Introduction
The combustion of terrestrial vegetation by natural processes and
anthropogenic activities are the primary sources of biomass burning (Simoneit et al., 1999).
Humans add to the global burden of greenhouse gases (Bowman et
al., 2009) through fire-related forest clearance. The impacts of greenhouse
gases and associated global climate change on the frequency, intensity,
duration, and location of biomass burning are not well understood and the
contribution of fire emissions to past and future atmospheric composition
are also unclear (IPCC, 2014). However, a
recent study found that the synthesized Holocene fire record in eastern
monsoonal China strictly tracks global atmospheric CO2 concentration
from Antarctica (Xue et al., 2018), but it is still not
clear if fire and CO2 triggered the rise in the other component or vice
versa. Therefore, more studies are needed to investigate interactions with
weather, climate, and landscape dynamics over a range of spatiotemporal
scales.
Lake sediments archive high-resolution histories of sediment flux, as well
as climatic, hydrological, and ecological changes, for as long as the lakes
preserve sediments through time (Yan and Wünnemann, 2014).
Numerous recent studies demonstrate climatic variations throughout China and
surrounding areas during the Holocene using lacustrine sedimentary records (Bird
et al., 2017; Dietze et al., 2013; Liu et al., 2009; Opitz et al., 2012;
Saini et al., 2017; Yanhong et al., 2006). The paleoclimate proxies used in
these studies – including carbonate percentages, mineralogy, grain-size
distribution, elemental geochemistry, stable isotope composition, leaf wax
long-chain n-alkanes, aquatic diatoms, and terrestrial pollen – collectively
record changes in hydroclimate and other environmental processes such as
vegetation growth, detrital influx, and volcanic eruptions. Within the Tibetan
Plateau (TP), only a few studies examine past biomass burning by using
charcoal (Herrmann
et al., 2010; Miao et al., 2017) or black carbon. Polycyclic aromatic
hydrocarbons (PAHs) are reported in the lake sediments from the TP spanning
the last two centuries (Yang et al., 2016). Monosaccharide
anhydrides (MAs), ammonia, and black carbon in ice cores have been used as
combustion proxies and indicators of fire on or influencing the Tibetan
Plateau, but these records mainly cover the last century (Kaspari
et al., 2011; Ming et al., 2008; Shugui et al., 2003; Xu et al., 2009; You
et al., 2016b). To the best of our knowledge, no studies examine PAHs or MAs
in sediments from the TP during the entire Holocene.
A combination of innovative molecular markers were used to infer past fires,
vegetation, and human interactions in sediment cores analysed from Guatemala (Schüpbach
et al., 2015) and East Africa (Battistel et al., 2016). Using a
similar approach, we use biomarkers that are produced under specific
environmental conditions and then transported, accumulated, and stored in
lacustrine sediments: monosaccharide anhydrides (MAs), fecal sterols and
stanols (FeSts), polycyclic aromatic hydrocarbons (PAHs), and
normal n-alkanes. Significant concentrations of these compounds are present
in soil and sedimentary archives with ages older than 10 cal kyr BP (D'Anjou
et al., 2012; Johnsen et al., 2005; Schüpbach et al., 2015), suggesting
that degradation, if happening, is a low-kinetic process (Battistel et al., 2016) and that
these compounds resist over the Holocene or longer timescales. Within the
listed biomarkers, MAs are specific tracers of vegetation combustion (Simoneit,
2002; Zangrando et al., 2013). Cellulose pyrolysis creates the molecular
marker levoglucosan (1,6-anhydro-β-D-glucopyranose; Simoneit et al., 1999),
while hemicellulose combustion produces the isomers mannosan
(1,6-anhydro-β-D-mannopyranose) and galactosan (1,6-anhydro-β-D-galactopyranose) (Kuo et
al., 2011). Several studies examine levoglucosan (L), mannosan (M), and
galactosan (G) in aerosols and ice cores (Kehrwald,
2012; Simoneit, 2002; Yao et al., 2013; Zennaro et al., 2014; Zhang et al.,
2008), as well as in sediment cores (Battistel
et al., 2016; Kirchgeorg et al., 2014; Schüpbach et al., 2015),
demonstrating the suitability of MAs as paleofire proxies. PAHs are a wide
group of organic compounds made up of two or more benzene rings combined
together in linear, angular, or clustered arrangements (Zakir
Hossain et al., 2013). The physical properties of PAHs, such as low aqueous
solubility and high lipophilicity, prevent microbial utilization and promote
their accumulation as particulates in terrestrial environments (Johnsen et al., 2005). This class of molecules
is produced by incomplete combustion during a wide range of natural and
anthropogenic processes, such as volcanic eruptions, vegetation and/or
garbage burning, fossil fuels, and cigarette or car emissions (Abdel-Shafy
and Mansour, 2016; Kim et al., 2013; Lima et al., 2005). PAHs are
semi-volatile, persistent, and ubiquitous in the environment with multiple
possible sources, and therefore commonly detected in soil, air, and water (Abdel-Shafy and Mansour, 2016; Johnsen
et al., 2005). The investigation of PAHs as tracers of biomass burning in
past climate archives such as sediments (Jiang et
al., 1998) and ice (Gabrieli et
al., 2010) is increasing in the last decades (Yan et al., 2014; Page
et al., 1999).
Leaf waxes are preserved in sediments and can help determine past vegetation
in a lake catchment. The cuticular wax layer of terrestrial plants consists
predominantly of long-chain hydrocarbons (n-alkanes) and creates a
protective barrier that helps maintain the plant's integrity within an
intrinsically hostile environment (Sheperd and Griffiths, 2006). The leaf wax
of higher plants is assumed to be stable, and is difficult to degrade during
transport, deposition, and burial (Cui et al., 2008). Different types of
plants have diverse distribution of n-alkane chain-lengths (Diefendorf and
Freimuth, 2017). Angiosperms generally produce more n-alkanes than
gymnosperms; however, chain-length distributions are highly variable within
plant groups, and especially for conifers where the Cupressaceae group tends
to have long-chain n-alkanes, while the Pinaceae group tends to have
relatively short-chain n-alkanes (Diefendorf and Freimuth, 2017; Diefendorf
et al., 2015). Sphagnum mosses are among the few plants that provide
a characteristic signal as these mosses are marked by the predominance of
C23 and C25 (Bush and McInerney, 2013). Long-chain n-alkanes
(C27–C33) with a strong odd/even predominance are usually
interpreted to be of terrestrial origin; mid-chain n-alkanes
(C20–C25) are mainly present in aquatic macrophytes; bacteria,
algae, and fungi primarily produce short-chain n-alkanes in the range
C14–C22, while n-C17 is an indicator for algae and
photosynthetic bacteria (Aichner et al., 2010; Ficken et al., 1998; Grimalt
and Albaigés, 1987; Han and Calvin, 1969). Due to the wide range of
possible chain lengths present within sediments, ratios of n-alkanes are
often used to determine the vegetation composition. The most commonly used
ratios are the average chain length (ACL; Poynter and Eglinton, 1990), the
carbon preference index (CPI; Bray and Evans, 1961), the submerged versus
emergent aquatic plants predominance ratio (Paq; Ficken et al.,
2000), and the grass to wood prevalence ratio (Norm31; Carr et al., 2014).
However, it is still unclear to what extent variations in leaf wax
composition within paleoenvironmental archives can be explained in terms of
changes in the relative proportions of different plant species on the
landscape and/or the reaction of a plant community to environmental
conditions (Carr et al., 2014).
Revealing human presence in lake catchments often relies on anthropological
evidence, but advances in proxy development during the past two decades now
allows for determining the presence of humans or pastoralism through steroid
fecal biomarker concentrations (Bull et al., 2002).
