Bias and uncertainty of delta13CO 2 isotopic mixing models

Patterns in the isotopic signal (stable C isotope composition; delta(13)C) of respiration (delta(13)C(R)) have led to important gains in understanding the C metabolism of many systems. Contained within delta(13)C(R) is a record of the C source mineralized, the metabolic pathway of C and the environmental conditions during which respiration occurred. Because gas samples used for analysis of delta(13)C(R) contain a mixture of CO(2) from respiration and from the atmosphere, two-component mixing models are used to identify delta(13)C(R). Measurement of ecosystem delta(13)C(R), using canopy airspace gas samples, was one of the first applications of mixing models in ecosystem ecology, and thus recommendations and guidelines are based primarily on findings from these studies. However, as mixing models are applied to other experimental conditions these approaches may not be appropriate. For example, the range in [CO(2)] obtained in gas samples from canopy air is generally less than 100 micromol mol(-1), whereas in studies of respiration from soil, foliage or tree stems, the range can span as much as 10,000 micromol mol(-1) and greater. Does this larger range in [CO(2)] influence the precision and accuracy of delta(13)C(R) estimates derived from mixing models? Does the outcome from using different regression approaches and mixing models vary depending on the range of [CO(2)]? Our research addressed these questions using a simulation approach. We found that it is important to distinguish between large (>1,000 micromol mol(-1)) and small (<100 micromol mol(-1)) ranges of CO(2) when applying a mixing model (Keeling plot or Miller-Tans) and regression approach (ordinary least squares or geometric mean regression) combination to isotopic data. The combination of geometric mean regression and the Miller-Tans mixing model provided the most accurate and precise estimate of delta(13)C(R) when the range of CO(2) is >or=1,000 micromol mol(-1).