Exoenzymes: Are they the secret to capturing non-equilibrium in microbe-SOM system?

Background/Question/Methods

There is growing interest in developing more models of soil processes that are more mechanistic. This is driven partially by the growth of microbial ecology and a natural desire to see this new body of knowledge captured in models. But more importantly, we have come to recognize the limitations of previous generations of biogeochemical models--they assume that the microbial communities involved in driving processes are effectively in equilibrium with their environment. However, particularly with climates changing and becoming more erratic, a quasi-steady state assumption is not valid in some cases and so models based on this approach perform poorly. The hope is that more mechanistically explicit models will be able to capture these dynamics. A growing body of models focus on extracellular enzymes as the proximal drivers of decomposition and so of C cycling. Enzyme driven models have great power for enhancing our understanding of decomposition processes and for predicting some aspects of decomposition that can not be explained by steady-state models. However, enzyme-driven decomposition models have their own assumptions that also create limitations in describing patterns of activity in environments with rapidly varying environmental conditions. 

Results/Conclusions

Exoenzyme-driven models are powerful for describing the biology and biochemistry of decomposition, for integrating stoichiometry as a control over decomposition, and for understanding microbial investment strategies. However, they assume that soil processes are regulated by those enzymatic processes, which is not necessarily true under all conditions. When moisture varies extremely for example, capturing respiration and C-cycling in a model requires primarily addressing the disconnection between processes generating available substrates and those consuming it. Recent research raises questions as to the nature of the processes generating bioavailable material (they may not be enzymatic) and to the most effective way of representing those processes within a model. Additionally, enzyme-driven models require a suite of parameters that can be difficult to measure or estimate. In this talk, I will discuss some of the problems of modeling processes in environments experiencing strong seasonal transitions and how extracellular enzymes may or may not facilitate that. I will consider both how researchers have formulated models and how these match against experimental studies.