FeSts, such as stanols and bile acids, in lake sediments reflect grazing in
a lacustrine catchment (D'Anjou et
al., 2012). Specific FeSts, such as 5β-stanols, are organic compounds
produced by the microbially mediated alteration of cholesterol in the
intestinal tracts of most mammals, making them ideal fecal biomarkers (Dubois and Jacob, 2016). Coprostanol and stigmastanol
derive from hydrogenation of cholesterol and stigmasterol by bacteria
present in the intestines of humans or animals and can indicate human
presence and animal husbandry, respectively (Daughton,
2012; Vane et al., 2010). These molecules are also used as chemical
indicators of fecal pollution of lakes, rivers, and drinking water (Daughton,
2012; Vane et al., 2010; Wu et al., 2009). In addition, FeSts can originate
from vegetation, e.g. β-sitosterol is synthesized by higher vascular
plants (Nishimura
and Koyama, 1977; Vane et al., 2010) and its derivative β-sitostanol
is generated from a reduction reaction in sediments (Martins et al., 2007).
In this study, we reconstruct fire activity and vegetation changes using a
multi-proxy analytical approach applied to lacustrine sediment samples from
the southeastern Tibetan Plateau. This is the first study to combine MA,
PAH, n-alkane, and FeSts analyses into a single analytical method
highlighting the interactions between fire, climate, and vegetation during
the Holocene. This combination of proxies, when synthesized with regional
climate records, helps determine the changing role of local and regional
fire activity throughout the Holocene.
Study area, modern climate, and Holocene climate history
The Qinghai–Tibet Plateau is a vast plateau in central Asia with an
average elevation of approximately 4500 m above sea level (a.s.l.). The TP
stretches nearly 1000 km north to south and 2500 km east to west, covering
an area of 2×106 km2 (Dong et al., 2010). In
addition to this wide geographic range, the TP also encompasses altitudes
ranging from 1500 to >8000 m a.s.l., resulting in highly
heterogeneous landscapes with considerable biodiversity. In general,
however, vegetation across much of the TP is dominated by meadow, steppe,
and shrub communities where species richness increases with increasing
altitude (Shimono et al., 2010). The TP is a
pivotal research area due to its sensitivity to century-scale or short-term
climatic changes and its influence on global climate (Liu et al., 1998). However, its remote
nature restricts access to possible paleoclimate studies, resulting in
relatively few investigations of past species diversity and plant community
changes (Wang et al., 2006).
The TP's climate is regulated by the critical and sensitive junction of four
climatic systems (Supplement Fig. S1): the westerlies, the East Asian
monsoon, the Siberian cold polar airflow (or winter monsoon), and the Indian
monsoon (Dong et al., 2010). The westerly winds and the Indian summer monsoon
(ISM) are considered to be the major wind patterns by which atmospheric
particulates derived from biomass burning reach the plateau (Yao et al.,
2013). Millennial-scale changes in insolation over the TP affect monsoon
variability and the associated moisture reaching the TP. Generally, during
periods of increased insolation, the monsoon extended farther north on the
TP, resulting in more vegetation growth across the plateau. During decreased
insolation, colder, drier conditions dominate the TP and regions influenced
by the ISM are restricted to more southerly portions of the plateau,
including the study area. During the late Pleistocene (∼16 cal kyr BP), a
cold and dry climate resulted in desert–steppe vegetation across much of the
TP (Tang et al., 2000). Global paleoclimate studies indicate that this last
glacial period concluded with a sudden warming event at ∼15 cal kyr BP (Severinghaus and Brook, 1999), in the context of
Bølling–Allerød and Younger Dryas events in the region (Liu et al.,
2008). The subsequent transition to the Holocene was characterized by
increasing temperature and precipitation that enhanced permafrost and snow
melting and facilitated tree growth in the TP after 12 cal kyr BP (Saini
et al., 2017; Tang et al., 2000). This period was depicted by frequent
oscillations between warm and cold phases, in Tibet as well as in other parts
of the world (Zhu et al., 2008; Liu et al., 2008, 2009). For example, Tang et
al. (2000) suggest that the evolution of the ISM has considerably fluctuated
throughout the Holocene. Lake Ximencuo (eastern
Tibet) sediments record cold events occurring between 10.3–10.0, 7.9–7.4,
5.9–5.5, 4.2–2.8, 1.7–1.3, and 0.6–0.1 cal kyr BP, where the cold
event at 4.2 cal kyr BP had the most substantial impact (Miao et al.,
2015; Mischke and Zhang, 2010). Superimposed on these oscillations, the
general temperature trends affecting the TP include a warm and humid climate
in the early to mid-Holocene, as registered in sediments and dust deposits
(Liu et al., 2008), and then a cooling trend during the mid-Holocene. The
high temperatures during the early Holocene accelerated evaporation and
caused many Tibetan lakes to evolve from open freshwater systems to saline
lakes (Dong et al., 2010), despite increased monsoonal precipitation (Bird et
al., 2014). TP vegetation also responded to these warmer temperatures with an
increase in forests and forest-meadows between 9.2 and 6.3 cal kyr BP
(Tang et al., 2000). During the mid- to late Holocene, warm and wet
conditions shifted towards a cooler and drier climate, due to weaker solar
insolation, and after 5 cal kyr BP temperature and precipitation decreased
linearly (Bird et al., 2014; Dong et al., 2010; Liu and Feng, 2012; Tang et
al., 2000). More recently, human activities and related climate change have
significantly altered the regional hydrology and vegetation distribution of
the plateau, with flora degeneration that led to desertification and frequent
dust storms (Wang et al., 2008).
(a) Map of the Tibetan Plateau and surrounding territories
showing the location of Paru Co (red pin) and of the other lakes mentioned in
the text (blue circles): Taro Co (TC), Nam Co (NC), Hidden Lake (HL), and
Lake Naleng (LN). (b) Satellite image of Paru Co.
(c) Average monthly precipitation at Paru Co based on TRMM data from
1998 to 2007 and average monthly temperatures at Paru Co (4845 m a.s.l.)
from Lhasa (3650 m a.s.l.) weather station data using a lapse rate of
-6.4 ∘C km-1 (Huffman et al., 2010). (d) Plot of
the age–depth model for Paru Co according to Bird et al. (2014).
Paru Co (0.1 km2) is located in the Nyainqentanglha Mountains
(29∘47′45.6′′ N, 92∘21′07.2′′ E; 4845 m a.s.l.; Fig. 1a and b) and is
dammed by moraines from past glaciations in its watershed. The biome
surrounding Paru Co is temperate subalpine steppe, where the lake is located
near the border of alpine coniferous forest and tropical and seasonal
rainforests (Li et al., 2016). The lake's
watershed is 2.97 km2 and consists of a sloping glacial valley
measuring 0.5 to 2.0 km in length with lateral mountain crests higher than
5000 m a.s.l. The maximum water depth of the modern lake is 1.2 m, with gently
sloping sides, but may tolerate a total water level of about 3 m. A central
ephemeral stream channel and a second incised channel drain the lake's
watershed and feed Paru Co with runoff. Outflow from the lake drains via a
small stream channel located approximately 430 m west of the primary outlet (Bird et al., 2014). The
Tropical Rainfall Measuring Mission data (TRMM) from 1998 to 2007 show that
approximately 92 % of mean annual precipitation (MAP; 1118 mm yr-1) at
Paru Co occurs between April and September during the ISM season (Fig. 1c; Bird et al., 2014).
