactions between Common eiders (Somateria mollissima) nesting on Mitivik Island, Nunavut, Canada (64°0'0" N 81°59'59" W), and their traditional egg predators, Herring gulls (Larus argentatus) during polar bear foraging. Whether polar bear foraging behaviour facilitates gull predation on Common eider eggs via the eiders' responses to polar-bear risk is currently unknown. Using aerial drone videography, I quantified gull egg-predation success on eider nests during polar bear foraging bouts. I recorded eider behavioural responses following their flush from nests and estimated if these responses influenced the success of gulls predating vacated nests. Preliminary results indicate that 15% of 192 nests vacated by eiders were visited by gulls, suggesting gulls may not be as ubiquitous predators as predicted. However, of the eiders that I observed returning to their nests shortly after flushing, only 10% returned to defend their nest from foraging gulls. Further analysis of results will allow me to determine whether polar bear foraging is facilitating gull predation, thereby causing an increased risk to Common eider reproductive success. Findings can be used in future studies to predict eider persistence as a result of indirect effects of climate change.
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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".