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Causal Bayesian networks in assessments of wildfire risks: Opportunities for ecological risk assessment and management

机译：因果贝叶斯网络评估野火风险：生态风险评估和管理的机会

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Wildfire risks and losses have increased over the last 100 years, associated with population expansion, land use and management practices, and global climate change. While there have been extensive efforts at modeling the probability and severity of wildfires, there have been fewer efforts to examine causal linkages from wildfires to impacts on ecological receptors and critical habitats. Bayesian networks are probabilistic tools for graphing and evaluating causal knowledge and uncertainties in complex systems that have seen only limited application to the quantitative assessment of ecological risks and impacts of wildfires. Here, we explore opportunities for using Bayesian networks for assessing wildfire impacts to ecological systems through levels of causal representation and scenario examination. Ultimately, Bayesian networks may facilitate understanding the factors contributing to ecological impacts, and the prediction and assessment of wildfire risks to ecosystems. Integr Environ Assess Manag 2021;00:1-11. Published 2021. This article is a U.S. Government work and is in the public domain in the USA.
机译：野火风险和损失在过去100年中增加，与人口扩张，土地利用和管理措施以及全球气候变化相关。虽然在建模野火的概率和严重程度方面进行了广泛的努力，但努力研究野火的因果关系，以影响生态受体和关键栖息地。贝叶斯网络是用于在复杂系统中进行绘图和评估因果知识和不确定性的概率性工具，这些工具只有有限地应用于对生态风险和野火的影响的定量评估。在这里，我们探讨使用贝叶斯网络评估野火对生态系统的影响的机会，通过因果代表和情景检查的水平。最终，贝叶斯网络可能有助于了解有助于生态影响的因素，以及对生态系统的野火风险的预测和评估。整合环境评估管理管理2021; 00：1-11。发布2021年。本文是美国政府工作，并在美国的公共领域。

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