Previous paleoclimate work at Paru Co (Bird et al., 2014)
indicates the occurrence of intense ISM rainfall between 10.1 and 5.2 cal kyr BP,
when 5-century-long high lake levels were recorded. The ISM weakened
after ∼5.2 cal kyr BP, with the exception of a pluvial event
centred at 0.9 cal kyr BP. Nir'pa Co, a small lake located near Paru Co,
suggests a wet period between 3.3 and 2.4 cal kyr and drier conditions from
2.4 to 1.3 cal kyr, due to lower silt and lithic content, coincident with
elevated sand and clay content and lower lake levels (Bird et al., 2017).
Methods
Coring and chronology
Paru Co core B11 was collected in 2011 and extends from 0 to 435 cm. Seven
radiocarbon ages determined by accelerator mass spectrometry (AMS 14C)
were measured on seven carbonized grass fragments extracted from the
surrounding sediments (Bird et al., 2014). The
sedimentation rate is approximately 0.35 mm yr-1 between 10.768 cal kyr BP and the present.
Between 10.937 and 10.789 cal kyr BP sedimentation rates
are approximately 10 times higher (3.3 mm yr-1). The final age–depth
model (Fig. 1d) was constructed using a linear regression between 434.9 and
364.1 cm and by fitting a third-order polynomial to the AMS 14C,
137Cs (-0.013 cal kyr – determined by direct gamma counting) and
sediment–water interface (-0.061 cal kyr) ages between 364.1 and 0.0 cm. The
associated model error is between 15 and 90 years (see Bird et al., 2014, for
further details). As the deepest part of the core shows a much higher
sedimentation rate that cannot be clearly explained, with the possibility of
data distortion, the subsequent description and discussion of the results
exclude the samples with ages between 10.784 and 10.937 cal kyr BP, limiting
the dataset interpretation to the period between 1.347 and 10.768 cal kyr BP.
Sample preparation
Subsamples (n=72) were selected from the core every 5 cm, spanning from
10.9 to 1.3 cal kyr BP with a time resolution of about 130 years on average.
Unfortunately, the uppermost samples covering the more recent period (1.3–0 cal kyr BP)
have not been processed for this study due to a lack of
sufficient sample amounts. The samples were sealed in plastic bags and
stored at -20 ∘C, weighed, freeze-dried, and ground and reweighed
in order to assure ∼1 g of dry material, allowing the
possibility of determining MAs, PAHs, n-alkanes, and FeSts from the same
sample. All samples were ground using a Mixer Mill MM 400 (Retsch GmbH,
Germany) ball miller.
The 72 Paru Co samples were extracted with a 9 : 1 v/v mixture of ultra-grade
(Romil Ltd, Cambridge, UK) dichloromethane and methanol (DCM : MeOH) with a
Thermo Scientific Dionex ASE 350 (accelerated solvent extractor system), in
order to extract both the polar and non-polar compounds. For each
extraction, we used 22 mL steel cells containing a 27 mm ø cellulose
filter, diatomaceous earth, the sample, ∼2 g of
Na2SO4 (to remove residual moisture), and ∼ 2 g of
activated copper (to remove sulphur that can interfere with PAH analysis).
We added the following internal standard solutions into each cell: 100 µL of 13C labelled levoglucosan at 1 ng µL-1 of
concentration, 100 µL of hexatriacontane at 40 ng µL-1, 100 µL of a mixture of 13C labelled PAHs (acenaphthylene,
phenanthrene, and benzo[a]pyrene) at 1 ng µL-1, and 100 µL of
cholesterol-3,4-13C2 at 1 ng µL-1. The extractions
were performed with three static cycles at 100 ∘C and 1500 psi. A
procedural blank was created and extracted for every batch of 12 samples,
where we filled the steel cell with all of the same reagents, but without a
sample.
Each sample was then purified with three steps to obtain a PAHs/n-alkanes
fraction, a FeSts fraction, and a MAs fraction. We combined and modified
published clean-up methodologies in order to obtain the necessary fractions (Battistel
et al., 2015; Douglas et al., 2012; Kirchgeorg et al., 2014; Martino, 2016).
Our resulting method uses 12 mL solid-phase extraction cartridges (SPE
DSC-Si 10 Tube; 12 mL; 52657 Supelco, Sigma-Aldrich) packed with 2 g of
silica gel (particle size 50 µm) and installed on
Visiprep™ (SPE Vacuum Manifold standard, Sigma-Aldrich) to
accelerate purification. We conditioned each cartridge with 30 mL of DCM and
30 mL of hexane (Hex). The first non-polar fraction (F1), containing PAHs
and n-alkanes, was eluted using 40 mL of a Hex : DCM 9 : 1 v/v mixture. Then, the
second polar fraction (F2), containing FeSts, was separated with 70 mL of
DCM. This fraction was derivatized, according to Battistel et al. (2015), at 70 ∘C for 1 h with 100 µL of BSTFA +1 % TMCS
(N,O-bis(trimethylsilyl)trifluoroacetamide with 1 %
trimethylchlorosilane, Sigma-Aldrich) to increase compound volatility and
detectability during gas chromatography mass spectrometry (GC-MS)
analysis. Finally, the third polar fraction (F3), containing MAs, was eluted
with 20 mL of MeOH. F1 and F2 were evaporated under a stream of pure N2
using a TurboVap II® system (Caliper Life Science, Hopkinton,
MA, USA) in order to reduce the volume to 150 µL. F3 was dried and
dissolved in 0.5 mL of ultra-pure water and sonicated to avoid any
adsorption of MAs to walls of glass evaporation tubes. Finally, the samples
were centrifuged (5 min, 14000 rpm) and transferred using decontaminated
Pasteur pipettes to the measurement vials.
Sample analysis
MAs were detected using methods published in Kirchgeorg et al. (2014) with ion
chromatography (IC; Dionex ICS 5000, Thermo Scientific, Waltham, USA) coupled
with a single quadrupole mass spectrometer (MSQ Plus™, Thermo
Scientific) equipped with a CarboPac MA1™ column (Thermo
Scientific, 2 mm ×250 mm) and an AminoTrap column (2 mm ×50 mm), resulting in a
good separation of the isomers levoglucosan, mannosan, and galactosan. The
injection volume was 50 µL. A solution of MeOH / NH4OH was added
post-column (0.025 mL min-1) to improve ionization of the aqueous
eluent before entering the electrospray ionization (ESI) in negative mode.
The analytes were quantified according to specific mass to charge ratios,
and with calibration curves and response factors containing unlabelled
molecules of L, M, G, as well as an internal standard molecule (13C
labelled levoglucosan).
Target molecules with their abbreviations, detected mass-to-charge
ratio (m/z) and method detection limit (ng) calculated as blank values plus
three standard deviations or with the signal-to-noise ratio when no analytes
were detectable in the blanks.
