Distinct fecal microbiome between wild and habitat-housed captive polar bears (Ursus maritimus): Impacts of captivity and dietary shifts
Bibliographic record
Abstract
Understanding the gut microbiome of polar bears can shed light on the effects of climate change-induced prolonged ice-free seasons on their health and nutritional status as a sentinel species. The fecal microbiome of habitat-housed captive polar bears who had consumed a high protein diet long-term was compared with that of the wild population. Individual differences, season, year and dietary inclusion of a brown seaweed (Fucus spiralis; part of the natural diet of wild polar bears), as a representation for nutritional change, were investigated for their effects on the fecal microbiome of captive polar bears. Microbial variations among fecal samples from wild and captive polar bears were investigated using 16s rRNA gene based metataxonomic profiling. The captive bears exhibited more diverse fecal microbiota than wild bears (p<0.05). The difference was due to significantly increased Firmicutes, Campilobacterota and Fusobacteriota, decreased Actinobacteriota (p<0.05), and absent Bdellovibrionota and Verrucomicrobiota in the captive bears. Compared with other factors, individual variation was the main driver of differences in fecal microbial composition in the captive bears. Seaweed consumption did not alter microbial diversity or composition, but this did not rule out dietary influences on the hosts. This is the first study, to the best of our knowledge, comparing the fecal microbiota of captive and wild polar bears and it reveals distinct differences between the two groups, which could result from many factors, including available food sources and the ratio of dietary macronutrients. Our findings provide preliminary insights into climate-change induced dietary shifts in polar bears related to climate-associated habitat change.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".