Foraging Patterns of Polar Bears (Ursus Maritimus) in a Rapidly Changing Arctic: Insights from Harvest-Based Sampling in Nunavut, Canada
Bibliographic record
Abstract
Arctic species are adapted to the seasonal changes in habitat conditions and resource availability; however, anthropogenic climate warming and associated sea ice loss are having widespread ecological consequences. Polar bears (Ursus maritimus) are top predators across their circumpolar range, where they rely on predictable sea ice patterns for life history characteristics. Thus, polar bears may be sensitive indicators of environmental change and their dietary patterns may reflect prey availability. Continued changes in environmental conditions are predicted to reduce foraging opportunities, however the mechanistic relationship between bear demography and habitat change are poorly understood. The objective of this dissertation was to identify patterns of polar bear diet composition and foraging success and examine how polar bears are responding to shifting environmental conditions in the Canadian Arctic. \nTo examine the foraging ecology of polar bears, I used adipose tissue samples from harvested and remote biopsied bears across Nunavut, Canada from 2010-2018 with additional samples from 1999-2003 for the Foxe Basin subpopulation. I used quantitative fatty acid signature analysis to estimate diet composition and the relative lipid content of adipose tissue was used as an index of body condition. I found that seasonal fluctuations in the body condition of polar bears was correlated with seasonal changes in sea ice conditions. Polar bear diet composition varied spatially and temporally based on local prey availability. In some cases, the dietary proportion, and frequency of prey occurrence in diets was influenced by sea ice conditions that promote prey susceptibility to predation or an increase in the availability of supplemental food (i.e., marine mammal carcasses). Age- and sex-specific variation in diet was associated with the broader dietary niche of adult male bears. This dissertation provides a more comprehensive understanding of the mechanisms affecting the responses and resilience of polar bears to climate-driven environmental change. Adipose tissue samples collected during subsistence harvests of polar bears have provided unprecedented insight into the foraging ecology of polar bears. Continued monitoring of polar bear body condition and diet will help inform effective management and conservation action.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".