Causal Bayesian networks in assessments of wildfire risks: Opportunities for ecological risk assessment and management. (8th June 2021) - Carriger, John F.; Thompson, Matthew; Barron, Mace G.
Abstract: 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. KEY POINTS: The ladder of causation has broad implications for understanding the role of models in supporting assessment and decision‐making goals. Each of the rungs of the ladder is examined in terms of environmental assessment and decision models. Our case studies focus on managing wildfire risks for ecological endpoints but will be broadly applicable to other environmental issues. Establishing a causal hierarchy for environmental models will benefit future environmental assessment and management endeavors.