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The Long chain Diol Index: A marine palaeotemperature proxy based on eustigmatophyte lipids that records the warmest seasons.

Sebastiaan W RampenThomas FriedlNataliya RybalkaVolker Thiel
Published in: Proceedings of the National Academy of Sciences of the United States of America (2022)
Long chain 1,13- and 1,15-diols are lipids which are omnipresent in marine environments, and the Long chain Diol Index (LDI), based on their distributions, has previously been introduced as a proxy for sea surface temperature. The main biological sources for long chain 1,13- and 1,15-diols have remained unknown, but our combined lipid and 23S ribosomal RNA (23S rRNA) analyses on suspended particulate matter from the Mediterranean Sea demonstrate that these lipids are produced by a marine eustigmatophyte group that originated before the currently known eustigmatophytes diversified. The 18S rRNA data confirm the existence of early-branching marine eustigmatophytes, which occur at a global scale. Differences between LDI records and other paleotemperature proxies are generally attributed to differences between the seasons in which the proxy-related organisms occur. Our results, combined with available LDI data from surface sediments, indicate that the LDI primarily registers temperatures from the warmest month when mixed-layer depths, salinity, and nutrient concentrations are low. The LDI may not be applicable in areas where Proboscia diatoms contribute 1,13-diols, but this can be recognized by enhanced contributions of C28 1,12 diol. Freshwater input may also affect the correlation between temperature and the LDI, but relative C32 1,15-diol abundances help to identify and correct for these effects. When taking those factors into account, the calibration error of the LDI is 2.4 °C. As a well-defined proxy for temperatures of the warmest seasons, the LDI can unlock important and previously inaccessible paleoclimate information and will thereby substantially improve our understanding of past climate conditions.
Keyphrases
  • particulate matter
  • air pollution
  • fatty acid
  • microbial community
  • heavy metals
  • machine learning
  • social media
  • drinking water
  • risk assessment