Molecular
Compounds
Abbreviation
Targeted
MDL
classes
ion (m/z)
(ng)
MAs
Levoglucosan
L
161
26.1
Mannosan
M
161
12.2
Galactosan
G
161
9.8
PAHs
Naphthalene
Naph
128
5
Acenaphthylene
Acy
152
800 pg
Acenaphthene
Ace
154
1.2
Fluorene
Flu
166
3.3
Phenanthrene
Phe
178
8
Anthracene
Ant
178
750 pg
Fluoranthene
FluA
202
2.4
Pyrene
Pyr
202
2.8
Benzo(a)anthracene
BaAnt
228
50 pg
Chrysene
Chr
228
250 pg
Retene
Ret
219
250 pg
Benzo(b)fluoranthene
BbFl
252
80 pg
Benzo(k)fluoranthene
BkFl
252
80 pg
Benzo(a)pyrene
BaPyr
252
700 pg
Benzo(e)pyrene
BePyr
252
860 pg
Benzo(ghi)perylene
Bghi
276
300 pg
Indeno(1,2,3-c,d)pyrene
IP
276
350 pg
Dibenzo(a,h)anthracene
DBahAnt
278
800 pg
n-alkanes
C10–C35
C10–C35
71
500
FeSts
Coprostanol
Cop
215
50 pg
Epicoprostanol
e-Cop
215
50 pg
Cholesterol
Chl
370
100.4
Cholestanol
5α-Ch
355
50 pg
Sitostanol
5α-Sit
215
0.5
Sitosterol
Sit
215/396
2.3
The 17 priority PAHs (according to US ATSDR, 1995) plus retene, n-alkanes
(from C10 to C35), and FeSts (coprostanol, epi-coprostanol,
cholesterol, 5α-cholestanol, sitosterol, sitostanol) were analysed
with gas chromatography (6890-N GC system) coupled to a single quadrupole
mass spectrometer (MS 5975, Agilent Technologies, Santa Clara, CA, USA)
(Argiriadis et al., 2014; Battistel et al., 2015; Gregoris et al., 2014;
Martino, 2016; Piazza et al., 2013). Each analysis used the same capillary
column (HP5-MS (5 %-phenyl)-methylpolysiloxane, Agilent Technologies,
Santa Clara, CA, USA). The conditions were an injection volume of
2 µL (split valve open after 1.5 min) and He as a carrier gas
(1 mL min-1). The MS was equipped with an electronic ionization (EI)
source used in positive mode. The analytes were quantified in single ion
monitoring mode (Table 1). We created response factors containing all of the
target compounds as well as internal standards (13C labelled
acenaphthylene, phenanthrene, and benzo[a]pyrene at 1 ng µL-1
– Cambridge Isotope Laboratories, Inc; hexatriacontane at
40 ng µL-1 and cholesterol-3,4-13C2 at
1 ng µL-1 – Sigma Aldrich). We ran a response factor after
every seven samples in order to monitor possible instrumental drift, as well
as running a full calibration curve of external PAH standards before each set
of analyses.
Target molecules with their respective analysed ions and method detection
limits (MDL) are listed in Table 1. Further method details and quality
assurance are available in previously published work (Argiriadis
et al., 2014; Battistel et al., 2015; Gregoris et al., 2014; Kirchgeorg et
al., 2014; Martino, 2016; Piazza et al., 2013). Chromatographic peak
identification and calculations were performed using the
Chromeleon™ 6.8 Chromatography Data System Software (Thermo
Scientific, Waltham, USA) and Agilent G1701DA GC/MSD ChemStation (Agilent
Technologies, Santa Clara, CA, USA).
Data analysis
All concentration values obtained from IC and GC-MS analyses were converted
to ng g-1 or µg g-1, and then transformed into fluxes in
order to correct the data for the influence of time and sedimentation.
Fluxes (ng cm-2 yr-1) were calculated by multiplying the
sedimentation rate (cm yr-1), wet density (g cm-3), and
concentration (ng g-1) of the respective analyte (Menounos, 1997). As explained in Sect. 3.1, we
investigate the data between 1.347 and 10.768 cal kyr BP, which has a constant
sedimentation rate. The concentrations of each analyte therefore have the
same trends as their resulting fluxes (Supplement Fig. S2). We present all
results as concentrations (ng g-1 or µg g-1).
N-alkane ratios useful for our study include the average chain length
(ACL), representing the composite of longer and shorter n-alkanes between
the chain length range of 21 to 33 and indicating the weighted predominant
length (Poynter and Eglinton, 1990); the carbon preference index (CPI), an
expression of odd/even predominance that represents how much of the original
biological chain length specificity is preserved in geological lipids (Meyers
and Ishiwatari, 1993); and the P-aqueous ratio (Paq), which
helps differentiate between submerged plants that tend to have
medium-chain-length n-alkanes and terrestrial plants that tend to have
longer chain lengths (Ficken et al., 2000). These ratios were calculated
according to the following equations:
ACL21–35=∑n21–35C21–35∑C21–35,CPI21–33=12∑Codd(21–33)/∑Ceven(20–32)∑Codd(21–35)/∑Ceven(22–34),Paq=C23+C25/C23+C25+C29+C31,
where n21–33 indicates the number of carbons in
the n-alkane chains and Cn represents the concentration of the
respective n-alkane. In order to help data interpretation, 2-tailed Pearson's
correlations were calculated in R for the biomarkers dataset with a 95 %
confidence interval (Supplement Fig. S3) with statistically significant results
when p values are <0.05.
Results
Paleofire indicators
MA concentrations values span from 29 to 6497, from 15 to 993, and from 17 to
1722 ng g-1 for levoglucosan, mannosan, and galactosan, respectively. In
the most recent sample from 1347 cal kyr BP, none of the three MAs were above
the detection limits, while in a few other samples mannosan and galactosan
were below the MDL. The Paru Co MA results reflect the general observation
in the literature that mannosan and galactosan concentrations are almost
always less than levoglucosan concentrations, which may be due to the
different thermal stability of their respective precursors, hemicellulose
and cellulose (Kuo
et al., 2011; Simoneit, 2002). Although levoglucosan (Fig. 2e) and
galactosan may have different precursors, their trends throughout the Paru
Co core are generally similar, while peaks in mannosan (Fig. 2f)
concentrations differ slightly from the other two isomers. The MA signal is
much higher during the early Holocene (10.8–8 cal kyr BP) and then slightly
increases again during the periods 7–5 and 3–2 cal kyr BP.
The lowest PAH value is 0.2 ng g-1 of benzo[b]fluoranthene (BbFl)
while the highest PAH concentration is 310.3 ng g-1, of naphthalene
(Naph). Phenanthrene (Phe), benzo[e]pyrene (BePyr), and Naph represent
20.9 %, 18.9 %, and 17.5 %, respectively, of the total PAH signal
in Paru Co (please see the Supplement Fig. S4 for single PAH concentrations).
The total sum of PAHs (∑PAHs, Fig. 2a) shows higher values in the
middle Holocene, with major peaks at 6.3, 5.8, 5.2, 4.8, 3.9, and
3.5–3.3 cal kyr BP. The general trend shows increases from 2.2 to
1.3 cal kyr BP. The molecular weight and/or number of aromatic rings of
PAHs allows for investigating the influence of different PAH types through
time. The group of 3-ring PAHs (Fig. 2b) includes Phe, anthracene (Ant) and
fluoranthene (FluA), demonstrating a similar pattern to the ∑PAHs. The
group of 4-ring PAHs (Fig. 2c) encompasses pyrene (Pyr), benzo[a]anthracene
(BaAnt), chrysene (Chr), retene (Ret), benzo[b]fluoranthene (BbFl), and
benzo[k]fluoranthene (Bkfl), which also has higher values during the middle
Holocene and then an increasing trend towards 1.3 cal kyr BP. The group of
5–6 ring PAHs (Fig. 2d) is composed of benzo[a]pyrene (BaPyr), BePyr,
benzo[ghi]perylene (Bghi), indeno[1,2,3,-c,d]pyrene (IP), and
dibenzo[a,h]anthracene (DBahAnt), with a more noisy trend and dissimilar
behaviour from the rest of the groups. 5–6 ring PAHs are high in the early
Holocene, peaking at 10.3–9.9 cal kyr BP, and then have separate high
concentrations at 9.3, 8.6, 7.2, 5.2, 3.9, 3.5, 2, and 1.3 cal kyr BP.
(a) Sum of PAH concentrations; (b) sum of 3-ring
PAH concentrations (Phe, Ant, FluA); (c) sum of 4-ring PAH
concentrations (Pyr, BaAnt, Chr, Ret, BbFl, Bkfl); (d) sum of
5–6 ring PAH concentrations (BaPyr, BePyr, Bghi, IP, DBahAnt). Data points
(black) with absolute error range (grey), LOWESS smoothing with SPAN
parameter 0.2 (red), b-spline interpolation (cyano).
