SOILSOILSOILSOIL2199-398XCopernicus PublicationsGöttingen, Germany10.5194/soil-2-551-2016Leaf waxes in litter and topsoils along a European transectSchäferImke K.imke.schaefer@giub.unibe.chLannyVerenaFrankeJörghttps://orcid.org/0000-0001-7897-4225EglintonTimothy I.ZechMichaelVysloužilováBarboraZechRolandInstitute of Geography and Oeschger Centre for Climate Change Research, University of Bern, 3012 Bern, SwitzerlandDepartment of Earth Science, ETH Zurich, 8092 Zurich, SwitzerlandLandscape- & Geoecology, Faculty of Environmental Sciences, Technical University of Dresden, 01062 Dresden, GermanyInstitute of Agronomy and Nutritional Sciences, Soil Biogeochemistry, Martin Luther University Halle-Wittenberg, 06120 Halle, GermanyInstitute of Archaeology of Academy of Science of the Czech Republic, Letenská 4, 11801 Prague 1, Czech RepublicLaboratoire Image, Ville, Environnement, UMR7362, CNRS/Université de Strasbourg, 67083 Strasbourg CEDEX, FranceImke K. Schäfer (imke.schaefer@giub.unibe.ch)25October20162455156420May201630May201610October201613October2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://soil.copernicus.org/articles/2/551/2016/soil-2-551-2016.htmlThe full text article is available as a PDF file from https://soil.copernicus.org/articles/2/551/2016/soil-2-551-2016.pdf
Lipid biomarkers are increasingly used to reconstruct past environmental and
climate conditions. Leaf-wax-derived long-chain n-alkanes and n-alkanoic
acids may have great potential for reconstructing past changes in vegetation,
but the factors that affect the leaf wax distribution in fresh plant
material, as well as in soils and sediments, are not yet fully understood and
need further research. We systematically investigated the influence of
vegetation and soil depth on leaf waxes in litter and topsoils along a
European transect. The deciduous forest sites are often dominated by the
n-C27 alkane and n-C28 alkanoic acid. Conifers produce few
n-alkanes but show high abundances of the C24n-alkanoic acid.
Grasslands are characterized by relatively high amounts of C31 and
C33n-alkanes and C32 and C34n-alkanoic acids. Chain
length ratios thus may allow for distinguishing between different vegetation
types, but caution must be exercised given the large species-specific
variability in chain length patterns. An updated endmember model with the new
n-alkane ratio (n-C31+n-C33) / (n-C27+n-C31+n-C33)
is provided to illustrate, and tentatively account for, degradation effects
on n-alkanes.
Introduction
To improve our understanding of ongoing environmental changes and to predict
consequences of future climate change more precisely, it is important to
investigate the magnitude of, and interactions between, climate and
environmental variations in the past. Lipid biomarkers are well preserved in
many geological archives and are increasingly used for palaeoclimate and
palaeoenvironmental reconstructions (Eglinton and Eglinton, 2008). Long-chain
n-alkanes (> C25) and n-alkanoic acids
(> C20), for example, are essential constituents of
epicuticular leaf waxes and thus serve as specific biomarkers for higher
terrestrial plants (Eglinton et al., 1962; Eglinton and Hamilton, 1967; Otto
and Simpson, 2005).
Leaf wax n-alkanes typically show an odd-over-even predominance
(OEP; Eglinton and Hamilton, 1967). The relative odd homologue abundance may
be useful to discriminate between different vegetation types: C27 and
C29 have been reported to be predominant in leaf waxes of trees and
shrubs, whereas C31 and C33 mostly derive from grasses and herbs
(Maffei, 1996; Maffei et al., 2004; Rommerskirchen et al., 2006; Zech et
al., 2009; Lei et al., 2010; Kirkels et al., 2013). From this distribution,
various n-alkane ratios have been proposed that allow estimating
the main contributing vegetation type to a palaeosample (Schwark et al.,
2002; Zech et al., 2009; Lei et al., 2010; Schatz et al., 2011; Wiesenberg
et al., 2015). Where pollen grains are preserved, leaf wax and pollen records are in
good agreement (e.g. Brincat et al., 2000; Schwark et al., 2002; Zech et
al., 2010; Tarasov et al., 2013). In contrast, a recent study that
summarizes n-alkane patterns in modern plants from all over the
world showed no discrimination power for vegetation reconstruction at a
global scale (Bush and McInerney, 2013), so regional calibration studies may
be more appropriate. Additionally, the accuracy of n-alkane records
remains somewhat uncertain due to several potential pitfalls:
Leaf wax production and concentration can vary widely between species
(e.g. Diefendorf et al., 2011; Bush and McInerney, 2013). Moreover, species
abundance in an ecosystem controls leaf wax signals in soils and sediment.
