Interrelated ecological impacts of climate change on an apex predator
Bibliographic record
Abstract
Climate change has broad ecological implications for species that rely on sensitive habitats. For some top predators, loss of habitat is expected to lead to cascading behavioral, nutritional, and reproductive changes that ultimately accelerate population declines. In the case of the polar bear (Ursus maritimus), declining Arctic sea ice reduces access to prey and lengthens seasonal fasting periods. We used a novel combination of physical capture, biopsy darting, and visual aerial observation data to project reproductive performance for polar bears by linking sea ice loss to changes in habitat use, body condition (i.e., fatness), and cub production. Satellite telemetry data from 43 (1991-1997) and 38 (2009-2015) adult female polar bears in the Baffin Bay subpopulation showed that bears now spend an additional 30 d on land (90 d in total) in the 2000s compared to the 1990s, a change closely correlated with changes in spring sea ice breakup and fall sea ice formation. Body condition declined for all sex, age, and reproductive classes and was positively correlated with sea ice availability in the current and previous year. Furthermore, cub litter size was positively correlated with maternal condition and spring breakup date (i.e., later breakup leading to larger litters), and negatively correlated with the duration of the ice-free period (i.e., longer ice-free periods leading to smaller litters). Based on these relationships, we projected reproductive performance three polar bear generations into the future (approximately 35 yr). Results indicate that two-cub litters, previously the norm, could largely disappear from Baffin Bay as sea ice loss continues. Our findings demonstrate how concurrent analysis of multiple data types collected over long periods from polar bears can provide a mechanistic understanding of the ecological implications of climate change. This information is needed for long-term conservation planning, which includes quantitative harvest risk assessments that incorporate estimated or assumed trends in future environmental carrying capacity.
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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 | 0.001 | 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) | 0.002 | 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".