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LIMNOLOGY AND OCEANOGRAPHY
Volume -, Issue -, Pages -Publisher
WILEY
DOI: 10.1002/lno.12462
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This study reveals the unique stable carbon isotopic composition of brGDGTs in cold seeps, indicating their origin from methane metabolizing communities. A machine-learning model is developed to identify seep-derived brGDGT signals, and it suggests potential methane emission events in a paleorecord.
Branched 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 (delta 13C = -32.9 parts per thousand to -82.7 parts per thousand), 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.
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