(e) Levoglucosan concentration; (f) Mannosan concentration.
Data points (black) with absolute error range (grey), LOWESS smoothing with
SPAN parameter 0.2 (purple), b-spline interpolation (dark blue). Pink bar
indicates the early Holocene period where levoglucosan and 5-ring PAHs show
high concentrations. (g) Ant / (Ant + Phe);
(h) IP / (IP + Bghi);
(i) FluA / (FluA + Pyr). Ratio values (black points) with
absolute error bars (grey) and diagnostic thresholds (red dashed lines).
(a) L / M ratio values (black points) with absolute
error bars (grey); LOWESS smoothing with SPAN parameter 0.2 (teal), b-spline
interpolation (blue). (b) CPI ratio values (black points); b-spline
interpolation (dark red). (c) Tree pollen (%) from Zhao et
al. (2011). (d) Sum of PAH concentrations, data points (black) with
absolute error range (grey), LOWESS smoothing with SPAN parameter 0.2
(orange), b-spline interpolation (red).
The ratios of both MAs and PAHs help reconstruct past vegetation and burning
sources. MA ratios can help determine past vegetation types and/or burning
temperatures. High combustion temperatures (∼300 ∘C) and longer combustion durations result in higher L / M and L / (M + G)
ratios, regardless of plant species (Kuo et
al., 2011). The L / M and L / (M + G) ratios in Paru Core range from 0.6 to 100
and 0.5 to 11.1, respectively (Supplement Fig. S5). The L / M ratios peak between
∼6 and 7 cal kyr BP, with the highest value of 98.8 (Fig. 3a).
The L / (M + G) values do not peak at the same time, but oscillate throughout
the Holocene, with the highest values centred around ∼2 cal kyr BP. Although MA ratios cannot precisely point to the type of past burnt
vegetation, these ratios can classify general vegetation types (Fabbri et al., 2009).
However, due to the fact that galactosan presents a different biodegradation
behaviour, the application of L / (M + G) ratio may be inadequate (Kirchgeorg, 2015). For this reason, we limited the discussion
only to L / M ratio results.
PAH diagnostic ratios used in this study are Ant / (Ant + Phe),
IP / (IP + Bghi), and FluA / (FluA + Pyr).
Ant / (Ant + Phe) values generally discriminate between petroleum (<0.10) and combustion (>0.10) sources; IP / (IP + Bghi)
distinguishes between different combustion sources, with values >0.5 for
grass, wood, or coal combustion, values between 0.2 and 0.5 for liquid fossil
fuel combustion and values <0.2 for petroleum sources;
FluA / (FluA + Pyr) is used to define the transition point (0.5)
between petroleum and combustion (Denis et al., 2012; Yunker et al., 2002a,
b, 2015; Zakir Hossain et al., 2013). In Paru Co these ratios are plotted
with absolute error bars (Fig. 2g–i) in order to highlight that the
influence of error propagation from the original analysis to the ratio values
should be carefully investigated (Hughes and Hase, 2010) when assigning
sources from the ratios. Considering the error bars, the three ratios show
values >0.10 for Ant / (Ant + Phe), >0.5 for
IP / (IP + Bghi), and >0.5 for FluA / (FluA + Pyr) for the
majority of the analysed samples.
Vegetation and human indicators
The variations in n-alkane ratios help reconstruct past vegetation changes,
as n-alkanes record the organic input into and within the lake. The
n-alkane concentrations oscillate between 0.6 ng g-1 (C10) and
321 µ g g-1 (C25) with C25 as the most abundant
(39.8 %) followed by C27 (15.8 %) and C29 (9.2 %).
ACL21–35 values fluctuate between 24.9 and 27.9, with a general
decreasing trend from 10.8 cal kyr BP until 7.2 cal kyr BP and then an
increasing pattern until 1.3 cal kyr BP (Fig. 4b). Paq ratios
(Fig. 4c) vary between 0.3 and 1 with a trend that is the opposite of
ACL21–35. CPI21–33 demonstrates a general
predominance of odds over evens, with values <1 only occurring in three
cases (1.8, 3.2, 8.6 cal kyr BP) and where the maximum value of 41.2
happens at 10.1 cal kyr BP (Fig. 3b).
The response of Paru Co aqueous vegetation to changing summer
insolation and associated monsoon intensity. (a) δD wax for
C27 and C29 n-alkanes referenced to Vienna Standard Mean Ocean
Water scale, data from Bird et al. (2014). (b) ACL ratio values
(purple points), adjacent-average smoothing with 5 points (black), b-spline
interpolation (purple line). (c) Paq ratio values (brown points),
adjacent-average smoothing with 5 points (black), b-spline interpolation
(brown line). (d) Principal component 1 values (blue) as indicative
of lake level changes, adjacent-average smoothing with 40 points (red), data
from Bird et al. (2014). (e) Summer insolation, data from Berger and
Loutre (1991).
FeSts contain very low values for the majority of the analysed compounds.
Only three FeSts are above the MDL in Paru Co, but these FeSts are not
quantifiable in all samples. These FeSts include sitostanol (5α-Sit)
that represents 58 % of the quantifiable total, sitosterol (Sit) with
37 %, and cholestanol (5α-Ch) with only
3 % of the total. The maximum FeSt concentration throughout the entire
core is from 5α-Sit (282 ng g-1) at 2 cal kyr BP
(Supplement Fig. S7). Due to the generally low concentrations, no diagnostic
ratios were calculated for the FeSts.
Discussion
Paleofire activity
It is not always easy to distinguish the pyrogenic, biogenic, and petrogenic
sources of PAHs in a specific place because (i) the same compound can be
derived from different sources; (ii) PAH profiles depend on the combustion
temperature, the duration of the process, the flame conditions (oxygen), and
the type of organic material (Daly
et al., 2007); and (iii) once deposited, PAHs undergo transformation processes
that depend on the chemical characteristics of the compounds and
environmental variables (Cai
et al., 2008; Ma et al., 2005; Maliszewska-Kordybach et al., 2009). Taking
these conditions into account, we interpret the PAH profiles in Paru Co as
fire-related as no evidence of other sources (e.g. volcanic eruptions,
anthropogenic emissions) was found.
During the early Holocene (10.8–8.5 kyr BP) levoglucosan, galactosan,
mannosan, and 5-ring PAHs show similar trends, with a general decreasing
pattern and some higher peaks at 10.5–10, 9.2, and 8.5 cal kyr BP. During the
middle Holocene, fires are recorded between 6.5 and 4 cal kyr BP by levoglucosan,
and more evidently by PAH spikes. The late Holocene shows increasing PAHs
from 3 to 1.3 kyr BP with levoglucosan peaks at ∼2 kyr BP (Fig. 2a–f).
The high concentrations of higher molecular weight PAHs during the early
Holocene could be explained with local fires of greater combustion
temperatures, due to the fact that a higher number of rings requires greater
burning energy (Denis et al., 2012). High percentages of 4–6 ring PAHs
generally suggest the contribution of local high-temperature combustion
origins (Yang et al., 2016), where such combustion may
be the source of BePyr, the congener with the second highest concentration
in Paru Co, but also of IP and Bghi, which are high-temperature markers
(Zakir Hossain et al., 2013). When fuel sources are uniform, hotter fires
(at and above 500 ∘C) commonly produce high concentrations of
BePyr, IP, Bghi (McGrath
et al., 2003; Zakir Hossain et al., 2013). The lower, but not lacking,
presence of 3-ring and 4-ring PAHs could be due to the fact that lower-molecular-weight
PAHs are more depleted due to weathering processes (Zakir
Hossain et al., 2013). Their lower concentrations may also be due to the
fact that the 3-ring and 4-ring PAHs could have travelled farther since they
are more volatile than the 5–6 ring PAHs. In addition, the 3-ring and
4-ring PAHs may have photochemically degraded in the gas phase after emission
to the atmosphere (Wang et al., 2010).
