Isotope partitioning of soil respiration: A Bayesian solution to accommodate multiple sources of variability
Abstract
Isotopic methods offer great potential for partitioning trace gas fluxes such as soil respiration into their different source contributions. Traditional partitioning methods face challenges due to variability introduced by different measurement methods, fractionation effects, and end-member uncertainty. To address these challenges, we describe in this paper a hierarchical Bayesian (HB) approach for isotopic partitioning of soil respiration that directly accommodates such variability. We apply our HB method to data from an experiment conducted in a shortgrass steppe ecosystem, where decomposition was previously shown to be stimulated by elevated CO2. Our approach simultaneously fits Keeling plot (KP) models to observations of soil or soil-respired δ13C and [CO2] obtained via chambers and gas wells, corrects the KP intercepts for apparent fractionation (Δ) due to isotope-specific diffusion rates and/or method artifacts, estimates method- and treatment-specific values for Δ, propagates end-member uncertainty, and calculates proportional contributions from two distinct respiration sources (“old” and “new” carbon). The chamber KP intercepts were estimated with greater confidence than the well intercepts and compared to the theoretical value of 4.4‰, our results suggest that Δ varies between 2 and 5.2‰ depending on method (chambers versus wells) and CO2 treatment. Because elevated CO2 plots were fumigated with 13C-depleted CO2,more »
- Authors:
-
- Arizona State Univ., Tempe, AZ (United States). School of Life Sciences
- Univ. of Western Sydney, NSW (Australia). Hawkesbury Inst. for the Environment; Univ. of Wyoming, Laramie, WY (United States). Dept. of Botany. Program in Ecology
- Publication Date:
- Research Org.:
- Univ. of Wyoming, Laramie, WY (United States); Arizona State Univ., Tempe, AZ (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); USDA Cooperative State Research, Education, and Extension Service (CSREES); National Science Foundation (NSF)
- OSTI Identifier:
- 1454917
- Alternate Identifier(s):
- OSTI ID: 1402370
- Grant/Contract Number:
- SC0006973; 2008-35107-18655; DEB1021559
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Geophysical Research. Biogeosciences
- Additional Journal Information:
- Journal Volume: 120; Journal Issue: 2; Journal ID: ISSN 2169-8953
- Publisher:
- American Geophysical Union
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Bayesian modeling; elevated CO2 experiment; end-member uncertainty; isotopic partitioning; Keeling plots; soil respiration
Citation Formats
Ogle, Kiona, and Pendall, Elise. Isotope partitioning of soil respiration: A Bayesian solution to accommodate multiple sources of variability. United States: N. p., 2015.
Web. doi:10.1002/2014JG002794.
Ogle, Kiona, & Pendall, Elise. Isotope partitioning of soil respiration: A Bayesian solution to accommodate multiple sources of variability. United States. https://doi.org/10.1002/2014JG002794
Ogle, Kiona, and Pendall, Elise. Tue .
"Isotope partitioning of soil respiration: A Bayesian solution to accommodate multiple sources of variability". United States. https://doi.org/10.1002/2014JG002794. https://www.osti.gov/servlets/purl/1454917.
@article{osti_1454917,
title = {Isotope partitioning of soil respiration: A Bayesian solution to accommodate multiple sources of variability},
author = {Ogle, Kiona and Pendall, Elise},
abstractNote = {Isotopic methods offer great potential for partitioning trace gas fluxes such as soil respiration into their different source contributions. Traditional partitioning methods face challenges due to variability introduced by different measurement methods, fractionation effects, and end-member uncertainty. To address these challenges, we describe in this paper a hierarchical Bayesian (HB) approach for isotopic partitioning of soil respiration that directly accommodates such variability. We apply our HB method to data from an experiment conducted in a shortgrass steppe ecosystem, where decomposition was previously shown to be stimulated by elevated CO2. Our approach simultaneously fits Keeling plot (KP) models to observations of soil or soil-respired δ13C and [CO2] obtained via chambers and gas wells, corrects the KP intercepts for apparent fractionation (Δ) due to isotope-specific diffusion rates and/or method artifacts, estimates method- and treatment-specific values for Δ, propagates end-member uncertainty, and calculates proportional contributions from two distinct respiration sources (“old” and “new” carbon). The chamber KP intercepts were estimated with greater confidence than the well intercepts and compared to the theoretical value of 4.4‰, our results suggest that Δ varies between 2 and 5.2‰ depending on method (chambers versus wells) and CO2 treatment. Because elevated CO2 plots were fumigated with 13C-depleted CO2, the source contributions were tightly constrained, and new C accounted for 64% (range = 55–73%) of soil respiration. The contributions were less constrained for the ambient CO2 treatments, but new C accounted for significantly less (47%, range = 15–82%) of soil respiration. Finally, our new HB partitioning approach contrasts our original analysis (higher contribution of old C under elevated CO2) because it uses additional data sources, accounts for end-member bias, and estimates apparent fractionation effects.},
doi = {10.1002/2014JG002794},
journal = {Journal of Geophysical Research. Biogeosciences},
number = 2,
volume = 120,
place = {United States},
year = {Tue Jan 06 00:00:00 EST 2015},
month = {Tue Jan 06 00:00:00 EST 2015}
}
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