Sustainability of the grizzly bear hunt in British Columbia, Canada
Bibliographic record
Abstract
ABSTRACT The sustainability of the grizzly bear ( Ursus arctos ) hunt in British Columbia, Canada has been questioned and is a high profile issue, particularly in the media. To investigate the hypothesis that the hunt is unsustainable, we examined the sustainable human‐caused mortality rate for grizzly bears using recent data on vital rates and population projection models and compare these to rates used by the management agency; examined the age and sex ratio of the kill and how the sex ratio changes with age for the entire province, each population unit, and cohorts born each year between 1977 and 1990; summarized population density estimates in hunted population units in British Columbia and compared these to unhunted areas in adjacent jurisdictions; and reviewed case studies of population units that have been highlighted as most likely to have had unsustainable kill levels. Because the natural mortality rate of adult grizzly bears is 1–2% and lower than was estimated when sustainable human‐caused mortality targets were developed, estimated sustainable kill rates are 4–10%, and generally higher than the 4–6% used in British Columbia. We estimated that males have been 3–4 times more vulnerable to being killed by hunters than females, yet males dominate the kill at all ages and the proportion of males increased with age, which is opposite of what is predicted for a heavily hunted population. The average age of female and male grizzly bears killed by hunters increased from 7.1 years and 7.4 years, respectively, in the 1980s to 7.7 years and 8.7 years, respectively, in the 2000s. An average of 107.4 females and 204.9 males per cohort born between 1977 and 1990 were eventually killed by people and recorded, suggesting that many more females than males died for unknown reasons. There have been more population inventories of grizzly bears in British Columbia than in any other jurisdiction. The average density estimate of 21 inventories in hunted areas without salmon ( Oncorhynchus spp.) in British Columbia (31.2 bears/1,000 km 2 ) was as high as or higher than nearby unhunted areas. The case studies had the highest kill densities or among the highest kill rates in the province and hunting targets were commonly exceeded and seasons closed or the hunter kill target reduced. Although population inventories in these areas found moderate or even high densities of bears, some are now in decline. Hunter kill data from declining populations had a high proportion of males and these were older, demonstrating that these indices of kill rates are sometimes unreliable. Although more population density, trend, and vital rate measurements would be beneficial, the hypothesis that the grizzly bear hunt has been unsustainable was not supported by our investigation of available information. © 2016 The Wildlife Society.
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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 | 0.001 | 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) | 0.000 | 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 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".