Higher-molecular-weight PAHs are more stable compounds compared to 3–4 ring
PAHs. If we assume that low-molecular-weight PAHs degrade at 500 ∘C, we have to assume that MAs may also degrade at this temperature, as
maximum concentrations occur at burning temperatures centred around 250 ∘C (Zennaro et al., 2015 and references
therein). In the Paru Co record, levoglucosan concentrations are higher than
PAHs during the early Holocene. Therefore, in order to explain this
discrepancy, regional early Holocene fires must have been more frequent than
local fires, producing high amounts of MAs, without excluding that
atmospheric transport of levoglucosan to Paru Co was more efficient during
the early Holocene. Therefore, this high abundance of levoglucosan may also
be related to a regional signal, as MAs are capable of travelling hundreds
of kilometres (Schüpbach et al.,
2015; Zennaro et al., 2014).
MAs continue to decrease from 8.5 to 1.5 kyr BP, whereas 3-, 4-, and 5-ring
PAHs start increasing before reaching their greatest values between 6.5 and
4 kyr BP (Fig. 2b–f). This difference may be due to higher percentages of
lignin burning (evidenced by retene peaks, Supplement Fig. S6) with respect to cellulose
burning (represented by MA concentrations). Pollen profiles (Zhao et al.,
2011) indicate an increased presence of trees between 7 and 3 kyr BP. The
combination of low concentrations of the 5–6 ring PAHs but abundant FluA,
Pyr, and BePyr suggests geographically small, but more frequent, wildfires
(Zakir Hossain et al., 2013). We interpret the Paru Co record between 6.5 and
4 cal kyr BP as the result of such relatively small, but recurrent, fires.
The explanation for the lack of levoglucosan and other MA peaks during the
period of the highest concentrations of PAHs (6.5–3 cal kyr BP) may be due
to (i) different burning temperatures and conditions, i.e. MAs are produced
in smouldering and low-temperature fires while high-temperature flaming
fires produce PAHs (Simoneit,
2002); and (ii) the lipophilic properties of PAHs, which have a low solubility in
water (Haritash and
Kaushik, 2009) while levoglucosan has a relatively higher water solubility,
with an estimated half-life of 5–8 days due to possible degradation
from aquatic microorganisms who utilize the “free” form of levoglucosan
(Norwood et al., 2013). Increased
presence of PAHs may also be due to the sedimentation itself. PAHs derived
from pyrogenic sources generally associate with soot-rich particles that
protect them from degradation in the atmosphere, water column, and sediments (Yunker et al., 2002b).
PAHs from forest fires only travel relatively local distances but are
protected from photolytic degradation due to their association with larger
particles, helping them survive the transport from the atmosphere into
climate archives such as sediments (Yunker et al., 2002b).
Combustion sources
The diagnostic ratios and associated error propagation (Fig. 2g–i) do not
allow quantitatively assigning PAH sources. The IP / (IP + Bghi) ratio contains values
above the 0.5 threshold for combustion of wood, wood soot, and/or grasses,
creosote, as well as almost all wood, and coal combustion aerosols and bush
fire (Yunker et al.,
2002b). The FluA / (FluA + Pyr) ratio, with values above 0.5 for the majority
of the samples, indicates the combustion of grass, wood or coal, although
this threshold is not definitive (Yunker et al., 2002b).
The Ant / (Ant + Phe) ratio with values >0.10 is generally related
to pyrogenic PAH sources, but overlapping values between petroleum and
combustion sources are reported (Yunker et al., 2002b).
In Paru Co, when including the error propagation, the majority of samples
show values of Ant / (Ant + Phe) >0.10. Due to the improbability
that petroleum sources were burned near Paru Co during the geological time
period covered by the analysed core, the obtained values for the ratio
Ant / (Ant + Phe) must be related to vegetation combustion. In general, Ant
undergoes more rapid photochemical reactions in the atmosphere than Phe. In
contrast, FluA/Pyr and IP/Bghi isomer pairs degrade at comparable rates and
the original composition information is preserved during atmospheric
transport (Yunker et
al., 2002b) suggesting that their ratios may be more reliable compared to
the Ant / (Ant + Phe) ratio. Given these considerations, we confirm that
diagnostic ratios are important tools for source assignment but cannot
be completely trusted due to overlapping values and error propagation that
may hinder the correct allocation of the signal origin. However, PAHs in
Paru Co can function as pyrogenic markers as we did not find any evidence of
other sources (e.g. volcanic eruptions, anthropogenic emissions).
The sum of PAH concentrations demonstrates similarities to the eastern TP
tree pollen record from the Zoige basin (Fig. 3; Zhao et al., 2011). The
Zoige basin is 450 km to the northeast of Paru Co and, in addition to
Hidden Lake (Tang et al., 2000), are among the closest
pollen records to Paru Co. Both records demonstrate Holocene vegetation
fluctuations in the region and are consistent with other pollen and
modelling studies (Dallmeyer
et al., 2011; Herzschuh et al., 2006; Lu et al., 2011), which identify
decreasing summer monsoon precipitation and changes in warm season
temperature as the mechanisms responsible for the vegetation shifts from
meadow to conifer forest to alpine steppe. The average forest fraction on
the TP shrank by almost one-third from the mid-Holocene (41.4 %) to the
present (28.3 %). Shrubs quadrupled in their mid-Holocene percentage to
present-day (12.3 %), replacing much of this forest. The grass fraction
also increased from 38.1 % during the mid-Holocene to the current
percentage of 42.3 % (Dallmeyer et al., 2011). This
forest decline and replacement by shrubs from 6 cal kyr to the present is
prevalent across much of the southeastern TP (Lu et al.,
2011).
PAH values are low in the early Holocene where, instead, tree pollen values
are quite high. However, in the mid-Holocene PAHs contain higher
concentrations from 6.5 cal kyr BP, concurrent with a peak in the percentage
of tree pollen. The subsequent decreasing trend in tree pollen, from 4.7 cal kyr BP onward, is associated with a drying and cooling climate that may have
intensified fire as recorded by PAHs in Paru Co, creating a positive
feedback resulting in even more decreasing tree coverage. This decreasing
trend in tree pollen reaches its lowest values after 3 cal kyr BP. The
regional wetter climatic conditions during the early and mid-Holocene (Bird et
al., 2014; Tang et al., 2000) may have favoured forest expansion, where this
biomass became available for successive burning during the more arid climate
of the late Holocene, when PAHs show an increasing trend (Fig. 2a).
In addition to the PAH ratios, L / M ratios can also help determine combustion
sources (Fig. 3a). L / M emission ratios ranging between 0.6 and 13.8 may be due
to softwood combustion, while ratios between 3.3 and 22 depict hardwood
burning, and ratios 2.0–33.3 may be due to burning grasses (Fabbri et al.,
2009 and references therein). Therefore, the Paru Co data suggest that the
fire signal from MAs after 10.74 cal kyr BP is likely due to conifer burning
in the region. Successively, grasses, softwood, and hardwood burning
oscillated until 8.6 cal kyr BP, where hardwood combustion prevailed until
7.7 cal kyr BP, followed by the predominance of grassland burning. Although
MA ratios can generally differentiate between grass versus wood burning (Kirchgeorg et al., 2014), specific
L / M and/or L / (M + G) ratios do not directly correspond to individual fuel
types (Matsubara Pereira, 2017) due to the problem
of overlapping ratios and similar burning conditions that influence the
ratios.
