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Distribution of glycerol dialkyl glycerol tetraethers in surface soils along an altitudinal
transect at cold and humid Mountain Changbai: Implications for the reconstruction of
paleoaltimetry and paleoclimate
LI Yue, ZHAO Shijin, PEI Hongye, QIAN Shi, ZANG Jingjie, DANG Xinyue and YANG Huan
Citation: SCIENCE CHINA Earth Sciences ; doi: 10.1007/s11430-017-9168-9
View online: http://engine.scichina.com/doi/10.1007/s11430-017-9168-9
Published by the Science China Press
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•RESEARCH PAPER•. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . https://doi.org/10.1007/s11430-017-9168-9
...........................................................................................................
Distribution of glycerol dialkyl glycerol tetraethers in surface soils
along an altitudinal transect at cold and humid Mountain
Changbai: Implications for the reconstruction of paleoaltimetry
and paleoclimate
Yue LI1, Shijin ZHAO1, Hongye PEI1, Shi QIAN1, Jingjie ZANG1, Xinyue DANG1&
Huan YANG1,2*
1School of Earth Sciences, China University of Geosciences, Wuhan 430074, China;
2State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China
Received August 7, 2017; revised November 15, 2017; accepted March 1, 2018; published online April 12, 2018
Abstract Glycerol Dialkyl Glycerol Tetraethers (GDGTs) serve as important tools for the quantitative reconstruction of
paleoclimate and paleoecology in both continental and marine environments. Previous studies of GDGTs in the terrestrial
environments focused primarily on the soils from the relatively warm-humid or cold-dry regions. However, it is still unclear how
GDGTs respond to environmental variables in the cold-humid regions. Here, we collected soils along an altitudinal transect of
Mountain (Mt.) Changbai, which has a typical cold-humid climate, to investigate the distribution of GDGTs and the response of
GDGT-based proxies to changes in climate along the transect. The shift in the distribution of archaeal isoprenoidal GDGTs
(isoGDGTs) revealed that the archaeal community varied significantly along the transect, which can affect the relationship
between TEX86 and mean annual air temperature (MAT). In addition, the increased temperature seasonality at higher altitudes
exerted a significant impact on TEX86. We proposed a global calibration of TEX86 for the growing season temperature re-
construction in the soil environments: T=85.19×TEX86− 46.30 (R2=0.84, p<0.001). The methylation indices for 5-methyl
branched GDGTs (brGDGTs) including MBT′5me and MBT5/6, showed correlation with soil water content but no relationship with
MAT, indicating that MBT′5meand MBT5/6 from cold-humid environments may be not suitable for temperature and altitude
reconstruction. In contrast, the recently developed pH proxies, including MBT′6me (the methylation index for 6-methyl
brGDGTs), CBT (Cyclisation index of Branched Tetraethers), IRIIa’ (Isomer ratio of IIa′) and IRIIIa′ (Isomer ratio of IIIa′)
exhibited significant correlations with soil pH, suggesting these proxies can still be used for soil pH reconstruction in the cold-
humid regions. The combination of MBT′5me and MBT′6me was strongly related to different types of climate (cold-dry, warm-
humid, cold-humid, and warm-dry). For example, MBT′5me<0.65 and MBT′6me>0.55 are diagnostic for the cold-humid climate.
Thus, the combination of MBT′5me and MBT′6me has the potential as a tool for the identification of different types of paleoclimate.
Keywords Cold-humid climate, GDGTs, Temperature, Soil pH, Soil water content
Citation: Li Y, Zhao S J, Pei H Y, Qian S, Zang J J, Dang X Y, Yang H. 2018. Distribution of glycerol dialkyl glycerol tetraethers in surface soils along an
altitudinal transect at cold and humid Mountain Changbai: Implications for the reconstruction of paleoaltimetry and paleoclimate. Science China Earth
Sciences, 61, https://doi.org/10.1007/s11430-017-9168-9
© Science China Press and Springer-Verlag GmbH Germany 2018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . earth.scichina.com link.springer.com
SCIENCE CHINA
Earth Sciences
* Corresponding author (email: yhsailing@163.com)
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1. Introduction
The reconstruction of paleoaltimetry is an important ap-
proach to understanding the plateau uplift and regional tec-
tonic movement. Recently, a number of proxies have been
developed for the reconstruction of paleoaltimetry. They can
be divided into three groups according to the principles that
they are based upon: (1) Hydrogen and oxygen isotopic
composition, e.g. δD of n-alkanes from the plant leaf waxes.
Significant hydrogen and oxygen isotopic fractionation oc-
curs when the water vapor ascends with the altitude. Proxies
can be developed according to the relationship between al-
titude and the fractionation of hydrogen and oxygen isotopes
(Jia et al., 2008;Zhuang et al., 2014;Bai et al., 2015). Water
with different oxygen isotopic compositions can be ingested
by animals and thus δ18O of the mammal teeth can be used to
reconstruct paleoaltimetry (Bershaw et al., 2010). (2) Tem-
perature generally decreases with altitude, and paleotherm-
ometers, e.g. ∆47 of the carbonate (Ghosh et al., 2006;
Hough et al., 2014), and TEX86 (tetraether index of archaea)
(Liu et al., 2013;Yang et al., 2010,2016), can be used to
reconstruct the paleoaltimetry when the lapse rate is de-
termined. (3) The assemblages of animal and plant fossils, e.
g. mammalian skeleton fossils (Deng et al., 2011) and pol-
lens (Sun et al., 2014). Plants and animals living in a specific
niche can be used to constrain the environmental conditions
including temperature, precipitation and altitude. However,
each proxy has its strengths and weakness. It is necessary to
deepen our understanding of the mechanisms of these
proxies via further modern observations.
