Integrating McGill Wetland Model (MWM) with peat cohort tracking and microbial controls.

Peatlands store a large amount of organic carbon and are vulnerable to climate change and human disturbances. However, ecosystem-scale peatland models often do not explicitly simulate the decrease in peat substrate quality, i.e., decomposability or the dynamics of decomposers during peat decomposition, which are key controls in determining peat carbon's response to a changing environment. In this paper, we incorporated the tracking of each year's litter input (a cohort) and controls of microbial processes into the McGill Wetland Model (MWMmic) to address this discrepancy. Three major modifications were made: (1) the simple acrotelm-catotelm decomposition model in MWM was changed into a time-aggregated cohort model, to track the decrease in peat quality with decomposition age; (2) microbial dynamics: growth, respiration and death were incorporated into the model and decomposition rates are regulated by microbial biomass; and (3) vertical and horizontal transport of the dissolved organic carbon (DOC) were added and used to regulate the growth of microbial biomass. MWMmic was evaluated against measurements from the Mer Bleue peatland, a raised ombrotrophic bog located in southern Ontario, Canada. The model was able to replicate microbial and DOC dynamics, while at the same time reproduce the ecosystem-level CO and DOC fluxes. Sensitivity analysis with MWMmic showed increased peatland resilience to perturbations compared to the original MWM, because of the tracking of peat substrate quality. The analysis revealed the most important parameters in the model to be microbial carbon use efficiency (CUE) and turnover rate. Simulated microbial adaptation with those two physiological parameters less sensitive to disturbances leads to a significantly larger peat C loss in response to warming and water table drawdown. Thus, the rarely explored peatland microbial physiological traits merit further research. This work paves the way for further model development to examine important microbial controls on peatland's biogeochemical cycling.