Feeding habits of Baffin Bay polar bears Ursus maritimus: insight from stable isotopes and total mercury in hair
Bibliographic record
Abstract
Loss of sea ice brought on by climate change affects polar bear Ursus maritimus access to prey. Here we investigated variation in feeding habits of the Baffin Bay (BB) polar bear subpopulation in relation to sea ice, habitat use, season, and demography using hair carbon (δ13C), nitrogen (δ15N), and sulfur (δ34S) stable isotope values and total mercury (THg) concentrations as ecological tracers. We analyzed hair samples from BB polar bears (n = 131) of all age and sex classes live-captured in West Greenland during the spring in 2009-2013. BB polar bears occupied a narrow isotopic space, suggesting limited variation in carbon sources and trophic position within the subpopulation. THg concentrations (median ± SE: 5.1 ± 0.2, range: 0.3-12.5 µg g-1 dry weight, DW) were related to age class, and nearly half exceeded the suggested threshold for neurological effects in polar bears at 5.4 µg g-1 DW. Although distinct coastal and offshore space-use strategies have been reported for BB polar bears, our results suggest that both strategies lead to similar carbon sources and trophic positions. We found seasonal variation in δ13C and δ34S across both space-use strategies, with δ34S suggesting that all BB polar bears may prey on a higher proportion of benthic-feeding bearded seals Erignathus barbatus in late summer relative to spring. Despite wide fluctuations in inter-annual sea ice conditions and differences in space-use strategies among individuals, stable isotope values and THg concentrations suggested limited variation in feeding habits among BB polar bears. The variation of habitat tracers (δ13C and δ34S) was related to season, whereas trophic tracer (δ15N and THg) variation was driven by demographic group. The specialized BB polar bear diet suggests limited feeding plasticity under continued climate warming.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".