Introduction
Soils play a key role in the Earth System as they control the hydrological,
erosional, biological and geochemical cycles. Moreover, they provide
services, goods and resources to humankind
(Berendse et al., 2015; Brevik
et al., 2015; Decock et al., 2015; Keesstra et al., 2012; Smith et al.,
2015). More specifically, soils are now being seen as a key component of the
carbon cycle
(Bruun et al.,
2015; Debasish-Sasha et al., 2014; Muñoz-Rojas et al., 2015; Novara et
al., 2015; Parras-Alcantara et al., 2015; Peng et al., 2015; Wasak and
Drewnik, 2015; Yu et al., 2014). This is especially the case for Arctic
soils, as they contain maximum stocks of soil organic matter (SOM) within
the whole pedosphere
(Fritz
et al., 2015; McGuire et al., 2009; Oliva et al., 2014; Ping et al., 2015;
Zubrzycki et al., 2014). Low temperature and high moisture conditions have
favoured the accumulation of large amounts of organic matter in permafrost
soils over thousands of years
(Schirrmeister
et al., 2011; Zubrzycki et al., 2013). However, current trends of climate
warming and permafrost thawing are exposing this pool of organic matter to
microbial degradation (Schuur et al., 2015), as well as
to fires and smouldering
(Rein, 2013; Tsibart et
al., 2014; Zaccone et al., 2014), for the first time in millennia. These
processes, involving SOM mineralisation and combustion, represent an
unprecedented source of carbon dioxide and methane to the atmosphere
(Christensen
et al., 1999; Gruber et al., 2004; Zimov et al., 2006), which might further
accelerate climate change effects (Schuur et al.,
2008). Because of that, Arctic SOM represents a vulnerable carbon pool,
susceptible to be remobilised under increasing temperatures. This is
especially relevant, considering that the Arctic is the region on Earth that
experienced the highest temperature increase in recent decades
(4 ∘C increase from 1968 to 1996, NOAA, 2015), and it is also
predicted to have the strongest warming in the coming years (between 2
and 9 ∘C by the year 2100, IPCC, 2007).
In order to better understand the implication of permafrost SOM to
greenhouse gas emissions, an accurate knowledge of its spatial distribution,
both in terms of quantity and quality (i.e. biodegradability, chemical
composition and humification degree) is needed
(Fritz et al., 2015). The current state of
knowledge estimates soil organic carbon (SOC) stocks of 1307 Pg (uncertainty
range between 1140 and 1476 Pg) throughout the northern circumpolar region
(Hugelius et al., 2014). These amounts
surpass previous estimates (Tarnocai
et al., 2009) and largely exceed the total carbon contained in the world
vegetation biomass (460–650 Pg) or in the atmosphere (589 Pg, IPCC, 2013). However, these SOC stock estimates
are still poorly constrained (Hugelius
et al., 2014). One main source of uncertainty is the fact that these
estimates have been calculated from observations which are highly spatially
clustered (Hugelius et al., 2013),
while extensive land areas still remain uncharacterised due to logistic
difficulties to reach these sites
(Horwath
Burnham and Sletten, 2010; Zubrzycki et al., 2013).
Uncertainties become even more important when SOM quality is concerned
(Mishra et al., 2013). The chemical composition of SOM
determines its decomposability and, therefore, it determines the rate at
which carbon may be transferred from soils to the atmosphere under warmer
conditions. Biodegradability of SOM has been related to the humification
degree, as more advanced stages in the humification process imply a
depletion of the labile molecules, as well as an increase in the bulk
aromaticity, which provides a higher stability of the SOM. A number of
proxies have been used to trace humification. Namely, H / C from humic acids
has been used as an index of molecular complexity, as higher degree of
conjugation implies a lower hydrogenation of the carbon chains
(Andersson et al., 2012) and it has been
found to decrease with humification
(Zaccone et al., 2007). The
optical index E4 / E6, being proportional to the average molecular weight of
humic compounds (Chen et al., 1977), has also been used to
trace humification (Dziadowiec
et al., 1994; Hugelius et al., 2012; Šīre and Klavinš, 2010),
during which an increase in the average molecular weight occurs due to
condensation and polymerisation processes
(McDonald et al., 2004). Similarly, C / N has
been used as a measure of decomposition degree in peat soils
(Kuhry and Vitt, 1996) and, moreover, has been found to be
directly related to SOM lability, as it determines the stoichiometric
availability of nitrogen to soil microorganisms
(Andersson
et al., 2012; Zaccone et al., 2007). Further, 13C-nuclear magnetic
resonance (NMR) spectroscopy provides information on the diversity in carbon
functional structures and has also been used to track changes in SOM during
decomposition and humification
(Kogel-Knabner, 1997; Zech et al., 1997)
in Arctic soils
(Abakumov
and Fattakhova, 2015; Calace et al., 1995, 2005; Chukov et al., 2015; Dai et
al., 2001; Ward and Cory, 2015). More specifically, high phenolic (150 ppm),
carboxyl-C (175 ppm) and alkyl-C (30 ppm) groups, together with low O-alkyl
carbons, have been related to advanced stages of humification
(Calace
et al., 2005; Dai et al., 2001; Zech et al., 1997).
