Extirpation Risk of an Isolated Black Bear Population Under Different Management Scenarios
Bibliographic record
Abstract
ABSTRACT Habitat loss and anthropogenic mortality are recognized as threats to populations of large carnivores worldwide, yet their relative importance to extinction risk has rarely been quantified. We used population viability analysis (PVA) to estimate extinction probability of an isolated population of black bears ( Ursus americanus ) on the Bruce Peninsula, Ontario, Canada under different management scenarios. We used random‐effects analysis of variance to estimate components of variance in extinction risk explained by 4 management actions: 1) preventing habitat destruction, 2) reducing or eliminating incidental non‐natural mortality, 3) reducing or eliminating harvest, and 4) reducing the fraction of reproducing females in the harvest. Habitat area reductions had the greatest effect on risk despite uncertainty in bear density. Incidental non‐natural mortality had a greater effect than the rate or age and sex distribution of harvest. Quantifying the variation in outputs of PVA models associated with different management options is an improvement over qualitative comparisons of relative risk and enhances the applicability of PVA to management. This study highlights the importance of protecting habitats on adjacent private lands when reserves are too small to support populations of bears, and of protecting reproducing females from non‐natural mortality—results that could aid managers of other large carnivores in focusing management efforts to ensure persistence of populations.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".