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

Abstract

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.

Orbital (Hydro)Climate Variability in the Ice-Free early Eocene Arctic

“…Specifically, in the modern system, soils typically show IIIa/IIa ratios below 0.59 and marine sediments show ratios above 0.92 (Xiao et al, 2016(Xiao et al, , 2020. Additionally, we used the total GDGT assemblage (isoGDGTs + brGDGTs) to infer the depositional setting using the machine learning algorithm "BigMAC" (Martínez-Sosa et al, 2023), capable of distinguishing marine, lake, peat and soil settings.…”

“…(f) GDGTs, with relative abundances (%) and absolute concentrations (ng/g of dry weight sediment) of all GDGTs. (g) BIT index, in which colors of datapoints mark the depositional environment indicated by the BIGMaC machine learning algorithm based on total GDGT distributions (Martínez-Sosa et al, 2023). (h) MBT'5me values, where green points mark datapoints with BIT > 0.4, which can be translated to MAF (top axis)).…”