Introduction
Understanding past climate variability is important for predicting future
climate change as well as how ecosystems, organisms, and human society could
be affected. The validation of climate proxies is imperative for the correct
interpretation of climate archives and therefore also for the climate models
building on these past climate data. Terrestrial environments play an
important role in global climate; however, continental climate
reconstructions are hindered by the lack of continental temperature proxies.
In the future, changes in terrestrial climate are likely to have a large
impact on human society just as they had in the past (e.g., Haug et al.,
2003). Availability of trustworthy temperature data from the terrestrial
environment will be essential for the development of reliable climate
models.
The distribution of branched glycerol dialkyl glycerol tetraethers (brGDGTs,
Fig. S1 in the Supplement), a group of membrane-spanning lipids that occur in heterotrophic
bacteria (Pancost and Sinninghe Damsté, 2003; Weijers et al., 2010)
pervasive in peat (Weijers et al., 2006) and worldwide in soils has proven
useful as a tool to obtain high-resolution, continental temperature
reconstructions (Weijers et al., 2007a; Schouten et al., 2008; Bendle et
al., 2010). Branched GDGTs are biosynthesized by bacteria (Sinninghe Damsté et
al., 2011, 2014) living in soils and the distribution of brGDGTs in soils is
affected by growth temperature and pH (Weijers et al., 2006). More
specifically, the degree of methylation of the brGDGTs (expressed as
methylation of branched tetraethers index, MBT; see Table S1 in the Supplement for a detailed
explanation of all GDGT indices used in this study) relates to mean annual
air temperature (MAT), and to a lesser extent soil pH, whereas the degree of
cyclization (DC) of the brGDGTs (also expressed as the cyclization of
branched tetraethers index, CBT) correlates solely with soil pH (Weijers et
al., 2007a). MBT has recently been amended to become MBT' by eliminating the
brGDGTs that rarely occur in soils (Peterse et al., 2012). These
observations led to the development of a continental paleoclimate proxy
based on the distribution of brGDGTs that has been applied in paleosoils
(Peterse et al., 2009; Weijers et al., 2007a, 2011). Branched GDGTs that are
produced in soils are washed by runoff into streams and rivers, where they
are transported to and deposited in river and coastal marine
sediments that are under the influence of major river systems. In this way,
brGDGTs have been used as recorders of the continental paleoclimate (Weijers
et al., 2007b; Bendle et al., 2010; Hren et al., 2010; Keating-Bitonti et
al., 2011).
Complications using brGDGTs as a proxy for MAT have arisen in some settings.
In marine sediments receiving a low input of soil organic matter (OM), it
was found that the distribution of brGDGTs and the reconstructed
temperatures were quite different from that observed in regional soils
(Peterse et al., 2009). Peterse et al. (2012) found in arid regions that
temperature is no longer an important control on the distribution of brGDGTs
and therefore MAT reconstructions in these areas should be interpreted with
care. In the Iberian Peninsula, Menges et al. (2014) found that MBT' was not
correlated with MAT but instead with the aridity index (AI), a
parameter for water availability in soils, and mean annual precipitation
(MAP). In drainage basins with varying soil sources that had different MATs
(i.e., mountainous vs. lowland), it was found that the provenance of the soil
matter must be considered when interpreting MAT reconstructions (Bendle et
al., 2010). In situ production of brGDGTs can occur within the river systems
(Yang et al., 2012; Zell et al., 2013; De Jonge et al., 2014b) and cause
brGDGT distributions and MAT reconstructions that differ from those in the
soils of the source area. These complications make it vital to investigate
how varying environmental conditions, the transport of these terrestrially
derived fossilized lipids, and in situ production affect the implementation
of brGDGTs for paleoclimate reconstructions.
Recently, a set of six new brGDGT isomers that differ in the position of the
methyl groups were identified and described (De Jonge et al., 2013). The
relative abundance of these novel, 6-methyl brGDGTs is strongly dependent
on pH, and so by excluding them from the MBT' index (newly defined as
MBT'5ME) the correlation with MAT is improved (De Jonge et al., 2014a).
The CBT index was also redefined in this study, as CBT', to include all of
the pH-dependent 6-methyl brGDGTs and consequently yielded a higher
correlation with soil pH as a result (De Jonge et al., 2014a). De Jonge et al. (2014a) also developed, based on a dataset of globally distributed
soils, a new pH calibration taking into account the new CBT' as well as new
MAT calibrations, defined as MATmr and MATmrs. In a global soil
set they were shown to improve the accuracy of reconstructions, especially
in arid regions. These indices and calibrations were applied in a coastal
sediment core in the northern Kara Sea off Siberia in a study emphasizing
the importance of examining the provenance of brGDGTs when using these
lipids for paleoclimate reconstructions (De Jonge et al., 2015).
The location of the study area on the Iberian Peninsula with the
stations where the four sediment cores were sampled (indicated by black
squares) along a transect from the Tagus River to off the Portuguese
continental margin, as well as the river SPM sampling site (indicated by a
white diamond), riverbank sediment sampling sites (indicated by red
circles), and soil sampling sites (indicated by black circles). The river
SPM, riverbank sediments, and soil samples were all collected in a previous
study. Digital elevation data are from Jarvis et al. (2006) and bathymetry from
IOC-IHO-BODC (2003).
A comprehensive study has been previously performed on the present-day
transport of brGDGTs in the Tagus River basin from source to sink (Zell et
al., 2014). The results from this study demonstrated that the distribution
of brGDGTs in the riverine suspended particulate matter (SPM) did not
reflect that of the soils, implying that, due to the aquatic production in
river and marine environments, the use of brGDGTs for paleoclimate
reconstructions in the region would be complicated (Zell et al., 2014,
2015). Here we examine whether the assessment of the provenance of brGDGTs in the
Tagus River basin can be improved by the application of analytical
methods allowing for the separation of the 5- and 6-methyl brGDGTs (De Jonge et
al., 2013). In addition, we examine whether the provenance of brGDGTs changed
over the Holocene and whether the distribution of brGDGTs in the past reflected
continental sources and thus past temperature and pH of the soils in the
drainage basin of the river. To this end we compare the down-core brGDGT
distributions in Holocene sediments retrieved from four locations along a
transect in the Tagus River basin – which includes the river floodplain
(Tagus River Floodplain core), the offshore mudbelt (Tagus Mudbelt core), and
marine sediments from the canyons (Lisbon Canyon Head core and
Lower Sétubal Canyon core; Fig. 1) – and compare them to brGDGT
distributions of soil and river SPM from the Tagus River watershed. This
allows insight into the potential and limitations of using the novel
MATmrs/CBT' proxies for climate reconstruction in this region and in
river systems in general.
Study area
The Tagus River drains the central part of the Spanish Plateau with an E–W
orientation (Benito et al., 2003). The waters originate at an elevation of
about 1600 m altitude in eastern Spain at the Iberian Range and the mouth of
the river feeds into the Atlantic Ocean near Lisbon (Vis and Kasse, 2009).
At 1200 km long, the Tagus River is the longest river of the Iberian
Peninsula and it occupies 82 × 103 km2, making it the
third largest in catchment area (Benito et al., 2003). The Tagus Basin is
surrounded by mountains on three sides with the Iberian Range to the east,
the Central Range to the north, and the Toledo Mountains to the south.
Present-day mean discharge at the Tagus River mouth is 400 m3 s-1
(Vale and Catarino, 1996; Vaz et al., 2011) and the largest contribution of
draining tributaries comes from the Central Range in the north (Benito et
al., 2003). The Tagus River is characterized by extreme seasonal and annual
variability, including periods of flooding with 30 times the mean discharge
and an annual discharge cycle characterized by two peaks in the winter
(December and then again in February to March) and a discharge minimum in the
summer (August; Benito et al., 2003). Since the 1940s dams have been built
along the expanse of the Tagus River for water supply, hydropower, and flood
prevention (Dias et al., 2002), which have likely impacted the transport of
brGDGTs in the Tagus River system since their construction.
Where the Tagus River debouches into the Atlantic Ocean, the narrow
continental shelf and steep continental slope are deeply incised by the
Lisbon–Setúbal canyon system. The head of the Lisbon branch of that
canyon system is located 13 km offshore from the Tagus River mouth at 120 m
water depth. From that point, the canyon descends over a length of 165 km
until it opens out onto the Tagus Abyssal Plain at 4860 m (Lastras et al.,
2009). Even though the shelf is very narrow, sparse amounts of continental
organic matter and clastic sediment reach the deep ocean in this region
(Jouanneau et al., 1998; de Stigter et al., 2011; Vis et al., 2016).
This is because the Lisbon–Setúbal canyon is not a very dynamic system
and has a weak down-canyon transport of sediments (Jouanneau et al., 1998;
Jesus et al., 2010; de Stigter et al., 2011). A part of the continental
shelf in this region is covered by mud deposits, which originate
predominantly from the Tagus estuary (Jouanneau et al., 1998). According to
this same study, the mouth of the much smaller Sado River is located further
to the southeast and contributes only a relatively minor sediments volume to
the shelf mud deposits.
