A hierarchical Bayesian approach to the classification of C3 and C4 grass pollen based on SPIRAL δ13C data

Michael A. Urban, David M. Nelson, Ryan Kelly, Tahir Ibrahim, Michael Dietze, Ann Pearson, Feng Sheng Hu

Research output: Contribution to journalArticlepeer-review

Abstract

Differentiating C3 and C4 grass pollen in the paleorecord is difficult because of their morphological similarity. Using a spooling wire microcombustion device interfaced with an isotope ratio mass spectrometer, Single Pollen Isotope Ratio AnaLysis (SPIRAL) enables classification of grass pollen as C3 or C4 based upon δ13C values. To address several limitations of this novel technique, we expanded an existing SPIRAL training dataset of pollen δ13C data from 8 to 31 grass species. For field validation, we analyzed δ13C of individual grains of grass pollen from the surface sediments of 15 lakes in Africa and Australia, added these results to a prior dataset of 10 lakes from North America, and compared C4-pollen abundance in surface sediments with C4-grass abundance on the surrounding landscape. We also developed and tested a hierarchical Bayesian model to estimate the relative abundance of C3- and C4-grass pollen in unknown samples, including an estimation of the likelihood that either pollen type is present in a sample. The mean (±SD) δ13C values for the C3 and C4 grasses in the training dataset were -29.6±9.5‰ and -13.8±9.5‰, respectively. Across a range of % C4 in samples of known composition, the average bias of the Bayesian model was <3% for C4 in samples of at least 50 grains, indicating that the model accurately predicted the relative abundance of C4 grass pollen. The hierarchical framework of the model resulted in less bias than a previous threshold-based C3/C4 classification method, especially near the high or low extremes of C4 abundance. In addition, the percent of C4 grass pollen in surface-sediment samples estimated using the model was strongly related to the abundance of C4 grasses on the landscape (n=24, p<0.001, r2=0.65). These results improve δ13C-based quantitative reconstructions of grass community composition in the paleorecord and demonstrate the utility of the Bayesian framework to aid the interpretation of stable isotope data.

Original languageEnglish (US)
Pages (from-to)168-176
Number of pages9
JournalGeochimica et Cosmochimica Acta
Volume121
DOIs
StatePublished - Nov 15 2013

ASJC Scopus subject areas

  • Geochemistry and Petrology

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