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DSSS: Is Brevity the Soul of Soil Models?


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Eric A. Davidson is President and Senior Scientist at the Woods Hole Research Center.  His research in biogeochemistry includes the exchange of plant nutrients from the land to streams and groundwater and the exchange of greenhouse gases between the soil and the atmosphere.  He works in forests, pastures, and agricultural fields in the Amazonian and Cerrado regions of Brazil and in regrowing forests of New England.  Davidson holds a Ph.D. in forestry from NCSU and held post-doctoral positions in soil microbiology and biogeochemistry at the UC-Berkeley and the NASA Ames Research Center before moving to Woods Hole in 1991.  He is a Fellow of the AAAS, the President of the Biogeosciences section of the AGU, and the Coordinator of the North American Center for the International Nitrogen Initiative.  Davidson has written a popular book, You Can’t Eat GNP, which explores the links between economics and ecology for students and laypersons.


Soils carbon stocks are 2-4 times greater than atmospheric CO2-C and 3-6 times larger than aboveground plant biomass-C.  Potential exists for C sequestration in soils, but there is also a large potential positive feedback to climate change as permafrost thaws and enzymatic decomposition of soil organic matter increases with warming.  Enzymatic reaction rates are temperature sensitive when substrate is not limiting.  However, substrate supply often, perhaps usually, limits enzymatic reaction rates in soils.  Soil microbial community composition varies temporally and spatially, and the reactive properties of extracellular enzymes also can probably be changed by microorganisms in response to environmental cues.  The C, N, and P assimilation enabled by extracellular enzyme activity affects the growth of microbial populations, their metabolism, and their enzyme synthesis.  Do models need to represent all of these processes in 3-D space and in time?  Ideally, the answer would be “yes,” but only if there is a viable approach to testing and validating model structures and parameterizations representing each process.  When that is not possible, some aggregation is needed.  A modular design enables progress on model components without losing sight of the way that components fit together.  Admittedly, the Dual Arrhenius and Michaelis–Menten (DAMM) model does not yet attain all of these lofty goals, but it offers promise to build upon an integrated, modular approach to represent as parsimoniously as possible numerous key interacting processes in a heterogeneous matrix, and to keep making improvements until we get the DAMM thing right.