Abstract

Abstract. Plant wax n-alkane chain length distribution and isotopes have been studied in modern ecosystems as proxies to reconstruct vegetation and climate of the past. However, most paleo-proxies focus on either concentrations or isotopes, whereas both carry complementary information on the mixing sources. We propose a multi-source mixing model in a Bayesian framework that evaluates both chain length distributions and isotopes simultaneously. The model consists of priors that include user-defined source groups and their associated parametric distributions of n-alkane concentration and δ13C. The mixing process involves newly defined mixing fractions such as fractional leaf mass contribution (FLMC) that can be used in vegetation reconstruction. Markov Chain Monte Carlo is used to generate samples from the posterior distribution of these parameters conditioned on both data types. We present three case studies from distinct settings. The first involves n-C27, n-C29, and n-C31 alkanes in lake surface sediments of Lake Qinghai, China. The model provides more specific interpretations on the n-alkane input from aquatic sources than the conventional Paq proxy. The second involves n-C29, n-C31, and n-C33 alkanes in lake surface sediments in Cameroon, western Africa. The model produces mixing fractions of forest C3, savanna C3, and C4 plants, offering additional information on the dominant biomes compared to the traditional two-end-member mixing regime. The third couples the vegetation source model to a hydrogen isotope model component, using biome-specific apparent fractionation factors (εa) to estimate the δ2H of mean annual precipitation. By leveraging chain length distribution, δ13C, and δ2H data of four n-alkane chains, the model produces estimated precipitation δ2H with relatively small uncertainty limits. The new framework shows promise for interpretation of paleo-data but could be further improved by including processes associated with n-alkane turnover in plants, transport, and integration into sedimentary archives. Future studies on modern plants and catchment systems will be critical to develop calibration datasets that advance the strength and utility of the framework.

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