Peatlands harbor vast stockpiles of carbon (approximately one-fifth to one-third of global soil carbon), which are susceptible to recent and future climate change. Particularly in northern wetlands, increasing temperature and vapor pressure deficit could induce a large feedback of CO2 and CH4, as these terrestrial carbon stockpiles degrade and return to the atmosphere. Sphagnum gross primary production (GPP) is a major entry point of carbon into peatland ecosystems, making it a central component of peatland carbon cycling. This study evaluates alternative mechanistic hypotheses, represented in a process-based model, for the drivers of seasonality in Sphagnum GPP. To rigorously evaluate the alternative hypotheses, parameters were estimated against Sphagnum GPP data using Markov chain Monte Carlo (MCMC) algorithms developed in a flexible modelling software, the Multi-Assumption Architecture and Testbed (MAAT). Predictions from the optimized models (hypotheses) were then evaluated against a validation dataset. Data were collected at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment sited in the Marcell Experimental Forest in northern Minnesota. Sphagnum magellanicum GPP fluxes were calculated from hourly measurements of gas exchange in LI-8100s situated in hollows throughout the growing seasons from 2014-2018.
Results/Conclusions
This study applied the developed MCMC algorithms to compare model fit between two alternate Sphagnum GPP models that represent two hypotheses – constant Shoot Area Index (SAI) and dynamic SAI. In this analysis, SAI is the photosynthesizing tissue area per unit ground area. Constant SAI assumes constant SAI during the growing season, while dynamic SAI assumes an interaction between photosynthesizing tissue surface area (i.e., SAI) and fluctuating water table height, reflecting the idea that submerged tissue is not photosynthetically active. The MCMC parameter estimation processes implemented in MAAT formally showed that the dynamic SAI model better explained the seasonal dynamics in the estimated GPP. Thus, this study demonstrated that accurate models of Sphagnum GPP at the Marcell Experimental Forest should incorporate the interaction between changing water table levels and the Sphagnum surface. In a broader context, the parameter estimation methods developed by this study enable the discovery of the most parsimonious model of Sphagnum GPP from a candidate set of models. By determining which Sphagnum GPP model is better equipped at describing a set of observed data, the most parsimonious model can subsequently be chosen for use in a predictive setting, thereby contributing to carbon cycle and climate change research.