Bacterial glycerol tetraethers as a potential tool to trace marine methane cycling

Author:

Zhang Zhe‐Xuan12ORCID,Li Jiwei1ORCID,Lu Hongxuan3,Yang Huan4,Zhang Yige5,Tang Yongjie1,Fu Meiyan6,Peng Xiaotong1ORCID

Affiliation:

1. Institute of Deep‐Sea Science and Engineering, Chinese Academy of Sciences Sanya China

2. Sorbonne Université, CNRS, EPHE, PSL, UMR METIS Paris France

3. State Key Laboratory of Loess and Quaternary Geology Institute of Earth Environment, Chinese Academy of Sciences Xi'an China

4. State Key Laboratory of Biogeology and Environmental Geology, Hubei Key Laboratory of Critical Zone Evolution School of Earth Sciences, China University of Geosciences Wuhan China

5. Department of Oceanography Texas A&M University College Station Texas USA

6. College of Energy, Chengdu University of Technology Chengdu China

Abstract

AbstractBranched glycerol dialkyl glycerol tetraethers (brGDGTs) are bacterial lipids that can be preserved in sedimentary archives for tens of millions of years and are ubiquitous in diverse environments, including cold seep systems. Their potential implications for detecting methane activity in deep time are, however, hampered by the multiple sources of brGDGTs in cold seeps and the lack of evidence of their stable carbon isotopes. Here, we show that brGDGTs in cold seeps are characterized by depleted stable carbon isotopic compositions of the alkyl moieties (δ13C = −32.9‰ to −82.7‰), indicating a methane metabolizing community origin, which is supported by the association between 16S rRNA genes and brGDGTs. We further identify unique seep‐derived brGDGT signals from the global published dataset by a tree‐based machine‐learning algorithm. This trained model, named light gradient‐boosting machine classification for paleoSEEP (GBM_SEEP), is further applied on a paleorecord across the Paleocene–Eocene Thermal Maximum (PETM), which suggests potential methane emission events during the PETM recovery phase. Collectively, our study links brGDGT production in cold seeps with methane metabolizing communities and provides a potential strategy to capture significant methane emission events using the machine‐learning model, which warrants further investigation.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Wiley

Subject

Aquatic Science,Oceanography

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