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    Author(s): Sharon HoodDuncan Lutes
    Date: 2017
    Source: Fire Ecology. 13(2): 66-84.
    Publication Series: Scientific Journal (JRNL)
    Station: Rocky Mountain Research Station
    PDF: Download Publication  (1.0 MB)


    Accurate prediction of fire-caused tree mortality is critical for making sound land management decisions such as developing burning prescriptions and post-fire management guidelines. To improve efforts to predict post-fire tree mortality, we developed 3-year post-fire mortality models for 12 Western conifer species - white fir (Abies concolor [Gord. & Glend.] Lindl. ex Hildebr.), red fir (Abies magnifica A. Murray bis), subalpine fir (Abies lasiocarpa [Hook.] Nutt.), incense cedar (Calocedrus decurrens [Torr.] Florin), western larch (Larix occidentalis Nutt.), lodgepole pine (Pinus contorta Douglas ex Loudon var. latifolia Engelm. ex S. Watson), whitebark pine (Pinus albicaulis Engelm.), ponderosa pines (Pinus ponderosa Lawson & C. Lawson var. scopulorum Engelm and var. ponderosa C. Lawson), Jeffrey pine (Pinus jeffreyi Balf.), sugar pine (Pinus lambertiana Douglas), Engelmann spruce (Picea engelmannii Parry ex Engelm.), and Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco var. glauca [Beissn.] Franco) - by pooling data collected from multiple fire-injury studies. Two sets of models were created: one for use in pre-fire planning in which only crown injury and tree diameter (DBH) were potential variables, and a second, optimal model for use in post-fire planning that used all significant variables. Predictive accuracy of all models was compared to the accuracy of the general, non-species specific mortality model used in the First Order Fire Effects Model (FOFEM) prior to version 5.7. The new species-specific models improved prediction of fire-caused tree mortality by 0 % to 48 %. Model accuracy increased the most for red fir, incense cedar, western larch, and whitebark pine, and increased the least for Engelmann spruce. The models in the post-fire option provided higher accuracy compared to the pre-fire models, but also required additional inputs. These new models were added to FOFEM beginning with version 5.7, and the options in the FOFEM Mortality Module were expanded. We describe the new options in FOFEM and how to use the software to predict tree mortality for pre-fire and post-fire planning, as well as modeling limitations and assumptions. The additions to FOFEM offer improved accuracy in predicting postfire tree mortality for 12 Western conifer species and allow direct inputs of fire injury to increase software applicability to prescribed fire and post-fire forest management.

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    Hood, Sharon; Lutes, Duncan. 2017. Predicting post-fire tree mortality for 12 western US conifers using the First-Order Fire Effects Model (FOFEM). Fire Ecology. 13(2): 66-84.


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    Abies, Calocedrus, decision support tools, fire-induced tree mortality, Larix, logistic regression, Picea, Pinus, prescribed fire, Psuedotsuga, salvage, validation

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