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Grasses fuel most fires on Earth and strongly influence local fire behaviour through traits that determine how flammable they are. Therefore, grass communities that differ in their species and trait compositions give rise to significant spatial variation in savanna fire regimes across the world, which cannot be otherwise explained. Likewise, fire regimes are continuously modified by alterations to savanna grass community traits, through species introductions and climatic changes. However, current representation of grassy fuels in global fire models misses important variation and therefore limits predictive power. The inclusion of grass trait diversity in models, using remotely sensed trait proxies, for example, will greatly improve our ability to understand and project savanna fires and their roles in the Earth system. Most fires on Earth occur in savannas, but vary considerably in their extent, frequency, and intensity. These patterns cannot fully be explained by climate or land management. An important 'missing-piece' in explaining the variation in savanna fire regimes is the diversity of grasses that fuel fires and whose traits strongly influence fire behaviour. The major differences in savanna fire behaviour across continents may lie in the contrasting species and trait assemblages in savanna grass communities. Fire regimes change significantly when savanna grass communities or traits are altered by the introduction of invasive species and climatic changes. Improved representation of grass trait diversity in fire models will be essential for reliable forecasts of the behaviour and impacts of the majority of fires on Earth. [ABSTRACT FROM AUTHOR]

The factors controlling the extent of fire in Africa south of the equator were investigated using moderate resolution (500 m) satellite-derived burned area maps and spatial data on the environmental factors thought to affect burnt area. A random forest regression tree procedure was used to determine the relative importance of each factor in explaining the burned area fraction and to address hypotheses concerned with human and climatic influences on the drivers of burnt area. The model explained 68% of the variance in burnt area. Tree cover, rainfall in the previous 2 years, and rainfall seasonality were the most important predictors. Human activities – represented by grazing, roads per unit area, population density, and cultivation fraction – were also shown to affect burnt area, but only in parts of the continent with specific climatic conditions, and often in ways counter to the prevailing wisdom that more human activity leads to more fire. The analysis found no indication that ignitions were limiting total burnt area on the continent, and most of the spatial variation was due to variation in fuel load and moisture. Split conditions from the regression tree identified (i) low rainfall regions, where fire is rare; (ii) regions where fire is under human control; and (iii) higher rainfall regions where burnt area is determined by rainfall seasonality. This study provides insights into the physical, climatic, and human drivers of fire and their relative importance across southern Africa, and represents the beginnings of a predictive framework for burnt area. [ABSTRACT FROM AUTHOR]