The Biogeography of Microbial Communities and Ecosystem Processes: Implications for Soil and Ecosystem Models

We are entering a period of rapid and pronounced environmental change. Understanding how this change will in! uence soil communities is essential for accurate prediction of future climate, biogeochemical cycles, and human well-being. Such reliable prediction is the fundamental test of scienti" c understanding (i.e. can we use knowledge of underlying mechanisms to predict accurately how phenomena change across space and time). Predicting how soils respond to environmental change will be central to adaptive management of ecosystems, given our reliance on soils for sustained food production, water puri" cation, carbon storage, and nutrient retention. This ability to predict with certainty how environmental change will in! uence soil processes requires mechanistic understanding of how soils work. Developing this mechanistic understanding is perhaps the “Grand Challenge” for soil ecologists if we are to advance our basic understanding of soils and apply this knowledge to effective environmental management. Mechanistic understanding improves our con" dence in predictions of the future that extrapolate observed relationships between regulatory variables (e.g. temperature) and process rates (e.g. soil respiration) ( Reynolds et al. 2001 ). Perhaps paradoxically, there may be much less agreement in predicted outcomes between ecosystem models when more mechanistic understanding is included. Although we might have “faith” that the true ecosystem response lies somewhere within the predictions from the models, predictions from mathematical models that rely on empirical (or statistical) relationships may be much more similar. Empirical models comprise the majority of ecosystem models used to predict soil biogeochemical processes (see Box 3.5.1 ). Our con" dence in the predictions by empirical models of future ecosystem response relies on the assumption that observations of regulatory variable-process rate relationships hold across space and time ( Reynolds et al. 2001 ). So, we are faced with a dilemma. Do we develop and employ mechanistic models that may be more accurate but also more uncertain or do we follow the more traditional approach, relying on empirical models that may predict ecosystem processes with more certainty but less accuracy? There is likely no single right answer to this question when developing models to predict how ecosystems will respond to environmental change. A family of models that covers this continuum of approach—between empiricism and mechanism –will permit us to determine where model predictions agree. Where they disagree likely indicates the greatest uncertainty in model predictions, and it is in addressing these disagreements that future research might yield most insight. The soil ecology community predominately relies on more empirical models and this reliance has repercussions beyond the discipline and can in! uence global policy. For example, the soil submodels