Several studies reported long-chain n-alkanes not only in
leaves but also in other plant parts (roots, stems, blossoms;
e.g. Wöstmann, 2006; Jansen et al., 2006; Kirkels et al., 2013; Gocke et al.,
2013), but in these studies the patterns show preferential synthesis of
shorter chains (<n-C25) with low OEPs, as well as much lower
n-alkane concentrations (3 to 10 times) than in leaves.
Leaf waxes are affected by mineralization and degradation (e.g. Zech et
al., 2009, 2011a; Nguyen Tu et al., 2011). As OEP values become
lower during degradation, Zech et al. (2009) and Buggle et al. (2010)
proposed procedures to quantify and correct n-alkane ratios for
degradation using the OEP.
Apart from vegetation type, many environmental parameters may influence
leaf wax patterns, for example temperature and precipitation (e.g. Poynter
et al., 1989; Sachse et al., 2006; Tipple and Pagani, 2013; Bush and
McInerney, 2015), as well as radiation, nutrient and water availability,
salinity, mechanical stress and pollution (e.g. Shepherd and Wynne
Griffiths, 2006; Guo et al., 2014). Sachse et al. (2006) and Duan and He (2011)
found longer chain lengths at lower latitudes, which could indicate
(i) enhanced loss of shorter n-alkanes with increasing evaporation
and (ii) preferential production of long-chain n-alkanes, providing
better protection against evaporation at higher temperatures and radiation.
Schefuß et al. (2003) reported a higher n-C31
vs. n-C29 abundance in dust samples from drier regions along the
West African margin and suggested that humidity may be the driving factor.
Bush and McInerney (2015) showed a correlation between average chain length (ACL)
and temperature along a transect throughout the central United States
and concluded that temperature is directly responsible for the synthesis of
longer chain length.
So far, only few studies have investigated homologue long-chain
n-alkanoic acid patterns in plants, soils and sediments
(e.g. Almendros et al., 1996; Marseille et al., 1999; Bull et al., 2000b;
Zocatelli et al., 2012; Feakins et al., 2014; Wiesenberg et al., 2015). Leaf
wax n-alkanoic acids have a distinct even-over-odd predominance
(EOP; Eglinton and Hamilton, 1967). Zocatelli et al. (2012) found
n-C26 to be predominant in grassland soil relative to forest
soil, whereas the forest soils contained n-C22 to
n-C28 in greater amounts. Since n-alkane patterns
alone seem to not always allow a reliable conclusion about the dominant
vegetation type, there is an urgent need to more systematically investigate
and understand the factors controlling the homologue n-alkanoic acid patterns.
In order to contribute to a better understanding of the various factors
controlling leaf wax patterns, we collected and analysed litter and topsoil
samples in deciduous and coniferous forests and from grasslands along a
transect in central Europe.
Specifically, our study aims to evaluate (1) the role of different vegetation types for n-alkane and
n-alkanoic acid chain length patterns and (2) the effects of degradation on leaf wax patterns.
Sampling of litter and topsoil samples rather than individual plants or plant
parts has the advantage of integrating over the whole ecosystem and
implicitly taking into account variable production and concentration of leaf
waxes between species, individual plants and plant parts. Such an approach
also implicitly accounts for the fact that vegetation types generally do not
totally dominate a specific ecosystem but mostly co-occur to a variable
degree. For example, we defined forests with a dominance > 80 %
of deciduous or coniferous trees as deciduous or coniferous forests,
respectively, and forests generally have some grass and herb understorey as
well. Our study will thus provide leaf wax patterns for ecosystems
dominated by specific vegetation types, and those patterns will be a sound
basis for comparing and interpreting leaf wax patterns from palaeosols or
sediments. Changes in leaf wax patterns from litter to the topsoil are
expected to indicate effects of degradation and microbial reworking of
organic material in the topsoil. Differences in climatic conditions along
the transect are not very pronounced, so this paper cannot focus on potential
direct climatic effects on leaf wax patterns.
Material and methodsGeographical setting and sampling
In 2012 and 2014, we collected litter (L) and topsoil samples (Ah1: 0–3 cm;
Ah2: 3–10 cm) from 26 locations along a transect in central Europe (Fig. 1).
Samples BRO, HUB, HUG, HUM, HUR and KOC were kindly provided by
B. Vysloužilová; more information about sampling and the regional setting
of these locations can be found in Vysloužilová et al. (2015). The
study area is in general characterized by relatively mild temperatures and
moderate rainfall. The mean annual air temperature along the transect ranges
from 5.5 to 11.0 ∘C, and mean annual precipitation ranges from
470 to 1700 mm (see Table S1 in the Supplement for individual data and data source). Altitudes
range from 16 to 899 m a.s.l. (above sea level). The natural vegetation consists of
grasslands in the south of the transect and a higher amount of deciduous
broadleaf and mixed forests (varying percentages of deciduous trees and
conifers) in the north. Also, the proportion of evergreen conifers increases northwards.
