of methanotrophs responded differently to simulated global change. The worst-case scenario is that the responses of the taxa to disturbance are randomly distributed across the phylogeny as illustrated by disturbance C in Fig. 8.3. In this case, calculation of the overall process rate requires the abundances of each individual taxon. A similar challenge would arise if the functional trait itself is not phylogenetically determined (Fig. 8.3B).

One promising set of tools for overcoming the challenges of these “worst-case” scenarios are metagenomic or metatranscriptomic approaches. For example, environmental gene tags (EGTs) could potentially be used as a proxy for physiological rates or disturbance responses across the whole community, even if these traits are unrelated to phylogeny (Tringe et al., 2005). With this technique, the abundances of genes specific to particular environmental processes (such as phosphate metabolism) could be extracted from community metagenomic data and used in modeling process rates. Another advantage of this approach is that multiple EGTs and processes can be examined in a single sample, rather than constructing separate clone libraries for each different functional gene of interest.

In our model, when does changing composition matter to ecosystem processes? Changing the abundance of a taxon will affect the process rate unless the abundances of other taxa also change to compensate. In undisturbed communities, exchanging one taxon for another (with similar biomass) can affect the community process rate if the two groups have different r₀ (physiological rate) values. With disturbance, composition matters if taxa with different m (disturbance response) values change in abundance (even if they had the same r₀ values). Although our simple model considers only the total abundances (i.e., biomasses) of different taxa, we note that changes in body size distributions within or across taxonomic groups could also affect ecosystem process rates as suggested by metabolic scaling theory (Enquist et al., 2003).

In communities with a large number of taxa, a “portfolio effect” may prevent the community process rate from changing with disturbance, even if the taxa change in abundance and are not functionally redundant (Doak et al., 1998). The portfolio effect can occur when positive responses of some taxa are averaged with negative responses of other taxa, resulting in no net change in function. Thus, the greater the number of taxa that perform a process, the more buffered the process is to environmental perturbations (Schimel, 1995). This portfolio mechanism (in addition to functional redundancy) could lead to similarity in community function despite changes in microbial composition.