Dr. Contreras-Quintana, Sergio

Quantitative reconstructions of past continental climates are vital for understanding contemporary and past climate change. Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are unique bacterial lipids that have been proposed as universal paleothermometers due to their correlation with temperature in modern settings. Thus, brGDGTs may serve as a crucial paleotemperature proxy for understanding past climate variations and improving regional climate projections, especially in critical but under constrained regions. That said, complications can arise in their application due to varying source contributions (e.g., soils vs. peats vs. lacustrine). As such, this study investigates brGDGT distributions in Chilean lake surface sediments and corresponding watershed soils to determine the source of brGDGTs to lake sediments. Global datasets of brGDGTs in lake sediments and soils were additionally compiled for comparison. Distinct brGDGT distributions in Chilean lakes and soils indicate minimal bias from soil inputs to the lacustrine sediments as well as in situ lacustrine production of brGDGTs, which supports the use of brGDGTs in lake sediments as reliable paleotemperature proxies in the region. The ΣIIIa/ΣIIa ratio, initially promising as a brGDGT source indicator in marine settings, shows global complexities in lacustrine settings, challenging the establishment of universal thresholds for source apportionment. That said, we show that the ratio can be successfully applied in Chilean lake surface sediments. Direct comparisons with watershed soils and further research are crucial for discerning brGDGT sources in lake sediments and improving paleotemperature reconstructions on regional and global scales moving forward. Overall, this study contributes valuable insights into brGDGT variability, essential for accurate paleoreconstructions.

Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are a family of bacterial lipids which have emerged over time as robust temperature and pH paleoproxies in continental settings. Nevertheless, it was previously shown that other parameters than temperature and pH, such as soil moisture, thermal regime or vegetation can also influence the relative distribution of brGDGTs in soils and peats. This can explain a large part of the residual scatter in the global brGDGT calibrations with mean annual air temperature (MAAT) and pH in these settings. Despite improvements in brGDGT analytical methods and development of refined models, the root-mean-square error (RMSE) associated with global calibrations between brGDGT distribution and MAAT in soils and peats remains high ( 5 °C). The aim of the present study was to develop a new global terrestrial brGDGT temperature calibration from a worldwide extended dataset (i.e. 775 soil and peat samples, i.e. 112 samples added to the previously available global calibration) using a machine learning algorithm. Statistical analyses highlighted five clusters with different effects of potential confounding factors in addition to MAAT on the relative abundances of brGDGTs. The results also revealed the limitations of using a single index and a simple linear regression model to capture the response of brGDGTs to temperature changes. A new improved calibration based on a random forest algorithm was thus proposed, the so called random Forest Regression for PaleOMAAT using brGDGTs (FROG). This multi-factorial and non-parametric model allows to overcome the use of a single index, and to be more representative of the environmental complexity by taking into account the non-linear relationships between MAAT and the relative abundances of the individual brGDGTs. The FROG model represents a refined brGDGT temperature calibration (R2 = 0.8; RMSE = 4.01 °C) for soils and peats, more robust and accurate than previous global soil calibrations while being proposed on an extended dataset. This novel improved calibra- tion was further applied and validated on two paleo archives covering the last 110 kyr and the Pliocene, respectively.