Divergent population trends following the cessation of legal grizzly bear hunting in southwestern British Columbia, Canada
Bibliographic record
Abstract
© 2019 Elsevier Ltd We conducted DNA capture-recapture monitoring of grizzly bears (Ursus arctos) from 5 to 17 years after hunting was stopped in two adjacent but genetically distinct populations in southwestern British Columbia, Canada. We used spatial capture-recapture and non-spatial Pradel robust design modelling to estimate population density, trends, and the demographic components of population change for each population. The larger population had 21.5 bears/1000 km 2 and was growing (λ Pradel = 1.02 ± 0.02 SE; λ secr = 1.01 ± 4.6 × 10 −5 SE) following the cessation of hunting. The adjacent smaller population had 6.3 bears/1000 km 2 and was likely declining (λ Pradel = 0.95 ± 0.03 SE; λ secr = 0.98 ± 0.02 SE). Estimates of apparent survival and apparent recruitment indicated that lower recruitment was the dominant factor limiting population growth in the smaller population. Factors limiting reproductive rates and population density could include poor habitat quality, particularly the abundance of high-energy foods, genetic Allee effects due to a long period of population isolation, or demographic effects affecting infanticide rates. The cessation of hunting was insufficient to promote population recovery for the low density, isolated population. Our research highlights the importance of considering mortality thresholds in addition to small population effects and habitat quality when recovering large carnivore 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".