Grizzly bear (<i>Ursus arctos</i>) movements and habitat use predict human-caused mortality across temporal scales
Bibliographic record
Abstract
While the location of wildlife mortalities provides some insight on the cause of death, identifying the risk factors associated with mortality events and in which cases these factors result in death requires information on individual behaviour prior to death. With access to a long-term database of grizzly bear ( Ursus arctos L., 1758) GPS locations, we investigated how behaviour differed between individuals that died of anthropogenic causes and those that survived across different temporal scales. We analyzed movement (diurnality and daily displacement) and habitat use (modelled risk and habitat quality) of grizzly bears residing in Alberta, Canada, from 2005 to 2021 to determine whether grizzly bears that died and grizzly bears that survived differed in these behaviours 2–4 years, 1 year, and 1 week prior to death, and whether patterns changed over time. We found that diurnality increased in the last year of life, while displacement increased in the last week of life, with differences becoming greater nearer the day of death. Grizzly bears that died used high-risk and low-quality habitat at all time scales, and these behaviours increased as death approached. Our analysis suggests that grizzly bear mortalities do not occur randomly but happen at times when individuals exhibit high-risk behaviours. This information can be used to make management decisions related to habitat management, road use, and human access.
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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".