Concurrent and lagged impacts of an anomalously warm year on autotrophic and heterotrophic components of soil respiration: a deconvolution analysis

• Published in: The New phytologist Vol. 187; no. 1; pp. 184 - 198
• Main Authors: Zhou, Xuhui, Luo, Yiqi, Gao, Chao, Verburg, Paul S.J, Arnone, John A. III, Darrouzet-Nardi, Anthony, Schimel, David S
• Format: Journal Article
• Language: English
• Published: Oxford, UK Oxford, UK : Blackwell Publishing Ltd 01.07.2010; Blackwell Publishing; Blackwell Publishing Ltd; Wiley Subscription Services, Inc
• Subjects: Analysis of Variance; Anomalies; Autotrophic Processes - physiology; autotrophic respiration; Bayesian; Biomass; Carbon - analysis; Carbon cycle; Carbon dioxide; Cell Respiration; Climate; Climate change; Climate effects; Components; Deconvolution; EcoCELL; Ecosystem models; Forest soils; Global warming; Heterotrophic Processes - physiology; heterotrophic respiration; Likelihood Functions; Markov chain Monte Carlo (MCMC); Markov chains; Mathematical models; Modeling; Models, Biological; Parameter identification; Parameter sensitivity; Parameters; Plant Cells; Plant cover; Plants - metabolism; Respiration; Sensitivity analysis; Soil; Soil - analysis; Soil ecology; Soil heating; Soil respiration; Soils; Statistical methods; Temperature; Terrestrial ecosystems; Terrestrial environments; warming; Water
• ISSN: 0028-646X; 1469-8137
• DOI: 10.1111/j.1469-8137.2010.03256.x

Partitioning soil respiration into autotrophic (RA) and heterotrophic (RH) components is critical for understanding their differential responses to climate warming. Here, we used a deconvolution analysis to partition soil respiration in a pulse warming experiment. We first conducted a sensitivity analysis to determine which parameters can be identified by soil respiration data. A Markov chain Monte Carlo technique was then used to optimize those identifiable parameters in a terrestrial ecosystem model. Finally, the optimized parameters were employed to quantify RA and RH in a forward analysis. Our results displayed that more than one-half of parameters were constrained by daily soil respiration data. The optimized model simulation showed that warming stimulated RH and had little effect on RA in the first 2 months, but decreased both RH and RA during the remainder of the treatment and post-treatment years. Clipping of above-ground biomass stimulated the warming effect on RH but not on RA. Overall, warming decreased RA and RH significantly, by 28.9% and 24.9%, respectively, during the treatment year and by 27.3% and 33.3%, respectively, during the post-treatment year, largely as a result of decreased canopy greenness and biomass. Lagged effects of climate anomalies on soil respiration and its components are important in assessing terrestrial carbon cycle feedbacks to climate warming.