We sampled soils in forests with a dominance of deciduous and coniferous
trees, as well as soils below grasslands (referred to as
“dec”, “con” and “grass” in the following text and figures).
Photographs of the sampling sites and descriptions of the dominant
vegetation are provided in Tables S2 and S1, respectively. For the dec
and con sites, we were able to collect litter samples, but the grass sites
had virtually no litter. Sampling sites were chosen in forests with large
old trees, indicating a stable environment for more than approximately 30
years. This limits the risk that a vegetation change might have influenced
the leaf wax signal in the soil, as leaf waxes are stable over long time
periods (e.g. Derenne and Largeau, 2001). Please note that our grass sites
can be dominated by grasses, herbs or heaths. Soil and litter samples at
each site were composites from three sampling points approximately 5 to
7 m apart from each other.
Lipid analysis
Lipids were extracted from 1–6 g freeze-dried and ground samples by
microwave extraction with 15 mL of dichloromethane (DCM)/methanol (MeOH) (9 : 1)
at 100 ∘C for 1 h. Each total lipid extract was passed over a
pipette column filled with aminopropyl silica gel as the stationary phase
(Supelco, 45 µm). The apolar fraction (including n-alkanes) was
eluted with hexane, more polar compounds (e.g. alcohols) with DCM/MeOH
(1 : 1), and acids (including n-alkanoic acids) with 5 % acetic acid in
diethyl ether. The n-alkanes were purified by passing the apolar
fraction over a pipette column filled with activated AgNO3 impregnated
silica gel (to retain unsaturated compounds) and another pipette column
filled with zeolite (Geokleen). After drying, the zeolite (containing
straight-chain compounds) was dissolved in HF and the n-alkanes
were recovered by liquid–liquid extraction with hexane. For quantification,
the n-alkane fractions were spiked with a known amount of the
5α-androstane and analysed with an Agilent 7890 gas chromatograph (GC)
equipped with a VF1 column (30 m length × 0.25 mm i.d., 0.25 µm
film thickness) and a flame ionization detector (FID).
The n-alkanoic acids were converted to fatty acid methyl esters (FAMEs)
with MeOH/HCl (95/5; 70 ∘C, 8 h). The FAMEs were recovered
by liquid–liquid extraction with hexane and cleaned over silica, AgNO3
and zeolite columns as described above before quantification with GC-FID.
For quantification of the FAMEs, 5α-androstane was again used as an
internal standard. Unfortunately, due to some problems during FAME
preparation, not all samples were available for methylation (missing
samples: BRO, HUB, HUG, HUM, HUR, KOC).
Sample locations (black dots) along the transect (map source: US National
Park Service, Esri, HERE, DeLorme, MapmyIndia, OpenStreetMap contributors and
the GIS user community).
Leaf wax proxies
Total n-alkane and n-alkanoic acid concentrations (ctot) were
calculated as the sum of C25 to C35 and C20 to C34 (odd as well
as even ones), respectively, and given in µg g-1 dry weight (dw).
Total concentrations of (a)n-alkanes and (b)n-alkanoic
acids in µg g-1 dry weight. Abbreviations: con, coniferous forest
sites (n= 9); dec, deciduous forest sites (n= 14); grass, grassland sites
(n= 22); L, litter; Ah1, topsoil 1 (0–3 cm); Ah2, topsoil 2 (3–10 cm).
Box plots show median (red line), interquartile range (IQR) with upper
(75 %) and lower (25 %) quartiles, lowest datum still within 1.5 × IQR
of lower quartile, and highest datum still within 1.5 × IQR of upper quartile.
Note that the y axis is logarithmic.
Changes in the average chain length (ACL) of n-alkyl lipids can
show changes in the input of vegetation type. The ACL was determined by
modifying the equation of Poynter et al. (1989). We used odd chain lengths
only for n-alkanes (Eq. 1) and even chain lengths for
n-alkanoic acids (Eq. 2).
ACL(n-alkanes)=27×n-C27+29×n-C29+31×n-C31+33×n-C33n-C27+n-C29+n-C31n-C33ACL(n-alkanoicacids)=24×n-C24+26×n-C26+28×n-C28+30×n-C30+32×n-C32n-C24+n-C26+n-C28+n-C30+n-C32
The OEP of the n-alkanes (Eq. 3) and
the EOP of the n-alkanoic acids (Eq. 4) can be
used as a proxy for degradation and were determined after Hoefs et al. (2002):
OEP=n-C27+n-C29+n-C31+n-C33n-C26+n-C28+n-C30+n-C32,EOP=n-C24+n-C26+n-C28+n-C30+n-C32n-C23+n-C25+n-C27+n-C29+n-C31.
High OEP values are characteristic of fresh plant material, while the OEP values decrease with
ongoing soil organic matter degradation in the topsoil (e.g. Buggle
et al., 2010; Zech et al., 2009, 2011b).
