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Fire and tree death: understanding and improving modeling of fire-induced tree mortality
Fire and tree death: understanding and improving modeling of fire-induced tree mortality
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Fire and tree death: understanding and improving modeling of fire-induced tree mortality
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Fire and tree death: understanding and improving modeling of fire-induced tree mortality
Fire and tree death: understanding and improving modeling of fire-induced tree mortality
Journal Article

Fire and tree death: understanding and improving modeling of fire-induced tree mortality

2018
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Overview
Each year wildland fires kill and injure trees on millions of forested hectares globally, affecting plant and animal biodiversity, carbon storage, hydrologic processes, and ecosystem services. The underlying mechanisms of fire-caused tree mortality remain poorly understood, however, limiting the ability to accurately predict mortality and develop robust modeling applications, especially under novel future climates. Virtually all post-fire tree mortality prediction systems are based on the same underlying empirical model described in Ryan and Reinhardt (1988 Can. J. For. Res. 18 1291-7), which was developed from a limited number of species, stretching model assumptions beyond intended limits. We review the current understanding of the mechanisms of fire-induced tree mortality, provide recommended standardized terminology, describe model applications and limitations, and conclude with key knowledge gaps and future directions for research. We suggest a two-pronged approach to future research: (1) continued improvements and evaluations of empirical models to quantify uncertainty and incorporate new regions and species and (2) acceleration of basic, physiological research on the proximate and ultimate causes of fire-induced tree mortality to incorporate processes of tree death into models. Advances in both empirical and process fire-induced tree modeling will allow creation of hybrid models that could advance understanding of how fire injures and kills trees, while improving prediction accuracy of fire-driven feedbacks on ecosystems and landscapes, particularly under novel future conditions.