Generally, the climate of the Tagus River basin is characterized by seasonal
variability and is considered continental Mediterranean (Le Pera and
Arribas, 2004). Summers in the Tagus region are hot and dry and the winters
are relatively mild and wet (Benito et al., 2003). During the summers, the
climate regime in the Tagus Basin is controlled by the Azores High and in
the winter by the westerlies (Benito et al., 2003). The MAT in the interior
regions of the Tagus River basin varies from 7.5 to 12.5 ∘C from the highlands to the lowlands
of the inner basin, respectively, and can
increase up to 16 ∘C along the Atlantic coast (Le Pera and
Arribas, 2004). The mean annual precipitation in the lowlands of the inner
basin is mostly below 500 mm, making it an arid region; however, some of the
highest altitudes of the mountainous areas have a larger mean annual
precipitation ranging from 750 to 1200 mm (Le Pera and Arribas, 2004).
The Iberian Peninsula is located between two major pressure systems, the
Azores High and the Iceland Low, which make up the North Atlantic
Oscillation (NAO). This climate phenomenon is caused by the varying pressure
gradient in the North Atlantic and greatly influences climate conditions all
over Europe (Hurrell, 1995; Hurrell and VanLoon, 1997). Because of the
Iberian Peninsula's advantageous position for studying the shifting NAO, the
climate in this region has been intensively investigated (Zorita et al.,
1992; Rodó et al., 1997; Trigo et al., 2004). Many of these studies are
from an oceanic perspective, obtaining sea surface temperatures from marine
sediments using the alkenone unsaturation indices (Abrantes et al., 2005,
2009; Rodrigues et al., 2009), coccolithophore assemblages (Cachao and
Moita, 2000; Palumbo et al., 2013), and stable isotopic oxygen composition
of foraminifera (Lebreiro et al., 2006; Bartels-Jónsdóttir et al.,
2006, 2009). The terrestrial climate has been examined using continental
paleoarchives such as speleothems (Munoz-Garcia et al., 2007;
Martin-Chivelet et al., 2011; Stoll et al., 2013), tree rings (Andreu et
al., 2007; Linan et al., 2012), and pollen (Huntley and Prentice, 1988;
Lebreiro et al., 2006; Davis et al., 2003; Fletcher et al., 2007; Corella et
al., 2013). The integrated continental and marine approach can give
complimentary information to past climate in a region, and by using the same
proxy on the continent, in the ocean, and at the ocean–continent interface
we would perhaps obtain a clearer picture of continental climate processes
in an area than by using separate studies or a multi-proxy approach.
Stations, sediment core names, locations of sampling, and water depth
for each sediment core used in this study.
Station
Core name
Latitude (N)
Longitude (W)
Water depth (m)
0501.029
Tagus River Floodplain
39∘23′07.80′′
08∘31′55.56′′
0
64PE332-30-2
Tagus Mudbelt
38∘39′02.20′′
09∘28′07.68′′
82
64PE332-44-2
Lisbon Canyon Head
38∘30′20.19′′
09∘15′04.87′′
259
64PE269-39-4
Lower Setúbal Canyon
38∘13′12.00′′
10∘10′00.00′′
4217
Material and methods
Sample collection
Soil samples, riverbank sediment samples, and river SPM
from the Tagus River basin (Fig. 1b) were collected previously (Zell et al.,
2014). These samples were complemented with four long sediment cores
collected along a transect running from the Tagus River to the lower
continental slope (Fig. 1). The Tagus River Floodplain core (0501.029) was
collected in a low-energy backswamp of the present-day floodplain of the
river at ∼ 4 km west of the Tagus channel (Table 1). The
sediment was collected using an Edelman hand auger for sediment above the
groundwater table and a gauge for sediment below the groundwater table (Vis
et al., 2008). The sediments were wrapped in the field for laboratory
analyses. The other three cores were collected using a piston corer, during
campaigns in May 2007 and March 2011 with RV Pelagia conducted by the NIOZ Royal
Netherlands Institute for Sea Research. The coring site for the Tagus Mudbelt core
(64PE332-30-2) was to the west of the Tagus Estuary mouth, for the Lisbon
Canyon Head core (64PE332-44-2) it was to the east of the Tagus Estuary
mouth, and for the Lower Setúbal Canyon core (64PE269-39) it was on the
crest of the northern levee of the lower Setúbal Canyon (Table 1). A
detailed description of the cores used in this study is given in the
Supplement.
Age models
Accelerated mass spectrometry (AMS) 14C measurements of
the three marine sediment cores were carried out at the Beta Analytic
laboratory (USA) on benthic or planktonic forams, gastropods, or shells
fragments (Table 2). As for the Tagus River Floodplain core, the radiocarbon
dating material was performed for a previous study and consisted of mostly
terrestrial botanical macrofossils, but other bulk material was used as well
(Vis et al., 2008). In order to establish consistent chronologies for the
four sediment cores, all the AMS dates were calibrated into calendar ages
using CALIB 7.0, available at http://radiocarbon.pa.qub.ac.uk/calib (Stuiver et al., 1998). For the three marine sediment
cores, Marine13 was used for calibration data and curve selection, and IntCal13 was used for the Tagus River Floodplain core
(Reimer et al., 2013). All radiocarbon dates mentioned have age spans in the
2σ range and are expressed as calibrated ages (cal BP; Table 2,
Fig. S2).
Summary of the data used to determine an age depth model for the
sediment samples in this study.
Sediment core
Lab code
Depth in
Mean depth
Uncorrected
Analytical
Ages
Ages
Analyzed material
core (cm)
in core (cm)
AMS14C
error (±1σ)
(ΔR = 0 yr)
(cal yr BP)
ages (yr BP)
(years)
(±2σ) (cal yr BP)
0501.029*
0–2
1
0
0501.029*
331–334
332.5
1136
38
964–1150
1057
Roots of fraction > 125 µm
0501.029*
331–334
332.5
1022
37
901–1001
951
Total organic fraction > 125 µm
0501.029*
604–607
605.5
3089
38
3209–3383
3296
Terrestrial botanical macrofossils
0501.029*
711–712
711.5
4129
42
4530–4821
4676
Terrestrial botanical macrofossils
0501.029*
1024–1029
1026.5
5790
40
6485–6676
6581
Terrestrial botanical macrofossils
0501.029*
1046–1050
1048
5900
45
6633–6805
6719
Terrestrial botanical macrofossils
64PE332-30-2
0–2
1
0
64PE332-30-2
BETA 348791
20–22
21
500
30
40–236
138
Gastropod fragments
64PE332-30-2
BETA 348792
428–430
429
1730
30
1221–1349
1285
Ammonia beccarii (benthic forams)
64PE332-30-2
BETA 348793
678–680
679
2320
30
1848–2032
1940
Gastropod
64PE332-30-2
BETA 317911
976–978
977
5370
30
5643–5849
5746
Bivalve shell fragments
64PE332-44-2
0–2
1
0
64PE332-44-2
BETA 317906
521–523
521.5
2330
30
1858–2044
1951
Mixed planktonic forams
64PE332-44-2
BETA 317907
770–772
771.5
5390
30
5664–5865
5765
Gastropod
64PE332-44-2
BETA 317908
924.5–926.5
925.5
8160
40
8515–8798
8657
Mixed planktonic forams
64PE269-39-4
0
0
0
64PE269-39-4
BETA 330562
5
5
930
30
486–608
547
G. bulloides (planktonic forams)
64PE269-39-4
BETA 330563
100
100
4980
50
5205–5466
5336
G. bulloides (planktonic forams)
64PE269-39-4
BETA 330564
200
200
10 190
40
11 092–11 271
11 182
G. bulloides (planktonic forams)
64PE269-39-4
BETA 348794
280
280
11540
40
12 865–13 150
13008
G. bulloides (planktonic forams)
* Data are from Vis et al. (2010).
Bulk isotope data
Prior to bulk carbon isotope analysis, sediment was
decalcified using a 2 N HCL solution for approximately 18 h. The sediment
was rinsed three times using double-distilled water and then freeze-dried
again. Total organic carbon (TOC) and δ13CTOC (Table 3)
were measured in duplicate using a Flash 2000 series organic elemental
analyzer (Thermo Scientific) equipped with a TCD detector. The δ13CTOC is expressed in relation to the Vienna Pee Dee Belemnite
(VPDB) standard, and the isotope analysis precision was
0.1 ‰.
