A MONTE CARLO APPROACH TO QUANTIFYING UNCERTAINTY IN C4 BIOMASS ESTIMATES FROM PALEOSOL ORGANIC MATTER AND CARBONATES

The Neogene C₃-C₄ transition is one of the most profound ecological reorganization in the Cenozoic Era. The causes behind C₄ plant proliferation remain actively debated, but are likely due to combined effects from climate, biogeography, and ecological processes. Nearly all modern trees, shrubs, and cool-growing-season gasses use the C₃ (Calvin cycle) photosynthetic pathway to fix atmospheric CO₂, which strongly favors ¹²C over ¹³C and results in very low δ¹³C values of plant tissue. In contrast, modern warm-growing season grasses use C₄ (Hatch-Slack) photosynthesis, which has a lower discrimination against ¹³C and results in plant tissues with higher δ¹³C values. Because the C₄ pathway is advantageous in times of low atmospheric pCO₂ and conditions of water stress, the relative proportion of C₃ to C₄ biomass from past landscapes is widely used as a paleoclimate proxy. Current methods used to estimate the fraction of C₄ biomass (f_C4) from pedogenic archives involve calculating simple linear mass-balance from representative endmember δ¹³C values for C₃ and C₄ plants, but this technique lacks robust uncertainty analysis. We developed a Monte Carlo resampling approach in order to incorporate uncertainties in calculating f_C4 from paleosols. This method includes realistic C₃ and C₄ plant δ¹³C distributions based on modern plants, uncertainty in past atmospheric CO₂ δ¹³C value, variation within measured substrates, uncertainty in temperature-dependent ¹³C/¹²C fractionation during carbonate precipitation, and laboratory analysis uncertainty. Applying this approach on paleosols from Miocene paleosols in North America and eastern Africa resulted in very large but realistic uncertainties in f_C4 estimates, despite efforts to cull input data based on a priori estimates of past rainfall levels. We argue that the Monte Carlo approach is an improved method for reporting f_C4 from paleosol substrates and will help to guide ongoing work seeking to improve paleovegetation reconstructions.