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;17:1168–1178. Published 2021. This article is a U.S. Government work and is in the public domain in the USA.

中文翻译：

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野火风险评估中的因果贝叶斯网络：生态风险评估和管理的机会

在过去 100 年中，野火风险和损失有所增加，这与人口膨胀、土地使用和管理实践以及全球气候变化有关。尽管在模拟野火的可能性和严重程度方面做出了广泛的努力，但研究野火与对生态受体和关键栖息地的影响之间的因果联系的努力却很少。贝叶斯网络是用于绘制和评估复杂系统中的因果知识和不确定性的概率工具，这些工具仅有限地应用于野火的生态风险和影响的定量评估。在这里，我们探索使用贝叶斯网络通过因果表示和情景检查的水平来评估野火对生态系统的影响的机会。最终，综合环境评估管理2021；17：1168–1178。2021 年出版。本文是美国政府的作品，在美国属于公共领域。