Correlates of seasonal change in the body condition of an Arctic top predator
Bibliographic record
Abstract
Climate-driven sea ice loss has led to changes in the timing of key biological events in the Arctic, however, the consequences and rate of these changes remain largely unknown. Polar bears (Ursus maritimus) undergo seasonal changes in energy stores in relation to foraging opportunities and habitat conditions. Declining sea ice has been linked to reduced body condition in some subpopulations, however, the specific timing and duration of the feeding period when bears acquire most of their energy stores and its relationship to the timing of ice break-up is poorly understood. We used community-based sampling to investigate seasonality in body condition (energy stores) of polar bears in Nunavut, Canada, and examined the influence of sea ice variables. We used adipose tissue lipid content as an index of body condition for 1,206 polar bears harvested from 2010-2017 across five subpopulations with varying seasonal ice conditions: Baffin Bay (October-August), Davis Strait and Foxe Basin (year-round), Gulf of Boothia and Lancaster Sound (August-May). Similar seasonal patterns were found in body condition across subpopulations with bears at their nadir of condition in the spring, followed by fat accumulation past break-up date and subsequent peak body condition in autumn, indicating that bears are actively foraging in late spring and early summer. Late season feeding implies that even minor advances in the timing of break-up may have detrimental effects on foraging opportunities, body condition, and subsequent reproduction and survival. The magnitude of seasonal changes in body condition varied across the study area, presumably driven by local environmental conditions. Our results demonstrate how community-based monitoring of polar bears can reveal population-level responses to climate warming in advance of detectable demographic change. Our data on the seasonal timing of polar bear foraging and energy storage should inform predictive models of the effects of climate-mediated sea ice loss.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".