So far, studies of SOM quality from Arctic soils have revealed a generalised
lowly-decomposed character of the organic molecules (Dziadowiec
et al., 1994), which preserve much of the chemical character of their
precursor material due to low progress of humification under cold conditions
(Davidson and Janssens, 2006). Also, recent studies
provide evidence of a high and long-term mineralisation potential of Arctic
SOM under increased temperatures
(Elberling et al., 2013;
Schädel et al., 2014; Schuur et al., 2015). Based on sites from Alaska
and Eastern Siberia, potential carbon losses have been
geographically extended for the whole high-latitude permafrost-affected
soils, predicting losses between 20 and 90 % of total soil organic carbon
under laboratory conditions within 50 years (Schädel
et al., 2014). At the same time, this study emphasised the importance of the
spatial variability of SOM quality in extrapolating mineralisation rates,
which underlines the need to extend the spatial coverage of empirical
observations of SOM quality and biodegradability to currently non-explored
areas.
One of these regions that remain under-characterised is the Yamal region,
located in Western Siberia, Russia. Previous research in this area
highlighted that this region is experiencing a more rapid increase in
ambient temperatures than other Arctic areas, challenging the
socio-ecological systems (Forbes et al.,
2009). This study presents, for the first time, a detailed characterisation
of the bulk organic matter and humic acids from soils collected in different
sites across the Gydan Peninsula. Our aim was to investigate the potential
vulnerability of soils from this region in the face of increasing
temperatures, and to contextualise it with current estimates of SOM
distribution. This was addressed by (a) quantifying SOC content, (b)
characterising SOM quality by means of elemental analysis, solid-state
13C-NMR spectroscopy and optical measurements, and (c) by analysing the
potential biodegradability of SOM.
Regional distribution of the study sites in the Gydan peninsula: 1
– Haranasale Cape, 2 – Yavay Cape, 3 – Gyda Yuribey Gulf, 4 – Enisey
Gulf, 5 – Belyi Island.
Study region
Samplings were performed in the Gydan Peninsula and Belyi Island, lying
within the Yamal-Nenets autonomous region of the Russian Federation
(northwestern Siberian coast). The Gydan Peninsula extends into the Kara
Sea, between the embayments of Ob and Yenisey rivers; whereas Belyi Island
is located at the top northern part of the Yamal Peninsula (Fig. 1). The
Gydan Peninsula is a predominantly flat territory entirely covered by
permafrost and tundra vegetation. Main vegetation types include grasses,
mosses and lichens, while small lignified bushes appear sparsely
distributed. This region, as is more than 60 % of the Russian land
surface, is underlaid by permafrost (Kotlyakov and Khromova,
2002). Soils have developed on Pleistocene sands underlaid by marine clays
and alluvial sediments deposited during the late Quaternary
(Walker et al., 2009), largely influenced by
cryogenesis. The average annual air temperature is -10 ∘C,
with the minimum monthly average being registered in January
(-25 ∘C) and the maximum in August (+8 ∘C). On average, the air temperature remains positive during 70 days per
year. The average annual precipitation in the region is 325 mm yr-1
and the average evaporation is between 50 and 100 mm yr-1
(Buchkina et al., 1998).
The Gydan Peninsula presents a diversity of soil types due to
regionally varying conditions of the cryopedogenesis process, including
depth of the active layer, texture and structure of parent materials and
bedrocks, and historic climatic conditions. As is most common for the whole of
Siberia, the Gydan Peninsula is covered by Cryosols – soil taxonomy
following the World Reference Base for Soil Resources (WRB) system,
(FAO, 2014) here and throughout the text – which are part of the
Gelisol unit. More specifically, the main soil types can be classified as
Cryic Histosols, Histic Cryosols and Histic Gleysols. They are all
permafrost-affected soils containing morphological features of
cryoturbation, but each exhibit slight differences in their organic matter
content. Histosols contain the maximum stocks of organic matter, whereas
typical Gleysols contain lower percentages of organic matter, although in
some cases it is more decomposed. Generally these Crysols are turbic, due to
the cryogenic mixing between different horizons which enhances the
accumulation of organic matter in the upper solum.
Soil morphology of sampling sites
Sampling sites were specifically located at the Yavay Peninsula, Gyda
Yuribey Gulf, Enisey Gulf, Haranasale Cape and Belyi Island (Fig. 1).
Typical Cryosol was sampled in the Yavay Peninsula (72∘21.642′ N,
75∘05.144′ E; Fig. 2a). It consisted of an upper histic horizon
of raw humus, some cryogenic cracks and a fairly homogeneous mineral soil
profile. The landscape of the Yavay peninsula is shown on Fig. 2b. The depth
of the permafrost table was between 70–80 cm. Features of gleyification were
evident on the permafrost table and along the mineral soil layer. The soil
was sampled at 0–5 cm.
