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Although cerrado trees have evolved with fire for millions of years, it is not well-understood which tree attributes are more important to survive fire in the Brazilian savanna. To address this issue, we used pre- and post-fire data on 367 cerrado trees (113 native species) planted in an arboretum in south-east Brazil and then left unburnt until 2019, when a prescribed burn was applied. Tree size (height and diameter) had been measured in 2017. Four months after the fire, we assessed tree size, relative bark thickness (bark-to-diameter ratio), leaf habit (evergreen or deciduous) and habitat preference (savanna specialist or generalist). These were the predictor variables used in generalised linear models exploring tree survival and resprouting type. Most trees survived fire: 59% resprouted epicormically, 25% resprouted basally, 6% had root suckers, and only four trees died. Basal and epicormic resprouting were related to tree size: small trees (diameter ≤ 5 cm) resprouted basally more frequently, whereas tall trees (height ≥ 3.7 m) resprouted epicormically more frequently. Our results suggest that rapid growth is more important than bark thickness, leaf habit or habitat preference to escape the fire trap, because it allows cerrado trees to reach a fire-resistant height more quickly.

To date, most studies of fire severity, which is the ecological damage produced by a fire across all vegetation layers in an ecosystem, using remote sensing have focused on wildfires and forests, with less attention given to prescribed burns and treeless vegetation. Our research analyses a multi-decadal satellite record of fire severity in wildfires and prescribed burns, across forested and treeless vegetation, in western Tasmania, a wet region of frequent clouds. We used Landsat satellite images, fire history mapping and environmental predictor variables to understand what drives fire severity. Remotely-sensed fire severity was estimated by the Delta Normalised Burn Ratio (ΔNBR) for 57 wildfires and 70 prescribed burns spanning 25 years. Then, we used Random Forests to identify important predictors of fire severity, followed by generalised additive mixed models to test the statistical association between the predictors and fire severity. In the Random Forests analyses, mean summer precipitation, mean minimum monthly soil moisture and time since previous fire were important predictors in both forested and treeless vegetation, whereas mean annual precipitation was important in forests and temperature seasonality was important in treeless vegetation. Modelled ΔNBR (predicted ΔNBRs from the best-performing generalised additive mixed model) of wildfire forests was higher than modelled ΔNBR of prescribed burns. This study confirms that western Tasmania is a valuable pyrogeographical model for studying fire severity of wet ecosystems under climate change, and provides a framework to better understand the interactions between climate, fire severity and prescribed burning.