Forest fragmentation, climate change and understory fire regimes on the Amazonian landscapes of the Xingu headwaters
Understory fire modeling is a key tool to investigate the cornerstone concept of landscape ecology, i.e. how ecological processes relate to landscape structure and dynamics. Within this context, we developed FISC—a model that simulates fire ignition and spread and its effects on the forest carbon balance. FISC is dynamically coupled to a land-use change model to simulate fire regimes on the Amazonian landscapes of the Xingu Headwaters under deforestation, climate change, and land-use management scenarios. FISC incorporates a stochastic cellular automata approach to simulate fire spread across agricultural and forested lands. CARLUC, nested in FISC, simulates fuel dynamics, forest regrowth, and carbon emissions. Simulations of fire regimes under modeled scenarios revealed that the major current and future driver of understory fires is forest fragmentation rather than climate change. Fire intensity proved closely related to the landscape structure of the remaining forest. While climate change may increase the percentage of forest burned outside protected areas by 30% over the next four decades, deforestation alone may double it. Nevertheless, a scenario of forest recovery and better land-use management would abate fire intensity by 18% even in the face of climate change. Over this time period, the total carbon balance of the Xingu’s forests varies from an average net sink of 1.6 ton ha−1 year−1 in the absence of climate change, fire and deforestation to a source of −0.1 ton ha−1 year−1 in a scenario that incorporates these three processes.
KeywordsCarbon fluxes Landscape dynamics Landscape metrics Spatially-explicit modeling Land-use management
We thank the Gordon and Betty Moore Foundation, the David and Lucille Packard Foundation, Conselho Nacional de Desenvolvimento Científico e Tecnológico, and the Large-Scale Biosphere Atmosphere Experiment for funding. Special thanks go to Jennifer Balch and the field team.
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