Space-use strategies of female polar bears in a dynamic sea ice habitat
Bibliographic record
Abstract
In environments with high spatiotemporal variability in resources, animals may exhibit nomadic movements for resource tracking as opposed to long-term area fidelity. Polar bears (Ursus maritimus) inhabit the dynamic sea ice, preying on seals, and demonstrate considerable intraspecific variation in space use. We studied patterns of fidelity and annual range size for 74 adult female polar bears captured in the Norwegian Arctic that were tracked for up to 5 years using satellite telemetry data. We used the autocorrelation structure of movements and distance between observations at a 1-year interval as measures of fidelity. The female polar bears had a circannual migration pattern. Annual range size varied with reproductive state and geographic location of the range. Females entering maternity dens had smaller annual ranges than females not entering dens. Nearshore females had smaller annual ranges than pelagic females, demonstrating different space-use strategies. Repeatability of movement patterns indicated strategy specialization. We suggest that the different space-use strategies result from variation in habitat and prey selection and in sea-ice dynamics. Factors affecting population and predatorprey dynamics may interact differently with the different space-use strategies and yield strategy-dependent outcomes, therefore a knowledge of movement strategies may be important for understanding polar bear population dynamics.
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 | 0.000 | 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".