Abstract
The differential flammability of individual plant species in landscape-scale fire behaviour is an important consideration, but one that is often overlooked. This is in part due to a relative dearth in the availability of plant flammability data. Here, we present a highly accurate predictive model of the likelihood of plant leaves entering flaming combustion as a function of leaf mass per area (LMA), leaf area (LA) and radiant heat flux using species of fire-prone dry sclerophyll forests of south-eastern Australia. We validated the performance of the model on two separate datasets, and on plant species not included in the model building process. Our model gives accurate predictions (75–84%) of leaf flaming with potential application in the next generation of fire behaviour models. Given the global wealth of species’ data for LMA and LA, in stark contrast to leaf flammability data, our model has the potential to improve understanding of forest flammability in the absence of leaf flammability information.
Highlights
Fire-Prone Dry Sclerophyll Forest.Climate change has resulted in long-term weather effects that create conditions favouring more frequent and intense wildfires [1,2,3,4,5,6,7]
Living plant leaves provide critical fuel for wildfires, so understanding differences in leaf flammability among species, and how leaf flammability is affected by fire intensity, is key to improving our predictive models of fire behaviour [12,13,14]
We present a predictive model of leaf flammability as a function of the leaf traits leaf mass per area (LMA) and leaf area (LA)
Summary
Fire-Prone Dry Sclerophyll Forest.Climate change has resulted in long-term weather effects that create conditions favouring more frequent and intense wildfires [1,2,3,4,5,6,7]. Living plant leaves provide critical fuel for wildfires, so understanding differences in leaf flammability among species, and how leaf flammability is affected by fire intensity, is key to improving our predictive models of fire behaviour [12,13,14]. Considering recent large and destructive wildfires across the world, there is an emerging possibility that the wealth of existing global data on these two leaf traits could be used to predict plant leaf flammability. Such predictive capacity would have important ramifications for models that use the traits and flammability of leaves as informative parameters for predicting wildfire behaviour [12,22,23,24,25]
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