Belowground consequences of vegetation change and their treatment in models

Authors: Jackson RB, HJ Schenk, EG Jobbágy, J Canadell, GD Colello, RE Dickinson, CB Field, P Friedlingstein, M Heimann, K Hibbard, DW Kicklighter, A Kleidon, RP Neilson, WJ Parton, OE Sala, MT Sykes

The extent and consequences of global land-cover and land-use change are increasingly apparent. One consequence not so apparent is the altered structure of plants belowground. This paper examines such belowground changes, emphasizing the interaction of altered root distributions with other factors, and their treatment in models. Shifts of woody and herbaceous vegetation with deforestation, afforestation, and woody plant encroachment typically alter the depth and distribution of plant roots, influencing soil nutrients, the water balance, and NPP. For example, our analysis of global soil datasets shows that the major plant nutrients C, N, P, and K are more shallowly distributed than are Ca, Mg, and Na, but patterns for each element vary with the dominant vegetation type. After controlling for climate, soil C and N are distributed more deeply in arid shrublands than in arid grasslands, and sub-humid forests have shallower nutrient distributions than do sub-humid grasslands. Consequently changes in vegetation may influence the distribution of soil carbon and nutrients over time (perhaps decades to centuries). Shifts in the water balance are typically much more rapid. Catchment studies indicate that the water yield decreases 25-40 mm for each 10% increase in tree cover, and increases in transpiration of water taken up by deep roots may account for as much as 50% of observed responses. Because models are increasingly important for predicting the consequences of vegetation change, we discuss the treatment of belowground processes and how different treatments affect model outputs. Whether models are parameterized by biome or plant life form (or neither), use single or multiple soil layers, or include N and water limitation, all affect predicted outcomes. Acknowledging and understanding such differences should help constrain predictions of vegetation change.

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