Sea ice dynamics influence movement patterns of adult female polar bears in southern Hudson Bay
Bibliographic record
Abstract
To access seals (Pinnipedia), polar bears (Ursus maritimus) move large distances over the sea ice in winter. Already documented declines in ice duration for Hudson Bay, Canada, are predicted to continue, likely affecting polar bear movement patterns. Using data from global positioning system (GPS) collars, 2007–2011, we describe movement patterns of adult female polar bears of the Southern Hudson Bay (SH) subpopulation. We tested effects of season and reproductive class on movement rates, distance travelled, displacement distance and direction, and home range size. Except for denning females, reproductive class had no effect on movement rates, which were greatest during freeze-up and least during summer. Across all reproductive classes and seasons, mean hourly movement rate was 0.63 km/hour. Mean annual distance moved by nondenning females was 4,771 km. During freeze-up, bears moved north-easterly from the Ontario coast toward the Belcher Islands and Québec following the forming ice edge. During breakup, bears moved southerly toward the Ontario coast and away from the residual ice that occurs north of the Ontario coast. In autumn, denning females moved southerly and inland to den. Mean annual minimum convex polygon (MCP) home-range size was 153,866 km2, with no effect of reproductive class nor change over time. Home range estimates from kernel density estimators and Brownian bridge movement models (BBMM) varied by reproductive class and were smaller than MCP ones. The BBMM estimates likely yield more realistic patterns of space use by polar bears. Using data from satellite collars, 1997–2003, we compared travel distance and home range size between periods (1997–2003; 2007–2011). We found weak evidence of a difference in distance moved between periods, perhaps due to a period of ice stability in the 2000s. Our results identified patterns of use of extensive areas of Hudson Bay by SH bears in winter, and results provide benchmarks for future studies of movement patterns.
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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 | 0.000 | 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".