Development and application of the Branched and Isoprenoid GDGT Machine learning Classification algorithm (BIGMaC) for paleoenvironmental reconstruction
Journal Article
Overview
abstract
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.
