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Glycerol dialkyl glycerol tetraethers (GDGTs), including both the archaeal isoprenoid GDGTs (isoGDGTs) and the bacterial branched GDGTs (brGDGTs), have been used in paleoclimate studies to reconstruct temperature in marine and terrestrial archives. However, GDGTs are present in many different types of environments, with relative abundances that strongly depend on the depositional setting. This suggests that GDGT distributions can be used more broadly to infer paleoenvironments in the geological past. In this study, we analyzed 1153 samples from a variety of modern sedimentary settings for both isoGDGT and brGDGTs. We used machine learning on the GDGT relative abundances from this dataset to relate the lipid distributions to the physical and chemical characteristics of the depositional settings. We observe a robust relationship between the depositional environment and the lipid distribution profiles of our samples. This dataset was used to train and test the Branched and Isoprenoid GDGT Machine learning Classification algorithm (BIGMaC), which identifies the environment a sample comes from based on the distribution of GDGTs with high accuracy. We tested the model on the sedimentary record from the Giraffe kimberlite pipe, an Eocene maar in subantarctic Canada, and found that the BIGMaC reconstruction agrees with independent stratigraphic information, provides new information about the paleoenvironment of this site, and helps improve paleotemperature reconstruction. In cases where paleoenvironments are unknown or are changing, BIGMaC can be applied in concert with other proxies to generate more refined paleoclimatic records.