Influence of fuel structure derived from terrestrial laser scanning (TLS) on wildfire severity in logged forests.

CONTEXT: Logging and wildfire can reduce the height of the forest canopy and the distance to the understorey vegetation below. These conditions may increase the likelihood of high severity wildfire (canopy scorch or consumption), which may explain the greater prevalence of high severity wildfire in some recently logged or burnt forests. However, the effects of these structural characteristics on wildfire severity have not clearly been demonstrated. OBJECTIVES: We aimed to assess how the structure of forests affected by logging and wildfire influence the probability of high severity wildfire. METHODS: We used terrestrial laser scanning to measure the connectivity of canopy and understorey vegetation in forests at various stages of recovery after logging and wildfire (approximately 0-80 years since disturbance). These sites were subsequently burnt by mixed severity wildfire during the 2019-20 'Black Summer' fire season in south-eastern Australia. We assessed how these forest structure metrics affected the probability of high severity wildfire. RESULTS: The probability of high severity fire decreased as the canopy base height increased, and the distance between the canopy base and understorey increased. High severity wildfire was less likely in forests with taller understoreys and greater canopy or understorey cover, but these effects were not considered causal. Fire weather was the strongest driver of wildfire severity, which was also affected by topography. CONCLUSIONS: These findings demonstrate a link between forest structure characteristics, that are strongly shaped by antecedent logging and fire, and fire severity. They also indicate that vertical fuel structure should be incorporated into assessments of fire risk.

Soil carbon in Australian fire-prone forests determined by climate more than fire regimes.

Knowledge of global C cycle implications from changes to fire regime and climate are of growing importance. Studies on the role of the fire regime in combination with climate change on soil C pools are lacking. We used Bayesian modelling to estimate the soil % total C (% CTot) and % recalcitrant pyrogenic C (% RPC) from field samples collected using a stratified sampling approach. These observations were derived from the following scenarios: 1. Three fire frequencies across three distinctive climate regions in a homogeneous dry sclerophyll forest in south-eastern Australia over four decades. 2. The effects of different fire intensity combinations from successive wildfires. We found climate had a stronger effect than fire frequency on the size of the estimated mineral soil C pool. The largest soil C pool was estimated to occur under a wet and cold (WC) climate, via presumed effects of high precipitation, an adequate growing season temperature (i.e. resulting in relatively high NPP) and winter conditions sufficiently cold to retard seasonal soil respiration rates. The smallest soil C pool was estimated in forests with lower precipitation but warmer mean annual temperature (MAT). The lower precipitation and higher temperature was likely to have retarded NPP and litter decomposition rates but may have had little effect on relative soil respiration. Small effects associated with fire frequency were found, but both their magnitude and direction were climate dependent. There was an increase in soil C associated with a low intensity fire being followed by a high intensity fire. For both fire frequency and intensity the response of % RPC mirrored that of % CTot: i.e. it was effectively a constant across all combinations of climate and fire regimes sampled.

Fire intensity drives post-fire temporal pattern of soil carbon accumulation in Australian fire-prone forests.

The impact of fire on global C cycles is considerable but complex. Nevertheless, studies on patterns of soil C accumulation following fires of differing intensity over time are lacking. Our study utilised 15 locations last burnt by prescribed fire (inferred low intensity) and 18 locations last burnt by wildfire (inferred high intensity), with time since fire (TSF) up to 43years, in a homogenous forest type in south eastern Australia. Following a stratified approach to mineral soil sampling, the soil % total C (% CTot) and % recalcitrant pyrogenic C (% RPC), were estimated. Generalised additive models indicated increases in % CTot at TSF >30years in sites last burnt by wildfire. Estimates in sites last subjected to prescribed fire however, remained constant across the TSF chronosequence. There was no significant difference in % CTot between the different fire types for the first 20years after fire. In the first 10years after wildfires, % RPC was elevated, declining to a minimum at ca. TSF 25years. After prescribed fires, % RPC was unaffected by TSF. Differences in response of % CTot and % RPC to fire type may reflect the strength of stimulation of early successional processes and extent of charring. The divergent response to fire type in % CTot was apparent at TSF longer than the landscape average fire return interval (i.e., 15 to 20years). Thus, any attempt to increase C sequestration in soils would require long-term exclusion of fire. Conversely, increased fire frequency is likely to have negligible impact on soil C stocks in these forests. Further investigation of the effects of fire frequency, fire intensity combinations and interaction of fire with other disturbances will enhance prediction of the likely impact of imposed or climatically induced changes to fire regimes on soil C.

Biophysical Mechanistic Modelling Quantifies the Effects of Plant Traits on Fire Severity: Species, Not Surface Fuel Loads, Determine Flame Dimensions in Eucalypt Forests.

The influence of plant traits on forest fire behaviour has evolutionary, ecological and management implications, but is poorly understood and frequently discounted. We use a process model to quantify that influence and provide validation in a diverse range of eucalypt forests burnt under varying conditions. Measured height of consumption was compared to heights predicted using a surface fuel fire behaviour model, then key aspects of our model were sequentially added to this with and without species-specific information. Our fully specified model had a mean absolute error 3.8 times smaller than the otherwise identical surface fuel model (p < 0.01), and correctly predicted the height of larger (≥1 m) flames 12 times more often (p < 0.001). We conclude that the primary endogenous drivers of fire severity are the species of plants present rather than the surface fuel load, and demonstrate the accuracy and versatility of the model for quantifying this.