Statistical analysis
First, we tested whether the data were normally distributed (Shapiro and Wilk,
1965) and whether variances of the samples were equal (Levene, 1960). In the case of
normality and equal variances, we conducted an analysis of variance (ANOVA)
test or otherwise a Kruskal–Wallis test to check for significant differences
(α= 0.05) between depths horizons within the same vegetation type
or between vegetation types within the same horizon, respectively. If the
ANOVA/Kruskal–Wallis test indicated significant differences in the means, we
applied a “post hoc” test to identify which of the means differ,
accounting for the effect of multiple testing. The appropriate post hoc test
after ANOVA was selected as recommended by Field (2013): for samples with
equal size and equal variance, we applied the Tukey's honest significance
test (Tukey, 1949). In the case of equal variance and unequal sample size, we
used the Hochberg test (Hochberg, 1988) and for unequal variances the
Games–Howell test (Games and Howell, 1976). After Kruskal–Wallis tests, we
performed the non-parametric Conover–Iman post hoc test with a Bonferroni
adjustment of p values (Conover and Iman, 1979; Conover, 1999). This test is
similar to the well-known Dunn test (Dunn, 1964) but is based on the
t distribution instead of the z distribution. It is statistically more
powerful than the Dunn test and better suited for our small sample size.
ResultsLeaf wax n-alkane abundances and chain length patterns
All samples show a dominance of long (> C25) odd-chain
n-alkanes, characteristic for epicuticular leaf waxes
(e.g. Eglinton et al., 1962; Eglinton and Hamilton, 1967; Rieley et al., 1991;
Collister et al., 1994). Total n-alkane concentrations (Ctot)
range from 0.4 to 1468 µg g-1 dw (Table S3). Such concentrations and
huge variability are in agreement with published data from fresh plant
material (e.g. Diefendorf et al., 2011; Hoffmann et al., 2013) and from soil
and sediments (e.g. Marseille et al., 1999; Freeman and Colarusso, 2001;
Liebezeit and Wöstmann, 2009). Differences exist depending on the
vegetation type and litter/soil horizon (Fig. 2a) but are mostly not
significant (Table S4).
Chain length patterns for odd long-chain n-alkanes in (a) litter,
(b) Ah1 and (c) Ah2, as well as long- and even-chain n-alkanoic
acids in (d) litter, (e) Ah1 and (f) Ah2.
Box plots of (a)n-alkane ACL, (b) (n-C31+n-C33) / (n-C27+n-C31+n-C33) ratio,
and (c) OEP. Con: coniferous forest sites (n= 9); dec: deciduous forest
sites (n= 14); grass: grassland sites (n= 22); L: litter; Ah1: topsoil 1
(0–3 cm); Ah2: topsoil 2 (3–10 cm). Box plots show median (red line),
interquartile range (IQR) with upper (75 %) and lower (25 %) quartiles,
lowest datum still within 1.5 × IQR of lower quartile, and highest datum still
within 1.5 × IQR of upper quartile.
Differences in chain length patterns between deciduous forests, coniferous
forests and grasslands are illustrated in Fig. 3 for litter (a), Ah1 (b) and
Ah2 (c). The deciduous forest samples are strongly dominated by
n-C27, although its relative abundance decreases from litter
to Ah2. However, most of our sampling sites are dominated by beech trees
(L11, L13, L14, L16, L17, L18, L20 and L23) that are known to produce mostly
n-C27 (Bush and McInerney, 2013, and references therein). To
check whether the observed n-C27 dominance could be explained
only with the presence of beech we performed the same correlations and plots
as mentioned above, excluding the beech-dominated sites. Figure S5 in the Supplement shows
that the dominance of n-C27 is less pronounced here and disappears in Ah2. Grass sites are dominated by n-C31 and are also
characterized by high abundances of n-C33 compared to
deciduous sites. The con sites show a pattern very similar to that of our grass sites.
The ACLs of the grass and con sites are significantly higher than those of
the dec sites (Fig. 4a; grass: Ah1 = 30.5; Ah2 = 30.3; dec: Ah1 = 28.6;
Ah2 = 29.6; see Table S6 for p values). Without the beech-dominated sites,
the dec sites' ACL shifts to higher values, but they are still significantly
lower than mean ACLs of the grass sites (Fig. S7a). However, significant
differences disappear in Ah2 (Table S6). The dec litter samples have lower
ACLs (27.5) than the Ah1 (28.6) and Ah2 (29.6). Our grass samples show
almost no decrease in ACL from Ah1 (30.5) to Ah2 (30.3), and the con sites
likewise show no significant decrease from L to Ah2 (Table S6).
Box plots for (a)n-alkanoic acid ACL and (b) EOP. Con:
coniferous forest sites (n= 9); dec: deciduous forest sites (n= 14);
grass: grassland sites (n= 14); L: litter; Ah1: topsoil 1 (0–3 cm);
Ah2: topsoil 2 (3–10 cm). Box plots show median (red line), interquartile
range (IQR) with upper (75 %) and lower (25 %) quartiles, lowest datum
still within 1.5 × IQR of lower quartile, and highest datum still within
1.5 × IQR of upper quartile.
