Although deforestation in the Amazon have been decreasing during the last decade, processes associated with forest degradation such as logging, fragmentation, and understory fires still contribute significantly to the carbon cycle. However, the impact of forest degradation processes on carbon cycle remains very uncertain, and observation-based studies are often too short to characterize the long-term dynamics of degraded forests. To overcome some of these limitations, we will develop a framework that integrates field measurements and remote sensing data to an individual- and process-based model (Ecosystem Demography model, ED) to study the long-term impact of forest degradation on carbon cycling. We will develop the ED model to represent key forest degradation processes, by including a parameterization of selective logging disturbances and by implementing a mechanistic fire model that predicts understory fires. We will use forest inventories, airborne LiDAR, and satellite-based land use chronosequences for three focal areas in the Amazon with distinct land use change histories (Paragominas, Santarém, and Feliz Natal) to determine initial conditions that capture the heterogeneous landscape of degraded forests, and use multi-temporal data to assess and calibrate the new model implementations and regrowth following disturbance. We will then simulate an array of future climate and land-use scenarios to further understand the relevance of changes in the micro-environment relative to changes in climate, their interactions, and the contributions of disturbance-driven losses and regeneration-driven gains to the carbon stores and cycle in the long term. The developments resulting from this project will provide a novel framework to study the impact of anthropogenic disturbances on ecosystems throughout the 21st century that includes a much broader spectrum of direct and indirect effects of land use changes.
News published in Agência FAPESP Newsletter about the scholarship: