Human-Bear Conflict in North America (1880-2020): A Comprehensive Analysis of Patterns, Outcomes and Interactions
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».