Emergent conservation outcomes of shared risk perception in human-wildlife systems |
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Authors: | Neil H. Carter Andres Baeza Nicholas R. Magliocca |
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Affiliation: | 1. School for Environment and Sustainability, University of Michigan, 440 Church Street, Ann Arbor, MI, 48109 U.S.A.;2. Center for Global Discovery and Conservation Science, Arizona State University, 1001 South McAllister Avenue, Tempe, AZ, 85287-8001 U.S.A.;3. Department of Geography, University of Alabama, Farrah Hall 331A, Box 870322, Tuscaloosa, AL, 35487-0322 U.S.A. |
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Abstract: | Human perception of risks related to economic damages caused by nearby wildlife can be transmitted through social networks. Understanding how sharing risk information within a human community alters the spatial dynamics of human-wildlife interactions has important implications for the design and implementation of effective conservation actions. We developed an agent-based model that simulates farmer livelihood decisions and activities in an agricultural landscape shared with a population of a generic wildlife species (wildlife-human interactions in shared landscapes [WHISL]). In the model, based on risk perception and economic information, farmers decide how much labor to allocate to farming and whether and where to exclude wildlife from their farms (e.g., through fencing, trenches, or vegetation thinning). In scenarios where the risk perception of farmers was strongly influenced by other farmers, exclusion of wildlife was widespread, resulting in decreased quality of wildlife habitat and frequency of wildlife damages across the landscape. When economic losses from encounters with wildlife were high, perception of risk increased and led to highly synchronous behaviors by farmers in space and time. Interactions between wildlife and farmers sometimes led to a spillover effect of wildlife damage displaced from socially and spatially connected communities to less connected neighboring farms. The WHISL model is a useful conservation-planning tool because it provides a test bed for theories and predictions about human-wildlife dynamics across a range of different agricultural landscapes. |
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Keywords: | agent-based models coexistence farmer decision making fencing social networks cercado coexistencia modelos basados en agentes redes sociales toma de decisiones de agricultores 基于主体的模型 共存 农场主的决策 建围栏 社会网络 |
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