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Carbon stocks in a miombo woodland landscape: spatial distributions and controls Emily Woollen, Mathew Williams, Casey Ryan and John Grace The University of Edinburgh, School of GeoSciences EGU - 6th April 2011, SOC dynamics at different spatial scales
Miombo woodland • Most common savanna woodland type • 2.7 million km2 • Livelihoods of millions Figure: Distribution and extent of miombo woodland in southern Africa. Source: Desanker et al. (1995) The Miombo Network: Framework for a Terrestrial Transect Study of Land-Use and Land-cover Change in the Miombo Ecosystem of Central Africa, Rep. No. 41. The International Geosphere-Biosphere Programme, Stockholm, Sweden.
Brachystegia, Julbernardia & Isoberlinia Seasonally dry deciduous woodlands Highly spatially heterogeneous C4 grasses Photo: Casey Ryan
What we don’t know - Previous work shows complex variations in soil C Williams et al. (2008) • Soil textural controls on C are not well understood • Links between soil and vegetation C unclear Williams et al. (2008) Forest Ecology and Manag. (254): 145-155 Ryan & Williams (in press) Biotropica
Key questions • How do C stocks in soils and vegetation vary across miombo woodland landscapes? • 2) To what degree and at what scales are these stocks linked?
Sampling Gorongosa National Park Vegetation Bare/wet ground Transect River Chitengo Nhambita Landsat 5 TM Transect IKONOS No IKONOS data 25 20 30 15 10 5 1 0.25 0 0.5 1 Km
Fine scale • Soil 5 cm • Soil 30 cm • Litter • Medium scale • - Leaf area index • Soil texture • AGB > 5 cm Coarse scale - AGB > 30 cm - Grass biomass Cyclic Sampling 100 m 100 m 100 m Transect continues 25 m radius 1 m2 314 m2 10m radius 1 m2 0.57 ha 314 m2 3 m 9 m 25 m 50 m One cycle 75 m
Spatial dependence (fine scale) Range = 14 m Range = 26 m Range = 17 m
Spatial dependence (coarse scale) Range = 1592 m Range = 1426 m
Spatial correlations R2 = 0.54 P < 0.01 y = - 0.09x+15.27 R2 = 0.82 P < 0.01 y = 0.05x-2.86 R2 = 0.24 P < 0.01 y = 0.14x+9.97 R2 = 0.38 P < 0.01 y = 3.99x+16.71
Spatial correlations Quantile regression Linear regression P < 0.01
Higher LAI Greater woody biomass volumes Less woody biomass volumes Greater soil C in the top 0.05 m Seasonally Saturated horizon Impermeable horizon 10 -100s meters Conceptual model Microrelief controls C stock distributions More Litter Lower LAI Clay and particle transport Less litter Coarse textured soil 1 -10s m Differing bedrock and weathering processes Less soil C in the deeper 0.3 m Less soil C in the top 0.05 m Greater soil C in the deeper 0.3 m Fine textured soil
Acknowledgements: Funding: Natural Environment Research Council Logistical support: Envirotrade Species identification: Meg Coates-Palgrave Field assistants: Albasine Mucavele, Alfonso Jornal, Ramaio Saimone, Neto Moulinho, Zito Lindo and Gary Goss Contact: firstname.lastname@example.org