Ecological implications from spatial patterns in human‐caused brown bear mortality
Bibliographic record
Abstract
Humans are important agents of wildlife mortality, and understanding such mortality is paramount for effective population management and conservation. However, the spatial mechanisms behind wildlife mortality are often assumed rather than tested, which can result in unsubstantiated caveats in ecological research (e.g. fear ecology assumptions) and wildlife conservation and/or management (e.g. ignoring ecological traps). We investigated spatial patterns in human‐caused mortality based on 30 years of brown bear Ursus arctos mortality data from a Swedish population. We contrasted mortality data with random locations and global positioning system relocations of live bears, as well as between sex, age and management classes (‘problem’ versus ‘no problem’ bear, before and after changing hunting regulations), and we used resource selection functions to identify potential ecological sinks (i.e. avoided habitat with high mortality risk) and traps (i.e. selected habitat with high mortality risk). We found that human‐caused mortality and mortality risk were positively associated with human presence and access. Bears removed as a management measure were killed in closer proximity to humans than hunter‐killed bears, and supplementary feeding of bears did not alter the spatial structure of human‐caused bear mortality. We identified areas close to human presence as potential sink habitat and agricultural fields (oat fields in particular) as potential ecological traps in our study area. We emphasize that human‐caused mortality in bears and maybe in wildlife generally can show a very local spatial structure, which may have far‐reaching population effects. We encourage researchers and managers to systematically collect and geo‐reference wildlife mortality data, in order to verify general ecological assumptions and to inform wildlife managers about critical habitat types. The latter is especially important for vulnerable or threatened populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.006 | 0.001 |
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 teacher head, 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".