Use of patch selection models as a decision support tool to evaluate mitigation strategies of human-wildlife conflict

  • Baruch-Mordo S
  • Webb C
  • Breck S
 et al. 
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Human-dominated landscapes offer spatially concentrated and reliable food resources that attract wildlife and lead to human-wildlife conflicts. Conflict management is often directed at humans (e.g., education) to reduce attractants, or foraging benefits to wildlife, or at wildlife (e.g., hazing) to increase foraging costs; but strategies can be expensive and ineffective. Because a key driver of conflict is the pursuit of food by wildlife, we used patch selection models, a dynamic, state-dependent modeling approach based on foraging theory, to assess how benefit reduction and cost increase resulting from conflict mitigation affect wildlife foraging decisions. We applied the patch selection models to a system in which American black bears (Ursus americanus) forage in urban and urban-interface patches and conflicts are common. We used survival as a fitness currency and body fat reserves as a state variable. We incrementally reduced availability of anthropogenic foods (benefit reduction) and increased energetic costs of movement in response to aversive conditioning treatments (cost increase) to search for thresholds resulting in avoidance of human-dominated patches. Benefit reduction ≥55% in urban patches and ≥70% in urban-interface patches resulted in avoidance by bears of almost all states. Cost increases achieving similar results exceeded 1100% and 400% in urban and urban-interface patches respectively, and are likely unrealistic to implement. Given modeling results and that control strategies targeting wildlife are unpopular with constituencies, we suggest allocating management resources to strategies that reduce availability of anthropogenic food. © 2013 Elsevier Ltd.

Author-supplied keywords

  • American black bear
  • Aversive conditioning treatments
  • Foraging theory
  • Urban ecology
  • Ursus americanus

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  • S. Baruch-Mordo

  • C.T. Webb

  • S.W. Breck

  • K.R. Wilson

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