Age, Sex and Relocation Distance as Predictors of Return for Relocated Nuisance Black Bears <i>Ursus americanus</i> in Ontario, Canada
Bibliographic record
Abstract
Relocation has been used as a management tool for nuisance black bears Ursus americanus for decades. Using tag recovery data from relocated nuisance bears in Ontario, Canada, we developed predictive models to assist managers in determining the efficacy of black bear relocation based on the sex and age of the animal, and a range of relocation distances. We considered relocation success in terms of nuisance recurrence rates and whether bears returned to within 20 km of the capture site. A minimum of 30% of relocated bears were subsequently involved in another nuisance event and adult females were the segment of the population most likely to re‐offend (48%). Adult bears consistently exhibited higher return rates than juveniles (73 vs 29%), hence we modeled these two groups separately. Based on logistic regression models, the probability of return for animals 1–3 years of age increased with age, females were more likely to return than males, and return rates decreased with increasing relocation distance. For bears ≥4 years of age, these variables were poor predictors of return; the model had difficulty identifying bears that did not return. We used receiver‐operating characteristic curves to estimate relocation distance thresholds for juvenile bears. The results suggest that 2‐ and 3‐year‐old male bears will not return to within 20 km of the capture area if relocated a minimum of 30 km and 64 km, respectively. Although relocation appears to be an effective strategy for the management of juvenile male bears, success rates were low for adults. Our findings can aid resource managers in making more informed decisions as to the potential effectiveness of relocation as a management tool.
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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) | 0.000 | 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".