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    Author(s): Travis Woolley; David C. Shaw; Lisa M. Ganio; Stephen Fitzgerald
    Date: 2012
    Source: International Journal of Wildland Fire. 21: 1-35. DOI: 10.1071/WF09039.
    Publication Series: Scientific Journal (JRNL)
    Station: Pacific Northwest Research Station
    PDF: Download Publication  (2.39 MB)


    Logistic regression models used to predict tree mortality are critical to post-fire management, planning prescribed bums and understanding disturbance ecology. We review literature concerning post-fire mortality prediction using logistic regression models for coniferous tree species in the western USA. We include synthesis and review of: methods to develop, evaluate and interpret logistic regression models; explanatory variables in logistic regression models; factors influencing scope of inference and model limitations; model validation; and management applications. Logistic regression is currently the most widely used and available technique for predicting post-fire tree mortality. Over 100 logistic regression models have been developed to predict post-fire tree mortality for 19 coniferous species following wild and prescribed fires. The most widely used explanatory variables in post-fire tree mortality logistic regression models have been measurements of crown (e.g. crown scorch) and stem (e.g. bole char) injury. Prediction of post-fire tree mortality improves when crown and stem variables are used collectively. Logistic regression models that predict post-fire tree mortality are the basis of simple field tools and contribute to larger fire-effects models. Future post-fire tree mortality prediction models should include consistent definition of model variables, model validation and direct incorporation of physiological responses that link to process modelling efforts.

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    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.


    Woolley, Travis; Shaw, David C.; Ganio, Lisa M.; Fitzgerald, Stephen. 2012. A review of logistic regression models used to predict post-fire tree mortality of western North American conifers.


    fire behavior, fire injury, modelling, prescribed fire, wildland fire

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