In order to obtain more information from the burning conditions, we compared
CPI values to L / M and PAHs (Fig. 3). Considering that PAHs and n-alkanes are
both local indicators, variations in CPI corresponding to spikes in local
fire markers may link combustion and vegetation types demonstrated by
n-alkane abundances. While no correlation exists between PAHs and CPI, the
CPI and L / M have a slight positive correlation (r=0.31, p value =0.03).
Medeiros and Simoneit (2008) found that the n-alkanes in
green vegetation smoke contained distributions ranging from C23 to
C35, with strong odd-to-even carbon number predominance evidenced by
CPI ranging from 9 to 58. MAs are better at recording smouldering fires than
are PAHs, which may in part explain the similarity between MA and CPI
variability through time. The Paru Co CPI values peak around 10 cal kyr BP,
in the period between 7.8 and 3.5 cal kyr BP, and at 2.3 cal kyr BP, with
values up to 41.2, similar to the peak distributions of L / M. Another
argument for the relationship between CPI and MA fire is the fact that
lower temperature fires (MAs) essentially steam-distil the vascular plant
lipids into the smoke, while high-temperature fires (PAHs) can result in
a decrease in the CPI, potentially due to the thermal generation of
n-alkanes of lower CPI from macromolecular material (Schefuss et al., 2003; Standley and Simoneit,
1987). In addition, the distance from the vegetation to the sediments may
influence the CPI record as plants that are in or near the water pools
contain shorter carbon chains, whereas more distant plants have higher CPI
values (García-Alix et al., 2017). Using these
considerations, we assume that when CPI and L / M are parallel to each other,
they record both fire from the surrounding areas as well as from near the
lake catchment.
Vegetation in the lake catchment
Past vegetation changes can also be derived from variations in n-alkane
ratios, as n-alkanes can record the organic input into and within the lake.
The ACL represents the composite of longer and shorter
n-alkanes (Poynter and Eglinton, 1990), encompassing the chain-length range
of 21 to 35. The peak in ACL21–35 values at
10.9–10 cal kyr BP may reflect fewer submerged aquatic plants, while
decreased ACL21–35 values between 10 and 5.5 cal kyr BP may
result from the prevalence of submerged aquatic plants (Fig. 4b). The
P-aqueous (Paq) ratio can help differentiate between submerged
plants that tend to have medium-chain-length n-alkanes and terrestrial
plants that tend to have longer chain lengths (Ficken et al., 2000). This
ratio is calculated as (C23+C25)/(C23+C25+C29+C31). When the Paq ratio is closer to 1, these values
indicate a greater percentage of submerged plants, and when the value is
closer to 0, these numbers pertain to a greater percentage of terrestrial
vegetation. The Paru Co Paq ratio (Fig. 4c) rapidly fluctuates in
the oldest section of the core, suggesting quick transitions between
terrestrial and aqueous vegetation as the dominant source of n-alkanes to
the lake. The ACL21–35 and Paq are highly negatively
correlated (r=-0.89; p value 4.8×10-22) throughout the Paru
Co core demonstrating that both ratios record similar vegetation changes
during the same time periods (Fig. 4). Higher ACL ratios and lower
Paq values demonstrate higher percentages of terrestrial plants,
and vice versa.
Fluctuations in lake levels (Fig. 4d) can be associated with fluctuations in
Paq, suggesting a general relationship between higher lake levels
and the prevalence of submerged plants between 10 and 5 cal kyr BP. The
opposite situation occurs between 5 and 1.3 cal kyr BP, when a decreasing
trend in lake level corresponds to diminishing Paq values. ACL
confirms this trend where the majority of values near 25 occur during higher
lake levels (10–5 cal kyr BP) and the majority of values around 27 occur
from 5 to 1.3 cal kyr BP. These high lake levels (Fig. 4d) are consistent
with wet conditions from a more intense ISM prevailing until ∼6 cal kyr BP, as evidenced by δD wax and pollen records
(Figs. 3, 4 and 5).
After 5.2 cal kyr BP, lake levels decreased, suggesting diminished ISM
rainfall, reduced clastic deposition, leading to an invasion of the littoral
zone on the core site and an increase in sand deposition (Bird et al., 2014). The
fluctuations in both ACL and Paq are consistent with these lake level
changes (Figs. 4 and 5). The mid- to late Holocene changes in the lake levels
and vegetation respond to decreased summer radiation and associated ISM
precipitation (Berger and Loutre, 1991).
The response of combustion proxies to changes in ISM intensity in
Paru Co. (a) Sum of PAH concentrations, data points (black) with
absolute error range (grey), LOWESS smoothing with SPAN parameter 0.2 (red),
b-spline interpolation (teal). (b) Lithics (%), data from Bird et
al. (2014). (c) MA concentrations, data points (black) with
absolute error range (grey), LOWESS smoothing with SPAN parameter 0.2 (red),
b-spline interpolation (teal).
The decline in forest vegetation and the rise in steppe vegetation from 5 to 4 cal kyr BP seems to coincide with an increased human presence on the TP.
Grazing indicators (increases in Rumex, Sanguisorba, and Apiaceae pollen) imply a human
influence on the environment since approximately 3.4 cal kyr BP near the
southeastern TP Lake Naleng (Kramer et al., 2010a), in
the region of Paru Co. Humans slashed and burned the forests near Lhasa to
open lands through fire over the past 4600 years
(Miehe et al., 2006). Other studies
also suggest links between fire activity and forest clearance in the
southern and southeastern TP during the late Holocene (Kaiser
et al., 2009a, b). Although evidence exists that humans
altered TP vegetation through burning in the late Holocene, the extent of
human-related vegetation change across the TP is still unknown. The absence
of anthropogenic FeSts in Paru Co sediments indicates that human and
associated pastoralism were not present in the local area. In Paru Co, the
only FeSts above the MDL were sitosterol and sitostanol (Supplement Fig. S7).
Sitosterol can derive from terrestrial vegetation, and the presence of its
derivative molecule sitostanol can indicate the microbial reduction of
sitosterol in the stomach of ruminant animals (Vane et al.,
2010), as well as sitosterol hydrogenation in sediments (Martins et al., 2007).
Sitostanol and sitosterol highly correlate with each other throughout the
Paru Co core (r=0.94, p value 3.79×10-8). The fact that sitosterol
and sitostanol were the only FeSts detected in Paru Co suggests the absence
of ruminant animals that would also deposit other FeSts, and we consider
vegetation and hydrogenation in sediments as the main sources of Paru Co
FeSts. Due to the absence of other human/animal indicators, we are inclined
to describe the variations in fire regimes and vegetation as primarily
climate-driven signals.
Isotopic and pollen information from surrounding lakes supports the climatic
variation from a warm and humid early Holocene to a cold and dry mid- to late
Holocene and also ascribe these climate changes to the ISM (Kramer
et al., 2010a, b; Ma et al., 2014; Zhu et al., 2010). Pollen assemblages
from two transects of lakes (east–west and north–south) across the TP
indicate sparse vegetation with low pollen concentrations characterized by
Artemisia/Cyperaceae alpine steppe (Li et al., 2016).
Lake Naleng, also located on the southeastern TP, records changes that are
similar to Paru Co paleoreconstructions (Kramer et al., 2010a). From
10.7 to 4.4 cal kyr BP, open Abies–Betula forests reflect intense summer monsoon and an
upward treeline shift. Temperature range reconstructions demonstrate a climate
2–3 ∘C warmer than the present and treeline position 400–600 m
higher than today. However, within this warm period, the climate had a
sudden, intense change between 8.1 and 7.2 cal kyr BP with temperatures 1–2 ∘C below early and mid-Holocene levels and forests retreating
downslope (Kramer et al.,
2010a). Multiple pollen studies using compilations of Chenopodiaceae,
Asteraceae, Cistaceae, Tamaricaceae, and Pottiaceae confirm the severe early
Holocene cold events at 8.7–8.3 and 7.4 cal kyr BP (Miao
et al., 2015; Mischke and Zhang, 2010). During the mid-Holocene (7.3–4.4 cal kyr BP), dense temperate steppe vegetation dominated the TP (Li et
al., 2016; Zhao et al., 2011). Tree pollen (primarily Picea) peaks during the
mid-Holocene at 6.5 cal kyr BP, and then decreases until 2 cal kyr BP (Zhao et al., 2011). During
this same time period, Cyperaceae becomes the dominant regional steppe
vegetation, and altitudinal vegetation belts shifted downslope in response
to reduced temperatures (Li et al., 2016).
