Fire-driven dynamics in Amazonia: Contrasting ancient legacies and modern degradation in soils and vegetation

Résumé fourni par la source

Fire regimes and human impacts on Amazonian forests have varied over decades to centuries, resulting in disturbances and recoveries that leave lasting legacies on vegetation and soil. While intact (old-growth) forests were once thought to be largely fire-free, our work has shown these forests to have a history of infrequent but recurrent fire. Wildfires have left their signatures in the soil in the form of black carbon (pyrogenic carbon or PyC), the product of incomplete combustion of organic matter. Vegetation has also likely responded to past fire; however, the mechanistic effects of these past disturbances remain poorly understood. Here, we examine Amazon disturbance and recovery processes over space and time (ancient to modern) in relation to fire. We utilise permanent forest plot data (soil PyC, physicochemical properties, vegetation) from two large-scale projects across the Amazon Basin, combined with remote sensing data. The analysis shows that soil texture and hydrology primarily explain the spatial variation of soil PyC at 30 cm depth, while historical climate played a relatively minor role. Furthermore, soil PyC from ancient wildfires is associated with increased soil fertility in intact forests. We also found that distinct groups of tree species in Amazonia are associated with ancient soil PyC. In contrast, modern fires increase soil PyC but result in a reduction in total SOC, degrade soil health, and reduce species richness. These findings indicate that infrequent ancient wildfires recurring at intervals spanning several hundred years had positive impacts on soil fertility and left legacy effects on modern forest composition. Conversely, modern fires, which are extensive and have short return intervals, negatively impact Amazon soils and vegetation on decadal scales. To better assess the long-term impacts of fire on soil carbon, we are incorporating soil PyC into Land Surface Models.

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