A HIERARCHICAL BAYESIAN APPROACH TO THE CLASSIFICATION OF C3 AND C4 GRASSES BASED ON THE δ13C DATA OF INDIVIDUAL POLLEN GRAINS

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 d¹³C values. To address several limitations of this novel technique, we expanded an existing SPIRAL training dataset of pollen d¹³C data from 8 to 31 grass species. For field validation, we analyzed d¹³C 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) d¹³C 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% C₄ for 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 d¹³C-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.