Glycerol dialkyl glycerol tetraethers (GDGTs) are a suite
of lipids widely used in the reconstruction of paleo-
temperature (Schouten et al., 2013). Based on this, they were
also thought to be potentially useful in the paleoaltimetry
reconstruction (Ding et al., 2015;Yang et al., 2016). They
can be divided into two groups: archaeal isoprenoidal
GDGTs (isoGDGTs) and bacterial branched GDGTs
(brGDGTs) (Figure 1). Thaumarchaeota can produce a di-
agnostic isoGDGT containing a cyclohexyl ring (Schouten et
al., 2013). The relative number of cyclopentyl rings in the
Thaumarchaeotal isoGDGTs are strongly correlated with the
sea surface temperature (SST), and TEX86 (tetraether index
of tetraethers consisting of 86 carbon atoms) was proposed to
be a proxy for the SST estimation (Schouten et al., 2002,
2013;Zhou et al., 2014). Kim et al. (2010) later proposed two
proxies,
TEX 86
H
and
TEX 86
L
, for the SST reconstruction in
the high and low temperature marine environments, respec-
tively. The application of TEX86 has been extended to the
lake environments (Blaga et al., 2008,2011;Powers et al,
2004,2010). However, due to the complexity of archaeal
community in the lakes and the impact of terrigenous ar-
chaeal isoGDGTs, the use of TEX86 for the lake surface
temperature estimation has been only limited to the deep
lakes with large catchment areas (Powers et al., 2010). In the
terrestrial environments, e.g. soils and karst caves, TEX86 in
the soils and stalagmites were demonstrated to be strongly
related to temperature, but the calibration equations for them
were different from those for the marine and lake environ-
ments (Liu et al., 2013;Blyth and Schouten, 2013;Blyth et
al., 2014;Yang et al., 2016). TEX86 has been also suggested
to reflect temperatures of different altitudes at mountains
with different climates, and the calibration of TEX86 for
temperature estimation was also different among studies
(Yang et al., 2010,2016;Liu et al., 2013).
BrGDGTs, which differ from isoGDGTs in the alkyl
chains, were presumed to be produced by unknown anaero-
bic heterotrophic bacteria (Weijers et al., 2006,2010;Op-
permann et al., 2010). Weijers et al. (2007) found that CBT
(the cyclization ratio of branched tetraethers), expressing the
relative amount of cyclopentyl moieties in brGDGTs,
strongly correlated with soil pH whereas MBT (the methy-
lation index of branched tetraethers), reflecting the relative
number of methyls in brGDGTs, were significantly related to
soil pH and mean annual air temperature (MAT). Peterse et
al. (2012) modified the original MBT proxy, and proposed a
global calibration of MBT′/CBT for the MAT reconstruction.
In the arid and semi-arid region of China, MBT′/CBT
showed a strong dependence on soil moisture and pre-
cipitation (Wang et al., 2014;Yang et al., 2014a). The ap-
plicability of MBT (MBT′)/CBT as a temperature proxy has
been tested in a multitude of altitudinal transects across the
globe, including Mt. Kilimanjaro in Tanzania (Sinninghe
Damsté et al., 2008), Mt. Gongga (Peterse et al., 2009), Mt.
Xiangpi (Liu et al., 2013), Mt. Jianfengling (Yang et al.,
2010), Mt. Shennongjia (Yang et al., 2016), the southeastern
margin of the Tibetan Plateau (Deng et al., 2016), southern
Alps (Zhuang et al., 2015), Mt. Meghalaya in India (Ernst et
al., 2013), Mt. Rungwe in Tanzania (Coffinet et al., 2014),
and eastern Cordillera in Colombia (Anderson et al., 2014).
The linear relationship between temperature and altitude
makes it possible to use MBT (MBT′)/CBT for the re-
construction of paleoaltimetry.
Recently, the improved chromatography methodology
provides a way to further understanding the mechanism of
the MBT (MBT′)/CBT proxy. De Jonge et al. (2013) found
that GDGT-II and -III had isomers that have different methyl
positions, namely, 5- and 6-methyl brGDGTs. The methy-
lation index of 5-methyl brGDGTs (MBT′5me) and 6-methyl
GDGTs (MBT′6me) were found to be dependent on tem-
perature and soil pH, respectively, in the global soils (De
Jonge et al., 2014a). The calculation of original MBT(MBT′)
involves 5- and 6-methyl brGDGTs, which can essentially
explain the dependence of MBT(MBT′) on both MAT and
soil pH. After precluding 6-methyl brGDGTs, MBT′5me can
produce more accurate MAT than MBT′/CBT (De Jonge et
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Figure 1 The structures of GDGTs (isoGDGTs and brGDGTs are shown above and below the dash line, respectively. Note that the glycerol arrangement in
archaeal isoGDGTs is tentative).
3...................................Li Y, et al. Sci China Earth Sci . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
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al., 2014a;Wang H et al., 2016). By extension, MBT′5me was
also suggested to be a proxy for the reconstruction of pa-
leoaltimetry (Yang et al., 2015). In some specific regions, e.
g. the Qinghai-Tibetan Plateau (QTP), MBT5/6, appeared to
be more accurate in inferring the MAT than MBT′5me (Ding et
al., 2015).
Although the application of soil GDGTs to the re-
construction of paleoaltimetry has been proposed, this re-
mains to be tested in more altitudinal transects. Due to a large
spatial heterogeneity in the terrestrial environments, the
same GDGT-based proxies may show distinct response to
environmental variables in different regions (Zheng et al.,
2016). Many previous studies showed the complexity in the
use of GDGT proxies in soils (e.g. Liu et al., 2013;Yang et
al., 2014b,2016) and lakes (e.g. Dang et al., 2016a;Wang M
D et al., 2016). The altitudinal transects of mountains from
different climatic zones can provide unique opportunities for
studying the response of GDGTs to multiple environmental
variables. To date, the GDGT proxies have been tested in
altitudinal transects with warm-humid and cold-dry climate,
including Mt. Shennongjia (Yang et al., 2016) and Mt.
