A review of logistic regression models used to predict post-fire tree mortality of western North American conifers
Travis Woolley A B , David C. Shaw A , Lisa M. Ganio A and Stephen Fitzgerald A
+ Author Affiliations
- Author Affiliations
A Oregon State University, 204 Peavy Hall, Corvallis, OR 97331, USA. Email: dave.shaw@oregonstate.edu; lisa.ganio@oregonstate.edu; stephen.fitzgerald@oregonstate.edu
B Corresponding author. Email: travis.woolley@oregonstate.edu
International Journal of Wildland Fire 21(1) 1-35 https://doi.org/10.1071/WF09039
Submitted: 23 April 2009 Accepted: 8 February 2011 Published: 18 November 2011
Abstract
Logistic regression models used to predict tree mortality are critical to post-fire management, planning prescribed burns 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.
Additional keywords: fire behaviour, fire injury, modelling, prescribed fire, wildland fire.
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