The soils of Gyda Yuribey Gulf (71∘18.812′ N, 77∘33.245′ E) consisted on Gleyic Cryosols with two gleyic horizons: an upper
and a contact one (Fig. 2c). Landscapes here (Fig. 2d) had a predominantly
flat orography and soils were more overmoisted than those in the Yavay
peninsula. The permafrost table was at 90 cm depth. The soil was sampled at
0–10 cm.
Examples of soils and landscapes analysed in this study. Yavay
Cape (a, b), Gyda Yuribey (c, d), Enisey Gulf, (e, f), Beliy Island
(g), Haranasale (h).
The landscape of the Enisey Gulf (72∘22.451′ N, 78∘38.586′ E) was predominantly flat, without any relevant relief
differentiation (Fig. 2f). Therefore, soils were highly overmoisted and
Histic Cryosols were dominant (Fig. 2e). The depth of the permafrost table
was 40–45 cm. The climate was considered as the most severe in comparison
with the other investigated plots. The soil was sampled at two depths: 0–5
and 5–9 cm.
Soils of the Beliy Island (73∘18.421′ N, 71∘23.555′ E)
were classified as Entic Podzol (Fig. 2g). The upper part of the horizon
presented weak features of podzolisation, and it was underlaid by material
affected by cryoturbation in the middle of the profile (20–45 cm). The lower
part was a Gleyic horizon (60–170 cm) of intense blue colour, underlaid by
the permafrost table at 200 cm depth. The soil was sampled at 0–10 cm.
In Haranasale Cape (71∘25.402′ N, 73∘03.758′ E) relief
forms were highly variable and presented hills and lowlands with relative
elevation changes of about 100 m. Soils here consisted mainly of Histic
Gleysols (Fig. 2h). The permafrost table was at 60–70 cm depth. Here two
different sites were sampled: one located on a hill slope, at 0–5 and
5–11 cm depth (hereafter referred to as Haranasale-1); and another one located on
a depression at 0–5, 5–10 and 20–30 cm depth (hereafter referred to as
Haranasale-2).
Methods
Bulk SOM characterisation
All chemical soil parameters were analysed on a fine earth of soil after
being passed through a 2 mm sieve. Total SOC content was determined by
dichromate oxidation–titration method (Walkely, 1947). Percent
content of carbon was multiplied by bulk soil density and by the soil layer
thickness to determine SOC stocks in gC m-2. Bulk soil density was
determined by weighing the micromonoliths of air dried soils. Total
mineralisable carbon was determined according to Anderson (1982): Aliquots
(5 g) of fresh soil were adjusted to a moisture content of 60 % of the
water-holding capacity and were incubated at 25 ∘C in sealed
plastic bottles with 1 M NaOH (Anderson, 1982) in
duplicate. The amount of CO2 trapped in the alkaline solution was
measured by titration every 7 days during 4 (Belyi Island and
Haranasale-2) and 9 (Yavay Cape, Enisey Gulf, Gyda Yurivey and
Haranasale-1) weeks. Soil pH was determined according to standard procedure
using soil : salt (CaCl2 0.1 N) ratio of 1:2.5.
SOM humic substances solution was obtained by diluting soil with 0.1 M NaOH
at a soil : solution mass ratio of 1:10 followed by gravity filtration. The
optical index E4 / E6 was measured on SOM humic substances solution as the
absorbance measured at 465 nm divided by that at 665 nm
(Chen et al., 1977).
Extraction of humic acids
Humic acids were extracted from each SOM humic substance solution according
to the following procedure (Schnitzer, 1982). The humic acids were
extracted with 0.1 M NaOH (soil : solution mass ratio 1:10) under nitrogen
gas. After 24 h of shaking, the alkaline supernatant was separated from the
soil residue by centrifugation at 1516 × g for 20 min and acidified
to pH = 1 with 6 M HCl to induce the precipitation of the humic acids. The
supernatant, which contained fulvic acids, was separated from the
precipitate by centrifugation at 1516 × g for 15 min. The humic
acids were then redissolved in 0.1 M NaOH and shaken for 4 h under N2
before the suspended solids were removed by centrifugation. The solution was
acidified again with 6 M HCl to pH = 1, and the humic acids were separated
by centrifugation and demineralised by shaking overnight in
0.1 M HCl / 0.3 M HF (solid/solution ratio 1:1). Next, they were repeatedly washed with
deionised water until pH = 3 was reached; and then they were finally
freeze-dried. Extraction yields of humic acids were calculated as the
percentage of carbon recovered from the original soil sample used for
extraction.
Properties of bulk soil organic matter. *Humic acids.
