Skip to Main Content
U.S. Forest Service
Caring for the land and serving people

United States Department of Agriculture

Home > Search > Publication Information

  1. Share via EmailShare on FacebookShare on LinkedInShare on Twitter
    Dislike this pubLike this pub
    Author(s): Lindsay M. Grayson; Robert A. ProgarSharon M. Hood
    Date: 2017
    Source: Forest Ecology and Management. 399: 213-226.
    Publication Series: Scientific Journal (JRNL)
    Station: Pacific Northwest Research Station
    PDF: Download Publication  (3.0 MB)


    Fire is a driving force in the North American landscape and predicting post-fire tree mortality is vital to land management. Post-fire tree mortality can have substantial economic and social impacts, and natural resource managers need reliable predictive methods to anticipate potential mortality following fire events. Current fire mortality models are limited to a few species and regions, notably Pinus ponderosa and Pseudotsuga menziesii in the western United States. The efficacy of existing mortality models to predict fire-induced tree mortality is central to effective forest management. This study validated 54 logistic regression mortality models from seven published articles and two sets of mortality guidelines from two sources. Survival and a suite of fire injury metrics were monitored for 3654 trees representing 14 species that burned in fires between 2002 and 2009 in the Pacific Northwest, USA. Tree species included Abies amabilis, A. concolor, A. grandis, A. lasiocarpa, Calocedrus decurrens, Chamaecyparis lawsoniana, C. nootkatensis, Thuja plicata, Pinus contorta, P. lambertiana, P. monticola, Picea engelmannii, Larix occidentalis, and Tsuga heterophylla. Existing logistic models adequately described post-fire mortality of A. concolor, A. lasiocarpa, C. decurrens, C. lawsoniana, L. occidentalis, P. engelmannii, P. contorta, and P. lambertiana. We also evaluated predictive accuracy of two published mortality guidelines that apply to species in the Pacific Northwest. In addition to validating existing models, we also developed new logistic regression models and simplified mortality guidelines, or thresholds. We created new logistic regression models for species with adequate sample size and which had no existing species-specific model (A. amabilis, A. grandis, P. monticola, and T. heterophylla). Most recommended models contained a crown scorch term and either a cambium injury term or a bark beetle infestation term. New postfire mortality thresholds were developed for A. amabilis, A. concolor, A. grandis, P. contorta, P. lambertiana, P. monticola, P. engelmannii, L. occidentalis, and T. heterophylla. We were not able to validate or develop acceptable logistic mortality models or thresholds for C. nootkatensis or T. plicata. Injury to cambium and crown were both significant predictors in all but one set of new thresholds. The validation of existing models and guidelines allows managers to determine which models will likely perform best and identifies knowledge gaps where no adequate models exist to predict post-fire tree mortality. The new logistic regression models and threshold guidelines provide improved accuracy, with simpler application for fire and forest management.

    Publication Notes

    • You may send email to to request a hard copy of this publication.
    • (Please specify exactly which publication you are requesting and your mailing address.)
    • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.


    Grayson, Lindsay M.; Progar, Robert A.; Hood, Sharon M. 2017. Predicting post-fire tree mortality for 14 conifers in the Pacific Northwest, USA: Model evaluation, development, and thresholds. Forest Ecology and Management. 399: 213-226.


    Google Scholar


    classification errors, logistic regression, modeling, post-fire tree mortality, Scott guidelines

    Related Search

    XML: View XML
Show More
Show Fewer
Jump to Top of Page