To study past changes in dec vs. grass vegetation, various n-alkane
ratios have been proposed and used (e.g. Zhang et al., 2006; Lei et al.,
2010; Bush and McInerney, 2013; Zech et al., 2013a, b). We
tested several n-alkane ratios (i.e. n-C33 / (n-C27+n-C33),
(n-C31+n-C33) / (n-C27+n-C31+n-C33), and
(n-C31+n-C33) / (n-C27+n-C29+n-C31+n-C33))
and found the largest differences between grass and dec samples for the
ratio (n-C31+n-C33) / (n-C27+n-C31+n-C33).
This ratio is low in the dec samples and high in the grass samples (Fig. 4b; dec L: 0.08;
Ah1: 0.29; Ah2: 0.56; grass Ah1: 0.79; Ah2: 0.78). Differences between
dec and grass are significant in Ah1 and Ah2 (Table S8). Without the beech-dominated sites, the ratio becomes higher for dec but still shows
significant differences between dec and grass (Fig. S7b, Table S8).
The OEP (or CPI, carbon preference index, which is very similar to the OEP)
is often regarded as a proxy for the preservation status of the leaf-wax-derived n-alkanes (e.g. Huang et al., 1996; Tipple and Pagani,
2010; Vogts et al., 2012; Wang et al., 2014, and references therein). High
OEPs are characteristic of fresh plant material and modern soils (Kirkels
et al., 2013; Diefendorf et al., 2011; Collister et al., 1994), whereas low
OEPs indicate degradation of n-alkanes during pedogenesis and early
diagenesis (Marseille et al., 1999; Freeman and Colarusso, 2001; Buggle et
al., 2010; Zech et al., 2011a; Wang et al., 2014). OEPs in the samples range
from 3 to 32.8, typical for fresh plant material and soils (Table S3).
Values significantly decrease from litter (18.4) to Ah1 (12.1) and Ah2 (6.8)
for the dec sites, and a minor decrease can be observed in the grass sites
(Fig. 4c, Table S9). Significant differences occur between dec and grass sites in
all horizons (Table S9).
Leaf wax n-alkanoic acid abundances and chain length patterns
All samples show high abundances of long (> C20) even-chain
n-alkanoic acids (Table S10), characteristic of epicuticular leaf
waxes (Eglinton and Hamilton, 1967). Many samples also have large amounts of
C16 and C18, yet those are ubiquitous and cannot be considered as
leaf wax biomarkers. Total n-alkanoic acid concentrations
(ctot refers here to the sum of C20 to C34) range from 3 to
854 µg g-1 dw, consistent with previous studies (e.g. Marseille et al.,
1999; Jandl et al., 2002). As for the n-alkanes, total
n-alkanoic acid concentrations vary between the vegetation types
and horizons (Fig. 2b). In general, ctot decreases from litter to Ah1
and Ah2. In contrast to the n-alkanes, the highest n-alkanoic
acid concentrations occur in our con samples. Decrease from litter to Ah1
and Ah2 are only significant in the dec samples (Table S11).
The n-alkanoic acid chain length patterns show differences between
the different vegetation types (Fig. 3d: litter; 3e: Ah1; 3f: Ah2). While
the dec samples are dominated by n-C28, the con samples show a
maximum for the shorter homologue n-C24 in the litter, Ah1 and
Ah2. The grass sites have high abundances of n-C24 to
n-C30, but when compared to dec and con, the relative high
n-C32 and n-C34 abundances are distinct.
Box plots for (a) indices C, (b) D and (c) G. Con: coniferous
forest sites (n= 9); dec: deciduous forest sites (n= 14); grass: grassland
sites (n= 14); L: litter; Ah1: topsoil 1 (0–3 cm); Ah2: topsoil 2
(3–10 cm). Box plots show median (red line), interquartile range (IQR) with upper
(75 %) and lower (25 %) quartiles, lowest datum still within 1.5 × IQR
of lower quartile, and highest datum still within 1.5 × IQR of upper quartile.
Ternary plots for the CDG indices: (a) litter, (b) Ah1 and
(c) Ah2, as well as (d) means for vegetation types. Each point represents the mean of
litter, Ah1 and Ah2, with regard to con, dec and grass.
Significant differences in the ACL between the three vegetation types exist
and reflect the above-mentioned predominance of various homologues (Fig. 5a,
Table S12). While the grass sites show a tendency towards higher ACLs, the con
sites tend to have the lowest values. Differences stay significant, even in
Ah1 and Ah2 (Table S12).