Site
Soil depth
Total C
E465/E650
pH
HA* extraction
Mineralisable C
(cm)
(%)
(CaCl2)
yield (%)
(mgC gsoil-1 day-1)
Yavay Cape
0–5
13.7
2.495
5.47
0.68
2.341
Gyda Yuribey
0–10
27.4
1.207
3.94
0.46
2.103
Enisey Gulf
0–5
36.4
2.654
3.64
1.85
2.318
Enisey Gulf
5–9
18
1.409
4.54
2.30
2.189
Haranasale-1
0–5
7.5
2.684
3.79
0.16
0.910
Haranasale-1
5–11
6.8
1.164
3.56
0.42
0.875
Beliy Island
0–10
5.7
2.244
5.07
0.46
1.572
Haranasale-2
0–5
12.3
2.816
6.26
1.85
1.945
Haranasale-2
5–10
15.2
1.952
4.75
0.99
1.553
Haranasale-2
20–30
0.4
6.679
3.01
0.13
0.583
Characterisation of humic acids
Humic acids were characterised for their elemental composition (C, N and H)
using a Euro EA3028-HT analyser. Data were corrected for water and ash
content. Oxygen content was calculated by difference taking into account the
ash content. The elemental ratios reported in this paper are based on
weight.
Solid-state 13C-NMR spectra of humic acids were measured with a Bruker
Avance 500 NMR spectrometer (Karlsruhe, Germany, 2003) in a 4 mm ZrO2 rotor.
The magic angle spinning speed was 20 kHz in all cases, and the nutation
frequency 13C fields for cross-polarisation was u1/2p 1/4 62.5 kHz.
Repetition delay and number of scans were 3 s. Groups of structural
compounds were identified by the chemical shifts values: 190–170 ppm – carboxyl group and amidic carbonyl (faCO2);
170–150 ppm – aromatic C of fenols and fenol esters (faP);
150–135 ppm – alkylaromatic (faS); 135–108 ppm – protonise aromatic
carbon, bridgehead C (faH); 108–100 ppm – cellulose anomeric
carbon and hemiacetal carbon (falO1); 100–70 ppm – resonance
region of C–H bonds, secondary alcohols, and other carbon atoms bound to
oxygen (falO2), 70–50 ppm – methyl group resonance region of
aliphatic and aromatic ethyl ethers, amino acid carbons, and methyl esters
of carboxylic groups (falOM); 50–32 ppm – resonance region of
quaternary carbon and CH carbons (falQ); 32–27 ppm – resonance
region of CH2 alkyl structures in transconformation
(faltrans); 27–10 ppm – resonance region of alkyl methyls and
CH2 units (falMet). The total aromatic content was determined
by integrating the signal intensity in the intervals 100–170 and 183–190
ppm, while the total aliphatic content was determined at the intervals 0–110
and 164–183. Both fractions were expressed as percentages to
the total spectral integration.
Statistical analyses
All statistical analyses were performed within the R environment
(R Development Core Team, 2015). Simple relationships between
variables were explored using univariate linear model correlations based on
Pearson's r. Prior to analysis, data were tested for normality using the
Shapiro test (base R). Normality was observed in all cases and no data
transformations were necessary. The only exception was an extreme outlier
for E4 / E6 which was removed from analysis. Those models which presented a
significant correlation where tested for homoscedasticity using the
Breusch–Pagan test (lmtest package for R, Zeileis and Hothorn, 2002). No
models presented significant heteroscedasticity, therefore, no adjustment of
the coefficients and their associated errors was necessary. Regressions were
considered significant at p > 0.05.
For the total mineralisable carbon, a multivariate linear model analysis was
performed to detect what SOM quantitative and qualitative variables were
significantly explaining SOM mineralisation. This was achieved using a best
subset selection procedure; that is, a least-squares regression model was
fit for every possible combination of explanatory variables. The best model
was considered to be that with a lowest Mallow's Cp (James et
al., 2013). Computations were performed using the leaps package for R
(Lumley, 2009).
In order to better understand the regional distribution of carbon structures
a multivariate approach was used. A non-metric multidimensional scaling
(NMDS) analysis using Bray–Curtis dissimilarities was performed on the
carbon functional groups defined by the 13C-NMR spectra. The quality of
the ordination was improved by submitting the data to Wisconsin double
standardisation. Interpretation of the NMDS ordination was enhanced by
overlaying information on the elemental composition of humic acids and
characteristics of bulk SOM. This was achieved by performing a vector
fitting analysis. Variables were centred by subtracting their mean and
scaled by dividing by their standard deviation. Fittings were considered
significant at p < 0.01. Computations for the multivariate analysis
were performed using the vegan package for R (Oksanen et al.,
2012).
Results
Bulk soil organic matter characteristics
Across the studied region, soils contained an average of 14.3 ± 10.8 % of total carbon (Table 1). Among the surface layers, the highest
amounts of carbon were found in Enisey Gulf (36.4 %), followed by Gyda
Yurivey Gulf (27.4 %). More moderate amounts were found in Yavay
Cape (13.7 %) and Haranasale-2 Cape (12.3 and 7.5 %). By contrast, Belyi
Island was the location with the lowest carbon content in surface soil
(5.7 %).
In Enisey Gulf, the high amounts of carbon in the surface decreased
substantially at the subsurface layer (from 36.4 % at 0–5 cm to 18.0 % at
5–9 cm), whereas in Haranasale Cape carbon content either remained stable
(7.5 % at 0–5 cm to 6.8 % at 5–11 cm, site 1) or even increased (12.3 %
at 0–5 cm to 15.2 % at 5–10 cm, site 2). Deeper in the same soil
profile (at 20–30 cm), carbon content was minimal (0.4 %).