Jianfengling (Yang et al., 2010), Mt. Xiangpi (Liu et al.,
2013) and QTP (Ding et al., 2015). However, it is still un-
clear how the GDGT-based proxies respond to the environ-
mental gradients under cold-humid climate. Here, we
collected soils from Mt. Changbai, which has a typical cold-
humid climate, to investigate the response of GDGTs in
surface soils to environmental factors along an altitudinal
transect.
2 Materials and methods
2.1 Study site
Mt. Changbai is a dormant volcano located in the southeast
of Jilin Province (Figure 2a). It is the border between China
and the democratic people’s republic of Korea (North Kor-
ea). Mt. Changbai has a typical cold-humid climate (Table
1). Moss and lichen can be found in the forest. As the highest
mountain of the eastern Eurasia continent, Mt. Changbai has
a typical vertical zonation of plants: broadleaf-conifer mixed
forest in the piedmont, the dark coniferous forests and betula
ermaniis forest at relatively higher altitudes, and tundra on
the top of the mountain. Dark brown soils in the Mt.
Changbai are rich in organic matter with relatively high soil
water content (SWC) and relatively low soil pH (Table 2).
Soils are developed on the basalt erupting from the volcano
hundreds of years ago. On the top of the mountain, soils are
only sporadically distributed due to a poor weathering of
basalt in the freezing environment.
2.2 Sample preparation
Surface soil samples were collected along an altitudinal
transect of Mt. Changbai at an altitude interval of about
100 m (Figure 2b). Five subsamples were collected after the
removal of the litter layer and were combined to form a
composite sample. The in situ air and soil temperatures at
each site were measured using a digital thermometer (±0.5°
C). Altitude was obtained from a portable GPS (Garmin)
with a precision of ±3 m. The MAT and mean annual pre-
cipitation (MAP) for each sampling site can be estimated
according to the linear relationship of MAT and MAP with
altitude for 6 meteorological stations nearby: Panshi (42°58′
N, 126°03′E), Huandian (42°57′N, 126°42′E), Jingyu (42°
21′N, 126°49′E), Donggang (42°06′N, 127°34′E), Tianchi
(42°01′N, 128°05′E), Yanji (42°53′N, 129°28′E) (1970–
2000):
R p
MAT(°C) = 0.005 ×Altitude
+6.587 (= 0.98, < 0.001), (1)
2
R p
MAP(mm) = 0.067 ×Altitude
+549.3 (= 0.80, < 0.001). (2)
2
Note that the eq. (2) didn’t include the meteorological data
from Panshi station.
Figure 2 The location of sampling. (a) The location of Mt. Changbai [the map of China [GS (2006) 2125] is used]; (b) the location of sampling sites.
4...........................Li Y, et al. Sci China Earth Sci . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
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Samples were transported to the laboratory and stored at
−20°C immediately after being weighed (Wo). Samples were
then freeze dried and weighted again to obtain the dry weight
(Wd). Soil water content (SWC) can be calculated from the
eq. (3):
W W WSWC= ( ) / . (3)
o d o
The dried samples were ground into powder. Five grams of
each sample was mixed with 12.5 mL water and centrifuged
to allow for the phase separation. The pH of supernatant was
measured using a pH meter (×3) and the average value for the
three measurements was used as the final pH of the soils
(Table 2).
An aliquot of each sample was extracted with a mixture of
dichloromethane (DCM) and methanol (MeOH) (9:1, v/v) in
a ultrasonic water bath (×5) to obtain the total lipid extract
(TLE). The TLE was condensed by a rotary evaporator to a
volume of 3−4 mL and dried under a gentle stream of ni-
trogen gas. The TLE was separated into apolar and polar
lipids on a column filled with activated silica with n-hexane
and DCM:MeOH=1:1 (v/v) as the eluents, respectively. The
polar lipid was base hydrolyzed for 2 h at 80°C, and the
neutral fraction was obtained by extracting the solutions with
n-hexane for 5 times. The neutral fraction, containing
GDGTs, was filtered with 0.45 μm PTFE syringe filter to
remove the particles and then dried under the nitrogen gas.
GDGTs were analyzed using an Agilent 1200 high per-
formance liquid chromatogram and triple quadruple mass
spectrometer (HPLC-MS/MS) equipped with an autosampler
and a Masshunter workstation. Samples were spiked with a
known amount of internal standard, C46 GTGT prior to in-
jection (Huguet et al., 2006). Sample was re-dissolved in 300
μl of n-hexane: ethyl acetate (EtOA) (84:16; v/v), and the
injection volume was 10 μL. The liquid chromatography
method followed Yang et al. (2015). The isomers of
brGDGTs were separated using two silica columns (150 mm
×2.1 mm, 1.9 μm, Thermo Finnigan; USA) in tandem fixed
at 40°C. A constant flow rate of 0.2 mL min–1 was used.
Samples were eluted with n-hexane (A) and EtOA (B)
(84:16; v/v) isocratically for the first 5 min. Then the fol-
lowing elution gradient was used: 82% A and 18% B for
60 min, %B increased to 100% for the next 21 min and
maintained for 4 min. 84 %A and 16% B was used to wash
the column for 30 min. GDGTs were detected using the
single ion monitoring (SIM), targeting m/z 1302, 1300, 1298,
1296, 1292, 1050, 1048, 1046, 1036, 1034, 1032, 1022,
1020, 1018 and 744. The nebulizer pressure and temperature
were 60 psi and 400°C, respectively. The flow rate of the
drying gas (N2) was 6 L min−1 at 200°C. The capillary vol-
tage was 3500 V, and the corona current was 5 μA (~3200 V
). We assumed a same response factor between GDGTs and
C46 GTGT in the mass spectrometer. The abundance of each
GDGT compound was determined by the comparison of peak
areas of GDGTs to that of C46 GTGT in the extracted ion
chromatography.
2.3 Calculation of GDGT-based proxies
TEX86 was calculated according to Schouten et al. (2002):
TEX = (GDGT 2 + GDGT 3 + cren ) /
(GDGT 1 + GDGT 2 + GDGT 3 + cren ), (4)
86
where cren′ represents crenarchaeol regioisomer.
The calculation of MBT, MBT′ and CBT followed Weijers
et al. (2007) and Peterse et al. (2012):
MBT= (Ia + Ia + Ib+Ib +Ic) /
(Ia+Ia + b+Ib + c+ IIa+ IIa + Ib+IIb +Ic
+IIc + IIa+IIIa + IIb+IIIb + IIc+IIc ), (5)
CBT= log[(Ib+ Ib + IIb+ IIb ) /
(Ia+ Ia + IIa+IIa )]. (6)
MBT = (Ia+Ia + b+Ib +c) /
(Ia+Ia + Ib+Ib + c+ IIa+ IIa +Ib
+IIb + Ic+Ic + IIa+IIIa ). (7)
MBT′5me, MBT′6me, CBT5me, and CBT6me were calculated
according to De Jonge et al. (2014a). IRx′ was calculated
according to De Jonge et al. (2014b).
MBT = (Ia+Ia + Ib+ Ib +Ic) /
(Ia+ Ia + Ib+Ib + Ic+ IIa+ IIb+IIc+IIIa). (8)
5me
MBT = (Ia+Ia + Ib+ Ib +Ic) /
(Ia+ Ia + Ib+Ib + Ic+IIa + IIb + IIc + IIIa ). (9)
6me
CBT = log[(Ib+ Ib +IIb) / (Ia+ Ia +IIa)]. (10)
5me
Table 1 Different climate types in the previous studiesa)
Site Altitude (m) MAT (°C) Soil pH SWC Climate type Reference
Mt. Jianfengling 86–1405 16–24 4.03−6.20 – Warm-humid Yang et al., 2010
Mt. Shennongjia 316–2907 1–16.7 5.03−8.01 7.8–45% Warm-humid to cold-
humid Yang et al., 2016
QTP 3066–5418 −5.5–7.6 6.22−8.37 – Cold-dry Ding et al., 2015
Mt. Xiangpi 3250–4104 −8.2–0.4 7.3−8.1 – Cold-dry Liu et al., 2013
Mt. Gongga 1180–3819 −1.5–14.3 4.4−7.9 – Cold-humid Peterse et al., 2009
Mt. Changbai 643–1910 −5–3 4.52−6.43 30.4−78.3% Cold-humid This study
a) MAT, mean annual temperature; SWC, soil water content; ‘–’ represents data unavailable; QTP, Qinghai-Tibetan Plateau
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CBT = log[(Ib+ Ib + IIb ) / (Ia+ Ia + IIa )]. (11)
6me
IR x x x= / ( + ), (12)
x
where, xrepresents 5-methyl brGDGTs, x′ represents 6-me-
thyl brGDGTs.
MBT5/6, previously defined by Ding et al. (2015), was
calculated according to the equation:
MBT = (Ia+ Ia + Ib+ Ib + Ic+IIa ) /
(Ia+ Ia + Ib+ Ib + Ic+ IIa+ IIa + IIb+ IIc+ IIIa+IIIa ). (13)
5/6
3. Results and discussion
3.1 The distribution of GDGTs and GDGT-based
proxies
The relative abundances of brGDGTs for the soils from Mt.
Changbai were much higher than those of isoGDGTs (Figure
3). GDGT-0 and crenarchaeol were generally the major
isoGDGT components, with an average abundance of 54.2%
and 24.9%, respectively (Figure 4a). GDGT-Ia, IIa and IIa′
accounted for the major proportions of brGDGTs, with an
average abundance of 39.7%, 32.3% and 10.3%, respectively
(Figure 4b) (The relative abundance of isoGDGTs and
brGDGTs are shown in Appendix (Tables S1 and S2,http://
link.springer.com), respectively; the GDGT proxies are
shown in Table 3).
TEX86 for these samples varied from 0.43 to 0.75. No clear
relationship between TEX86 and MAT can be found. MBT′5me,
MBT′6me, and MBT5/6 were in the range of 0.38–0.65, 0.39–
0.97 and 0.40–0.66, respectively (Table 3). We cannot find a
clear relationship between MBT′5me and MAT or soil pH
(Figure 5). Likewise, the relationship between MBT5/6 and
Table 2 Sample information for the soils collected from Mt. Changbaia)
Sample No. Sampling
T(°C)
Sampling air
T(°C) Alt (m) GPS GPS MATe(°C) MAPe(mm) SWC Soil pH
CB 1-1 8.2 14.8 742 42°23′55″N 128°05′29E 2.9 599 70.5% 5.54
CB 1-2 8.9 14.2 742 42°23′55″N 128°06′28″E 2.9 599 63.0% 5.15
CB 1-3 8.5 15.1 748 42°23′55″N 128°06′28″E 2.9 599 42.7% 5.49
CB 2-1 7.5 14.5 859 42°19′52″N 128°06′36″E 2.3 607 71.0% 5.30
CB 2-2 7.7 13.1 859 42°19′53″N 128°06′34″E 2.3 607 78.3% 5.25
CB 2-3 7.3 13.7 860 42°19′52″N 128°06′33″E 2.3 607 51.2% 4.61
CB 3-1 6.7 14.0 1008 42°15′10″N 128°09′48″E 1.6 617 51.7% 5.09
CB 3-2 6.9 14.6 1004 42°15′09″N 128°09′47″E 1.6 617 55.1% 6.05
CB 3-3 7.2 13.7 1005 42°15′09″N 128°09′48″E 1.6 617 56.2% 5.68
CB 4-1 5.8 14.1 1130 42°10′48″N 128°11′01″E 0.9 625 61.8% 5.96
CB 4-2 5.7 13.0 1134 42°10′48″N 128°11′02″E 0.9 625 46.6% 5.47
CB 4-3 5.9 13.9 1139 42°10′48″N 128°11′03″E 0.9 626 54.4% 5.70
CB 5-1 7.3 10.8 1248 42°08′35″N 128°11′34″E 0.4 633 50.5% 4.96
CB 5-2 6.9 12.0 1248 42°08′34″N 128°11′34″E 0.4 633 49.5% 5.46
CB 5-3 8.6 12.0 1249 42°08′35″N 128°11′34″E 0.3 633 41.0% 4.55
CB 6-1 5.6 7.9 1349 42°06′32″N 128°13′04″E −0.2 640 59.7% 4.67
CB 6-2 6.6 7.9 1349 42°06′31″N 128°13′04″E −0.2 640 63.7% 4.87
CB 6-3 7.5 7.9 1350 42°06′31″N 128°13′03″E −0.2 640 73.5% 4.52
CB 7-1 9.3 14.9 643 42°30′03″N 128°08′02″E 3.4 592 47.8% 5.87
CB 7-2 11.9 14.9 646 42°30′05″N 128°08′02″E 3.4 593 30.4% 6.13
CB 7-3 11.9 14.0 646 42°30′05″N 128°08′02″E 3.4 593 45.1% 6.43
CB 8-1 – – 1350 42°07′09″N 128°06′23″E −0.2 640 – 4.40
CB 9-1 – – 1910 42°03′40″N 128°04′01″E −3.0 677 – 4.68
CB 9-2 – – 1910 42°03′40″N 128°04′01″E −3.0 677 – 4.71
CB 10-1 – – 790 42°24′06″N 128°05′39″E 2.6 602 – 4.99
CB 10-2 – – 790 42°24′06″N 128°05′39″E 2.6 602 – 5.03
CB 11-1 – – 1700 42°04′29″N 128°03′54″E −1.9 663 – 4.75
CB 11-2 – – 1700 42°04′29″N 128°03′54″E −1.9 663 – 4.73
CB 12-1 – – 1200 42°10′07″N 128°09′25″E 0.6 630 – 4.71
a) T, temperature; Alt, altitude; MATe, estimated mean annual temperature; MAPe, estimated mean annual precipitation; ‘–’ represents data unavailable.
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MAT was poor. In contrast, MBT′5me exhibited a significant
negative correlation with SWC (R2=0.54, p<0.001, an outlier
was excluded). MBT′6me showed a negative correlation with
soil pH (R2=0.88, p<0.001).
CBT, CBT5me and CBT6me ranged from 0.43–1.64, 0.40–
1.70, and 0.4–1.51, respectively (Table 3). All of them
showed a close relationship with soil pH (Figure 5) with
similar regression lines (Figure 6a). IRIIa′ and IRIIIa′ varied
from 0.01–0.64 and 0.02–0.66, respectively. They were
shown to be significantly dependent on soil pH (Figures 5
Figure 3 The total ion chromatography of GDGTs for selected samples (CB 1-2 and CB 5-3) from Mt. Changbai.
Figure 4 The average abundance of (a) isoGDGTs and (b) brGDGTs for all the soils collected from Mt. Changbai.
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and 6b;Table 3).
To further assess the applicability of IRIIa′ and IRIIIa′ as pH
proxies in more diverse environments, the published data for
Mt. Shennongjia (Yang et al., 2016), QTP (Ding et al., 2015)
and the global soils (De Jonge et al., 2014a) were combined
with data in this study. These two indices still exhibited
significant correlations with soil pH in global soils from a
variety of environments (Figure 6c and 6d), indicating that
IRIIa′ and IRIIIa′ can be applied to different types of soils
globally and are more reliable than CBT.
3.2 Influence of archaeal community and seasonality
on TEX86
TEX86 for Mt. Xiangpi (Liu et al., 2013), Mt. Shennongjia
(Yang et al., 2016), Mt. Jianfengling (Yang et al., 2010) all
showed significant correlations with altitude or MAT, al-
though these three mountains have distinct climates. The
regression lines for them are different, indicating that some
other factors may also have an influence on the relationship
between TEX86 and MAT.
Unlike above three mountains, TEX86 for soils from Mt.
Changbai were scattered and showed no correlation with
MAT (Figure 7a). Blaga et al. (2008) proposed the ratio of
GDGT-0/cren as a proxy for the contribution of methanogens
in the archaeal community. The ratio>2 means methanogens
and Bathyarchaeota contribute a significant amount of
isoGDGTs, and vice versa (Besseling et al., 2017). Ap-
proximately 59% of samples for Mt. Changbai showed a
GDGT-0/cren ratio>2. Likewise, the majority of the samples
from Mt. Jianfengling also showed a GDGT-0/cren ratio>2.
This is likely because Mt. Jianfengling and Changbai both
have very humid climate and soils (Yang et al., 2010). In
contrast, only 4% of the samples showed GDGT-0/cren>2 at
Table 3 GDGT-based proxies for soils along an altitudinal transect of Mt. Changbaia)
No. Sample No. TEX86 MBT′ MBT′5me MBT′6me MBT5/6 CBT CBT5me CBT6me IRIIa′ IRIIIa′
1 CB 1-1 – 0.38 0.48 0.64 0.54 0.93 0.94 0.82 0.33 0.30
2 CB 1-2 0.53 0.42 0.46 0.82 0.50 1.32 1.33 1.16 0.16 0.10
3 CB 1-3 0.43 0.41 0.52 0.65 0.58 0.82 0.85 0.72 0.35 0.38
4 CB 2-1 0.56 0.37 0.41 0.78 0.45 1.12 1.14 0.93 0.17 0.13
5 CB 2-2 – 0.34 0.38 0.80 0.41 1.29 1.31 1.07 0.13 0.09
6 CB 2-3 – 0.58 0.61 0.92 0.62 1.39 1.42 1.26 0.11 0.11
7 CB 3-1 – 0.50 0.61 0.75 0.64 0.99 1.03 0.89 0.32 0.36
8 CB 3-2 – 0.40 0.53 0.62 0.58 0.76 0.81 0.67 0.37 0.44
9 CB 3-3 – 0.47 0.57 0.73 0.61 1.06 1.09 0.96 0.31 0.33
10 CB 4-1 0.73 0.38 0.51 0.59 0.58 0.90 0.93 0.79 0.41 0.39
11 CB 4-2 0.75 0.42 0.50 0.72 0.56 1.04 1.08 0.92 0.28 0.25
12 CB 4-3 – 0.39 0.49 0.65 0.55 0.90 0.93 0.79 0.33 0.28
13 CB 5-1 – 0.55 0.58 0.93 0.59 1.33 1.35 1.16 0.08 0.07
14 CB 5-2 – 0.47 0.54 0.77 0.58 1.21 1.23 1.11 0.26 0.19
15 CB 5-3 – 0.61 0.65 0.91 0.66 1.45 1.49 1.32 0.15 0.04
16 CB 6-1 0.75 0.52 0.55 0.91 0.56 1.61 1.70 1.45 0.10 0.13
17 CB 6-2 0.61 0.47 0.53 0.82 0.56 1.36 1.41 1.16 0.18 0.18
18 CB 6-3 – 0.58 0.61 0.94 0.62 1.64 1.69 1.51 0.10 0.06
19 CB 7-1 0.63 0.34 0.53 0.50 0.60 0.56 0.55 0.51 0.52 0.56
20 CB 7-2 0.68 0.38 0.59 0.52 0.66 0.72 0.71 0.68 0.56 0.57
21 CB 7-3 0.68 0.29 0.54 0.39 0.61 0.43 0.40 0.41 0.64 0.66
22 CB 8-1 – 0.55 0.58 0.91 0.60 1.52 1.60 1.36 0.12 0.04
23 CB 9-1 – 0.39 0.40 0.97 0.40 1.48 1.52 1.23 0.01 0.02
24 CB 9-2 – 0.40 0.43 0.86 0.44 1.62 1.59 1.33 0.10 0.13
25 CB 10-1 0.52 0.48 0.55 0.79 0.59 1.01 1.02 0.90 0.25 0.19
26 CB 10-2 0.55 0.48 0.55 0.78 0.59 1.02 1.02 0.90 0.26 0.18
27 CB 11-1 – 0.36 0.38 0.86 0.41 1.54 1.55 1.25 0.09 0.05
28 CB 11-2 – 0.43 0.46 0.87 0.48 1.37 1.46 1.14 0.11 0.06
29 CB 12-1 – 0.54 0.57 0.89 0.60 1.37 1.36 1.22 0.16 0.05
a) ‘–’: GDGT-0/cren>2
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Mt. Shennongjia, where the climate is less humid than both
Mt. Changbai and Jianfengling (Yang et al., 2016).
The distribution of archaeal isoGDGTs and bacterial
brGDGTs changed with soil pH (Figure 3). The relative
abundance of GDGT-0 was higher in the soils with lower soil
pH whereas the relative abundance of crenarchaeol was
higher in the circumneutral environments. The humic sub-
stance accumulates in the cold and humid forest at Mt.
Changbai due to a relatively low microbial activity, leading
to a high soil organic carbon content and relatively low soil
pH. Soils at Mt. Changbai are thus favorable to the growth of
methanogens and Bathyarchaeota that can produce more
GDGT-0. In addition, acidic Thaumarchaeota and Thau-
marchaeota Group I.1c and Group I.3 occur in a high
abundance in the acidic forest soils (Lehtovirta et al., 2009,
2016;Cao et al., 2012). These archaea, e.g. Nitrosotalea
devanaterra, an acidophilic Thaumarchaeotal isolate, can
produce more abundant GDGT-4 than crenarchaeol, which is
different from other archaea falling into Thaumarchaeota
Group I.1a and Group I.1b (Lehtovirta et al., 2016). Besides,
some other studies showed that Thermoplasma and metha-
nogens might also biosynthesize crenarchaeol (Li et al.,
2017). Therefore, it is likely that soil pH-induced changes in
the archaeal community may affect the composition of
isoGDGTs and in turn have an influence on the TEX86 value.
The complex archaeal communities at soils of Mt. Changbai
may be one of the causes of the weak correlation between
TEX86 and MAT for the cold and humid climate.
The average TEX86 value at lower altitudes (MAT>2°C)
was larger than that at higher altitudes (MAT<2°C) (Figure
7b). This can be explained by an increased seasonal pro-
duction of archaea in the colder soils. Most microbes prefer
to inhabit temperate environments, and their growth may be
significantly suppressed in the frozen soils. The MAT is very
low and the SWC is relatively high at the peak of Mt.
Changbai, where the soils keep frozen during most seasons
of a year. Only the temperature signal when the soils are not
frozen and archaea are actively growing can be recorded by
TEX86. In contrast, soil will not be frozen at lower altitudes
and the TEX86 can record the temperature signal throughout a
year. This may lead to systematically higher TEX86 values at
higher than low altitudes. We complied the published TEX86
values for soils (Yang et al., 2010;Liu et al., 2013;Blyth et
al., 2014;Yang et al., 2016) and the pure cultures of Group
I.1b Thaumarchaeota (Pitcher et al., 2010;Sinninghe Damsté
et al., 2012;Elling et al., 2017) and plotted them against
MAT or growing temperatures. TEX86 for data points with
MAT<2°C were higher than those with MAT>2°C (Figure
7c). For those data points with MAT<2°C, the months from
April to October, when the mean temperature of each month
Figure 5 Redundancy analysis (RDA) showing the relationship between environmental factors and the brGDGT-based proxies for soils from Mt. Changbai.
MAT, mean annual temperature; SWC, soil water content; Alt, altitude. The numbers denote the corresponding samples in Table 3.
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is larger than 0°C, was favorable to the growth of archaea. If
the MAT for Mt. Xiangpi and higher altitudes of Mt.
Changbai (MAT<2°C) was substituted with the mean tem-
perature for these months, i.e., the growing season tem-
perature, TEX86 showed a significant correlation with the
mean growing season temperatures (Figure 7d). As archaea
producing isoGDGTs in these soils mainly belong to Group
I.1b Thaumarchaeota, it is reasonable to observe that data
points for the pure cultures of Thaumarchaeota Group I.1b at
different temperatures were plotted near the regression line.
