A hierarchical Bayesian approach to the classification of C3 and C4 grass pollen based on SPIRAL Î ́13C data

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 d C values. To address several limitations of this novel technique, we expanded an existing SPIRAL training dataset of pollen dC data from 8 to 31 grass species. For field validation, we analyzed dC 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 C3and C4-grass pollen in unknown samples, including an estimation of the likelihood that either pollen type is present in a sample. The mean (±SD) dC 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, r 2 = 0.65). These results improve dC-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. ! 2013 Elsevier Ltd. All rights reserved.