On the basis of the above-mentioned significant differences between the ACL
and the three vegetation types we propose the following three indices,
referred to as CDG indices (Eqs. 5–7) for coniferous forests (C), deciduous forests (D)
and grasslands (G):
IndexC=n-C24n-C24+n-C28+n-C32+n-C34,IndexD=n-C28n-C24+n-C28+n-C32+n-C34,IndexG=n-C32+n-C34n-C24+n-C28+n-C32+n-C34.
Index C ranges from 0.25 to 0.91, with the highest values for the con sites
(Fig. 6a), and shows significant differences between our con and dec samples,
as well as between the con and grass in all horizons, but not between the
dec and grass locations (Table S13). Index D ranges from 0.03 to 0.62 and
has highest values for the dec sites (Fig. 6b). It differs significantly
between all vegetation types in L, Ah1 and Ah2, except for con and grass
sites in Ah2. It also significantly decreases from L to Ah1 and Ah2 in the
dec sites (Table S14). Index G ranges from 0.00 to 0.37 and discriminates
between forest and grass sites, with systematically higher values for the
grass sites (Fig. 6c). Like index D, index G shows significant differences
between all three vegetation types in all horizons, except for the con and
dec locations in Ah1. The index likewise shows a significant decrease from L
to Ah2 in the dec samples (Table S15). The CDG indices can conveniently be
plotted in ternary diagrams, which illustrate the clusters for the different
vegetation types and the scatter within the cluster (Fig. 7).
EOPs in our samples range from 1.2 to 9.9 (Table S16), typical for
n-alkanoic acids that originate from epicuticular leaf waxes and
are found in soils (Killops and Killops, 2005). The EOP decreases
significantly from litter to Ah1 and Ah2 in the dec samples (L: 4.3; Ah1: 3.46;
Ah2: 2.85; Fig. 5b, Table S16), and without significance in the con
samples (L: 4.0; Ah1: 3.8; Ah2: 2.81).
Discussionn-alkane pattern in litter and topsoil
The lower ctot values of the con litter samples compared to the dec
litter (median con: 30.9 µg g-1 dw; dec: 80.4 µg g-1 dw) are in
good agreement with findings of much lower n-alkane abundances in
conifer needles than deciduous leaves (e.g. Sachse et al., 2006; Zech et
al., 2009; Diefendorf et al., 2011; Norris et al., 2013; Tarasov et al.,
2013) and in forest soils below conifers (e.g. Almendros et al., 1996).
Given these low reported n-alkane concentrations in conifer
needles, we ascribe the n-alkane patterns in the con litter and
topsoil samples to the n-alkane input from the understorey, and we
focus in the following discussion on the differences between grass and dec
sites. Our grass soils have very low n-alkane abundances (median
Ah1, 3.5 µg g-1 dw), which we interpret to be an artefact of (former)
plowing and admixture with inorganic soil material. Based on the data we
therefore cannot infer a low n-alkane production in grass sites.
n-alkanes to distinguish between vegetation types
The domination of n-C27 in our dec samples in all horizons,
and the relatively high concentration of n-C31 and
n-C33 in the grass samples, implies that the established
source-specific compounds, at least along the transect, allow for conclusions to be made
regarding the vegetation type that generated them. However, Fig. S5 proves that the
pattern is less specific when the beech-dominated sites are excluded. This
supports former results and shows that n-C27 is strongly
produced by beech trees (Bush and McInerney, 2013, and references therein).
The ACL and our proposed n-alkane ratio of
(n-C31+n-C33) / (n-C27+n-C31+n-C33)
show significant differences between the dec and the grass sites in Ah1 and
Ah2, even when the beech-dominated sites are excluded (Tables S6 and S8).
Although n-C27 is not the dominant long odd-chain
n-alkane in Ah2 at the dec locations that are not dominated by
beeches, its percentage in the dec samples is still higher than at the grass
sites (Fig. S5), whereas the percentage of n-C31 and
n-C33 is the highest in the grass sites in Ah1 as well as in
Ah2. Therefore, our results corroborate, at least for the studied transect, that the
ACL and our proposed n-alkane ratio of
(n-C31+n-C33) / (n-C27+n-C31+n-C33)
allow for differentiation between the input of dec and grass vegetation. Care
has to be taken when interpreting palaeovegetation changes solely on the
dominance of one n-alkane compound over the others (e.g. proxies
like Cmax; Wiesenberg et al., 2015), because this might lead to an
underestimation of the deciduous tree input, at least when beech trees were
not the main contributors to the soil. Nevertheless, we strongly emphasize
that the observed patterns are very likely a regional phenomenon and our
results should not be transmitted to other regions with different climate
and vegetation types, because n-alkane patterns do not work on a
global scale (Bush and McInerney, 2013). Thus, our results underline the
need for regional calibrations for the n-alkane pattern, because
they corroborate its potential for palaeovegetation reconstruction on a
regional base.
Although significant differences occur between the dec and grass sites OEP
in Ah1 and Ah2, we would not recommend using the OEP as a proxy to
distinguish between the two vegetation types, because it can very likely
show significant decreases with increasing soil depth, as it does in the dec
samples (Table S9), so it is probably strongly influenced by degradation and
microbial reworking.
