Higher sensitivity and lower specificity in post-fire mortality model validation of 11 western US tree species
Jeffrey M. Kane A D , Phillip J. van Mantgem B , Laura B. Lalemand B and MaryBeth Keifer C
+ Author Affiliations
- Author Affiliations
A Department of Forestry and Wildland Resources, Humboldt State University, 1 Harpst Street, Arcata, CA 95521, USA.
B U.S. Geological Survey, Western Ecological Research Center, Redwood Field Station, Arcata, CA 95521, USA.
C National Park Service, National Interagency Fire Center, Boise, Idaho 83705, USA.
D Corresponding author. Email: jkane@humboldt.edu
International Journal of Wildland Fire 26(5) 444-454 https://doi.org/10.1071/WF16081
Submitted: 6 May 2016 Accepted: 28 February 2017 Published: 20 April 2017
Abstract
Managers require accurate models to predict post-fire tree mortality to plan prescribed fire treatments and examine their effectiveness. Here we assess the performance of a common post-fire tree mortality model with an independent dataset of 11 tree species from 13 National Park Service units in the western USA. Overall model discrimination was generally strong, but performance varied considerably among species and sites. The model tended to have higher sensitivity (proportion of correctly classified dead trees) and lower specificity (proportion of correctly classified live trees) for many species, indicating an overestimation of mortality. Variation in model accuracy (percentage of live and dead trees correctly classified) among species was not related to sample size or percentage observed mortality. However, we observed a positive relationship between specificity and a species-specific bark thickness multiplier, indicating that overestimation was more common in thin-barked species. Accuracy was also quite low for thinner bark classes (<1 cm) for many species, leading to poorer model performance. Our results indicate that a common post-fire mortality model generally performs well across a range of species and sites; however, some thin-barked species and size classes would benefit from further refinement to improve model specificity.
Additional keywords: fire effects modelling, fire severity, fuel treatments, prescribed fire, trees.
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