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

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 km2

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): 145-155

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

15 102025 5

Transect

Vegetation

Bare/wet ground

IKONOS

0 0.5 1 Km0.25

130

Nhambita Chitengo

Gorongosa National Park

Landsat 5 TM

Sampling

Cyclic Sampling

Transect continues

314 m2

1 m2

25 m radius

3 m

10m radius

25 m 50 m

9 m

0.57 ha

314 m21 m2

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 m100 m 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 mRange = 1592 m

Spatial correlations

R2 = 0.82

P < 0.01

y = 0.05x-2.86

R2 = 0.54

P < 0.01

y = - 0.09x+15.27

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

Impermeable horizon

Seasonally Saturated horizon

Higher LAI

Greater woody biomass volumes

10 -100s 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

MoreLitter

1 -1

0s 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: e.s.woollen@sms.ed.ac.uk