脆弱性(计算)
野生动物
风险评估
危害
气候变化
地理
基线(sea)
环境资源管理
持续性
环境科学
栖息地
环境保护
生态学
计算机科学
渔业
计算机安全
生物
作者
Nuntikorn Kitratporn,Wataru Takeuchi
标识
DOI:10.1016/j.scitotenv.2022.155174
摘要
As natural resources decrease, competition between humans and large endangered wildlife increases, hindering the sustainability of animal conservation and human development. Despite the multi-dimensional nature of such interactions, proactive assessments that consider both the biosphere and anthroposphere remain limited. In this study, we proposed a human elephant conflict risk assessment framework and analyzed the spatial distribution of risk at the baseline (2000-2019) and in the near future (2025-2044) for Thailand, so that it may address the multifaceted characteristics and impending effects of climate change. Future scenarios were based on the combination of RCP45/SSP2 or RCP85/SSP5 and spatial policy, with or without elephant buffer zones. The composite risk index, comprised of hazard, exposure, and vulnerability, was constructed using the geometric mean, and validation was performed with the area under the curve (AUC). Our results projected a shift with increasing future risk toward higher latitudes and altitudes. Increasing future risk (average +1.7% to +7.4%) in the four forest complexes (FCs) in northwestern regions was a result of higher hazard and vulnerability from more favorable habitat conditions and increasing drought probability, respectively. Reduction in future risk (average -3.1% to -57.9%) in other FCs in lower regions was mainly due to decreasing hazard because of decreasing habitat suitability. Our results also highlight geographically explicit strategies to support long-term planning of conservation resources. Areas with increasing future risk are currently facing low conflict; hence it is recommended that future strategies should enhance adaptive capacity and coexistence awareness. Conversely, areas with lowering future risk from a decrease in habitat quality are recommended to identify buffer strategies around protected areas to support existing large elephant populations.
科研通智能强力驱动
Strongly Powered by AbleSci AI