Carbon stocks in a miombo woodland landscape: spatial distributions and controls Emily Woollen, Mathew Williams, Casey Ryan and John Grace The University.

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Presentation transcript:

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 - 6 th April 2011, SOC dynamics at different spatial scales

Miombo woodland 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. Most common savanna woodland type 2.7 million km 2 Livelihoods of millions

Photo: Casey Ryan Brachystegia, Julbernardia & Isoberlinia C4 grasses Seasonally dry deciduous woodlands Highly spatially heterogeneous

What we don’t know - Previous work shows complex variations in soil C - 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): Ryan & Williams (in press) Biotropica Williams et al. (2008)

1) 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? Key questions

Transect River No IKONOS data Transect Vegetation Bare/wet ground IKONOS Km Nhambita Chitengo Gorongosa National Park Landsat 5 TM Sampling

Cyclic Sampling Transect continues 314 m 2 1m21m2 25 m radius 3 m 10m radius 25 m50 m 9 m 0.57 ha 314 m 2 1 m 2 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 100 m One cycle 75 m

Vegetation trends

Soil trends

Spatial dependence (fine scale) Range = 14 m Range = 26 m Range = 17 m

Spatial dependence (coarse scale) Range = 1426 m Range = 1592 m

Spatial correlations R 2 = 0.82 P < 0.01 y = 0.05x-2.86 R 2 = 0.54 P < 0.01 y = x R 2 = 0.24 P < 0.01 y = 0.14x+9.97 R 2 = 0.38 P < 0.01 y = 3.99x+16.71

Spatial correlations Quantile regressionLinear regression P < 0.01

Impermeable horizon Seasonally Saturated horizon Higher LAI Greater woody biomass volumes s meters Clay and particle transport Less woody biomass volumes Greater soil C in the top 0.05 m Conceptual model Coarse textured soil Less soil C in the deeper 0.3 m Fine textured soil Greater soil C in the deeper 0.3 m Less litter Less soil C in the top 0.05 m More Litter 1 -10s m Lower LAI Microrelief controls C stock distributions Differing bedrock and weathering processes

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: