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Frontiers of Earth Science

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Front. Earth Sci.    2018, Vol. 12 Issue (4) : 862-876    https://doi.org/10.1007/s11707-018-0722-z
RESEARCH ARTICLE
Distribution of glycerol ethers in Turpan soils: implications for use of GDGT-based proxies in hot and dry regions
Jingjie ZANG1, Yanyan LEI2, Huan YANG1()
1. State Key Laboratory of Biogeology and Environmental Geology, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China
2. Department of Geology and Environmental Science, University of Pittsburgh, Pittsburgh, PA 15260, USA
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Abstract

Proxies based on glycerol dialkyl glycerol tetraethers (GDGTs), a suite of membrane lipids occurring ubiquitously in soils, have generated increasing interest in quantitative paleo-environmental reconstruction. Hot and dry climates are likely to have occurred in the geological past; however, the limitations and applicability of these proxies to hot and dry environments are still unknown. In this study, we analyzed the GDGT distribution in the Turpan (TRP) basin of China, where the highest soil temperature can be approximately 70°C, and the mean annual precipitation (MAP) is 15.3 mm. We compared GDGT-based proxies among TRP soils, Nanyang (NY) soils, and Kunming (KM) soils; these three sites exhibit similar mean annual air temperature (MAAT) albeit contrasting temperature seasonality and MAP. Archaeal isoprenoidal GDGTs (isoGDGTs) dominate over bacterial branched GDGTs (brGDGTs) in most TRP soils; this is a characteristic GDGT distribution pattern for soils from dry regions globally. Another feature is the anomalously high GDGT-0/crenarchaeol ratio, which is generally attributed to the contribution of anaerobic methanogenic archaea by previous studies; however, these anaerobic archaea are unlikely to be highly abundant in the dry TRP soils, indicating that certain uncultured halophilic Euryarchaeota are likely to produce a significant amount of GDGT-0 that finally results in a high GDGT-0/Cren ratio. The changes in the salinity of the TRP soils appear to be an important factor affecting the MBT’5ME and the relative abundance of 6- vs. 5-methyl pentamethylated brGDGTs (IRIIa’). This is likely to introduce certain scatters in the correlations between MBT’5ME and MAAT and that between IRIIa’ and pH determined at the global scale. A comparison of the MBT’5ME-inferred temperature between TRP, NY, and KM soils does not indicate a significant bias toward summer temperature, indicating that brGDGT paleo-thermometers in soils could reflect the MAAT.
Keywords GDGTs      Turpan soils      semi-arid and arid areas      salinity      MBT’5ME     
Corresponding Author(s): Huan YANG   
Just Accepted Date: 28 August 2018   Online First Date: 21 September 2018    Issue Date: 20 November 2018
 Cite this article:   
Jingjie ZANG,Yanyan LEI,Huan YANG. Distribution of glycerol ethers in Turpan soils: implications for use of GDGT-based proxies in hot and dry regions[J]. Front. Earth Sci., 2018, 12(4): 862-876.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-018-0722-z
https://academic.hep.com.cn/fesci/EN/Y2018/V12/I4/862
Fig.1  Structures of archaeol, archaeal isoprenoid GDGTs (isoGDGTs), and bacterial branched GDGTs (brGDGTs).
Fig.2  Sampling sites in TRP basin. The sampling site for each soil sample is listed below: Site 1, TRP-01; Site 2, TRP-02 and TRP-03; Site 3, TRP-04; Site 4, TRP-07 and TRP-08; Site 5, TRP-09, TRP-10, and TRP-11; Site 6, TRP-12, TRP-13, and TRP-14; Site 7, TRP-15 and TRP-16; Site 8, TRP-18, TRP-19, and TRP-20; and Site 9, TRP-21 and TRP-22.
Fig.3  Mean monthly temperature (a) and precipitation (b) in TRP basin, Nanyang (NY), and Kunming (KM) in China. The mean monthly air temperatures and precipitation were both averaged over the 30 y from 1981 to 2010.
Sample Location Altitude/m Sampling information
TLF-01 89°13′2″E, 42°54′55″N (site 1) −17 TLF-01 was collected at a site neighboring a grape-growing region
TLF-02 89°14′51″E, 42°53′26″N (site 2) −60 TLF-02 and TLF-03 were collected from uncultivated land with sporadically distributed bush. The soils were very dry, hardened, and mixed with gravels
TLF-03 89°14′51″E, 42°53′26″N (site 2) −60
TLF-04 89°15′19″E, 42°41′30″N (site 3) −154 TLF−04 was collected from a barren desert with sporadically distributed bush
TLF-07 89°30′35″E, 42°55′4″N (site 4) −40 TLF-07 and TLF-08 were collected near the Flaming Mountain. The surface temperature could reach up to 60°C in summer. Soils were very dry without any vegetation
TLF-08 89°30′35″E, 42°55′4″N (site 4) −40
TLF-09 89°13′32″E, 42°58′35″N (site 5) 78 TLF-9 and TLF-10 was collected from a woodland and TLF-11 was collected from a farmland
TLF-10 89°13′32″E, 42°58′35″N (site 5) 78
TLF-11 89°13′32″E, 42°58′35″N (site 5) 78
TLF-12 89°7′39″E, 42°58′45″N (site 6) 69 TLF-12, TLF-13, and TLF-14 were collected from the Karez paradise
TLF-13 89°7′39″E, 42°58′45″N (site 6) 69
TLF-14 89°7′39″E, 42°58′45″N (site 6) 69
TLF-15 89°4′9″E, 42°56′45″N (site 7) −4 TLF-15 and TLF-16 were collected at a site near the Jiaohe Ruins with trees growing
TLF-16 89°4′9″E, 42°56′45″N (site 7) −4
TLF-18 89°4′33″E, 42°56′40″N (site 8) 10 TLF-18, TLF-19, and TLF-20 were collected from a mountain slope
TLF-19 89°4′33″E, 42°56′40″N (site 8) 10
TLF-20 89°4′33″E, 42°56′40″N (site 8) 10
TLF-21 89°11′7″E, 42°57′17″N (site 9) 17 TLF-21 and TLF-22 were collected from a mountain slope
TLF-22 89°11′7″E, 42°57′17″N (site 9) 17
Tab.1  Soil-sample information
Fig.4  Average fractional abundances of isoGDGTs (a) and brGDGTs (b) in the TRP soils. The error bars display the range of the fractional abundances of isoGDGTs and brGDGTs.
Fig.5  Box plots showing the range of (a) Ri/b, (b) BIT, (c) IRIIa’, and (d) MBT’6ME in the TRP, NY, and KM soils.
Fig.6  Box plots showing range of (a) MBT’ and (b) GDGT-0/Cren for the TRP, NY, and KM soils and other Chinese soils (CHN) as reported by Yang et al. (2014).
Fig.7  Box plot showing pH range for TRP, NY, and KM soils.
Fig.8  Scatter plot showing correlation between MBT’ and MAP (a), between MBT’6ME and MAP (b), and between MBT’5ME and MAAT (c). Data for global soils (De Jonge et al., 2014a), soils from Northern China (Wang et al., 2016), the NY soils (Lei et al., 2016), the KM soils (Lei et al., 2016), and the TRP soils are compiled here. Here, IR6ME for all the selected data are>0.5. Two outlier soil samples (Ecuador-19 and Egypt-1; De Jonge et al., 2014a) are excluded from the global soils. Meanwhile, the samples collected from northern China around Da Hinggan Mountains are also excluded from the plot because the soil pH of these soils<7. The dotted line in (c) exhibits the linear correlation between the MBT’5ME and MAAT of the global soils (R2 = 0.66, p<0.05), as reported by De Jonge et al. (2014a).
Fig.9  RDA plots showing relationship between environmental variables and GDGT proxies. The numbers in the plots represent the corresponding samples in the table (Table 1, in the order of TLF-01 to TLF-22).
Fig.10  Scatter plots showing relationship of (a) archaeol/GDGT-0, (b) f(6ME), (c) IRIIa’, and (d) MBT’5ME with soil salinity.
Sample f(GDGT-0) f(GDGT-1) f(GDGT-2) f(GDGT-3) f(Cren) f(Cren’) f(IIIa) f(IIIa’) f(IIIb) f(IIIb’) f(IIa) f(IIa’) f(IIb) f(IIb’) f(IIc) f(IIc’) f(Ia) f(Ib) f(Ic)
TLF-01 0.31 0.06 0.08 0.03 0.46 0.06 0.09 0.05 0.00 0.01 0.02 0.55 0.01 0.09 0.00 0.00 0.13 0.04 0.00
TLF-02 0.56 0.05 0.05 0.02 0.29 0.03 0.09 0.08 0.02 0.00 0.01 0.54 0.01 0.12 0.00 0.00 0.08 0.04 0.01
TLF-03 0.44 0.06 0.06 0.03 0.37 0.04 0.01 0.24 0.01 0.01 0.02 0.48 0.01 0.10 0.00 0.01 0.07 0.04 0.01
TLF-04 0.51 0.01 0.11 0.03 0.31 0.03 0.06 0.15 0.01 0.02 0.09 0.29 0.04 0.11 0.01 0.02 0.10 0.08 0.02
TLF-07 0.69 0.02 0.03 0.01 0.23 0.02 0.04 0.14 0.01 0.02 0.08 0.36 0.04 0.10 0.01 0.01 0.10 0.07 0.01
TLF-08 0.78 0.01 0.02 0.02 0.16 0.01 0.02 0.17 0.00 0.00 0.08 0.41 0.04 0.10 0.00 0.00 0.12 0.07 0.00
TLF-09 0.92 0.00 0.01 0.00 0.06 0.01 0.02 0.12 0.01 0.02 0.03 0.38 0.03 0.15 0.00 0.01 0.12 0.10 0.01
TLF-10 0.49 0.02 0.03 0.02 0.41 0.03 0.03 0.09 0.01 0.01 0.04 0.32 0.05 0.16 0.01 0.01 0.13 0.12 0.03
TLF-11 0.87 0.01 0.00 0.00 0.10 0.01 0.02 0.11 0.01 0.02 0.03 0.39 0.03 0.15 0.00 0.01 0.13 0.10 0.01
TLF-12 0.83 0.01 0.01 0.01 0.13 0.01 0.02 0.11 0.00 0.02 0.04 0.37 0.02 0.15 0.00 0.01 0.14 0.11 0.02
TLF-13 0.83 0.01 0.01 0.01 0.13 0.01 0.02 0.11 0.01 0.02 0.04 0.32 0.02 0.15 0.00 0.01 0.16 0.14 0.02
TLF-14 0.77 0.01 0.02 0.01 0.17 0.02 0.02 0.11 0.02 0.02 0.04 0.32 0.02 0.16 0.00 0.01 0.15 0.12 0.02
TLF-15 0.83 0.01 0.01 0.01 0.13 0.01 0.02 0.17 0.00 0.02 0.04 0.41 0.02 0.15 0.00 0.01 0.11 0.06 0.01
TLF-16 0.86 0.01 0.01 0.01 0.10 0.01 0.01 0.19 0.00 0.02 0.02 0.44 0.01 0.15 0.00 0.01 0.09 0.06 0.01
TLF-18 0.70 0.02 0.02 0.01 0.22 0.03 0.01 0.01 0.01 0.01 0.03 0.55 0.03 0.14 0.00 0.01 0.10 0.08 0.02
TLF-19 0.68 0.02 0.02 0.01 0.24 0.03 0.01 0.13 0.01 0.01 0.02 0.42 0.02 0.12 0.00 0.01 0.09 0.06 0.10
TLF-20 0.55 0.02 0.04 0.02 0.34 0.03 0.01 0.12 0.00 0.02 0.03 0.43 0.03 0.12 0.00 0.01 0.11 0.08 0.02
TLF-21 0.83 0.02 0.01 0.01 0.13 0.01 0.00 0.19 0.00 0.00 0.03 0.51 0.01 0.07 0.00 0.00 0.14 0.05 0.00
TLF-22 0.69 0.02 0.02 0.01 0.25 0.01 0.01 0.24 0.01 0.00 0.01 0.56 0.00 0.07 0.00 0.00 0.07 0.02 0.00
Tab.2  Fractional abundance of isoGDGTs and brGDGTs in the TRP soils. The fractional abundance of each isoGDGT component is based on only the isoGDGTs, whereas the fractional abundance of the brGDGTs is calculated relative to the total brGDGTs
Sample pH SWC Salinity/
(mg·g−1)		 Archaeol/
GDGT−0		 GDGT-0/Cren BIT IRIIIa’ IRIIa’ MBT MBT’ MBT’5ME MBT’6ME CBT CBT5ME CBT6ME f(5ME) f(6ME) Ri/b MAATa) MAATmrb)
TLF-01 7.81 25.43 0.15 0.74 0.66 0.78 0.36 0.96 0.17 0.17 0.58 0.19 0.70 0.48 0.72 0.15 0.85 0.50 9.6 3.8
TLF-02 7.99 0.16 4.69 5.27 1.94 0.89 0.46 0.98 0.13 0.13 0.54 0.15 0.57 0.29 0.59 0.15 0.85 0.35 8.3 3.6
