FROG: A global machine-learning temperature calibration for branched GDGTs in soils and peats
Date
2022Author
Véquaud, Pierre
Thibault, Alexandre
Derenne, Sylvie
Anquetil, Christelle
Collin, Sylvie
Contreras, Sergio
Nottingham, Andrew T.
Sabatier, Pierre
Werne, Josef P.
Huguet, Arnaud
Publisher
Geochimica et Cosmochimica ActaDescription
Artículo de publicación WOS - SCOPUSMetadata
Show full item recordAbstract
wBranched 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 devel-
opment of refined models, the root-mean-square error (RMSE) associated with global calibrations between brGDGT distri-
bution 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.