We can therefore obtain a global calibration of TEX86 for the
growing season temperature reconstruction in soil environ-
ments:
T R p= 85.19 × TEX 46.30 (= 0.84, < 0.001). (14)
86
2
Compared with the global calibration of TEX86 for marine
and lake environments (Table 4), the slope of eq. (14) is
higher and the intercept is lower, indicating a distinct re-
sponse of archaeal membrane lipids to temperature between
the aquatic and soil environments. This is likely because the
major isoGDGT-producing archaea in soils belong to Group
I.1b Thaumarchaeota but the dominant archaea in marine and
deep lake environments fall into Thaumarchaeota Group I.1a
(Schouten et al., 2013). This global calibration (eq. (14)) may
be potentially useful in the quantitative reconstruction of
paleotemperature in the loess-paleosol.
3.3 Impact of SWC on the methylation index of
brGDGTs
A number of previous studies suggested that MBT′5me and
MBT5/6 correlated with MAT (De Jonge et al., 2014a;Yang et
al., 2015;Ding et al., 2015). However, MBT′5me and MBT5/6
for Mt. Changbai exhibited no correlation with MAT (Figure
5) and were more variable than data for QTP (cold-dry cli-
mate), Mt. Gongga (cold-humid) and Mt. Shennongjia
(warm-humid) (Figure 8a and 8b). This is largely because
Mt. Changbai have low MAT and humid climate. In fact, data
for Mt. Gongga, which also has a cold and humid climate,
showed a large scatter when MAT is <5°C.
After removal of an outlier from the data set for Mt.
Changbai (CB 6-3), MBT′5me and MBT5/6 showed significant
Figure 6 (a) Linear correlations between CBT, CBT5me, and CBT6me for soils from Mt. Changbai and soil pH; (b) Liner correlations between IRIIa′, IRIIIa′ for
soils from Mt. Changbai and soil pH; the linear relationship of soil pH with (c) IRIIa’ and (d) IRIIIa’ for the complied dataset including data from this study, the
QTP (Ding et al., 2015), Mt. Shennongjia (Yang et al., 2016) and the global soils (De Jonge et al., 2014a).
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correlations with SWC (R2=0.54 and 0.68, respectively, pof
them both<0.001) (Figure 8c and 8d). This is also the case
for Mt. Shennongjia (MBT′5me and MBT5/6 vs. SWC, R20.57
and 0.49, respectively, pboth<0.001) (Figure 8e and 8f).
Although it appears that MBT′5me and MBT5/6 for Mt.
Changbai and Mt. Gongga both exhibited correlations with
SWC, SWC for Mt. Shennongjia was correlated with MAT
(R2=0.61, p<0.001) whereas SWC for Mt. Changbai had no
relationship with MAT (R2=0.01, p>0.05). Therefore, the
correlation between MBT′5me and SWC for Mt. Shennongjia
was caused by the correlation between SWC and MAT. The
relationship between MBT′5me and SWC for Mt. Changbai
was not impacted by MAT, indicating that SWC may affect
MBT′5me under cold and humid climate (MAT<5°C). In fact,
temperature proxies based on microbial lipids, e.g.
TEX 86
L
of isoGDGTs (Kim et al., 2010), do not respond to tem-
perature sensitively when temperature is close to 0°C. This
may be due in part to the limit of the membrane lipid ad-
justment to the low temperature or the changes in the mi-
crobial communities when temperature decreases. SWC may
cause the changes of oxygen content in soils, which also
Figure 7 (a) Correlation between TEX86 (data with GDGT-0/cren<2) for Mt. Changbai and MAT (mean annual temperature); (b) comparison of TEX86
between data points with MAT<2°C and >2°C in the box plot; (c) correlation between MAT and TEX86 from the compiled data for soils from Mt. Xiangpi
(Liu et al., 2013), Mt. Jianfengling (Yang et al., 2010), Mt. Shennongjia (Yang et al., 2016), and soils recovered from caves in the UK and Australia (Blyth et
al., 2014) and for the pure cultures of Group I.1b Thaumarchaeota (Pitcher et al., 2010;Sinninghe Damsté et al., 2012;Elling et al., 2017); (d) a global
calibration of TEX86 and growing season temperature based on data from Mt. Xiangpi (Liu et al., 2013), Mt. Jianfengling (Yang et al., 2010), Mt.Shennongjia
(Yang et al., 2016) and soils recovered from caves in the UK and Australia (Blyth et al., 2014) and for the pure cultures of Group I.1b Thaumarchaeota
(Pitcher et al., 2010;Sinninghe Damsté et al., 2012;Elling et al., 2017). Note that the growing seasons include May to September at Mt. Xiangpi, and April to
October at an altitude from 1130 to 1350 m for Mt. Changbai.
Table 4 The published calibrations of TEX86 for temperature re-
constructiona)
The calibration equation
of TEX86
Sample Reference
SST=56.2×TEX86−10.8 Sea sediments Kim et al., 2008
SLST=50.8×TEX86−10.4 Lake sediments Powers et al., 2010
WLST=57.3×TEX86
−17.5 Lake sediments Powers et al., 2010
a) SST, sea surface temperature; SLST, lake surface temperature in
summer; WLST, lake surface temperature in winter.
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exerts a significant impact on the metabolism of bacteria in
soils. As SWC is not a limiting factor for the growth of
bacteria in the soils of Mt. Changbai, we speculate that
SWC-induced changes in oxygen content is more likely to be
the direct environmental control on MBT′5me and MBT5/6.
