Predators and scavengers: Polar bears as marine carrion providers
Bibliographic record
Abstract
Scavenging is a foraging strategy widely used across the animal kingdom and apex predators provide a large amount of energy in a food web by provisioning carrion. In the harsh environmental conditions of the Arctic, apex predators such as polar bears Ursus maritimus can provide scavenging opportunities for many species. Carrion can act as a buffer when food resources are low, and some terrestrial species use the marine environment for cross‐ecosystem resource subsidies. We present an overview of scavenging as a foraging strategy in the Arctic marine environment and examine the contribution of prey provided by polar bears to the Arctic scavenging assemblage. As obligate predators of seals, polar bears contribute a substantial amount of carrion to the marine ecosystem, particularly to the sea ice surface where it is accessible for seasonal scavenging opportunities. We estimated that an average polar bear kills approximately 1001 kg of marine mammal biomass annually and given preferential feeding of blubber and abandonment of carcasses, we estimate that 30% of the biomass is left as available carrion. Consequently, polar bears provision approximately 7.6 × 10 6 kg year –1 of carrion biomass for scavengers across their range, equivalent to 3.93 × 10 7 MJ of energy. Eleven vertebrate species are known to scavenge polar bear kills, and an additional eight are possible scavengers. While foraging associations with polar bear kills for some species are better understood, others are scarce or undocumented. We provide an overview of what is known about the role of polar bears as carrion providers, the network of scavenging species on the sea ice, and the possible consequences of trophic downgrading in this ecosystem and recipient ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".