Development and Application of the Branched and Isoprenoid GDGT Machine Learning Classification Algorithm (BIGMaC) for Paleoenvironmental Reconstruction.

Glycerol dialkyl glycerol tetraethers (GDGTs), both archaeal isoprenoid GDGTs (isoGDGTs) and bacterial branched GDGTs (brGDGTs), have been used in paleoclimate studies to reconstruct environmental conditions. Since GDGTs are produced in many types of environments, their relative abundances also depend on the depositional setting. This suggests that the distribution of GDGTs also preserves useful information that can be used more broadly to infer these depositional environments in the geological past. Here, we combined existing iso‐ and brGDGT relative abundance data with newly analyzed samples to generate a database of 1,153 samples from several modern sedimentary settings. We observed a robust relationship between the depositional environment and the relative abundances of GDGTs in our samples. This data set was used to train and test the Branched and isoGDGT Machine learning Classification (BIGMaC) algorithm, which identifies the environment a sample comes from based on the distribution of GDGTs with high precision and recall (F1 = 0.95). 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 and palynological information, provides new information about the paleoenvironment of this site, and helps improve its paleotemperature reconstruction. In contrast, we also include an example from the PETM‐aged Cobham lignite as a cautionary example that illustrates the limitations of the algorithm. We propose that in cases where paleoenvironments are unknown or are changing, BIGMaC can be applied in concert with other proxies to generate more refined paleoclimate records. Key Points: The distribution of glycerol dialkyl glycerol tetraethers (GDGTs) is particular to each depositional environment and has unique responses to environmental factorsThe BIGMaC algorithm captures the relationship between both branched and isoprenoid GDGTs (isoGDGTs) with depositional environmentsOur approach can provide paleoclimatological and paleoenvironmental information based only on GDGTs [ABSTRACT FROM AUTHOR]