4.5 Article

Multi-scale assessment of post-fire tree mortality models

期刊

INTERNATIONAL JOURNAL OF WILDLAND FIRE
卷 28, 期 1, 页码 46-61

出版社

CSIRO PUBLISHING
DOI: 10.1071/WF18031

关键词

fire mortality modelling; FOFEM; Rim Fire; Sierra Nevada mixed-conifer; Smithsonian Forest-GEO; spatial patterns; Yosemite Forest Dynamics Plot

类别

资金

  1. Joint Fire Science Program [16-1-04-02]
  2. National Park Service [P14AC00122, P14AC00197]
  3. Utah Agricultural Extension Station, Utah State University [9072]
  4. Ecology Center at Utah State University

向作者/读者索取更多资源

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.

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