Simple measures of climate, soil properties and plant traits predict national-scale grassland soil carbon stocks

Manning, Peter; de Vries, Franciska T.; Tallowin, Jerry R. B.; Smith, Roger; Mortimer, Simon R.; Pilgrim, Emma S.; Harrison, Kate A.; Wright, Daniel G.; Quirk, Helen; Benson, Joseph; Shipley, Bill; Cornelissen, Johannes H. C.; Kattge, Jens; Bönisch, Gerhard; Wirth, Christian; Bardgett, Richard D.; Wilsey, Brian (2015). Simple measures of climate, soil properties and plant traits predict national-scale grassland soil carbon stocks. Journal of Applied Ecology, 52(5), pp. 1188-1196. Blackwell Scientific Publications 10.1111/1365-2664.12478

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Soil carbon (C) storage is a key ecosystem service. Soil C stocks play a vital role in soil fertility and climate regulation, but the factors that control these stocks at regional and national scales are unknown, particularly when their composition and stability are considered. As a result, their mapping relies on either unreliable proxy measures or laborious direct measurements.
Using data from an extensive national survey of English grasslands, we show that surface soil (0–7 cm) C stocks in size fractions of varying stability can be predicted at both regional and national scales from plant traits and simple measures of soil and climatic conditions.
Soil C stocks in the largest pool, of intermediate particle size (50–250 μm), were best explained by mean annual temperature (MAT), soil pH and soil moisture content. The second largest C pool, highly stable physically and biochemically protected particles (0·45–50 μm), was explained by soil pH and the community abundance-weighted mean (CWM) leaf nitrogen (N) content, with the highest soil C stocks under N-rich vegetation. The C stock in the small active fraction (250–4000 μm) was explained by a wide range of variables: MAT, mean annual precipitation, mean growing season length, soil pH and CWM specific leaf area; stocks were higher under vegetation with thick and/or dense leaves.
Testing the models describing these fractions against data from an independent English region indicated moderately strong correlation between predicted and actual values and no systematic bias, with the exception of the active fraction, for which predictions were inaccurate.
Synthesis and applications. Validation indicates that readily available climate, soils and plant survey data can be effective in making local- to landscape-scale (1–100 000 km2) soil C stock predictions. Such predictions are a crucial component of effective management strategies to protect C stocks and enhance soil C sequestration.

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