Expanding lipid proxies to the next dimension: Developing methods for determination of oxygen isotope ratios in plant waxes

Abstract:

We seek to understand the δ¹⁸O signal of n-alkanols, a biosynthetically similar compound group to the highly studied n-alkanes. Alkanols of >24 carbons are produced at the leaf level, incorporating a transpiration enriched oxygen and hydrogen signal. The use of δ¹⁸O as a proxy is of great interest because of the more simplistic biosynthetic sourcing of oxygen in lipids. Complete equilibration of organic and water bound oxygen atoms is achieved in the Calvin cycle (all oxygen atoms are at some point in an exchangeable carbonyl group). This leads to a uniform signal among oxygen atoms incorporated through photosynthesis. Although it is analytically useful, the larger abundance of hydrogen isotopes in the same molecule leads to a more ambiguous signal, especially when integrating through soils and sediments. This study stems from recent work in our lab, which has shown significant relationships between an applied evapotranspiration deficit and δ¹⁸O in bulk lipid (hexane) extracts of plant material. While it is exciting that bulk lipids show this relationship, it is critical to first demonstrate that the isotopic signal is stable in order to use the signal from preserved alkanols as an integrator in soils and sediments. In this experiment, we show for the first time that a series of n-alkanols do not exchange oxygen with environmental water. Moving forward we are developing methods to address the analytical challenges of measuring oxygen isotope ratios of these compounds. Once this is overcome, we will be able to measure δD, δ¹⁸O, and δ¹³C from a single compound in a homogenized sample. The end result will be an improvement in the ability to interpret changes in field scale evapotranspiration, moving from a 2 dimensional (δD and δ¹³C only) to 3 dimensional (i.e. the addition of δ¹⁸O) model. This will apply to both modern and paleohydrologic relationships, improving our ability to reconstruct and predict the impacts of water balance variability across spatiotemporal scales, especially in terrestrial environments.