Contaminant Biomagnification in Polar Bears: Interindividual Differences, Dietary Intake Rate, and the Gut Microbiome
Bibliographic record
Abstract
Some persistent hydrophobic pollutants biomagnify, i.e., achieve higher contaminant levels in a predator than in its prey ( C predator / C prey > 1). This ratio is called the biomagnification factor (BMF) and is traditionally determined using tissues from carcasses or biopsies. Using a noninvasive method that relies on equilibrium sampling in silicone-film-coated vessels and chemical analysis of paired diet and feces, we determined on three occasions the thermodynamic biomagnification limit (BMF lim ) and feces-based biomagnification factor (BMF F ) for three zoo-housed polar bears who experience seasonal periods of hyperphagia and hypophagia. All bears had high biomagnification capabilities (BMF lim was up to 200) owing to very efficient lipid assimilation (up to 99.5%). The bears differed up to a factor of 3 in their BMF lim . BMF lim and BMF F of a bear increased by up to a factor of 4 during the hypophagic period, when the ingestion rate was greatly reduced. Much of that variability can be explained by differences in the lipid assimilation efficiency, even though this efficiency ranged only from 98.1 to 99.5%. A high BMF lim was associated with a high abundance of Bacteroidales and Lachnospirales in the gut microbiome. Biomagnification varies to a surprisingly large extent between individuals and within the same individual over time. Future work should investigate whether this can be attributed to the influence of the gut microbiome on lipid assimilation by studying more individual bears at different key physiological stages.
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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 | 0.001 | 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".