Influence of soil depth on the n-alkane pattern
Although we observed no significant decreases in Ctot from L to Ah1 and
Ah2 for the dec and con sites, as well as from Ah1 to Ah2 for all three vegetation
forms (Table S4), the slightly decreasing trends in Fig. 2a are most
likely due to lipid degradation and admixture with inorganic soil material.
As stated above, the significant decrease in OEP from L to Ah2 in the dec
samples (Fig. 4c, Table S9) is probably due to degradation effects. Therefore,
despite the wide range of OEPs in modern plants (Bush and McInerney, 2013,
and references therein), the OEP can serve as a degradation proxy along our
transect. The OEPs in the grass samples show no significant decrease from
Ah1 (7.2) to Ah2 (6.5), but the degradation effects here are probably biased
by plowing and mixing of Ah1 and Ah2.
The decrease in the relative percentage of the dominant n-alkane(s)
in the dec and grass samples from L to Ah1 and from Ah1 to Ah2 (Fig. 3a–c)
as well as the significant increase in ACL from L to Ah2 in the dec samples
(Fig. 4a, Table S6) is probably another indication of the effect of
degradation on the n-alkane pattern. Our grass samples show an
insignificant but decreasing trend in ACL from Ah1 (30.5) to Ah2 (30.3).
These observed changes are consistent with the notion that the more abundant
homologues are preferentially degraded and lost during pedogenesis. This
affects n-alkane patterns in soils and sediments (Zech et al.,
2009, 2013a, b). Degradation also affects our
(n-C31+n-C33) / (n-C27+n-C31+n-C33)
ratio, which is expressed in the significant increases in the dec samples from
litter (0.08) to Ah1 (0.3) to Ah2 (0.56) and in the slightly decreasing
trend in the grass samples from 0.84 to 0.80 (Fig. 4b, Table S8).
Endmember plot modified after Zech et al. (2013b). Degradation
lines refer to the complete dataset. Samples that deviated markedly from the
degradation lines are labelled and discussed in the text.
In order to illustrate and correct for degradation effects, Zech et al. (2009)
proposed an endmember model, which was later modified by Zech et al. (2013a, b).
We added the dec and grass samples to the
dataset for Europe, provided by Zech et al. (2013b). Figure 8 shows the new
endmember plot and illustrates that our n-alkane ratio differs
between grass and dec samples and that it changes depending on the OEP,
i.e. with degradation. As already described above, the n-alkane ratio is
wider for grass samples and lower values are more typical for dec. With
increasing degradation, differences seem to become less: the trend lines, or
“degradation lines”, for grass and dec converge. In principle, the
endmember model allows for degradation effects to be tentatively corrected for and
for the contribution of grasses versus deciduous trees in
(palaeo)samples to be quantified: the equations in Fig. 8 are used to calculate the grass and
tree endmember for a specific OEP, and following the rule of proportion
% grass can then be estimated as
%grass=n-alkaneratiosample-equationdegradation line treesequationdegradation line grass-equationdegradation line trees.
Again, we tested whether the endmember plot can be explained only by the
presence of beech by applying the same model and plot without the beech-dominated sites. The dec degradation line shifts upward, closer to the grass
degradation line (Fig. S17), and R2 drops from 0.3 to only 0.1, but
it still shows a separation of the dec samples from the grass samples. Four
sites plot particularly high above the dec degradation line and deserve a
closer look. Sample location L19 is a birch forest surrounded by fields, and
L26-2 dec is an open forest with birch and oak trees, with a larger number of grasses
in the understorey. For both sites, the n-alkane ratios for Ah1 and
Ah2 plot much closer to the grass degradation line than the litter samples,
and we speculate that both sites may have been grasslands in the past that
were only recently reforested. Since turnover times of n-alkanes
are in the order of decades (e.g. Amelung et al., 2008, and references
therein; Wiesenberg et al., 2004) we would expect to see the
n-alkane pattern prior to reforestation in the upper soil.
Unfortunately, we do not have information about former land use at the study
locations to verify this speculation. The sample locations L22 (acer, elder,
ash, poplar) and L26-1 (acer, oak, beech, fir) are characterized by litter
samples that plot close to the grass degradation line. We cannot exclude the
possibility that these litter samples and sites are affected by n-alkane input
from grasses, but likely the data simply reflect the large species-specific
variability in n-alkane patterns reported repeatedly in the
literature (e.g. Diefendorf et al., 2011; Bush and McInerney, 2013).