We complied GDGT data from other three regions, i.e.,
QTP (Ding et al., 2015), Kunming (Lei et al., 2016), Turpan
(Zang et al., 2018), which have cold-dry, warm-humid and
warm-dry climate, respectively and made comparison with
that for Mt. Changbai (Figure 9). Soils from the cold-dry
regions showed low MBT′5me and MBT′6me. In contrast, soils
from the cold and humid Mt. Changbai exhibited low MBT′5me
yet relatively higher MBT′6me. We defined a MBT′5me
threshold (0.65) to distinguish the warm and cold regions and
a MBT′6me threshold (0.55) to distinguish the dry and humid
regions. Figure 9 showed that MBT′5me>0.65 and MBT′6me
>0.55 occurred in warm-humid regions, MBT′5me<0.65 and
MBT′6me>0.55 in cold-humid regions, MBT′5me<0.65 and
MBT′6me<0.55 in cold-dry regions, and MBT′5me>0.65 and
MBT′6me<0.55 in warm-dry regions. The increased dry cli-
mate can enhance the alkalinity of soils and decrease the
SWC, both of which are thought to lead to a lower MBT′6me
Figure 8 (a) Correlation between MBT′5me for QTP (Qinghai-Tibetan Plateau) (Ding et al., 2015), Mt. Gongga (De Jonge et al., 2014a), Mt. Shennongjia
(Yang et al., 2015), Mt. Changbai and MAT; (b) correlation between MBT5/6 for QTP (Qinghai-Tibetan Plateau) (Ding et al., 2015), Mt. Gongga (De Jonge et
al., 2014a), Mt. Shennongjia (Yang et al., 2015), Mt. Changbai and MAT; (c) correlation between MBT′5me for Mt. Changbai and SWC (soil water content);
(d) correlation between MBT5/6 for Mt. Changbai and SWC (soil water content); (e) correlation between MBT′5me for Mt. Shennongjia and SWC (soil water
content) (Yang et al., 2015); (f) correlation between MBT5/6 for Mt. Shennongjia and SWC (soil water content) (Yang et al., 2015).
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value. Therefore, the combination of MBT′5me and MBT′6me
can be used as a proxy to indicate different types of climates
in the paleoclimate reconstruction.
3.4 The potential of MBT′/CBT in the paleoaltitude
reconstruction under cold-humid climate
Although no relationship can be found between MBT′5me or
MBT5/6 and MAT under cold-humid climate, the MBT′/CBT,
a classic GDGT-based proxy, exhibited a significant corre-
lation with altitude of Mt. Changbai. The calibration equa-
tion is:
m
R p
Alt( ) = 1019.85 × CBT 1407.36 × MBT
+552.86 (= 0.60, < 0.001). (15)
2
This equation is different from those for other altitudinal
transects (Yang et al., 2014b), which might be caused by the
differences between the lapse rates of these transects. Dang
et al. (2016b) proposed the relative amount of 6-methyl
brGDGTs, IR6me, to distinguish the environmental control on
MBT′. When IR6me<0.5, MBT′ was thought to be dependent
primarily on MAT. After precluding data points with IR6me
>0.5 (CB 7-1, CB 7-2, CB 7-3), the linear correlation be-
tween MBT′, CBT and MAT was not improved significantly:
m T
R p
Alt( ) = 1014.15 × CBT 1386.20 × MB
+551.98 ( = 0.51, < 0.001). (16)
2
The premise that MBT′/CBT can be used to reconstruct
altitude is that this proxy can be used to reconstruct MAT.
However, the correlation between MBT′/CBT and altitude
was not necessarily caused by the relationship between MBT′
and MAT for some transects. For example, MBT′ for Mt.
Jianfengling and Meghalaya showed no correlation with al-
titude. The relationship between the MBT′/CBT and altitude
was primarily dependent on the significant correlation be-
tween CBT and altitude (Yang et al., 2014b). Likewise, The
reconstructed altitudes for Mt. Changbai from the eq. (15)
showed a significant correlation with soil pH (R2=0.62,
p<0.001), indicating that the relationship of MBT′/CBT with
altitude of Mt. Changbai was also derived from the corre-
lation between pH and altitude. In the paleoaltimetry re-
construction, however, soil pH at different altitude may be
not necessarily associated with the altitude. Therefore, in
terms of paleoaltimetry reconstruction, the MBT′/CBT proxy
is somewhat complex because in some cases it is CBT that
causes the correlation between this proxy and altitude, rather
than MBT′. The use of MBT′/CBT proxy in the paleoalti-
metry reconstruction should be taken with caution.
4. Conclusions
Bacterial brGDGTs over dominated archaeal isoGDGTs in
the soils from an altitudinal transect of Mt. Changbai that has
a typical cold and humid climate. The ratio of GDGT-0/
crenarchaeol for most soil samples were larger than 2, in-
dicating that anaerobic methanogens or Bathyarchaeota ac-
count for a large amount of archaeal community. The
seasonal production of archaea in soils from cold regions makes
the response of TEX86 to temperature complex. MBT′5me
was primarily dependent on the SWC in this cold humid
environment. CBT, IRIIa′, and IRIIIa′ all exhibited significant
correlations with soil pH. The combination of MBT′6me and
MBT′6me has the potential to be a proxy for the identification
of climatic types. The low MBT′5me (<0.65) and high MBT′6me
(>0.55) are diagnostic for the soils developed under cold
humid climate.
Figure 9 Cross plots of MBT′5me and MBT′6me , which can be used to diagnose different climate types. QTP: Qinghai-Tibetan Plateau (Ding et al., 2015),
MAT −5.5–7.6°C, MAP 194–495 mm; TRP: Turpan (Zang et al., unpublished), MAT 15.1°C, MAP 1.3 mm; KM: Kunming (Lei et al., 2016), MAT 14.9°C,
MAP 1012 mm; CB: Mt. Changbai (this study), MAT −3.0–3.4°C, MAP 592.4–677.5 mm, SWC 0.30–0.78.
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Acknowledgements We thank Jia Juan from the Institute of Botany,
Chinese Academy of Sciences, for providing some soil samples. This work
was supported by the National Natural Science Foundation of China (Grant
Nos. 41602189 & 41330103) and the Cradle Plan of China University of
Geosciences (Grant No. CUGL170403).
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(Responsible editor: Huayu LU)
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