Local environmental covariates are important for predicting fire history from tree stem diameters
J. Lazzari A D , H. J. Yoon B , D. A. Keith C and D. A. Driscoll A
+ Author Affiliations
- Author Affiliations
A Fenner School of Environment and Society, Australian National University, Canberra, ACT 2601, Australia.
B Statistical Consulting Unit, Australian National University, Canberra, ACT 2601, Australia.
C School of Biological, Earth and Environmental Sciences, University of New South Wales, Kensington, NSW 2052, Australia.
D Corresponding author. Email: juliana.lazzari@anu.edu.au
International Journal of Wildland Fire 24(6) 871-882 https://doi.org/10.1071/WF15069
Submitted: 29 October 2014 Accepted: 31 March 2015 Published: 1 June 2015
Abstract
In fire-prone landscapes, knowing when vegetation was last burnt is important for understanding how species respond to fire and to develop effective fire management strategies. However, fire history is often incomplete or non-existent. We developed a fire-age prediction model for two mallee woodland tree species in southern Australia. The models were based on stem diameters from ~1172 individuals surveyed along 87 transects. Time since fire accounted for the greatest proportion of the explained variation in stem diameter for our two mallee tree species but variation in mean stem diameters was also influenced by local environmental factors. We illustrate a simple tool that enables time since fire to be predicted based on stem diameter and local covariates. We tested our model against new data but it performed poorly with respect to the mapped fire history. A combination of different covariate effects, variation in among-tree competition, including above- and below-ground competition, and unreliable fire history may have contributed to poor model performance. Understanding how the influence of covariates on stem diameter growth varies spatially is critical for determining the generality of models that predict time since fire. Models that were developed in one region may need to be independently verified before they can be reliably applied in new regions.
Additional keywords: fragmentation, mallee woodland, plant-stem morphometrics, prediction model, time since fire (TSF).
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