Lipid extraction and GDGT analysis
Between 1 and 3 g of freeze-dried sediment
was extracted using Dionex™ accelerated solvent extraction (ASE)
with dichloromethane (DCM) : methanol (9:1, v/v) as the solvent at a
temperature of 100 ∘C and a pressure of 1500 psi for 5 min with
60 % flush and 60 s of purge. The extract was then collected and dried
using a Caliper Turbovap® LV. Next, using DCM, the lipid extract
was dried over a column of anhydrous Na2SO4 and then blown down
under a gentle stream of N2. In order to quantify GDGTs, 1 µg of
an internal standard (C46 GDGT; Huguet et al., 2006) was added to the
total lipid extract before it was separated over a column of Al2O3 (activated for 2 h at 150 ∘C) into three fractions using
hexane : DCM (9:1, v:v) for the apolar fraction, hexane : DCM (1:1, v:v) for the
ketone fraction, and DCM : MeOH (1:1, v:v) for the polar fraction. The polar
fraction, which contained the GDGTs, was dried under a N2 stream and
then re-dissolved in hexane : isopropanol (99:1, v:v) at a concentration
10 mg mL-1. Finally, it was passed through a 0.45 µm PTFE filter and
analyzed with high-performance liquid chromatography–atmospheric pressure
positive ion chemical ionization mass spectrometry (HPLC-APCI-MS) with a
separation method that allows the separation of 5- and 6-methyl brGDGTs
(Hopmans et al., 2015). For the study of Zell et al. (2014) the samples
were split into two different fractions before the analysis: the intact
polar lipid (IPL) fraction and core lipid (CL) fractions. For the purposes
of this study the IPL and CL fractions of the river SPM were analyzed
separately on the HPLC-APCI-MS for GDGTs (Hopmans et al., 2015) and then the
amounts of GDGTs found in the CL and IPL fractions were combined. After
analysis, some of the GDGT-based indices were recalculated for the entire
sample set.
Concentrations of GDGTs and brGDGT-based indices for each sample
set along the transect.
Sample name
Age
TOC*
δ13CTOC*
Concentration
BIT
MBT'5me
DC'
IR
IRII
IRIII
(cal kyr BP)
(wt %)
(‰ VPDB)
index
Crenarchaeol
Sum
brGDGTs
(µg g OC-1)
Tagus soils
TRS-8b
n/a
3.0
-27.8
2.2
19.2
0.88
0.51
0.13
0.39
0.36
0.43
TRS-7
n/a
5.0
-27.5
0.0
7.5
1.00
0.56
0.01
0.13
0.12
0.17
TRS-9
n/a
0.5
-27.2
0.1
7.8
0.99
0.68
0.01
0.14
0.14
0.15
TRS-3
n/a
0.7
-29.0
0.6
4.1
0.87
0.40
0.05
0.41
0.43
0.39
TRS-4
n/a
0.7
-28.7
1.2
5.3
0.81
0.42
0.04
0.39
0.41
0.30
TRS-5
n/a
2.2
-28.4
2.4
15.3
0.85
0.29
0.10
0.37
0.37
0.35
TRS-10
n/a
1.5
-28.5
1.4
1.6
0.49
0.60
0.19
0.84
0.88
0.83
TRS-12
n/a
0.2
-25.1
4.0
2.4
0.34
0.62
0.22
0.87
0.87
0.91
TRS-14b
n/a
0.8
-25.3
1.7
2.1
0.48
0.57
0.32
0.85
0.89
0.86
TRS-13
n/a
6.9
-27.0
0.9
1.2
0.54
0.33
0.13
0.74
0.80
0.71
TRS-15
n/a
0.9
-25.7
1.9
2.0
0.48
0.46
0.21
0.86
0.90
0.86
TRS-16
n/a
0.1
-24.8
1.6
3.0
0.62
0.56
0.19
0.92
0.93
0.93
TRS-20
n/a
0.1
-26.1
0.6
3.8
0.84
0.52
0.38
0.83
0.87
0.83
TRS-19
n/a
0.1
-25.7
0.7
19.7
0.95
0.56
0.44
0.84
0.85
0.89
Tagus riverbank sediments
TRS-6
n/a
1.7
-26.3
16.7
76.0
0.77
0.53
0.30
0.54
0.46
0.59
TRS-8a
n/a
0.2
-26.5
10.8
68.2
0.83
0.55
0.26
0.77
0.71
0.83
TRS 2a
n/a
3.3
-23.6
3.4
12.8
0.70
0.63
0.42
0.79
0.81
0.77
TRS 2b
n/a
0.5
-25.1
8.1
55.1
0.80
0.57
0.45
0.67
0.72
0.72
TRS 1a
n/a
0.9
-26.9
9.7
18.8
0.62
0.54
0.21
0.68
0.68
0.66
TRS1b
n/a
1.5
-27.6
26.1
19.7
0.28
0.64
0.53
0.72
0.82
0.80
TRS-11
n/a
1.3
-27.0
6.3
3.9
0.33
0.48
0.24
0.75
0.79
0.75
TRS-14a
n/a
3.0
-27.1
1.2
21.7
0.92
0.38
0.44
0.55
0.56
0.68
TRS-17
n/a
3.7
-29.6
1.6
37.8
0.93
0.41
0.51
0.70
0.65
0.66
TRS-22
n/a
0.6
-29.8
3.1
24.8
0.86
0.41
0.40
0.80
0.75
0.82
Tagus River SPM
TR 2 Sup July
n/a
2.6
-29.2
6.2
33.1
0.80
0.51
0.28
0.60
0.53
0.64
TR 3#1 Sup Sept.
n/a
1.8
-28.4
5.6
20.7
0.73
0.52
0.30
0.59
0.51
0.61
TR4 #1 Oct.
n/a
2.5
-30.9
8.6
38.1
0.77
0.52
0.30
0.61
0.54
0.62
TR5 #1 Sup Nov.
n/a
1.3
-28.9
5.8
54.8
0.85
0.49
0.25
0.62
0.55
0.68
TR 6 #1 Sup Dec.
n/a
2.4
-29.4
11.5
86.8
0.85
0.47
0.26
0.63
0.56
0.67
TR7 #1 Sup Jan.
n/a
2.4
-29.8
9.8
53.6
0.77
0.48
0.27
0.63
0.56
0.67
TR8 #1 Sup Feb.
n/a
1.0
-29.4
16.8
46.9
0.69
0.50
0.27
0.58
0.51
0.62
TR9 #1 Sup Mar.
n/a
2.2
-29.0
6.3
21.9
0.72
0.50
0.29
0.58
0.51
0.61
TR10 #1 Sup Apr.
n/a
1.9
-28.5
0.8
36.5
0.96
0.52
0.29
0.58
0.50
0.61
TR11 #1 Sup May
n/a
1.7
-28.5
26.3
80.8
0.69
0.52
0.29
0.56
0.48
0.59
TR12 #1 Sup June
n/a
1.3
-27.8
9.5
22.8
0.65
0.55
0.23
0.51
0.48
0.58
Tagus River Floodplain
sediments (0501.029), depth (cm)
10.0
0.0
1.7
-26.3
2.1
29.7
0.92
0.34
0.31
0.39
0.39
0.44
95.0
0.3
2.6
-26.5
6.4
66.8
0.89
0.36
0.24
0.43
0.43
0.45
195.0
0.6
1.5
-27.4
2.5
111.8
0.97
0.34
0.37
0.38
0.40
0.43
241.0
0.8
1.5
-26.0
5.2
62.0
0.90
0.40
0.24
0.35
0.36
0.38
341.5
1.0
11.2
-27.5
0.9
47.6
0.97
0.45
0.39
0.36
0.34
0.37
401.0
1.5
12.1
-27.7
1.2
90.6
0.98
0.38
0.38
0.40
0.36
0.46
453.0
2.0
8.0
-26.7
3.0
120.9
0.97
0.34
0.35
0.43
0.43
0.50
542.0
2.8
7.7
-27.3
3.2
91.9
0.95
0.37
0.39
0.40
0.41
0.42
577.0
3.1
4.6
-27.2
3.3
84.2
0.95
0.43
0.35
0.38
0.37
0.41
641.0
3.8
5.3
-28.1
1.0
59.6
0.97
0.52
0.39
0.34
0.35
0.39
681.0
4.3
6.6
-28.6
1.0
52.3
0.97
0.46
0.45
0.37
0.37
0.42
741.0
4.9
4.2
-24.4
4.2
74.3
0.92
0.43
0.45
0.38
0.38
0.36
862.0
5.6
16.2
-26.8
2.2
58.4
0.94
0.45
0.45
0.46
0.43
0.52
982.0
6.3
8.7
-27.7
1.5
46.5
0.95
0.46
0.49
0.37
0.36
0.37
1041.0
6.7
5.2
-27.2
4.4
48.2
0.87
0.48
0.45
0.40
0.39
0.41
Continued.