In terms of carbon stocks, among surface soils there was an average of 12.1 ± 7.2 gC m-2 (Table 1). From surface to subsurface layers (0–5 to
5–10 cm depth), there was a notorious increase in the Haranasale Cape sites
(from 5.3 and 8.6 gC m-2, to 11.5 and 25.8 gC m-2 in sites 1 and
2, respectively). However, there was a 50 % decrease in the Enisey Gulf
site (from 25.5 to 12.6 gC m-2). The deep mineral soil layer from
Haranasale-2 had minimal carbon stocks (0.75 gC m-2).
E4 / E6 averaged 2.383 ± 0.592 in surface soils (Table 1). This average
decreased at subsurface layers to 1.508 ± 0.403, although this
difference was not statistically significant (t test, p > 0.05).
The mineral layer of Haranasale-2 showed a unique signal consisting in an
outlying high value of 6.679.
Elemental composition of humic acids
Humic acid extractions produced maximum yields in Enisey Gulf (especially in
the subsurface layer, 2.30 %) and in Haranasale-2 surface sample
(1.85 %), whereas minimum yields were obtained for the deep mineral layer
in Haranasale-2 (0.13 %, Table 1). The elemental compositions of humic
acids and their corresponding elemental ratios were characterised by very
low variabilities (CV < 9.1 %, Table 2). H / C was the elemental
ratio with the lowest variability (CV = 3.5 %) and averaged 0.109. The
highest values of this ratio were found in Enisey Gulf, Belyi Island and at
the deep mineral layer of Haranasale-2 Cape (20–30 cm). By contrast, the
lowest H / C ratios were found in Gyda Yurivey and Yavay Cape (0.105 in both
sites). The H / C ratio was found to be significantly negatively correlated
with the C / N ratio (Fig. 3), explaining more than 40 % of its variance
(r2= 0.439, p < 0.05).
Elemental composition and elemental ratios of the extracted humic
acids. *Coefficient of variation.
Site
Soil depth (cm)
N %
C %
H %
O %
C / N
O / C
H / C
Yavay Cape
0–5
4.3
51.4
5.4
38.9
12.0
0.8
0.1
Gyda Yuribey
0–10
3.9
50.2
5.3
40.6
12.9
0.8
0.1
Enisey Gulf
0-5
5.0
52.3
6.1
36.7
10.4
0.7
0.1
Enisey Gulf
5–9
5.3
50.5
5.6
38.7
9.6
0.7
0.1
Haranasale-1
0–5
4.4
50.1
5.3
40.2
11.4
0.8
0.1
Haranasale-1
5–11
4.4
49.7
5.3
40.6
11.3
0.8
0.1
Beliy Island
0–10
4.4
48.7
5.5
41.3
11.0
0.9
0.1
Haranasale-2
0–5
4.9
51.5
5.6
38.0
10.5
0.7
0.1
Haranasale-2
5–10
4.3
49.4
5.3
40.9
11.4
0.8
0.1
Haranasale-2
20–30
4.6
43.7
5.0
46.8
9.6
1.11
0.1
CV (%) *
9.1
4.3
4.9
6.1
9.6
11.2
3.5
H / C vs. C / N scatter plot. Numbers in parentheses indicate the
sampled soil depth in centimetres.
The C / N content averaged 11.0 ± 1.0. In depth, it was found to be
slightly lower at deeper layers in Haranasale-1 and Enisey Gulf (Table 2).
However, in Haranasale-2 there was an increase from the surface layer
(0–5 cm) to the subsurface one (5–10 cm) from 10.46 to 11.41. The O / C ratio
averaged 0.81 ± 0.09 and the mineral layer in Haranasale-2 had a
remarkably higher value. No clear patterns could be observed between O / C and
the other elemental ratios.
13C-NMR characterisation of humic acids
13C-NMR spectra revealed that there were only small differences in the
structural diversity of carbon atoms both among the surface samples, as well
as within soil layers. In all samples there was a statistically significant
predominance of aliphatic carbons (68.85 ± 3.60 %) over aromatic
ones (31.15 ± 3.60 %; t test, p < 0.001; Table 4). The
standard deviation of all measured 13C-NMR spectra (Fig. 4 and 5)
showed that the highest variability among samples occurred in the resonance
areas between 50–70 ppm (resonance area for methoxyl carbon, including
amino acids and peptides) and 27–32 ppm (corresponding to CH2 alkyl
structures like alkanes and fatty acids), both of them corresponding to
aliphatic carbon structures.
13C-NMR section integrals (percent of total carbon) for key
carbon structures of soil humic acids.
δ(13C)ppm
10–27
27–32
32–50
50–70
70–100
100–108
108–135
135–150
150–170
170–190
Site
Key structures
falMet
faltrans
falQ
falOM
falO2
falO1
faH
faS
faP
faCO2
Yavay Cape
0–5 cm
12.55
4.59
11.97
17.92
14.76
3.69
15.80
4.37
8.77
5.59
Gyda Yuribey
0–10 cm
14.99
4.60
11.29
17.14
12.70
3.41
15.52
5.11
9.13
6.11
Enisey Gulf
0–5 cm
16.72
5.68
12.57
20.35
14.12
2.97
11.50
3.27
7.53
5.29
Enisey Gulf
5–9 cm
15.24
5.13
12.52
19.29
14.09
3.13
12.70
3.64
8.78
5.48
Haranasale-1
0–5 cm
16.08
6.09
12.26
15.77
12.45
3.30
15.05
4.52
9.10
5.37
Haranasale-1
5–11 cm
16.43
5.40
12.17
19.00
13.67
3.42
12.99
3.73
8.78
4.42
Beliy Island
0–10 cm
17.76
6.62
12.12
19.91
13.55
2.98
11.46
3.25
7.75
4.60
Haranasale-2
0–5 cm
12.86
3.82
10.93
19.77
12.10
3.90
16.37
6.00
7.59
6.67
Haranasale-2
5–10 cm
13.54
4.05
11.04
18.34
12.06
3.58
15.86
6.68
8.39
6.47
Haranasale-2
20–30 cm
15.17
4.97
11.58
16.46
11.24
3.12
15.78
5.38
8.18
8.12
13C-NMR spectra of surface soil samples. The grey spectrum
corresponds to the standard deviation of all 13C-NMR spectra.
When only surface samples are considered, 13C-NMR spectra show (Fig. 4) that Enisey Gulf and Belyi Island had a lower signal intensity across
the aromatic resonance areas compared with the rest of the sites. By contrast,
their signal at 70 ppm was markedly higher, indicating an increase of
O-alkyl C structures. Haranasale-1 site presents a distinctive signature,
consisting in a high methoxyl C peak at 55 ppm. Finally, Enisey Gulf, Belyi
Island and Haranasale-2 sites had slightly higher signal intensities in the
region between 0 to 50 ppm, which corresponds to alkyl C structures.
In Enisey Gulf and in the two Haranasale sites, some patterns could be
observed in depth (Fig. 5). In the three sites it could be observed that the
signal intensity for aromatic carbons (135–150 ppm) presented minor changes.
However, some variation could be observed in the aliphatic regions.
Interestingly, every site presented different patterns. In Haranasale-1
site, the subsurface soil layer exhibited a higher signal intensity at the
O-alkyl C peak at 70 ppm, whereas such signal remained constant at
Haranasale-2, and increased in Enisey Gulf. On the other hand, the signal at
the methoxyl C peak (55 ppm) remained constant in Haranasale-1 and Enisey
Gulf, whereas it clearly decreased in depth in Haranasale-2. Finally, in the
region of 0–50 ppm, the signal at the surface and subsurface layers remained
stable in the three sites. However, in Haranasale-2 site, where a third
depth was sampled (20–30 cm), it can be seen that in this mineral layer there
was a further increase of the signal intensity, indicating a higher
prevalence of simple carbon bonds.
Total aromatic and aliphatic carbons, and maximal signal intensity
at the three ppm of maximum variability in the 13C-NMR spectra. Data
are expressed as percent of total carbon.
Site
Depth (cm)
Aromatic
Aliphatic
27 ppm
55 ppm
70 ppm
Yavay Cape
0–5
33.22
66.78
0.10
0.07
0.10
Gyda Yuribey
0–10
33.90
66.10
0.12
0.09
0.08
Enisey Gulf
0–5
25.59
74.41
0.13
0.06
0.12
Enisey Gulf
5–9
28.55
71.45
0.12
0.08
0.10
Haranasale-1
0–5
32.44
67.56
0.14
0.07
0.08
Haranasale-1
5–11
29.11
70.89
0.13
0.08
0.10
Beliy Island
0–10
25.71
74.29
0.14
0.07
0.11
Haranasale-2
0–5
34.48
65.52
0.09
0.14
0.09
Haranasale-2
5–10
35.11
64.89
0.10
0.12
0.08
Haranasale-2
20–30
33.43
66.57
0.13
0.09
0.08
13C-NMR spectra at different soil depths. The grey spectrum
corresponds to the standard deviation of all 13C-NMR spectra.
Total mineralisable carbon
The total mineralisable carbon during the first 7 days of incubation was
found to be positively correlated with the total carbon in soil, explaining
nearly 58 % of its variability (r2= 0.576, p < 0.01).
Accordingly, the highest rates were found in Yavay Cape (2.34 mgC gsoil-1 d-1) and in Enisey Gulf (2.32 and
2.19 mgC gsoil-1 d-1 at 0–5 and 5–9 cm, respectively; Table 1). Among the surface soil
samples, the lowest mineralisation rate was found in Belyi Island
(1.57 mgC gsoil-1 d-1). In the three sites were different depths were
measured, located at Enisey Gulf and Haranasale Cape, the mineralisation
rates decreased with depth, and were minimal in the mineral deep horizon
(20–30 cm) of the Hystic Gleysol in Haranasale (0.58 mgC gsoil-1 d-1). During the following weeks of incubation
(9 for Yavay Cape, Enisey Gulf and Haranasale-1, and 4 for Belyi
Island and Haranasale-2 sites), mineralisation rates remained constant, so
that CO2 degassing increased linearly over time (adj r2
> 0.860, p < 0.05) in all sites without reaching a
saturation point.
