Abstract

<p>Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are bacterial lipids that are used as paleotemperature and paleo-pH proxies. Developed originally for application in paleosoils, and geo-archives dominated by soil organic matter, they are also used for temperature reconstructions in lake sediments. For this, brGDGT ratios such as the MBT'<sub>5ME</sub> are translated into mean annual air temperatures using linear transfer functions. However, water depth (Yao et al., 2019; Stefanescu et al., 2021) has recently been shown to influence the MBT'<sub>5ME</sub> values in freshwater lakes. In addition, variable inputs of soil-derived GDGTs can skew the MBT'<sub>5ME</sub> ratio values encountered in lake sediments. Unfortunately, the diagnostic ratios used to recognize either changes in water depth (HP5) or soil input (ΣIII/ΣII), are based on the relative abundance of the same 2 compounds (IIa and IIIa).</p><p>Currently, most of the work on environmental drivers of brGDGT lipids has been done on modern lake sediments. A view on the paleo-variability, i.e. the variability on brGDGTs in lacustrine archives, was still lacking. In this contribution, we will revisit several published and unpublished brGDGT records in last glacial and/or Holocene lake sediments that report a change in soil input and/or water level with time (e.g. Cao et al., 2021; Robles et al., 2022, Ramos-Román et al., submitted). We will use a compilation of these records to highlight how changes in hydrology and soil input influence brGDGT compositions. To distinguish between soil and lake-derived GDGTs, we will employ a novel machine learning approach (linear discriminant analysis). This method allows to identify soil and lake brGDGT distributions in modern soils and sediments (85% accuracy), and is now tested for the first time in paleolacustrine settings.</p><p>We show that natural or anthropogenic changes in the landscape can impact the diagnostic GDGT ratios for soil input and the MBT'<sub>5ME</sub> ratio. The machine learning approach also allows to identify those depths where soil input is significant. This exercise is a first step in investigating the paleo-variability of brGDGTs with a machine learning approach, to determine variables that impact their downcore variability.</p><p> </p><p>Cao J., et al. 2021. Lake-level records support a mid-Holocene maximum precipitation in northern China. Science China Earth Sciences 64, 2161–2171.</p><p>Ramos-Román, M. J., et al. Lipid biomarker (brGDGT)- and pollen-based reconstruction of temperature change during the Middle to Late Holocene transition in the Carpathians. Submitted to Global and Planetary Change.</p><p>Robles, M., et al. 2022. Impact of climate changes on vegetation and human societies during the Holocene in the South Caucasus (Vanevan, Armenia): A multiproxy approach including pollen, NPPs and brGDGTs. Quaternary Science Reviews 277, 107297.</p><p>Stefanescu, I.C., et al. 2021. Temperature and water depth effects on brGDGT distributions in sub-alpine lakes of mid-latitude North America. Organic Geochemistry 152, 104174.</p><p>Yao, Y., et al. 2020. Correlation between the ratio of 5-methyl hexamethylated to pentamethylated branched GDGTs (HP5) and water depth reflects redox variations in stratified lakes. Organic Geochemistry 147.</p>

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