Sample name
Age
TOC*
δ13CTOC*
Concentration
BIT
MBT'5me
DC'
IR
IRII
IRIII
(cal kyr BP)
(wt %)
(‰ VPDB)
index
Crenarchaeol
Sum
brGDGTs
(µg g OC-1)
Mudbelt sediments
(64PE332-30-2), depth (cm)
1.0
0.0
1.2
-23.7
200.9
28.5
0.09
0.58
0.35
0.51
0.45
0.54
25.0
0.2
1.0
-24.2
194.7
40.7
0.13
0.54
0.28
0.49
0.43
0.52
53.0
0.2
0.9
-24.5
189.3
36.4
0.13
0.52
0.26
0.47
0.44
0.50
75.0
0.3
1.0
-24.4
206.5
43.8
0.14
0.52
0.26
0.48
0.43
0.51
101.0
0.4
0.9
-24.3
228.9
44.4
0.13
0.53
0.27
0.48
0.43
0.51
151.0
0.5
0.9
-24.3
194.8
40.2
0.13
0.53
0.27
0.49
0.44
0.53
201.0
0.6
1.0
-24.5
187.9
31.9
0.11
0.51
0.27
0.47
0.43
0.50
248.0
0.8
0.6
-24.5
224.0
58.0
0.14
0.53
0.26
0.46
0.42
0.49
297.0
0.9
1.1
-24.5
168.3
30.6
0.12
0.50
0.26
0.47
0.42
0.51
347.0
1.1
1.0
-24.1
202.5
24.3
0.08
0.52
0.30
0.50
0.45
0.53
397.0
1.2
1.1
-24.1
180.4
27.3
0.10
0.49
0.30
0.48
0.43
0.52
429.0
1.3
1.2
-24.0
77.3
8.7
0.08
0.50
0.34
0.53
0.45
0.57
496.0
1.5
1.0
-24.3
143.8
15.1
0.07
0.52
0.32
0.53
0.46
0.57
546.0
1.6
0.9
-24.1
11.2
1.4
0.08
0.52
0.33
0.51
0.45
0.55
596.0
1.7
1.1
-27.3
141.3
14.8
0.07
0.51
0.34
0.52
0.45
0.56
645.0
1.9
1.1
-29.9
108.7
12.8
0.08
0.52
0.34
0.52
0.45
0.55
680.0
2.0
1.1
-27.4
142.1
17.4
0.08
0.54
0.31
0.50
0.44
0.54
741.0
2.7
0.8
-27.3
176.1
16.0
0.06
0.52
0.35
0.53
0.46
0.55
791.0
3.4
0.7
-24.5
240.8
19.8
0.05
0.50
0.37
0.55
0.48
0.58
840.0
4.0
0.9
-24.4
139.3
11.0
0.05
0.48
0.37
0.54
0.48
0.55
890.0
4.6
0.6
-24.6
180.2
14.2
0.05
0.48
0.39
0.55
0.47
0.57
977.0
5.7
0.7
-27.5
125.8
11.8
0.06
0.46
0.40
0.53
0.46
0.52
Lisbon Canyon Head sediments
(64PE332-44-2), depth (cm)
1.0
0.0
1.5
-22.9
420.5
27.1
0.04
0.60
0.41
0.55
0.48
0.56
45.0
0.2
1.2
-22.8
409.9
47.3
0.07
0.58
0.40
0.55
0.48
0.57
85.0
0.3
1.1
-23.4
440.4
48.1
0.07
0.54
0.36
0.53
0.46
0.55
130.5
0.5
1.2
-23.4
429.6
41.2
0.06
0.55
0.36
0.53
0.46
0.57
187.5
0.7
1.2
-23.7
308.4
27.6
0.06
0.56
0.36
0.53
0.45
0.56
221.5
0.8
1.1
-24.4
157.9
35.1
0.12
0.54
0.28
0.47
0.42
0.51
278.5
1.0
1.2
-23.2
283.6
22.7
0.05
0.54
0.36
0.51
0.45
0.54
326.5
1.2
1.0
-22.6
361.7
31.1
0.05
0.53
0.42
0.56
0.47
0.60
371.5
1.4
1.1
-22.6
374.1
26.5
0.05
0.53
0.41
0.55
0.47
0.58
429.0
1.6
1.0
-22.6
399.6
29.2
0.05
0.55
0.42
0.56
0.49
0.59
480.0
1.8
1.1
-23.4
242.3
18.0
0.05
0.54
0.41
0.55
0.48
0.57
502.0
1.9
1.1
-23.9
365.1
25.4
0.04
0.52
0.42
0.56
0.49
0.58
522.0
2.0
1.0
-23.4
376.8
30.9
0.05
0.55
0.39
0.55
0.48
0.57
550.0
2.4
1.0
-23.0
363.1
26.7
0.05
0.54
0.43
0.57
0.50
0.59
570.0
2.7
0.7
-22.6
493.6
34.3
0.04
0.53
0.43
0.57
0.50
0.59
630.0
3.6
0.7
-22.4
389.6
25.7
0.04
0.53
0.44
0.58
0.51
0.60
686.0
4.5
0.5
-22.4
640.2
40.7
0.04
0.54
0.45
0.59
0.51
0.60
728.0
5.1
0.5
-22.1
100.1
6.7
0.04
0.50
0.45
0.57
0.51
0.58
771.0
5.8
0.7
-23.3
620.7
37.3
0.04
0.53
0.44
0.58
0.50
0.59
805.0
6.4
0.3
-22.1
414.0
25.9
0.04
0.52
0.45
0.58
0.51
0.58
869.5
7.6
0.3
-22.3
538.9
35.7
0.04
0.54
0.41
0.57
0.50
0.56
925.5
8.7
0.3
-23.1
409.4
30.1
0.04
0.52
0.46
0.59
0.51
0.58
Lower Setúbal Canyon
sediments (64PE269-39-4), depth (cm)
1.0
0.0
0.8
-23.5
265.3
20.3
0.05
0.68
0.38
0.73
0.60
0.77
20.0
1.3
0.7
-22.3
506.5
15.6
0.02
0.60
0.39
0.77
0.60
0.80
40.0
2.3
0.6
-22.0
751.5
18.1
0.02
0.58
0.39
0.73
0.61
0.75
60.0
3.3
0.8
-22.2
540.7
16.2
0.02
0.56
0.46
0.75
0.61
0.74
80.0
4.3
0.8
-22.3
460.5
13.5
0.02
0.56
0.41
0.75
0.63
0.77
100.0
5.3
0.8
-22.8
204.7
7.3
0.03
0.57
0.37
0.74
0.66
0.75
120.0
6.5
0.7
-22.8
199.2
8.0
0.03
0.57
0.38
0.77
0.66
0.78
140.0
7.7
0.5
-26.1
433.3
13.6
0.02
0.51
0.41
0.73
0.67
0.74
160.0
8.8
0.5
-22.8
395.6
16.9
0.03
0.45
0.43
0.69
0.64
0.64
180.0
10.0
0.4
-24.9
709.2
25.6
0.02
0.48
0.43
0.69
0.58
0.65
200.0
11.2
0.5
-25.7
703.9
22.1
0.02
0.54
0.41
0.66
0.52
0.64
220.0
11.6
n/a
n/a
n/a
n/a
0.02
0.48
0.47
0.66
0.52
0.59
240.0
12.1
n/a
n/a
n/a
n/a
0.01
0.56
0.57
0.70
0.56
0.63
260.0
12.6
n/a
n/a
n/a
n/a
0.03
0.58
0.49
0.71
0.58
0.64
280.0
13.0
n/a
n/a
n/a
n/a
0.01
0.52
0.48
0.69
0.57
0.65
* Data for Tagus soils, riverbank sediments, and SPM have previously
been published by Zell et al. (2014).
Calculation of GDGT-based proxies
The Roman numerals used to calculate the following GDGT-based proxies refer to the GDGTs indicated in Fig. S1. The GDGT indicated by IV
is crenarchaeol, the isoprenoid GDGT specific to Thaumarchaeota (Sinninghe
Damsté et al., 2002).
The BIT index (Hopmans et al., 2003), which results in a value between 0 and
1, with those values closer to 0 designating a more marine signal and a value
close to 1 indicating a more terrestrial signal, was calculated using the
following formulae that specifically include the novel 6-methyl brGDGTs
according to De Jonge et al. (2015):
BIT index=(Ia+IIa+IIIa+IIa′+IIIa′)/(Ia+IIa+IIIa+IIa′+IIIa′+IV)
The isomer ratio (IR) signifies the quantity of the penta- and
hexamethylated 6-methyl brGDGTs compared to the total brGDGTs and was calculated
according to De Jonge et al. (2015):
IR=(IIa′+IIb′+IIc′+IIIa′+IIIb′+IIIc′)/(IIa+IIb+IIc+IIIa+IIIb+IIIc+IIa′+IIb′+IIc′+IIIa′+IIIb′+IIIc′).
The relative abundances of the penta- and hexamethylated 6-methyl brGDGTs are
calculated according to (De Jonge et al., 2014b):
IRII=IIa′/(IIa+IIa′),IRIII=IIIa′/(IIIa+IIIa′).
The MBT'5Me (which excludes the 6-methyl brGDGTs) was used to calculate
MAT according to De Jonge et al. (2014a):
MBT′5Me=(Ia+Ib+Ic)/(Ia+Ib+Ic+IIa+IIb+IIc+IIIa),MAT=-8.57+31.45×MBT5Me′.
The equation to determine DC (Sinninghe Damsté et al., 2009) was
reformulated to specifically include the pentamethylated 6-methyl brGDGTs:
DC′=(Ib+IIb+IIb′)/(Ia+Ib+IIa+IIb+IIa′+IIb′).
To calculate pH and MAT the novel MATmr/CBT' calibration was used (De
Jonge et al., 2014a):
CBT′=10log(Ic+IIa′+IIb′+IIc′+IIIa′+IIIb′+Ic′)/(Ia+IIa+IIIa),pH=7.15+1.59×CBT′,MATmr=7.17+17.1×[Ia]+25.9×[Ib]+34.4×[Ic]-28.6×[IIa],MATmrs=5.58+17.91×[Ia]-18.77×[IIa].
Statistical analysis
Using the R software package for statistical analysis, we
employed principal component analysis (PCA) based on the correlation matrix.
The PCA was performed on the fractional abundances of all 15 of the 5- and
6-methyl brGDGTs for the entire sample set along the transect from the land
to the ocean.
Results
We report bulk and brGDGT data for four cores covering Holocene
sedimentation in the Tagus River basin and its outflow into the Atlantic. We
compare these data with new results acquired through an improved LC method
able to distinguish between the 5- and 6-methyl brGDGTs (De Jonge et al.,
2013) on the soils, riverbank sediments, and SPM samples previously obtained
by Zell et al. (2014).
Box plots of (a) δ13CTOC (‰)
of the organic carbon and (b) TOC (wt %) for each sample set along the
Tagus River source to sink transect. The increasing δ13CTOC values in the sediment core locations with increasing
distance from the coast indicate that more of the organic carbon in these
sediments is marine-derived.
Bulk parameters of the sediments
The age–depth models for the marine sediment cores (Fig. S2, Table 1) are
based on radiocarbon dating of selected foraminifera, gastropods, and shell
fragments. The data show that of the four sediment cores from the transect,
the Tagus River Floodplain sediments date to 6.7 cal kyr BP, the Tagus Mudbelt
sediments date to 5.8 cal kyr BP, the Lisbon Canyon Head sediments date
to 8.7 cal kyr BP, and the Lower Setúbal Canyon penetrated the oldest
strata (13.0 cal kyr BP). Reported values for sediments from each
location were averaged over the interval 0–6.0 cal kyr BP so as to avoid
a bias in the data since not all of the sediment cores covered more than
6.0 kyr.
Box plots of (a) crenarchaeol concentrations (µg g OC-1),
(b) sum of brGDGTs (µg g OC-1), (c) BIT index, (d) DC',
(e) IR, and (f) MBT'5me for each sample set in the transect from the land to the
ocean off the Portuguese coast.
The bulk carbon isotope data for the Tagus River SPM, riverbank sediments,
and soils have been previously discussed in Zell et al. (2014). The TOC
values for the Tagus River Floodplain sediments are relatively high and also
highly variable, with a range of 1.5–16 wt % and a mean of 6.5 ± 4.3 wt % (average ± standard deviation), and the mean
δ13CTOC was -27.0 ± 1.0 ‰ (Fig. 2; Table 3). In the Tagus Mudbelt sediments the TOC is less variable than in the Tagus
River Floodplain sediments, ranging from 0.6 to 1.2 wt % and with an
average of 0.9 ± 0.2 wt % (Fig. 2; Table 3). The average δ13CTOC in the Tagus Mudbelt sediments, -24.3 ± 0.2 ‰,
is higher than in the Tagus River Floodplain
sediments. The average δ13CTOC of the Lisbon Canyon Head
sediments, -23.0 ± 0.6 ‰, is higher than the Tagus
Mudbelt sediments and the TOC content is similar to that of the Tagus Mudbelt
sediments, ranging from 0.25 to 1.5 wt % with the mean of 0.9 ± 0.3 wt % (Fig. 2; Table 3). The average δ13CTOC values in
the Lower Setúbal Canyon sediments (-23.4 ± 1.5 ‰) are similar to those of the Lisbon Canyon Head
sediments, with a TOC content ranging from 0.51 to 0.85 wt % with a mean
value of 0.65 ± 0.14 wt % (Fig. 2; Table 3).
Concentrations and distributions of GDGTs
Tagus soils and riverbank sediments
The average concentration of crenarchaeol is higher in the riverbank
sediments (∼ 8.7 ± 7.8 µg g OC-1) than in the
soils (∼ 1.4 ± 1.1 µg g OC-1; Fig. 3a–b;
Table 3). The same trend is true for the brGDGTs, with the average
concentration being higher in the riverbank sediments (∼ 33.9 ± 24.5 µg g OC-1) than the soils
(∼ 6.8 ± 6.5 µg g OC-1; Fig. 3a–b; Table 3). The values of the
BIT index were similar to those previously reported (Zell et al., 2014) for
both the soils and riverbank sediments and ranged from 0.3 to 1.0 with an
average of 0.7 ± 0.2 (Fig. 3c; Table 3). The reanalysis of the brGDGTs
in the soils reveals that the relative abundance of the novel 6-methyl
brGDGTs is highly variable (ranging from 0.13 to 0.92) and can be quite high;
the average values for the IR are 0.6 ± 0.3 (Fig. 3e; Table 3). IR is
even higher but less variable for the riverbank sediments, with an average of
0.7 ± 0.1 (Fig. 3e; Table 3). In general the penta- and hexamethylated
brGDGTs show the same ratio of 5- and 6-methyl isomers (Fig. 4); however, in
soils from an altitude of > 350 m the 6-methyl brGDGTs are
especially dominant (Fig. S3). Values for the new MBT'5me index, which
excludes the 6-methyl brGDGTs (cf. De Jonge et al., 2014a), of the soils and
riverbank sediments are quite similar, with an average of 0.5 ± 0.1 in
both cases (Fig. 3f; Table 3). The DC' ratio deviates between the soils and
the riverbank sediments (Fig. 3d; Table 3). The DC' for the soils is highly
variable but on average low (0.2 ± 0.1); for the riverbank sediments it
is higher with an average of 0.4 ± 0.1 (Fig. 3d; Table 3).
Isomer ratio for the non-cyclized pentamethylated brGDGT
(IRII) plotted against that of the non-cyclized hexamethylated brGDGT
(IRIII).
Tagus River SPM
The SPM was obtained from the Tagus Estuary near the mouth of the Tagus
River once a month over the course of a year (excluding the month of
August). Data from the Tagus River SPM showed that the summed brGDGT and
crenarchaeol concentrations in the river SPM varied throughout the year and
were on average 45 ± 23 and 9.8 ± 6.8 µg g OC-1 (Fig. 3a–b; Table 3), respectively, resulting in only
small variations in the BIT index (i.e., 0.8 ± 0.1; Fig. 3c; Table 3).
The distribution of brGDGTs (Fig. 5c) was relatively constant throughout the
year, as is evident from the values for MBT'5me (0.5 ± 0.0), DC'
(0.3 ± 0.0), and IR (0.6 ± 0.0) for the river SPM (Fig. 3d–f;
Table 3).
Tagus River Floodplain sediments
The average crenarchaeol concentration is fairly low in the Tagus River
Floodplain sediments, 2.8 ± 1.7 µg g OC-1; conversely, the
average sum of the brGDGTs in the sediments, 70 ± 26 µg g OC-1, is the largest out of the entire transect (Fig. 3a–b; Table 3).
The BIT index is fairly high and constant throughout the sediment core, with
an average value of 0.9 ± 0.0 (Fig. 3c; Table 3). The distribution of
brGDGTs (Fig. 5d) is somewhat similar to that of the riverine SPM (Fig. 5c)
and shows no major changes over the Holocene. The Tagus River Floodplain
sediments has the lowest average values for MBT'5me (0.4 ± 0.1)
and IR (0.4 ± 0.0) of all the sediment records in the transect (Fig. 3e–f). The mean DC' throughout the sediments in this sample set is
0.4 ± 0.1 (Fig. 3d; Table 3).
Average distribution of brGDGTs for each sample set along the
transect of samples that runs from the land to the ocean off the coast of
Lisbon. Evident from this figure is that the distribution of brGDGTs within
this sample set varies greatly. Distributions of brGDGTs in marine sediments
only reflect the distribution of the brGDGTs from the Tagus soils to a
minor extent. The color of the bars reflects the brGDGT structure as labeled
in the legend, and the range indicated with the error bars equals 2xs the
standard deviation.