In order to better understand the relationship between SOM biodegradability
and its chemical quality, a multivariate linear regression analysis was
performed, introducing the variables related to bulk SOM properties (total
carbon content, pH, E4 / E6 ratio), as well as those related specifically with
the humic acids (elemental composition and the carbon structures revealed by
13C-NMR spectra). Using best subset selection, it was found that these
qualitative variables did not significantly improve the predictability
provided by the univariate linear regression with total carbon content (the
inclusion of new variables to the model increased the Cp).
Multivariate analysis of soil organic matter properties
The spatial distribution of the carbon structural diversity of humic acids
in the Gydan Peninsula was explored using an NMDS analysis. The fitted NMDS
ordination (fit-based r2= 0.999, non-metric fit r2= 1, Fig. 6),
defined a gradient from aromatic (Haranasale-2 and Gyda Yurivey Gulf sites)
to aliphatic carbon predominance (Belyi Island, Enisey Gulf and Haranasale-1
sites) on the primary axis. An additional vector fitting analysis (variables
significant at p < 0.01) onto the NMDS ordination revealed that the
secondary axis created an oxygenation gradient between a predominance of C % and H % on
the positive side (r2= 747, p < 0.01 and r2= 0.772,
p < 0.01, respectively), and O % and O / C % on the negative side
(r2= 0.814, p < 0.01 and r2= 0.778, p < 0.01,
respectively). According to that, the humic acids of Yavay Cape, and upper
layers of Enisey Gulf and Haranasale-2 are related to low levels of
oxygenation, whereas those of Haranasale-1 and the deep layer of Haranasale-2
are related to higher levels. Furthermore, the positive secondary axis
grouped sites with higher pH (r2= 0.6074, p < 0.05) and total
mineralisable carbon (r2= 0.6655, p < 0.05). Interestingly, the
N elemental composition and C / N ratio of humic acids where not significantly
correlated within the NMDS ordination. Similarly, the optical index E4 / E6
and the total carbon content were found not to be related with the
distribution of carbon structures defined by 13C-NMR spectra.
Discussion
In this study we performed a detailed characterisation of SOM from the Gydan
Peninsula, an Arctic region in Western Siberia whose soils remained, to
date, unexplored. Total carbon quantification was complemented with a
multi-proxy approach to determine SOM composition from multiple
perspectives: elemental analysis, functional composition and optical
characteristics. These methodologies consistently showed that soils of the
Gydan Peninsula contain carbon stocks which are in the range of the latest
estimations (Hugelius et al., 2014).
Moreover, most of this carbon is contained in aliphatic carbon structures
which have little compositional variability, both regionally and in soil
depth. Finally, soil incubations highlighted the high mineralisation
potential of these carbon compounds.
Non-metric multidimensional scaling (NMDS) analysis of humic acids
based on their carbon functional groups, derived from 13C-NMR spectral
integration. Dark-grey and light-grey groups indicate aliphatic and aromatic
carbon structures, respectively. Carbon functional group symbols are
specified in Sect. 4.3. Numbers in parentheses indicate the sampled soil
depth in centimetres.
Total soil carbon content
Hugelius et al. (2014) estimated that, in the region of the Gydan Peninsula,
there are 15–30 gC m-2 in the 0–30 cm soil layer. In this study, we did
not sample to 30 cm depth at most sites. However, if we integrate the
carbon stocks in the first 10 cm that we sampled, then we can do some
estimative comparison. According Hugelius et al. (2014) values, in the first
10 cm layer (which is the range sampled in this study), there should be a
minimum of 5–10 gC m-2 (if we consider that SOM is evenly vertically
distributed) and a maximum of 15–30 gC m-2 (if we consider that all SOM
is accumulated at the upper part), considering that SOM content is usually
higher at the surface and decreases with depth. According to our data, in the
first 10 cm depth there would be stocks within this range (9.53 to 19.18 gC m-2). Therefore, our results are in agreement with current estimations
of Arctic SOC stocks based on spatial extrapolations.
Stability of SOM
Results on SOM elemental analysis and 13C-NMR spectroscopy revealed
very low levels of compositional variability, both regionally and in depth.
The main drivers of soil humic acids' composition have been identified as the
quality of the precursor materials (Andersson
et al., 2012), followed by the climatic conditions (i.e. temperature and
moisture) which shape the biophysical conditions necessary for humification
to take place (Zech et al., 1997). Hence,
the regional homogeneity in SOM and humic acids' composition observed in this
study reflects, on the one hand, the fact that all sites are covered by
similar tundra plant communities. The main vegetation types are grasses,
mosses and lichens, rich in aliphatic compounds such as carbohydrates,
lipids and proteins. By contrast, the source of lignins – main precursor
molecules for the synthesis of humic substances
(McDonald et al., 2004) – is restricted to
sparse bushes and, therefore, the low presence of lignins would be a main
restriction for the formation of humic acids, hence favouring the presence
of aliphatic compounds. This is reflected in our findings, which revealed
the presence of more than 60 % of aliphatic carbon structures in all
sites.
