Deciphering environmental controls over biome switches in temperate and tropical savannas

Savannas and evergreen forests are the most important tropical vegetation types in terms of area, biodiversity, total carbon stocks, and use by humans, so shifts in the distribution of these biomes have large implications for conservation, global change, and human welfare. Predicting these shifts, however, is challenging; some modeling studies forecast widespread expansion of forest into tropical savanna, while others forecast massive collapse of tropical forest. The difficulty of predicting and managing savanna-forest transitions arises largely from complex fire-vegetation feedbacks that are influenced by humans, climate, previous fire history, and the characteristics of the species that compose the vegetation. An improved understanding of these feedbacks is necessary for developing the next generation of earth system models and for informing management of the seasonally dry tropics. This project will advance our ability to predict these changes by combining field data and modeling to test for and quantify the causes of these complex dynamics. Specifically, the work will (1) quantify key processes that underlie switches between savanna and forest states in the tropics, (2) use this information to refine and parameterize the CLM(ED-SPITFIRE) model for simulating savanna-forest dynamics, and (3) perform simulations to understand environmental controls on the distribution of tropical savanna and forest, with emphasis on causes of hysteresis.