Xuhui Zhou, The University of Oklahoma, Yiqi Luo, University of Oklahoma, Paul S.J. Verburg, Desert Research Institute, Jay Arnone, Desert Research Institute,, and David S. Schimel, National Ecological Observatory Network.
Background/Question/Methods Partitioning soil respiration into autotrophic (RA) and heterotrophic (RH) components is critical for understanding their differential responses to climate change. Past research with conventional methods has encountered considerable difficulty in RA vs. RH partitioning. In this study, we used a kinetic-based deconvolution analysis for partitioning of automated soil respiration data from a pulse warming experiment in the EcoCELLs facility of Desert Research Institute, Nevada. We first conducted a sensitivity analysis with all parameters to determine which parameters can be identified by observations of soil respiration. A Markov chain Monte Carlo (MCMC) technique was then used to optimize the identified parameters (i.e., C transfer coefficients and variables of environmental effects) in a 10-pool terrestrial ecosystem (TECO) model. At last, the optimized model was employed to quantify RA and RH in a forward analysis. Results/Conclusions
Our results show that 7 or 8 of 13 parameters were constrained by daily soil respiration. Warming stimulated RH and had little effects on RA in the first two months of the experiment and then decreased them in the rest of the treatment year and one post-treatment year in comparison with those in control. Overall, warming significantly decreased RA and RH by 28.4% and 19.8%, respectively, during the treatment year and by 26.5% and 32.7%, respectively, during the one post-treatment year. Depression of both RA and RH under warming and post- treatment largely resulted from decreased canopy greenness and biomass in the EcoCELL. Our deconvoluted results suggest that lagged effects of climate anomalies were important on soil respiration and its components and should be taken into consideration in assessing terrestrial carbon-cycle feedback to climate warming. This study also shows that this kinetic-based deconvolution analysis with the Bayesian approach is an effective tool to partition soil respiration into RA and RH and examine their responses to climate warming.
