An interspecific foraging association with polar bears increases foraging opportunities for avian predators in a declining Arctic seabird colony
Bibliographic record
Abstract
Abstract Interspecific foraging associations (IFAs) are biological interactions where two or more species forage in association with each other. Climate‐induced reductions in Arctic sea ice have increased polar bear (Ursus maritimus) foraging in seabird colonies, which creates foraging opportunities for avian predators. We used drone video of bears foraging within a common eider (Somateria mollissima) colony on East Bay Island (Nunavut, Canada) in 2017 to investigate herring gull (Larus argentatus) foraging in association with bears. We recorded nest visitation by gulls followingn = 193 eider flushing events from nests during incubation. The probability of gulls visiting eider nests increased with higher number of gulls present (β = 0.14 ± 0.03 [SE],p < .001) and for nests previously visited by a bear (β = 1.14 ± 0.49 [SE],p < .02). In our model examining the probability of gulls consuming eggs from nests, we failed to detect statistically significant effects for the number of gulls present (β = 0.09 ± 0.05 [SE],p < .07) or for nests previously visited by a bear (β = −0.92 ± 0.71 [SE],p < .19). Gulls preferred to visit nests behind bears (χ2 = 18, df = 1,p < .0001), indicating gulls are risk averse in the presence of polar bears. Our study provides novel insights on an Arctic IFA, and we present evidence that gulls capitalize on nests made available due to disturbance associated with foraging bears, as eiders disturbed off their nest allow gulls easier access to eggs. We suggest the IFA between gulls and polar bears is parasitic, as gulls are consuming terrestrial resources which would have eventually been consumed by bears. This finding has implications for estimating the energetic contribution of bird eggs to polar bear summer diets in that the total number of available clutches to consume may be reduced due to avian predators.
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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 | 0.001 | 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) | 0.002 | 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".