Integrating plant-soil interactions into global carbon cycle models

Ostle, Nicholas J. and Smith, Pete and Fisher, Rosie and Ian Woodward, F. and Fisher, Joshua B. and Smith, Jo U. and Galbraith, David and Levy, Peter and Meir, Patrick and McNamara, Niall P. and Bardgett, Richard D. (2009) Integrating plant-soil interactions into global carbon cycle models. Journal of Ecology, 97 (5). pp. 851-863. ISSN 0022-0477

Abstract

1. Plant-soil interactions play a central role in the biogeochemical carbon (C), nitrogen (N) and hydrological cycles. In the context of global environmental change, they are important both in modulating the impact of climate change and in regulating the feedback of greenhouse gas emissions (CO2, CH4 and N2O) to the climate system. 2. Dynamic global vegetation models (DGVMs) represent the most advanced tools available to predict the impacts of global change on terrestrial ecosystem functions and to examine their feedbacks to climate change. The accurate representation of plant-soil interactions in these models is crucial to improving predictions of the effects of climate change on a global scale. 3. In this paper, we describe the general structure of DGVMs that use plant functional types (PFTs) classifications as a means to integrate plant-soil interactions and illustrate how models have been developed to improve the simulation of: (a) soil carbon dynamics, (b) nitrogen cycling, (c) drought impacts and (d) vegetation dynamics. For each of these, we discuss some recent advances and identify knowledge gaps. 4. We identify three ongoing challenges, requiring collaboration between the global modelling community and process ecologists. First, the need for a critical evaluation of the representation of plant-soil processes in global models; second, the need to supply and integrate knowledge into global models; third, the testing of global model simulations against large-scale multifactor experiments and data from observatory gradients. 5. Synthesis. This paper reviews how plant-soil interactions are represented in DGVMs that use PFTs and illustrates some model developments. We also identify areas of ecological understanding and experimentation needed to reduce uncertainty in future carbon coupled climate change predictions.