Diet-driven mercury contamination is associated with polar bear gut microbiota
Bibliographic record
Abstract
Abstract The gut microbiota may modulate the disposition and toxicity of environmental contaminants within a host but, conversely, contaminants may also impact gut bacteria. Such contaminant-gut microbial connections, which could lead to alteration of host health, remain poorly known and are rarely studied in free-ranging wildlife. The polar bear (Ursus maritimus) is a long-lived, wide-ranging apex predator that feeds on a variety of high trophic position seal and cetacean species and, as such, is exposed to among the highest levels of biomagnifying contaminants of all Arctic species. Here, we investigate associations between mercury (THg; a key Arctic contaminant), diet, and the diversity and composition of the gut microbiota of polar bears inhabiting the southern Beaufort Sea, while accounting for host sex, age class and body condition. Bacterial diversity was negatively associated with seal consumption and mercury, a pattern seen for both Shannon and Inverse Simpson alpha diversity indices (adjusted R2 = 0.35, F1,18 = 8.00, P = 0.013 and adjusted R2 = 0.26, F1,18 = 6.04, P = 0.027, respectively). No association was found with sex, age class or body condition of polar bears. Bacteria known to either be involved in THg methylation or considered to be highly contaminant resistant, including Lactobacillales, Bacillales and Aeromonadales, were significantly more abundant in individuals that had higher THg concentrations. Conversely, individuals with higher THg concentrations showed a significantly lower abundance of Bacteroidales, a bacterial order that typically plays an important role in supporting host immune function by stimulating intraepithelial lymphocytes within the epithelial barrier. These associations between diet-acquired mercury and microbiota illustrate a potentially overlooked outcome of mercury accumulation in polar bears.
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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 | 0.000 | 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".