TLF-03 8.18 0.23 0.65 5.99 1.18 0.86 0.96 0.96 0.12 0.12 0.74 0.12 0.60 0.28 0.62 0.05 0.95 0.37 14.7 3.1
TLF-04 8.04 0.38 29.57 9.27 1.67 0.82 0.71 0.77 0.21 0.21 0.51 0.26 0.32 0.21 0.32 0.25 0.75 0.50 7.6 3.3
TLF-07 8.01 0.00 14.48 16.93 3.03 0.85 0.76 0.83 0.18 0.19 0.52 0.23 0.40 0.21 0.43 0.22 0.78 0.56 7.8 3.0
TLF-08 7.96 0.00 23.95 8.68 4.83 0.68 0.89 0.84 0.19 0.19 0.58 0.22 0.48 0.27 0.51 0.17 0.83 2.31 9.6 2.7
TLF-09 7.74 0.20 3.53 0.04 14.72 0.57 0.86 0.92 0.24 0.24 0.75 0.27 0.27 0.07 0.28 0.12 0.88 8.14 14.9 5.5
TLF-10 7.58 0.41 18.49 1.11 1.17 0.66 0.71 0.88 0.28 0.28 0.68 0.32 0.17 0.00 0.19 0.19 0.81 0.74 12.8 6.2
TLF-11 7.61 0.36 1.11 0.06 8.58 0.57 0.84 0.93 0.24 0.24 0.74 0.26 0.30 0.08 0.32 0.12 0.88 4.93 14.6 5.4
TLF-12 7.81 5.22 1.58 0.07 6.48 0.63 0.86 0.91 0.27 0.27 0.76 0.30 0.29 0.13 0.30 0.12 0.88 3.01 15.3 5.9
TLF-13 7.87 0.20 0.68 0.06 6.33 0.58 0.84 0.90 0.31 0.32 0.79 0.35 0.22 0.08 0.22 0.13 0.87 3.56 16.2 7.0
TLF-14 7.84 0.69 0.35 0.05 4.52 0.54 0.84 0.88 0.28 0.29 0.75 0.32 0.23 0.13 0.23 0.15 0.85 3.21 15.2 6.1
TLF-15 7.88 8.88 0.35 0.08 6.13 0.55 0.89 0.91 0.18 0.18 0.70 0.20 0.40 0.29 0.40 0.10 0.90 4.44 13.4 3.7
TLF-16 7.92 1.49 0.21 0.13 8.23 0.63 0.97 0.96 0.14 0.16 0.81 0.17 0.40 0.16 0.40 0.04 0.96 3.53 16.9 4.1
TLF-18 7.98 0.53 1.40 0.30 3.19 0.76 0.51 0.95 0.19 0.20 0.74 0.21 0.43 0.06 0.47 0.10 0.90 0.98 14.6 4.7
TLF-19 7.86 0.00 0.36 0.28 2.84 0.74 0.94 0.95 0.25 0.26 0.83 0.27 0.42 0.16 0.45 0.08 0.92 0.99 17.5 7.0
TLF-20 7.51 0.10 1.84 0.70 1.60 0.61 0.91 0.94 0.22 0.22 0.75 0.24 0.38 0.08 0.42 0.10 0.90 1.28 14.9 5.3
TLF-21 7.17 0.00 4.09 0.96 6.29 0.74 1.00 0.95 0.19 0.19 0.85 0.20 0.71 0.43 0.72 0.04 0.96 2.29 18.2 4.2
TLF-22 7.59 0.35 5.02 0.19 2.79 0.73 0.98 0.99 0.09 0.09 0.84 0.09 0.82 0.48 0.83 0.03 0.97 1.32 17.7 2.7
Average 7.81 2.35 5.92 2.68 4.54 0.70 0.80 0.92 0.20 0.21 0.71 0.23 0.43 0.21 0.44 0.12 0.88 2.26 13.7 4.6
Standard deviation ±0.23 ±6.02 ±8.88 ±4.59 ±3.48 ±0.11 ±0.18 ±0.06 ±0.06 ±0.06 ±0.11 ±0.07 ±0.18 ±0.14 ±0.18 ±0.06 ±0.06 ±2.04 ±3.45 ±1.42
Tab.3  Environmental variables and proxies for the TRP soils (n = 19)
Fig.11  (a) Box plots of MAAT estimates for the TRP, NY, and KM soils, calculated using the global calibration of MBT’5ME (De Jonge et al., 2014a). It displays the minimum, maximum, median, lower quartile (25%), and upper quartile (75%) information for the MAAT; (b) Box plots of MAATmr estimates for the TRP, NY, and KM soils, produced using multiple linear regression calibration (Eq. (11)). The MAATmr estimates for the TRP soils are significantly lower than those for the NY and KM soils.
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