Fecal DNA metabarcoding shows credible short-term prey detections and explains variation in the gut microbiome of two polar bear subpopulations
Bibliographic record
Abstract
This study developed and evaluated DNA metabarcoding to identify the presence of pinniped and cetacean prey DNA in fecal samples of East Greenland (EG) and Southern Beaufort Sea (SB) polar bearsUrsus maritimussampled in the spring of 2015-2019. Prey DNA was detected in half (49/92) of all samples, and when detected, ringed sealPusa hispidawas the predominant prey species, identified in 100% (22/22) of EG and 81% (22/27) of SB polar bear samples with prey DNA detected. Bearded sealErignathus barbatusDNA was found in 19% (5/27) of SB polar bear samples for which prey DNA was detected. Prey DNA detection frequencies and relative abundances were compared to estimates from quantitative fatty acid signature analysis (QFASA) for a subset of SB polar bears. Ringed seal and bearded seal were the main prey identified by both methods, but QFASA also identified 2 cetacean prey species not found by prey DNA. Differences in DNA metabarcoding vs. QFASA results were likely related to the different dietary timescales captured by each approach, i.e. short-term vs. long-term diet, respectively. Prey DNA detection, sex/age class, and subpopulation significantly explained variation in polar bear gut bacterial composition. Polar bear samples with prey DNA detected were associated with higher abundances of the bacterial classes Clostridia and Bacilli and lower abundances of Negativicutes. Fecal DNA metabarcoding is thus useful for identifying recent prey of polar bears, complementing quantitative and likely longer-term QFASA estimates, and may help understand variation in the polar bear gut microbiome.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".