Human-Bear Conflict in North America (1880-2020): A Comprehensive Analysis of Patterns, Outcomes and Interactions
Bibliographic record
Abstract
Human-bear (Ursus spp.) conflict (HBC) is an important issue facing wildlife managers across North America. It is essential that we understand the factors associated with HBCs in North America so that wildlife managers can make appropriate, science-based recommendations about how to avoid, and if necessary, survive such incidents. To that end, we present this comprehensive analysis of > 2,100 HBCs in the United States and Canada, ranging from 1880 to the present. This analysis includes the three native North American bear species: black bears (Ursus americanus), grizzly bears (U. arctos), and polar bears (U. maritimus) and assesses the role that twelve key variables played in human-bear conflict. We collected data from various sources, including newspapers, official government reports, and verified personal accounts. In the first chapter, we summarized data, looked for patterns and conducted statistical analysis (AIC weighted linear regression modeling and chi square analysis) to determine significance of variables in relation to human injury during HBC encounters. Our results found that human-bear conflict incidents in North America are rare but are increasing at a steady rate. HBCs involving grizzly bears were far more numerous and more likely to result in an injury, but black and polar bear HBCs were more likely to be fatal. Most incidents were classified as surprise encounters followed by bears being curious. The most common activity people were engaged in when an incident began was hiking or walking, followed by hunting and camping. Single bears were involved more than all other cohorts combined. There was a clear negative correlation between the use of a bear deterrent (firearms and/or bear spray) and the occurrence of human injury. Similarly, as group size increased, odds of human injury steeply decreased. In the second chapter, we present an analysis of human actions and associated bear reactions that occurred during each encounter. Each action-reaction pair was analyzed at four levels, increasing from the least detailed (e.g., "aggressive" or "defensive" actions) to the most detailed (e.g., person used a firearm, or person played dead). These summaries provide insights regarding the outcomes (i.e., how bears responded) of specific actions people have taken towards bears. For both black and grizzly bears, "aggressive" actions by humans resulted in the lowest rates of bear attack responses, while "neutral" human actions produced the highest attack rates. Third level analysis provided a more specific insight into these results, indicating that the success of "aggressive" actions is generally driven by the use of a deterrent, while the high attack rates of "neutral" actions are most often a result of people being taken by surprise with "no time to react".
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".