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Transplant experiments can also be used to separate environmental versus compositional effects on process rates (Reed and Martiny, 2007). If different microbial communities produce different process rates in a common environment, then it can be inferred that the compositional differences are responsible for the functional differences. Balser and Firestone (2005) provide a good example of how the transplant approach can also be used to make linkages between microbial taxa and process rates under disturbance. They transplanted soil microbial communities across a climate gradient and demonstrated that community composition affected process rates independent of climate. Furthermore, they used phospholipid fatty acid data to correlate process rates with specific members of the microbial community and concluded that nitrification potential and N2O flux were likely driven by Gram-negative bacteria.

Although not often possible, direct manipulations of microbial composition can provide useful information about the functional status of microbial groups, especially when coupled with process rate measurements. For example, specific taxa can be targeted for elimination from a community via chemical or physical means and process rates compared in communities with and without the taxa (Santos and Whitford, 1981; Griffiths et al., 2000; Austin et al., 2006). Wertz et al. (2007) manipulated soil microbial composition by serial dilution and reinnoculation of sterile microcosms; they found no effect of composition on functioning in the microcosms. Alternatively, communities can be artificially constructed to contain specific taxa and to establish links between composition and process rates (Naeem et al., 2000). For instance, Bell et al. (2005) showed that the diversity and composition of bacteria influenced respiration rates in aquatic microcosms.

The literature reviewed in the sections above suggests that microbial composition is often altered by disturbances and does not recover over some time. Furthermore, these changes often impact the rates of ecosystem processes, suggesting that at least some microbial taxa are functionally dissimilar. In light of these observations, we propose a broad framework in the next section for integrating information about microbial composition into predictive models of ecosystem processes.


As more data are collected on the relationship between microbial composition and ecosystem functioning, explicitly incorporating microbes into process models will become increasingly tractable. Indeed, analogous efforts have been successful with plant functional groups and ecosystem models. However, there are some gaps to bridge between microbial ecolo-

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