Sensitivity analysis and quantification of uncertainty for isotopic mixing relationships in carbon cycle research |
| |
| Institution: | 1. Department of Mathematics, University of Utah, 155 S 1400 E, Salt Lake City, UT 84112, United States;2. Technische Universität München, Lehrstuhl für Grünlandlehre, D-85350 Freising-Weihenstephan, Germany;3. Department of Biology, University of Utah, 257 S 1400 E, Salt Lake City, UT 84112, United States;1. Atmospheric Chemistry Research Group, School of Chemistry, University of Bristol, Cantock''s Close, Bristol BS8 1TS, UK;2. rdscientific, Newbury, Berkshire, UK;3. Atmospheric Chemistry Services, Okehampton, Devon EX20 4QB, UK;4. The Centre for Atmospheric Science, The School of Earth, Atmospheric and Environmental Science, The University of Manchester, Simon Building, Brunswick Street, Manchester M13 9PL, UK;1. Department of Neurosurgery, Johns Hopkins University, Baltimore, MD, USA;2. Department of Neurosurgery, Korea University Guro Hospital, Seoul, Republic of Korea;3. Boehringer Ingelheim, Ridgefield, CT, USA;1. Prehistoric Museum, Utah State University Eastern, Price, UT 84501, USA;2. University of Colorado Museum, Boulder, CO 80309, USA;3. Oklahoma Museum of Natural History, Norman, OK 73072, USA;1. Key Laboratory of Vertebrate Evolution and Human Origin of Chinese Academy of Sciences, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing, China;2. Palaeontological Research and Education Centre, Mahasarakham University, Kantarawichai, Mahasarakham 44150, Thailand;3. Zhucheng Dinosaur Museum, Bureau of Tourism, Zhucheng, Shandong, China;1. Centre for Isotope Research (CIO), Energy and Sustainability Research Institute Groningen (ESRIG), University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands;2. Energy Research Centre of the Netherlands (ECN), P.O. Box 1, 1755 ZG Petten, The Netherlands;1. Mech-Sense, Department of Gastroenterology & Hepatology, Aalborg University Hospital, Aalborg, Denmark;2. Mech-Sense, Department of Radiology, Aalborg University Hospital, Aalborg, Denmark;3. Department of Clinical Medicine, Aalborg University, Aalborg, Denmark;4. Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark |
| |
| Abstract: | Quantifying and understanding the uncertainty in isotopic mixing relationships is critical to isotopic applications in carbon cycle studies at all spatial and temporal scales. Studies that depend on stable isotope approaches must also address quantification of uncertainty for parameters derived from isotopic studies. An important application of isotopic mixing relationships is determination of the isotopic content of ecosystem respiration (δ13CS) via an inverse relationship (a Keeling plot) between atmospheric CO2 concentrations (CO2]) and carbon isotope ratios of CO2 (δ13C). Alternatively, a linear relationship between CO2] and the product of CO2] and δ13C (a Miller/Tans plot) can also be applied.We used three datasets of CO2] and δ13C in air to examine contrasting approaches to determine δ13CS and its uncertainty. These datasets were from the Niwot Ridge, Colorado, AmeriFlux site, the Biosphere-Atmosphere Stable Isotope Network (BASIN), and from the Grünschwaige Grassland Research Station in Germany. The analysis of this data included Keeling plots and Miller/Tans plots fit with both Model I (ordinary least squares) and Model II regressions (geometric mean regression and orthogonal distance regression).Our analysis confirms previous observations that increasing the range of the measurements (CO2] range) used for a mixing line reduces the uncertainty associated with δ13CS. Using a Model II regression technique to determine δ13CS introduces a negatively skewed bias in δ13CS which is especially significant for small CO2] ranges. This bias arises from comparatively greater variability in the dependent variable than the independent variable for a linear regression. For carbon isotope studies, uncertainty in the isotopic measurements has a greater effect on the uncertainty of δ13CS than the uncertainty in CO2]. As a result, studies that estimate parameters via a Model II regression technique maybe biased in their conclusions. In contrast to earlier studies, we advocate Model I (ordinary least squares) regression to calculate δ13CS and its uncertainty. Reducing the uncertainty of isotopic measurements reduces the uncertainty of δ13CS, even when the CO2] range of samples is small (<20 ppm). As a result, improvement in isotope (rather than CO2]) measuring capability is presently needed to substantially reduce uncertainty in δ13CS. We find for carbon isotope studies no inherent advantage or disadvantage to using either a Keeling or Miller/Tans approach to determine δ13CS. We anticipate that the mathematical methods developed in this paper can be applied to other applications where linear regression is utilized. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