Tagus Mudbelt sediments
The average concentration of the brGDGTs in the Tagus Mudbelt sediments, 25 ± 14 µg g OC-1, is lower than in the Tagus River Floodplain
sediments; however, the concentration of crenarchaeol, 170 ± 50 µg g OC-1, is higher in the Tagus Mudbelt sediments (Fig. 3a–b; Table 3). This
results in a lower mean value of the BIT index (i.e., 0.09 ± 0.03; Fig. 3c; Table 3). The brGDGT distribution is relatively constant over the
Holocene and is fairly similar to that of the Tagus River Floodplain
sediments, with slightly higher fractional abundances of Ia and IIIa' (cf.
Fig. 5d–e; Table 3). The average value of the MBT'5me (0.5 ± 0.0)
is similar to the Tagus River SPM value (Fig. 3f). The average value of the
DC' is 0.3 ± 0.1 and the mean value of the IR is 0.5 ± 0.0
(Fig. 3d–e; Table 3).
Lisbon Canyon Head sediments
The average sum of the brGDGTs, 31 ± 9.3 µg g OC-1, is
about the same in the Lisbon Canyon Head sediments as in the Tagus Mudbelt
sediments, but the amount of crenarchaeol, 390 ± 130 µg g OC-1, is larger in the Lisbon Canyon Head
sediments (Fig. 3a–b; Table 3). This results in lower BIT index values (0.05 ± 0.02) than in the Tagus Mudbelt
sediments (Fig. 3c; Table 3). The average brGDGT distribution (Fig. 5f) is
fairly similar to that of the Tagus River Floodplain and Tagus Mudbelt sediments and is
relatively constant over the Holocene. The average of the MBT'5me
(0.5 ± 0.0) is statistically identical to that in the Tagus Mudbelt sediments
(Fig. 3f; Table 3). However, the average IR (0.6 ± 0.0) and DC' (0.4 ± 0.0) values are both a bit higher (Fig. 3d–e; Table 3).
Lower Setúbal Canyon sediments
The concentrations of the brGDGTs in these most distal sediments are quite
low, on average 16 ± 5.5 µg OC-1 (Fig. 3a; Table 3), while
the amount of crenarchaeol in this sediment core is the highest out of the
entire transect at 470 ± 200 µg g OC-1 (Fig. 3b; Table 3). This
results in a low average BIT index value of 0.02 ± 0.01 (Fig. 3c; Table 3). The average distribution of brGDGTs in these sediments (Fig. 5g) is
different from the marine sediments from the other two sites, with a higher
fractional abundance of IIIa'. However, another component with the same
molecular ion eluted at around the same time as IIIa' in the Lower
Setúbal Canyon sediments (which we determined was not the “mixed
5,6-dimethyl isomer”; cf. Weber et al., 2015), complicating integration and
quantification. This indicates that the brGDGT results from these sediments
must be interpreted with some caution. The average MBT'5me (0.6 ± 0.1) and DC' (0.4 ± 0.1) values are fairly similar to the Lisbon Canyon Head
sediments averages, but the average IR (0.7 ± 0.0) is the highest of all
sediments (Fig. 3d–f; Table 3).
Principal component analysis based on the fractional abundances of
the 15 brGDGTs of samples in the transect that runs from inland to off the
coast of Portugal plotting (a) the scores of the brGDGT compounds on the
first two principal components and (b) the scores of the samples from
each sample set used in this study.
PCA
In order to determine the variation in the distribution of brGDGTs, we
performed PCA on the distributions of brGDGTs
of all the samples examined. Most variation is explained by principal
component 1 (PC1; 29.8 %) and is clearly related to the fractional
abundance of the 5-methyl vs. 6-methyl brGDGTs (Fig. 6a). With the
exception of IIIc (which is typically a minor brGDGT with a fractional
abundance of < 1 %; Fig. 5), all of the 5-methyl brGDGTs score
positively on PC1 and the 6-methyl brGDGTs score negatively. For the overall
dataset, PC1 is highly negatively correlated with the IR ratio (Fig. 7a,
R2= 0.78). PC2 explains 25.6 % of the variance of the PCA. Branched
GDGTs that score positively on PC2 are generally comprised of cyclized and
more methylated brGDGTs (Fig. 6a). With the exception of IIIc (which is
typically a minor brGDGT with a fractional abundance of < 1 %;
Fig. 4), all of the tetra- and penta-methylated brGDGTs containing no
cyclopentane moiety (i.e., Ia, IIa, and IIa') score negatively on PC2.
Consequently, PC2 is highly positively correlated with DC' for the whole
dataset (Fig. 7b, R2= 0.84).
Discussion
Environmental parameters affecting brGDGT distribution in Tagus
soils
What is evident from the earlier study by Zell et al. (2014) was that the
distribution of the brGDGTs in Tagus soils varies widely. The primary
environmental parameters influencing brGDGT distributions in soil (Weijers
et al., 2006), i.e., MAT and pH, differed substantially in the Tagus River
basin. MAT varies from 10 to 17 ∘C and pH from 5.5 to 8.6 (Zell et al.,
2014) and both parameters show a distinct correlation with altitude
(R2= 0.93 and 0.73, respectively). Applying the brGDGT global soil
calibration of Peterse et al. (2012), Zell et al. (2014) arrived at
unrealistically low (0–10 ∘C) estimated MATs using the brGDGT
distributions. This was attributed to the arid conditions in the region
(MAP < 800 mm yr-1), which have in other studies, including one
that analyzed soils from the Iberian Peninsula, been indicated as a likely
cause of the discrepancy between actual and reconstructed MAT using brGDGT
distributions (Peterse et al., 2012; Dirghangi et al., 2013; Menges et al.,
2014). Our reanalysis of the soils taking into account the novel 6-methyl
brGDGTs now provides the possibility to re-evaluate these data. It is clear
that the fractional abundances of the novel 6-methyl brGDGTs vary to a large
extent. The IRII and IRIII vary from 0.1 to 0.9 (Fig. 4) and some
of the soils score very negatively on PC1 (Fig. 6b), which is predominantly
determined by the fractional abundance of the 6-methyl brGDGTs. From the
global soil brGDGT dataset (De Jonge et al., 2014a) it was evident that the
main factor influencing the fractional abundance of the 6-methyl brGDGTs is
soil pH, with an increased abundance in high pH soils. In the Tagus River
basin soil pH indeed shows a large variation, i.e., from 5.5 to 8.6, and this
likely explains the large variation in IR. When we calculate the pH from the
brGDGT distribution using the new Eq. (9) of De Jonge et al. (2014a),
which is based predominantly on the fractional abundances of 6-methyl
brGDGTs, we find a highly significant correlation between measured and
reconstructed pH (R2= 0.89) following the 1:1 line (Fig. 8a).
Differences in soil pH also affect the degree of cyclization of brGDGTs
(Weijers et al., 2007a; De Jonge et al., 2014b), and indeed we find a
significant positive correlation between DC' and soil pH (R2= 0.74).
The effect of MAT is not clearly revealed in the dataset. For the global
soil brGDGT dataset a strong relationship exists between MAT and
MBT'5Me (De Jonge et al., 2014a). Although we observe substantial
variation for MBT'5Me in soils (i.e., 0.3–0.7; Fig. 3f) for this
dataset, we do not observe a statistically significant relationship of MAT
with MBT'5Me. Also, reconstructed MATs are far too low, i.e.,
0.5–13 ∘C using Eq. (6) and 2.6–11 ∘C using
Eq. (10). Evidently, the “cold bias” of the brGDGT distributions in
the soils of the Tagus river basin (Zell et al., 2014) is not solved when 5-
and 6-methyl brGDGTs are individually quantified.
Scatterplots of (a) PC1 against the IR (R2= 0.78) and
(b) PC2 against DC' (R2= 0.84) for the entire set of samples used in this
study.
Previously it was postulated that, in this region, aquatic in situ production
and arid conditions are complicating the use of brGDGTs for climate
reconstructions (Menges et al., 2014; Zell et al., 2014). Within the soil
sample set, a strong negative relationship exists between the DC' and the
measured MAT in the Tagus basin (R2= 0.79), whereas the degree of
cyclization up until this point has only been reported to be related to pH
and not to MAT (Weijers et al., 2007a). Conversely, though, the MATmrs
reconstructed values for the soils have a positive correlation with DC'
(R2= 0.51) and it is lower than with the measured MAT. Although at
this point we are unsure whether this association occurs in other arid areas as
well, we do believe this strong relationship between the DC' and the MAT
could be affecting the applicability of brGDGTs for temperature
reconstructions in this region.
Provenance of brGDGTs in the Tagus River and its outflow
The application of brGDGTs in marine sediments influenced by river outflows
for reconstruction of the continental paleoclimate (e.g., Weijers et al.,
2006) rests on the premise that the distribution of the brGDGTs produced in
the soils must be conserved throughout riverine transport to the sediments
where they are archived. Therefore, we compare brGDGT distributions and
concentrations from the rest of the sample set in the source-to-sink
transect to determine whether the soil signal is conserved during transport in
the Tagus River basin. The PCA results (Fig. 6b) indicate that for the most
part the distribution of brGDGTs from the river SPM and sediments along the
transect is not similar to those from the soils or the Tagus Watershed.
