Shade alters the growth and architecture of tropical grasses by reducing root biomass

Tropical peat composition may provide a negative feedback on fire occurrence and severity

Loss of peat through increased burning will have major impacts on the global carbon cycle. In a normal hydrological state, the risk of fire propagation is largely controlled by peat bulk density and moisture content. However, where humans have interfered with the moisture status of peat either via drainage, or indirectly via climate change, we hypothesise that its botanical composition will become important to flammability, such that peats from different latitudes might have different compositionally-driven susceptibility to ignition. We use pyrolysis combustion flow calorimetry to determine the temperature of maximum thermal decomposition (Tmax) of peats from different latitudes, and couple this to a botanical composition analysis. We find that tropical peat has higher Tmax than other regions, likely on account of its higher wood content which appears to convey a greater resistance to ignition. This resistance also increases with depth, which means that loss of surface peat in tropical regions may lead to a reduction in the subsequent ignitability of deeper peat layers as they are exposed, potentially resulting in a negative feedback on increased fire occurrence and severity.

Savanna fire regimes depend on grass trait diversity

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.