Fire dynamics and carbon cycling in miombo woodlands
Bowers, Samuel Jonathan
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Savannah ecosystems play a prominent role in the global carbon (C) cycle, yet fluxes are poorly quantified, and the key processes regulating vegetation dynamics are uncertain. Insight is particularly deficient in southern Africa’s miombo woodlands, a woody savannah that is home to over 100 million people. This biome is heavily disturbed, with widespread deforestation and degradation associated with agriculture, charcoal and timber extraction, and frequent fires from anthropogenic sources. In this thesis I combine plot inventory data with remote sensing and modelling techniques to improve our understanding of the miombo woodland C cycle. Using a network of forest inventory plots, I characterise floristic and functional diversity in a savannah-forest mosaic in southeastern Tanzania. Divergent vegetation structures are associated with variation in fire frequency, water supply, and soil chemo-physical properties. Corresponding differences are noted in fire resilience, water-use, and nutrient acquisition plant functional traits, suggesting that multiple interrelated environmental filters act to assemble heterogeneous tree communities. Re-inventory of forest plots was used to quantify key aspects of the woody C cycle. Tree growth rates are slow, calling for careful management of woodland resources, and significantly reduced where stems were damaged. Stem mortality is rare, though elevated in the smallest trees and where damage was recorded. Contemporary strategies to incentivise the conservation of miombo woodland ecosystems, such as the REDD+ programme of the United Nations, advocate payments for sustaining ecosystem services such as C sequestration. I report on a pilot REDD+ project aiming to reduce woodland degradation from frequent high intensity fires in southeastern Tanzania. Model simulations suggest that woody biomass is being gradually lost from the region, and that setting early season fires has the potential to reverse this trend. Realising substantial changes in C storage requires a demanding reduction to late fire frequency, and uncertainty in model predictions remains high. I quantify the C cycle of southern African woodlands by combining observational data with a diagnostic C cycle model under a model-data fusion framework. Model outputs show substantial variation in primary production, C allocation patterns, and foliar and canopy traits, which are associated with differences in woody cover, fire, and precipitation properties. C cycle dynamics correspond poorly to conventional land cover maps, indicating they may be unsuited to upscaling measurements and models of the terrestrial C cycle.