, Volume 146, Issue 1, pp 148-156
Date: 04 Aug 2005

Controlling for anthropogenically induced atmospheric variation in stable carbon isotope studies

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Abstract

Increased use of stable isotope analysis to examine food-web dynamics, migration, transfer of nutrients, and behavior will likely result in expansion of stable isotope studies investigating human-induced global changes. Recent elevation of atmospheric CO2 concentration, related primarily to fossil fuel combustion, has reduced atmospheric CO2 δ13C (13C/12C), and this change in isotopic baseline has, in turn, reduced plant and animal tissue δ13C of terrestrial and aquatic organisms. Such depletion in CO2 δ13C and its effects on tissue δ13C may introduce bias into δ13C investigations, and if this variation is not controlled, may confound interpretation of results obtained from tissue samples collected over a temporal span. To control for this source of variation, we used a high-precision record of atmospheric CO2 δ13C from ice cores and direct atmospheric measurements to model modern change in CO2 δ13C. From this model, we estimated a correction factor that controls for atmospheric change; this correction reduces bias associated with changes in atmospheric isotopic baseline and facilitates comparison of tissue δ13C collected over multiple years. To exemplify the importance of accounting for atmospheric CO2 δ13C depletion, we applied the correction to a dataset of collagen δ13C obtained from mountain lion (Puma concolor) bone samples collected in California between 1893 and 1995. Before correction, in three of four ecoregions collagen δ13C decreased significantly concurrent with depletion of atmospheric CO2 δ13C (n ≥ 32, P ≤ 0.01). Application of the correction to collagen δ13C data removed trends from regions demonstrating significant declines, and measurement error associated with the correction did not add substantial variation to adjusted estimates. Controlling for long-term atmospheric variation and correcting tissue samples for changes in isotopic baseline facilitate analysis of samples that span a large temporal range.

Communicated by David Post