Assessing polar bear (Ursus maritimus) in a changing Arctic using non-invasive DNA metabarcoding
Bibliographic record
Abstract
Dietary studies of apex predators enhance our understanding of species’ life histories, predator-prey interactions and food webs. Studies of diet over time can reveal changes in these life history and ecology that result from climate change and landscape disturbances. Polar bears (Ursus maritimus) reside at the top of Arctic food webs. Because polar bears use both sea ice and land throughout their annual cycle, they integrate trophic information from both marine and terrestrial ecosystems across their vast territories, including availability and diversity of prey species. Polar bears act as sentinels of environmental change as they are highly adapted to using sea ice as a platform for foraging and are therefore vulnerable to current and projected changes in Arctic sea ice conditions. Given that these changes in sea ice conditions are projected to alter the composition and accessibility of polar bear prey species, and the pressing need for more non-invasive wildlife monitoring strategies, this study investigates what dietary information could be retrieved from 743 polar bear fecal samples from across the Canadian Arctic using novel multi-marker DNA metabarcoding to identify Arctic birds, fish, and mammals at family- and species-level resolution. I designed two novel blocking primers specific to polar bear DNA to maximize detection of prey taxa within fecal samples. Over 8 million metabarcoding sequences were retrieved, identifying 31 prey species, belonging to 19 families, representing 14 orders. As expected, polar bears consume Arctic seal species (Phocidae), present in >70% of successfully sequenced fecal samples – more common than any other prey group. The dietary signature of taxa such as seals, small toothed whales, various seabirds, and caribou confirm previous observations of components of polar bear diet, however, the frequency of detection of several terrestrial taxa and seabird species are consumed may represent novel insights reflecting predator-scavenger relationships and shifts in the diet of the Arctic’s top predator due to climate change. My study is the first to use multi-marker, fecal-based DNA metabarcoding to provide a comprehensive assessment of the relative consumption of fish, bird, and mammal prey taxa by polar bears across the Canadian Arctic. This method offers a cost-effective, non-invasive approach for assessing regional trophic interactions and prey diversity, and reliably monitoring the diets of wide-ranging predators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".