Dr. Contreras-Quintana, Sergio

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.

Gram-negative bacteria produce specific membrane lipids, i.e. 3-hydroxy fatty acids with 10 to 18 C atoms. They have been recently proposed as temperature and pH proxies in terrestrial settings. Nevertheless, the existing correlations between pH or temperature and indices derived from 3-OH FA distribution are based on a small soil dataset (ca. 70 samples) and only applicable regionally. The aim of this study was to investigate the applicability of 3-OH FAs as mean annual air temperature (MAAT) and pH proxies at the global level. This was achieved using an extended soil dataset of 168 topsoils distributed worldwide, covering a wide range of temperatures (5 to 30 ∘C) and pH (3 to 8). The response of 3-OH FAs to temperature and pH was compared to that of established branched glycerol dialkyl glycerol tetraether (GDGT)-based proxies (MBT'5Me/CBT). Strong linear relationships between 3-OH-FA-derived indices (RAN15, RAN17 and RIAN) and MAAT or pH could only be obtained locally for some of the individual transects. This suggests that these indices cannot be used as palaeoproxies at the global scale using simple linear regression models, in contrast with the MBT'5Me and CBT. However, strong global correlations between 3-OH FA relative abundances and MAAT or pH were shown by using other algorithms (multiple linear regression, k-NN and random forest models). The applicability of the three aforementioned models for palaeotemperature reconstruction was tested and compared with the MAAT record from a Chinese speleothem. The calibration based on the random forest model appeared to be the most robust. It generally showed similar trends with previously available records and highlighted known climatic events poorly visible when using local 3-OH FA calibrations. Altogether, these results demonstrate the potential of 3-OH FAs as palaeoproxies in terrestrial settings.