These alpine steppes contain arid vegetation elements (including Cyperaceae,
Poaceae (grass family), Amaranthaceae (pigweed and amaranths), and
characteristic high-alpine herb families) between 4.4 and 0 cal kyr BP
(Herzschuh et al.,
2006; Tang et al., 2000). Lake records from Nam Co
and Taro Co, located in the same vegetation zone as Paru Co, suggest a
weakening in the ISM and the increased influence of the westerlies from 5.6
to 0.9 cal kyr BP (Bird
et al., 2014; Li et al., 2011; Ma et al., 2014). This synthesis on changes
in Holocene vegetation suggests that variations in monsoonal precipitation
and insolation-driven temperature are the predominant driving forces for
changes in alpine vegetation in the central TP (Li et al., 2016).
Atmospheric transport
The TP is ringed by high mountains that create natural barriers that block
the transport of smoke aerosols to the TP from the south, west, and
northwest (You et al., 2016a). However, the ISM system may
help transport both mineral and organic aerosols over the mountain ridges
and into the TP during the summer monsoon months when winds rush from the
south across the Himalayas. The ISM is the main source of precipitation
across much of the southern TP, where this rainfall provides moisture for
plant growth. The strength of the ISM over millennial timescales is driven
by solar radiation, where increased insolation results in the ISM moisture
moving northward across the TP. Climatic records from areas surrounding the
TP demonstrate that the Pleistocene–Holocene transition was characterized by
increasing temperatures until approximately 8.2 cal kyr BP, when a sudden
cooling occurred (Mischke et al.,
2016). The ISM was more intense than current levels between ca. 10 and 6 cal kyr BP due to increased insolation, and reached a maximum
in the southeastern
TP at 8 cal kyr BP (Tang et al., 2000).
The mid-Holocene (∼6 cal kyr BP) had higher average summer sea
surface temperatures (SST) and a stronger summer monsoon than during the
present, resulting in a warm and wet climate (Wei et al., 2007; Zhao et al.,
2011). This timing is consistent with paleomonsoon records from southern
China and with the idea that the interactions between summer insolation and
other large-scale boundary conditions, including SST and sea-level change,
control regional climate (Zhao et al., 2011). A drying trend during the past
6 cal kyr is documented in many records from the northern subtropics and
tropics (Liu and Feng, 2012). The cooling trend after the Holocene Climatic
Optimum (6.5–4.7 cal kyr BP) correlates with decreasing solar insolation
(Zhao et al., 2011), resulting in the decreasing strength of the Asian
monsoon systems and in a drier climate across much of the TP. Decreased solar
insolation resulted in a dramatic drying at ∼4.2 cal kyr BP,
directly or indirectly leading to the observed collapses of many Chinese
Neolithic cultures (Liu and Feng, 2012; Wang et al., 2005). During the past
750 years, precipitation changes have influenced fire-regimes and vegetation
shifts in the Altai Mountains, where ecosystems are highly
sensitive to occasional decadal-scale drought events which, in the future,
may trigger unprecedented environmental reorganization under global-warming
conditions (Eichler et al., 2011).
This monsoonal history may affect the transport of fire products to Paru Co.
The difference between the Paru Co MA and PAH records may be influenced
not only by the burning temperatures that produce the different products
(Sect. 5.1) but may also reflect changing atmospheric transport. MAs peak
during the ISM maximum at Paru Co between 10 and 7 cal kyr BP, which may
reflect the long range transport of these fire aerosols associated with
biomass burning on regional scales (Fig. 4). MAs are generally considered as
regional signals due to their ability to be transported longer distances
than the more local PAHs, where this early Holocene levoglucosan peak may
reflect either increased fire activity and/or changes in atmospheric
transport. We may hypothesize that high levoglucosan concentrations during
the early Holocene in Paru Co reflect the interplay between increasing
influence of the ISM in the early Holocene resulting in wetter conditions
and increase biomass on the southern TP (An et al., 2012), as
well as increased early Holocene winter monsoons causing a cold and dry
climate on the northeastern TP that is cited as a main driver for fire
activity during this time period (Miao
et al., 2017). Major transport to Paru Co could have come from the south via
the ISM but, to the best of our knowledge, no studies encompassing Holocene fire
history exist from the possible southern source areas.
Even though modern transport is not indicative of atmospheric circulation in
the early Holocene, recent studies can depict the distribution of air masses
affecting the southern Tibetan Plateau under current monsoon conditions, and
demonstrate the geographic reach of possible source areas. Modern transport
patterns demonstrate that air masses over Ranwu, a sampling site in the
Tibetan Plateau, 450 km east of Paru Co (Wang et al., 2016), mostly arrive
from Bangladesh and the Indo-Gangetic Plain during both the winter
(64.3 %) and spring (70.2 %) seasons (Wang et al., 2016). The remaining
fraction of air masses in winter (35.7 %) come from the Middle East,
Afghanistan, Pakistan, and northwest India, while the spring air masses
largely derive from northwest India (26.3 %). Winter air masses over
Beiluhe, a sampling site 600 km north of Paru Co, in the central Tibetan
Plateau, come mostly from the southern slope of the Himalayas (79.6 %)
with the remaining air masses originating in the Middle East and Central
Asia (20.4 %). However, spring air masses largely originate from
northwestern China (45.9 %), followed by Central Asia (32.4 %) as well
as the southern slope of the Himalayas (21.6 %). The Paru Co levoglucosan
record therefore encompasses possible source regions that may extend beyond
the TP.
Although PAHs are more of a local fire indicator than levoglucosan
concentrations, PAHs are also affected by changes in atmospheric transport
and associated precipitation. PAHs peak during periods of less intense ISM
precipitation, as indicated by Paru Co lithics percentages in the periods
10.5–10.1, 7–5.8, and 5.2–3.2 cal kyr B (Fig. 4), as more intense
rainfall results in greater lithic deposition (Bird et al., 2014). During
these drier phases, aridity could have increased regional fire activity
(Sect. 5.1). However, this relationship between aridity and fire is not
constant for the late Holocene Paru Co record as the increasing PAH signal
from 3 to 1.3 cal kyr BP coincides with increasing lithic abundances that
may be related to more ISM precipitation. Therefore, the 3–1.3 cal kyr BP
increasing PAHs could be related to a fire signal transported by ISM
precipitation. Rainfall occurring together with or soon after fire
events scavenges PAH particles from the atmosphere and increases deposition
(Denis et al., 2012).
The late-Holocene PAH fire signal in Paru Co is consistent with charcoal
records demonstrating an increasing fire trend in the eastern monsoonal
region of China during this same time period (Xue et al., 2018). The
increasing fires in Paru Co and surrounding areas are synchronous with the
general change in the regional fire pattern during the late Holocene,
coincident with increases in population and crop areas (Marlon et al., 2013).
Although human FeSts were absent in Paru Co, FeSts are a very local indicator
of the presence of humans, and anthropogenic activity could still influence
fire records across regional scales. For example, Miao et al. (2017) link
northeastern Tibet Bronze Age sites and associated human activity to increasing charcoal
concentrations from 3.6 cal kyr BP to the present.