Sediments from three of the sample sets in the transect – the Tagus River
Floodplain sediments, the Tagus Mudbelt sediments, and the Lisbon Canyon Head
sediments – all plot differently from the soils, and although the
distributions of the Lower Setúbal Canyon sediments and the Tagus River
SPM plot closer, there is still an offset from the soils. The Tagus riverbank sediments plot the most closely to the soil samples in the Tagus River basin; however, again a slight offset still exists. Thus, even without
considering the effects of environmental parameters on brGDGT distributions,
we can already conclude that the brGDGTs in the sediments and river SPM only
reflect the distribution of brGDGTs in the Tagus soils to a minor extent,
and thus it is unlikely that Tagus soils are a major source for brGDGTs in
the marine sediments.
Using PCA (Fig. 6) we tried to determine what factors are causing the
variation in the distribution of brGDGTs in the Tagus River basin. PC1 is
primarily related to the predominance of 5-methyl vs. 6-methyl brGDGTs
(Fig. 6a) and thus pH (cf. De Jonge et al., 2014a). This was confirmed for the soils in the Tagus Basin, where the calculated pH based on the fractional abundance of predominantly 6-methyl brGDGTs shows good agreement with the measured pH (see Sect. 4.1). De Jonge et al. (2014b) showed that, in the SPM of the
alkaline waters of the river Yenisei, 6-methyl brGDGTs also predominate,
indicating that pH in all kinds of environmental settings determines the
ratio between 5 and 6-methyl brGDGTs. The Tagus riverbank sediments, river
SPM, and the Lower Setúbal Canyon sediments score mostly negatively on
PC1, as do soils from higher altitudes (> 350 m; Fig. 6b). The
Tagus Mudbelt sediments, Lisbon Canyon Head sediments, the Tagus River Floodplain
sediments, and the lower-altitude soils (< 350 m) have similar
abundances of the 5- and 6-methyl brGDGTs or higher abundances of the
5-methyl brGDGTs and plot mostly positively on PC1. Since the Tagus River
Floodplain sediments, the Tagus Mudbelt sediments, and the Lisbon Canyon Head
sediments do not have a predominance of 6-methyl brGDGTs, this indicates either
that they received an equal contribution of soil-derived organic
matter from the lower-altitude soils in the region (< 350 m) as from
the higher-altitude region (> 350 m) or, more likely, that in situ
production of brGDGTs is a large source of brGDGTs in these sample sets.
Panels (a)–(c) show scatterplots of the Tagus soil samples for
(a) reconstructed and measured pH (R2= 0.89), (b) reconstructed MATmr
(∘C) and measured MAT (∘C; R2= 0.27), and (c) reconstructed MATmrs (∘C) and measured MAT (∘C;
R2= 0.38). For panels (b)–(c) the soil samples from an altitude greater
than 350 m are indicated in black and those from an altitude below 350 m are
indicated in green. Panels (d)–(f) show scatterplots of the Tagus riverbank
sediments for (d) reconstructed and measured pH (R2= 0.14), (e) reconstructed MATmr (∘C) and measured MAT (∘C; R2= 0.31), and (f) reconstructed MATmrs (∘C) and measured
MAT (∘C; R2= 0.23). Panel (g) is a scatterplot showing the
reconstructed and measured pH for the Tagus River SPM samples
(R2= 0.09).
PC2 also explains a substantial part of the variance in the dataset (25.6 %, Fig. 6b) and is correlated with DC' (R2= 0.84, n= 109,
Fig. 7b). Since pH is also the main driver of DC' (Weijers et al., 2007a),
this suggests that differences in pH are also responsible for the variance seen
in PC2. The samples that stand out are the sediments from the Lower
Setúbal Canyon core, which are the most marine sediments in the sample
set and plot most positively, and the lowest-altitude soils (28–344 m),
which plot the most negatively. These latter soils are characterized by a
low measured pH. The oldest (11.6–13.0 kyr BP) sediments of the Lower
Setúbal Canyon score most positively on PC2. A high degree of
cyclization of brGDGTs has been observed previously in marine sediments from
a Svalbard fjord and attributed to marine in situ production in the alkaline
pore waters of marine sediments (Peterse et al., 2009; Weijers et al., 2014).
Reanalysis of the Svalbard sediments for brGDGTs actually showed that this
cyclization affects the tetra- and pentamethylated brGDGTs to a much larger
extent than that of the hexamethylated brGDGTs (Sinninghe Damsté, 2016),
and the same observation can be made for the sediments of the Lower
Setúbal Canyon (Fig. 5g). Evidently, the high degree of cyclization of
brGDGTs as a response to pH is not as clearly seen in the soils since the
high-altitude, high-pH soils from the Tagus watershed (Fig. S3c) do not
exhibit the pattern (i.e., fractional abundance of IIb' larger than that of
IIa') observed in the Lower Setúbal Canyon sediments (Fig. 5g). This
pattern is, to a lesser degree, also seen in the sediments of the Lisbon
Canyon Head core (Fig. 5f). As mentioned earlier, the Lower Setúbal
Canyon sediments also display a predominance of 6-methyl brGDGTs over the
5-methyl counterparts, especially with regard to the hexamethylated
brGDGTs. In the Lower Setúbal Canyon sediments, IIIa' is by far the most
abundant brGDGT, consisting of 29 % of the entire brGDGT pool (Fig. 5g).
This is comparable to Svalbard sediments (Sinninghe Damsté, 2016), where
IIIa' is also the most abundant brGDGT. Taken together this clearly
indicates the influence of in situ production in the Lower Setúbal
Canyon sediments. However, the degree of cyclization for Ia-c and IIa-c is
not as high as observed for the Svalbard sediments, which still suggests
some allochthonous input of brGDGTs even in these remote marine sediments.
Another way to determine whether in situ production is a factor affecting the
brGDGT distribution in aquatic environments is by the calculation of
reconstructed pH values. If in situ production is heavily contributing to
the brGDGT pool, then the reconstructed pH values should reflect that of the
aquatic environment in which they were produced. The average reconstructed
pH of the sample sets in the transect is relatively high with a clear trend
toward higher values with increasing distance from the river mouth (Fig. 9a),
which would be in line with increased in situ production of brGDGTs in the
alkaline pore waters of marine sediments. However, these values are still
within the range of the measured (5.5–8.5) and reconstructed (Fig. 9a) pH of
the soils, and so this does not prove in situ production as a major
contributor of brGDGTs in these sample sets. Conversely, the newly
calculated DC', also a reflection of pH, is quite variable throughout the
sample sets in the transect except for in the river SPM, where it is fairly
constant (Fig. 3d; Table 3). Since the DC' is lowest in the soils
(0.2 ± 0.1) and then higher in the rest of the samples in the transect
(0.3–0.4), this suggests in situ production is an issue (cf. Zell et al.,
2014) in all of the sample sets (Fig. 3d; Table 3).
Box plots of all the sample sets within the transect from the land
to the deep ocean off the Portuguese coast for (a) reconstructed pH,
(b) MATmrs (∘C), and (c) MATmr (∘C). Red dotted
line indicates estimated present-day MAT for the Tagus River basin
(14.6 ∘C).
Branched GDGTs as indicators of terrestrial OM transport by the Tagus
River
Classically, the assessment of the contribution of terrestrial OM to marine
sediments is performed by measuring δ13CTOC (Hedges and
Oades, 1997, and references cited therein). In the earlier study of the Tagus
River system, Zell et al. (2014) determined that the average δ13CTOC of the riverine SPM
(∼ -29 ± 0.8 ‰), like the Tagus soils, is consistent with a
predominant C3 of higher plants origin (Fry and Sherr, 1984).
Additionally, this study found the δ13CTOC in marine
surface sediments off the Portuguese coast in front of the Tagus River
increase with increasing distance offshore by an increased contribution of
13C-enriched marine OM. This trend is also evident for the Holocene
sediments studied here. The most terrestrial sediments of the transect, i.e.,
from the Tagus River Floodplain, also have a δ13CTOC value
(∼ 27 ± 1.0 ‰; Fig. 2a; Table 3)
consistent with a predominant Corigin of higher plants. Moving
offshore, the less negative δ13CTOC values of the Tagus Mudbelt
sediments (-24 ± 0.2 ‰), the Lisbon Canyon Head
sediments (-23 ± 0.6 ‰), and the Lower Setúbal
Canyon sediments (-23 ± 1.5 ‰) all indicate that
the majority of the TOC off the Portuguese shelf is of marine origin (Fig. 2a; Table 3). Therefore, as Zell et al. (2014) found with marine surface sediments
off the Portuguese coast, the δ13CTOC
(‰) averages from the sediments in our transect also
increase with increasing distance offshore, demonstrating that the present
trend in the δ13CTOC signal remained the same over the
Holocene.
Zell et al. (2014) previously showed that in the present-day Tagus River
system the amount of brGDGTs (µg g OC-1) increases from the soils
to the riverbank sediment to the river SPM and explained this increase as
proof of riverine in situ production of brGDGTs. Concentrations of summed
brGDGTs in surface sediments in transects from the Portuguese coast rapidly
declined with increasing distance from the coast, suggesting that brGDGTs
could still be used as a tracer for terrestrial organic matter (Zell et al.,
2015). The trends observed in these earlier studies are confirmed here for
the Holocene. The Tagus River Floodplain sediments have the highest
concentration of brGDGTs (67 ± 26 µg g OC-1) in the entire
transect, much higher than in the soils (Fig. 3b; Table 3). However, the
sediments in this core are somewhat atypical for the Tagus River Floodplain as
some layers consist of peat as a result of the low-energy backswamp
conditions in the vicinity, which could explain the difference in brGDGT
concentrations from the surrounding soils. This could also be due to the
addition of aquatically produced brGDGTs from the river during times of
flooding, although it should be noted that the concentration of brGDGTs is
even higher than in riverine SPM (Fig. 3b). The summed brGDGT concentration
decreases and is fairly similar among the Tagus Mudbelt sediments (25 ± 14 µg g OC-1) and the Lisbon Canyon Head sediments
(31 ± 9.3 µg g OC-1), and then decreases further moving away from the
coastline to the Lower Setúbal Canyon sediments (16 ± 5.5 µg g OC-1) demonstrating the decrease in input of riverine brGDGTs moving
away from the shoreline (Fig. 3b). However, even though the sum of the
brGDGTs is lower in the marine sediment than in the Tagus River Floodplain
sediments, the amount of brGDGTs in all four sediment cores is higher than
in the Tagus soils (∼ 6.8 ± 6.5 µg g OC-1),
indicating the origins of the brGDGTs in the sediment cores are not all soil-derived and pointing instead to riverine in situ production as well as
possibly in aquatic sediments (Fig. 3b).
A previous study by Zell et al. (2015) determined that, in the surface
sediments off the coast of Portugal, the BIT index is influenced by both
declining brGDGT concentrations and increased crenarchaeol production with
increasing distance from the coast. For the Holocene sediments studied here,
the average concentration of crenarchaeol in the Tagus River Floodplain
sediments is low (2.8 ± 1.7 µg g OC-1) and similar to that
of the Tagus soils (1.4 ± 1.1 µg g OC-1; Fig. 3a; Table 3).
The crenarchaeol concentration increases in the sediments with increasing
distance from the shoreline, signifying the increase in marine production
with water depth and distance from the coast (Fig. 3a). Consequently, the
BIT index is the highest in the Tagus River Floodplain sediments
(0.94 ± 0.03) out of the entire transect (Fig. 3c), and then the BIT
index decreases within the sediments along the transect with increasing
distance from the Portuguese coast, potentially signifying a decrease in
terrestrial input moving away from the shoreline.
Factors affecting the application of brGDGTs for paleoclimate
reconstructions off the Iberian Peninsula
Despite the caveats with respect to in situ production of brGDGTs in aquatic
environments as described in the previous section, we tested how the new
soil calibration based on individually quantified 5-methyl and 6-methyl
brGDGTs (De Jonge et al., 2014a) performed to reconstruct continental MAT in
this region. For this comparison we will consider the present-day MAT of the
entire Tagus River basin, 14.6 ± 2.2 ∘C (Zell et al., 2014),
assuming that soil-derived brGDGTs from along the whole river basin are
contributing to the marine sediments. The assumption that the brGDGTs from
the entire Tagus River basin are being contributed to oceanic sediments is
probably invalid for modern times as the construction of dams along the
Tagus River, which began in the 1940s, most likely prevents part of the
terrestrial material from upstream making it downstream and out off the
coast of Portugal. However, since we are not looking at marine surface
sediments in this study but instead sediments deposited during the Holocene,
the placement of dams in the river should not affect our results except in the case of the riverine SPM. Despite the separation of the 5- and 6-methyl brGDGT
isomers and the application of the new proxy, the reconstructed MATs using
both riverine SPM and Holocene sediments is still substantially lower than
14.6 ∘C (Fig. 9b and c), as noted for the Tagus Basin soils (see Sect. 4.1).
Using the MATmr calibration, the reconstructed average temperature for the Lower Setúbal Canyon sediments (11.2 ± 0.7 ∘C) is the most similar to modern-day MAT in the regions (Fig. 9b). Using the MATmr calibration, the average reconstructed temperature of the Lisbon Canyon Head sediments, 12.4 ± 0.5 ∘C, comes closest to the modern-day MAT in the region (Fig. 9c).
Even though we used the new calibration to reconstruct MAT, it should be
noted that the low BIT values (< 0.15; Fig. 3c) of the Holocene
sediments deposited at the three marine sites indicate that there were
probably not enough soil-derived brGDGTs making it out to ocean and being
deposited in the sediments over the Holocene for reliable climate
reconstructions (cf. Weijers et al., 2014). When considering the summed concentration of brGDGTs along the entire transect, since the concentration is lowest in the soils, this indicates that the origin of the brGDGTs may not be solely soil-derived. Therefore, even though the BIT index seems high enough for MAT reconstructions in the riverbank sediments and river SPM, in situ production of brGDGTs could be complicating the applications of brGDGTs for paleoclimate reconstructions throughout the transect of samples as was previously discussed (see Sect. 4.2). This further supports earlier conclusions from
previous studies (Yang et al., 2012; Zell et al., 2013) stating that the
amount and origin of brGDGTs in a system need to be examined along with the
BIT index when determining whether brGDGTs can be applied for MAT reconstruction.
Conclusions
We have established that the distribution of brGDGTs varies greatly within
the Tagus River basin (Fig. 5), and although this may be partly explained by
the varying contributions of higher altitude, which contain a greater
proportion of 6-methyl isomers, vs. lower-altitude soils in the sample
sets, it is more likely due to the contribution of aquatically produced
brGDGTs in some of the sample sets. In order to use sedimentary brGDGTs for
paleoclimate reconstructions, the distribution of brGDGTs in the soils must
be related to the MAT and conserved throughout riverine transport to the
sediments where they are deposited; however, our results corroborate
previous studies stating that most of the terrestrial matter is not making
it out to the ocean and being deposited in sediments close to shore. The
lack of soil-derived OM in offshore sediments along with the substantial
input of aquatically produced brGDGTs is complicating MAT reconstructions
from sedimentary, marine brGDGTs in this region.
Additionally, we confirm the findings of Zell et al. (2014, 2015) that
in situ production of brGDGTs is occurring in the river and marine systems
of the Tagus River basin and go on to show that there are indications that
it occurred in the past as well. Although in situ production is complicating
environmental reconstructions using marine sediments, another issue is that
accurate MAT reconstructions using brGDGTs cannot currently be performed on
the soils, even with the separation of the 6-methyl brGDGTs from the
5-methyl isomers using the new method and calibrations. Previous studies
have concluded that paleoclimate reconstructions in arid regions using
brGDGTs are complicated due to a breakdown in the relationship with MBT' and
MAT (Peterse et al., 2012; Menges et al., 2014). In this study we confirm
that there is no strong relationship between the MBT'5me and
measured MAT in this arid region. However, we also do not observe the same
relationship with MAP and MBT'5me that has been previously
reported between MAP and MBT' in arid regions and has been implied in
making reconstructions difficult. Instead, we see a strong relationship with
the DC' and measured MAT in the area not observed before. We also see a
predominance of 6-methyl isomers, previously only reported in river SPM, in
the Tagus soils from greater than 350 m altitude. Although this might be a
characteristic of arid soils and related to MAP since it is below 550 mm yr-1 in most of the soil samples above 350 m, the two highest-elevation
soil samples, which both have a MAP above 550 mm yr-1, also demonstrate
this trend. Future studies need to be performed in arid environments to
determine whether a strong relationship between MAT and DC', as well as a
predominance of 6-methyl isomers, is a characteristic of arid regions and
contributing to the complications found using brGDGTs for paleoclimate
reconstructions. Also, higher-elevation environments should be further
studied to determine whether a predominance of 6-methyl brGDGTs is a feature of
higher altitudes and complicating climate reconstructions.
Because of these unique features in this region, perhaps the development of
a local calibration could assuage difficulties in using brGDGTs as a
paleoclimate proxy for soils in the Tagus River basin. This would not,
however, solve the issue of in situ-produced brGDGTs overwhelming the amount
of soil-derived brGDGTs in aquatic sediments. We did find that the new CBT'
and pH calibrations do an excellent job reconstructing pH in the soils of the
Tagus Basin, and since pH is related to other environmental factors such as
MAP, this will be useful for paleoclimate reconstructions in terrestrial
sites over the Iberian Peninsula, where in situ production is not a
complicating factor.