Isotope partitioning of soil respiration: A Bayesian solution to accommodate multiple sources of variability

Ogle, Kiona; Pendall, Elise

doi:10.1002/2014JG002794

Isotope partitioning of soil respiration: A Bayesian solution to accommodate multiple sources of variability

Journal Article · Tue Jan 06 00:00:00 EST 2015 · Journal of Geophysical Research. Biogeosciences

DOI:https://doi.org/10.1002/2014JG002794· OSTI ID:1454917

Ogle, Kiona ^[1]; Pendall, Elise ^[2]

Arizona State Univ., Tempe, AZ (United States). School of Life Sciences; Arizona State University
Univ. of Western Sydney, NSW (Australia). Hawkesbury Inst. for the Environment; Univ. of Wyoming, Laramie, WY (United States). Dept. of Botany. Program in Ecology

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.

View Accepted Manuscript (DOE)

Research Organization:: Arizona State Univ., Tempe, AZ (United States); Univ. of Wyoming, Laramie, WY (United States)

Sponsoring Organization:: National Science Foundation (NSF) (United States); USDA Cooperative State Research, Education, and Extension Service (CSREES); USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)

Grant/Contract Number:: SC0006973

OSTI ID:: 1454917

Alternate ID(s):: OSTI ID: 1402370

Journal Information:: Journal of Geophysical Research. Biogeosciences, Journal Name: Journal of Geophysical Research. Biogeosciences Journal Issue: 2 Vol. 120; ISSN 2169-8953

Publisher:: American Geophysical UnionCopyright Statement

Country of Publication:: United States

Language:: English

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54 ENVIRONMENTAL SCIENCES
Bayesian modeling
Keeling plots
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end-member uncertainty
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Isotope partitioning of soil respiration: A Bayesian solution to accommodate multiple sources of variability

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