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Predictive models of tree mortality and survival are vital for management planning and understanding fire effects in forest communities and landscapes. Post-fire tree mortality has been traditionally modeled as an empirical function of tree defenses (e.g., bark thickness) and fire injury (e.g., crown scorch). We used a post-fire tree mortality dataset built from 40 contributed datasets from across the USA to formally evaluate the accuracy of fire-induced tree mortality models from the First Order Fire Effects Model (FOFEM) software system. The Fire-Induced Tree Mortality (FITM) database includes observations of fire injury and survival or mortality for 160 tree species and >173,000 trees, which were burned in 420 prescribed fires and wildfires occurring from 1981 to 2016. The basic model in FOFEM (FOFEM5) uses bark thickness and percent of the live crown volume (CVS) scorched to predict post-fire mortality, and can be applied to any species for which bark thickness can be calculated. FOFEM also includes 29 species-specific tree mortality models, with unique predictors and coefficients. We assessed accuracy of the FOFEM5 model for 45 tree species and assessed 24 species-specific models for 13 species. The FOFEM5 model consistently over-predicted mortality for angiosperms; 6 of 11 angiosperms had AUCs <0.7. For conifers, FOFEM5 over-predicted mortality for thick-barked species. It also under-predicted mortality at low levels of CVS for conifers with moderate bark thickness. The species-specific models had significantly higher AUCs than the FOFEM5 models for 15 of the 22 models. Approximately 75% of models tested had either excellent or good predictive ability. The models that performed poorly were primarily angiosperms or thin-barked conifers. This suggests that different approaches—such as different model forms, better estimates of bark thickness, and additional predictors—may be warranted for these taxa. Future data collection and research should target the data gaps and poorly performing models identified in this study.

This seminar was part of the 2019-2020 Missoula Fire Sciences Laboratory Seminar Series.

Media Record Details

Dec 12, 2019
C. Alina Cansler

Cataloging Information

Topic(s):
Fire Effects
Fire & Fuels Modeling

NRFSN number: 20635
Record updated: