Seasonal habitat selection by adult female polar bears in western Hudson Bay
Bibliographic record
Abstract
Abstract Individual variation in habitat selection has emerged as an important component necessary for understanding population ecology. For threatened species, where habitat loss and alteration affect population trends, understanding habitat use provides insight into mechanisms of population change. Polar bears, Ursus maritimus, in the Western Hudson Bay subpopulation have experienced declines in body condition, survival, and abundance associated with delayed freeze‐up and earlier break‐up of sea ice due to climate change. Although this subpopulation has been intensively studied, sea ice habitat selection remains poorly understood. We developed a habitat selection model using a mixed conditional logistic regression to determine habitat selection across seasons (freeze‐up, early winter, late winter, break‐up) and assess individual variation in habitat selection. We used 8487 locations collected between 2004 and 2010 from 64 GPS satellite linked radio‐collars on adult females to compare habitat selected to habitat available. Selection changed across seasons and varied the most among individuals during the freeze‐up and break‐up seasons. During later winter, there was less variation in habitat selection among individuals and bears showed the least amount of selection in habitat use. Distance to the denning area, a core terrestrial refuge habitat, was the top‐ranked covariate in all seasons suggesting site fidelity plays a role in habitat selection. Some individual variation may have been due to reproductive status, though we could not account for this directly. Recognizing individual differences, especially in a rapidly changing environment, allows managers to identify critical habitats instead of simply average resources, and may lead to more successful efforts to protect habitats.
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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".