Further, prolonged cold temperatures and high moisture conditions typical of
the Arctic climate would limit the progress of humification
(Weintraub and Schimel, 2003) and keep organic
molecules in soils preserving to a large extent their original chemical
properties. This preservation effect from humification explains the vertical
homogeneity observed in the chemical composition of humic acids, as revealed
by 13C-NMR spectra. This vertical homogeneity was most prominent for
the aromatic functional groups, indicated by a low standard deviation in
those resonance shift regions corresponding to aromatic carbon,
alkylaromatic and carboxyl-carbonyl C groups (108 to 170 ppm). This
vertically constant aromatic signal has been described to be typical of
initial stages of the humification process
(Zech et al., 1997). By contrast, the
aliphatic fraction showed some vertical patterns. The main variability was
found in the resonance regions of alkyl-C, methoxyl and O-alkyl C groups
(at 27, 55 and 70 ppm). However, the patterns in depth were not consistent
among sites. This may be due to the presence of cryoturbation, a common
process in Cryosols (Bockheim and Tarnocai, 1998), which
hinders the vertical distribution of SOM quality to be comparable among
sites. However, such variability reflects the lower stability that aliphatic
compounds have in comparison to the aromatic ones, being more prone to
microbial processing. The functional composition of humic acids in our sites
were consistent with those previously reported for Arctic tundra soils from
Alaska
(Dai
et al., 2001; Ward and Cory, 2015) and Eastern Siberia
(Abakumov et al., 2014), sharing the common fact that there was
a predominance of alkyl-C and O-alkyl C groups over aromatic and
carboxyl–carbonyl carbons.
High sensitivity of SOM to mineralisation
Low aromaticity has been considered to be indicative of a higher
availability of SOM to mineralisation. Hence, a number of proxies tracing
SOM aromaticity have been used to infer its lability degree. Both the
elemental composition and optical properties have been interpreted in terms
of lability (Kalbitz et
al., 2003). Further, Dai et al. (2001) found carboxyl–carbonyl C groups to
be correlated with SOM mineralisation. However, we did not find any
correlation between the total mineralisable carbon and any of the measured
qualitative SOM variables. By contrast, the total mineralisable carbon was
found to be related only to the total carbon content in soil. This lack of
relationship between SOM mineralisation and SOM quality might be attributed
to two reasons: (a) the presence of a high proportion of highly labile SOM
compounds, which may be hindering a quality-related limiting effect, and (b)
the qualitative variability in our samples would be too low to recreate any
relationship between aromaticity and mineralisation. The former reason could
be proved using longer-term incubations, as some limiting effects may be
observed once the most highly labile pool of SOM is depleted. Evidence in
this line arose in some recent papers which provided unique data on
long-term permafrost soil incubations
(Elberling et al., 2013; Schädel et
al., 2014). This unprecedented data revealed a major role of C / N on the
landscape-scale variation of SOM mineralisation
(Schädel et al., 2014). Even though we did not find a
significant relationship between C / N and SOM mineralisation, we did find a
significant correlation between C / N and H / C (i.e. level of conjugation of
humic acids), providing indirect evidence of the influence of C / N on SOM
biodegradability. Overall, our results indicate that the mineralisation rate
of SOM from the Gydan Peninsula is not limited by qualitative aspects, at
least at early decomposition stages. Moreover, longer incubation times would
be needed in order to further identify specific qualitative aspects of SOM
that control its biodegradability on longer timescales.
Conclusions
This study presents, for the first time, a detailed characterisation of the
humic acids of soils from the Gydan Peninsula, Western Siberia. Thereby, it
expands the regional coverage of recent local-scale studies of SOM lability
in permafrost soils in periglacial environments, what may contribute to
better depict the regional variability of humic acids' characteristics across
the Arctic region.
Our results reveal that the total carbon content of soils of this region is
in agreement with latest regional estimates of Arctic SOC stocks. Moreover,
we found that SOM compounds are predominantly aliphatic (more than 60 %)
and that their elemental and functional composition remains highly constant,
both regionally and in depth. This aliphatic compositional homogeneity
reflects common vegetation cover (which supplies restricted lignified
material), together with common cold climatic conditions across the
studied region (which keep humification rates low).
Our results also suggest that the mineralisation rate of this
highly aliphatic material depends primarily on the total quantity of organic
carbon in soil, while qualitative aspects (elemental and functional
composition, and optical properties) were not found to have a significant
influence. We argue that this may be due to a high predominance and
availability of labile compounds, so that during our short-term incubations
(weekly scale) compositional aspects did not play a limiting role of the
mineralisation rate.
Overall, we conclude that soils of the Gydan Peninsula contain SOM whose
composition is highly stable at present climatic conditions. However, this
stability can be jeopardised in the future by increasing warming
temperatures which can induce the mineralisation of high amounts of stored
labile organic compounds.