Contrasting Temporal Patterns of Mercury, Niche Dynamics, and Body Fat Indices of Polar Bears and Ringed Seals in a Melting Icescape
Bibliographic record
Abstract
Polar bears ( Ursus maritimus ) and ringed seals ( Pusa hispida ) have a strong predator–prey relationship and are facing climate-associated Arctic habitat loss and harmful dietary exposure to total mercury (THg) and other pollutants. However, little is known about whether both species inhabiting the same area exhibit similar temporal patterns in Hg concentration, niche dynamics, and body fat indices. We used THg, δ 13 C, and δ 15 N values of western Hudson Bay polar bear hair (2004–2016) and ringed seal muscle samples (2003–2015) to investigate temporal trends of these variables and multidimensional niche metrics, as well as body fat indices for both species. We found a decline in THg concentration (by 3.8% per year) and δ 13 C (by 1.5‰) in ringed seals suggesting a change in feeding habits and carbon source use over time, whereas no significant changes occurred in polar bears. In contrast, the polar bear 3-dimensional niche size decreased by nearly half with no change in ringed seal niche size. The δ 13 C spacing between both species increased by approximately 1.5× suggesting different responses to annual changes in sympagic-pelagic carbon source production. Ringed seal body fat index was higher in years of earlier sea ice breakup with no change occurring in polar bears. These findings indicate that both species are responding differently to a changing environment suggesting a possible weakening of their predator–prey relationship in western Hudson Bay.
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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 | 0.001 | 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) | 0.000 | 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".