Multi-scale assessment of post-fire tree mortality models
Tucker J. Furniss
A B E , Andrew J. Larson C , Van R. Kane D and James A. Lutz A B
+ Author Affiliations
- Author Affiliations
A Wildland Resources Department, Utah State University, 5230 Old Main Hill, Logan, UT 84322, USA.
B The Ecology Center, Utah State University, 5205 Old Main Hill, Logan, UT 84322, USA.
C Department of Forest Management, University of Montana, Missoula, MT 59812, USA.
D School of Environmental and Forest Sciences, University of Washington, Box 352100, Seattle, WA 98195, USA.
E Corresponding author email: tucker.furniss@usu.edu
International Journal of Wildland Fire 28(1) 46-61 https://doi.org/10.1071/WF18031
Submitted: 2 March 2018 Accepted: 30 October 2018 Published: 3 December 2018
Journal compilation © IAWF 2019 Open Access CC BY-NC-ND
Abstract
Post-fire tree mortality models are vital tools used by forest land managers to predict fire effects, estimate delayed mortality and develop management prescriptions. We evaluated the performance of mortality models within the First Order Fire Effects Model (FOFEM) software, and compared their performance to locally-parameterised models based on five different forms. We evaluated all models at the individual tree and stand levels with a dataset comprising 34 174 trees from a mixed-conifer forest in the Sierra Nevada, California that burned in the 2013 Rim Fire. We compared stand-level accuracy across a range of spatial scales, and we used point pattern analysis to test the accuracy with which mortality models predict post-fire tree spatial pattern. FOFEM under-predicted mortality for the three conifers, possibly because of the timing of the Rim Fire during a severe multi-year drought. Locally-parameterised models based on crown scorch were most accurate in predicting individual tree mortality, but tree diameter-based models were more accurate at the stand level for Abies concolor and large-diameter Pinus lambertiana, the most abundant trees in this forest. Stand-level accuracy was reduced by spatially correlated error at small spatial scales, but stabilised at scales ≥1 ha. The predictive error of FOFEM generated inaccurate predictions of post-fire spatial pattern at small scales, and this error could be reduced by improving FOFEM model accuracy for small trees.
Additional keywords: fire mortality modelling; FOFEM; Rim Fire; Sierra Nevada mixed-conifer; Smithsonian ForestGEO; spatial patterns; Yosemite Forest Dynamics Plot.
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