GDGT distribution in tropical soils and its potential as a terrestrial paleothermometer revealed by Bayesian deep-learning models

Christoph Haggi*, B D A Naafs, Daniele Silvestro, Dailson J. Bertassoli Jr., Thomas K. Akabane, Vinicius R. Mendes, Andre O. Sawakuchi, Cristiano M. Chiessi, Carlos A. Jaramillo, Sarah J. Feakins

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

9 Citations (Scopus)

Abstract

Branched and isoprenoidal glycerol dialkyl glycerol tetraethers (br- and isoGDGTs) are membrane lipids produced by bacteria and archaea, respectively. These lipids form the basis of several frequently used paleoclimatic proxies. For example, the degree of methylation of brGDGTs (MBT’5Me) preserved in mineral soils (as well as peats and lakes) is one of the most important terrestrial paleothermometers, but features substantial variability that is so far insufficiently constrained. The distribution of isoGDGTs in mineral soils has received less attention and applications have focused on the use of the relative abundance of the isoGDGT crenarchaeol versus brGDGTs (BIT index) as an indicator of aridity. To expand our knowledge of the factors that can impact the br- and isoGDGT distribution in mineral soils, including the MBT’5Me index, and to improve isoGDGT-based precipitation reconstructions, we surveyed the GDGT distribution in a large collection of mineral surface soils (n = 229) and soil profiles (n = 22) across tropical South America. We find that the MBT’5Me index is significantly higher in grassland compared to forest soils, even among sites with the same mean annual air temperature. This is likely a result of a lack of shading in grasslands, leading to warmer soils. We also find a relationship between MBT’5Me and soil pH in tropical soils. Together with existing data from arid areas in mid-latitudes, we confirm the relationship between the BIT-index and aridity, but also find that the isoGDGT distribution alone is aridity dependent. The combined use of the BIT-index and isoGDGTs can strengthen reconstructions of past precipitation in terrestrial archives. In terms of site-specific variations, we find that the variability in BIT and MBT’5Me is larger at sites that show on average lower BIT and MBT’5Me values. In combination with modelling results, we suggest that this pattern arises from the mathematical formulation of these proxies that amplifies variability for intermediate values and mutes it for values close to saturation (value of 1). Soil profiles show relatively little variation with depth for the brGDGT indices. On the other hand, the isoGDGT distribution changes significantly with depth as does the relative abundance of br- versus isoGDGTs. This pattern is especially pronounced for the isoGDGTIsomerIndex where deeper soil horizons show a near absence of isoGDGT isomers. This might be driven by archaeal community changes in different soil horizons, potentially driven by the difference between aerobic and anaerobic archaeal communities. Finally, we use our extensive new dataset and Bayesian neural networks (BNNs) to establish new brGDGT-based temperature models. We provide a tropical soil calibration that removes the pH dependence of tropical soils (n = 404; RMSE = 2.0 °C) and global peat and soil models calibrated against the temperature of the months above freezing (n = 1740; RMSE = 2.4) and mean annual air temperature (n = 1740; RMSE = 3.6). All models correct for the bias found in arid samples. We also successfully test the new calibrations on Chinese loess records and tropical river sediments. Overall, the new calibrations provide improved temperature reconstructions for terrestrial archives.
Original languageEnglish
Pages (from-to)41-64
Number of pages24
JournalGeochimica et Cosmochimica Acta
Volume362
Early online date20 Sept 2023
DOIs
Publication statusPublished - 1 Dec 2023

Bibliographical note

Funding Information:
This project was funded by a Swiss National Science Foundation (SNF) mobility fellowship (grant P400P2_183856 ) to CH. We acknowledge undergraduate laboratory assistants at USC: Betelhem Assefa, Jonnie Dolan, Lindsay Luchinsky, Dea Kurti, Christopher Rincon and Sharon Tu. Soils were imported under USDA Permit P330-19-00164. We thank NERC for partial funding of the National Environmental Isotope Facility (NEIF; contract no. NE/V003917/1) and associated HPLC-MS capabilities at the University of Bristol. B.D.A.N. acknowledges a Royal Society Tata University Research Fellowship for funding. AOS is supported by CNPq (grant 307179/2021-4 ) and FAPESP (grant 2018/23899-2 ). Sampling of soils in eastern Amazon was funded by FAPESP (grant 2016/02656-9 ). DJB was financially supported by the São Paulo Research Foundation (FAPESP) (grants #2019/24977-0 and #2022/06440-1 ). TKA acknowledges the financial support from FAPESP (grants 2019/19948-0 and 2021/13129-8 ). D.S. received funding from the Swiss National Science Foundation ( PCEFP3_187012 ) and from the Swedish Research Council ( VR: 2019-04739 ), and the Swedish Foundation for Strategic Environmental Research MISTRA within the framework of the research programme BIOPATH (F 2022/1448). CMC acknowledges the financial support from FAPESP (grants 2018/15123-4 and 2019/24349-9 ), CNPq (grant 312458/2020-7 ) and the 2019-2020 BiodivERsA joint call for research proposals, under the BiodivClim ERA-Net COFUND programme. We thank Hongxuan Lu, Weiguo Liu, Hong Yang for making the data from the Xifeng and Lantian loess sections available and Changyan Tang and Shucheng Xie for providing the data for the Weinan loess section. We thank four anonymous reviewers for their helpful comments.

Funding Information:
This project was funded by a Swiss National Science Foundation (SNF) mobility fellowship (grant P400P2_183856) to CH. We acknowledge undergraduate laboratory assistants at USC: Betelhem Assefa, Jonnie Dolan, Lindsay Luchinsky, Dea Kurti, Christopher Rincon and Sharon Tu. Soils were imported under USDA Permit P330-19-00164. We thank NERC for partial funding of the National Environmental Isotope Facility (NEIF; contract no. NE/V003917/1) and associated HPLC-MS capabilities at the University of Bristol. B.D.A.N. acknowledges a Royal Society Tata University Research Fellowship for funding. AOS is supported by CNPq (grant 307179/2021-4) and FAPESP (grant 2018/23899-2). Sampling of soils in eastern Amazon was funded by FAPESP (grant 2016/02656-9). DJB was financially supported by the São Paulo Research Foundation (FAPESP) (grants #2019/24977-0 and #2022/06440-1). TKA acknowledges the financial support from FAPESP (grants 2019/19948-0 and 2021/13129-8). D.S. received funding from the Swiss National Science Foundation (PCEFP3_187012) and from the Swedish Research Council (VR: 2019-04739), and the Swedish Foundation for Strategic Environmental Research MISTRA within the framework of the research programme BIOPATH (F 2022/1448). CMC acknowledges the financial support from FAPESP (grants 2018/15123-4 and 2019/24349-9), CNPq (grant 312458/2020-7) and the 2019-2020 BiodivERsA joint call for research proposals, under the BiodivClim ERA-Net COFUND programme. We thank Hongxuan Lu, Weiguo Liu, Hong Yang for making the data from the Xifeng and Lantian loess sections available and Changyan Tang and Shucheng Xie for providing the data for the Weinan loess section. We thank four anonymous reviewers for their helpful comments. Research data, including the brGDGT, isoGDGT abundances, derived indices and allied metadata corresponding to the soil samples can be accessed in the Supplementary Material and at www.pangaea.de. BNN models presented in this study are available as Python code on the GITHUB following repository https://github.com/dsilvestro/gdgt-ai.

Publisher Copyright:
© 2023 The Authors

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