In summary, our results show that (1)n-alkane patterns are systematically different between the
investigated dec and grass sites, (2)
soil depth/degradation affects the homologue patterns, and (3)
endmember modelling is a useful tool for palaeovegetation
reconstruction along the transect, but one needs to be aware of the
uncertainties related mainly to the large species-specific variability in
the n-alkane patterns. However, the fact that coniferous trees
produce only a few n-alkanes makes respective palaeovegetation
reconstructions “blind” for coniferous trees.
n-alkanoic acid pattern in vegetation and topsoil
To the best of our knowledge, this is the first study which systematically
investigates long-chain n-alkanoic acid patterns in litter and
topsoil along a transect that encompasses a range of environmental
conditions and vegetation types. Since differences in con concentration
compared to dec are much more pronounced in the topsoil than in the litter,
we infer that better preservation of n-alkanoic acids in soils
under coniferous forests is the reason for the observed differences, and not
higher alkanoic acid production by conifers. This is further consistent with
studies showing better preservation of alkanoic acids in soils with low pH
typical for coniferous forests, while n-alkanes are better
preserved in soils with a high pH, more typical for deciduous forests (Bull
et al., 2000a; Zocatelli et al., 2012). We again attribute the low ctot
in the grass sites mainly to plowing and admixture with inorganic soil material.
n-alkanoic acids to distinguish between vegetation types
The n-alkanoic acid distribution in the vegetation types implies
that specific compounds can be used to characterize them (Fig. 3d–f).
The n-C24 alkanoic acid can represent the input of conifers in
L, Ah1 and Ah2; n-C28 shows the contribution of deciduous
trees in all horizons; and the relative amount of n-C32 and
n-C34 can be used to estimate the grass contribution. From
that, we suggest the CDG indices. They show strong differences between the
three vegetation types (Fig. 6, Tables S13–S15), which are significant in
nearly all horizons, apart from index C, which does not allow a distinction
between dec and grass sites (Table S13). The ternary plots of the three
indices visualize the discrimination potential by showing clusters for the
different vegetation types, although we must emphasize that outliers
exist (Fig. 7a–c). Index C based on the dominance of the shorter chain
n-alkanoic acid C24, which might be more strongly affected by
microbial degradation and reworking compared to the longer-chain
counterparts n-C28, n-C32 and
n-C34 that are included in the D and G indices (Sect. 4.2.2).
The ACL of the n-alkanoic acids, on the other hand, may not be a
particularly useful proxy for palaeovegetation because the observed
differences are small (although they are significant, Table S12) and mixing
a con and grass signal could falsely yield a dec signal.
Influence of soil depth on the n-alkanoic acid pattern
The significant decrease in Ctot of the dec and con samples from L to
Ah2 (Table S11) is most likely attributed to enhanced degradation effects on
the acids with increasing soil depth.
The preferential loss of n-C28 (n-C24) in the
dec samples (con samples) can be visualized by comparing the homologue
patterns of litter, Ah1 and Ah2 (Fig. 3d–f). This degradation effect is
not documented in a significant change in the ACL, and only the con sites show a
slight but non-significant increasing trend with increasing soil depth (Fig. 5a,
Table S12). The degradation effect of a certain homologue on the CDG
indices is illustrated in Figs. 6 and 7. Index D, for example, is high for
the dec litter samples but significantly decreases from litter to Ah1 and
Ah2 (Table S14). The same applies for index C and G with regard to the con
and grass samples, respectively, although the changes are not significant.
With the preferential loss of the most abundant compound
(n-C28 for dec and n-C24 for con), the respective
characteristic index decreases; however, the other two indices unavoidably
increase. This is illustrated by the clusters moving closer together (Fig. 7b–d).
Nevertheless, they still allow for discrimination between the three
vegetation types as at least index D and G show significant differences
between all three vegetation types in all horizons (Tables S14 and S15). All
three indices show a significant decrease (index D) or increase (index C and
index G) with soil depth in the dec samples, which implies that they are more
prone to degradation under the more alkaline deciduous forest soils. The
grass samples do not show changes in ACL or in the indices from Ah1 to Ah2,
which we again ascribe to plowing.
The significant decrease in EOP from litter to Ah1 and Ah2 in the dec
samples (dec litter: 4.3; Ah1: 3.46; Ah2: 2.85; Fig. 5b) resembles the
decrease in the OEP for the n-alkanes and suggests that the most
abundant (even-numbered) compounds are preferentially degraded during
pedogenesis. The decrease in EOP in the con samples is not significant,
which we again ascribe to a better preservation of n-alkanoic acids
in the acidic soils under conifers. Nevertheless, Fig. 5b indicates a
decreasing trend in the EOP in the con samples from L to Ah2, probably due
to slight degradation effects on the acids in the con Ah1 and Ah2 samples.
Grass samples do not show a trend in EOP, which is most likely because of
the plowing that affected these sites. Like the OEP, the EOP might thus
serve as a proxy for degradation.
Although our results demonstrate that the leaf-wax-derived
n-alkanoic acids in soils under coniferous forests are less prone
to degradation compared to soils under deciduous forests, the risk still
exists that the leaf wax contribution from coniferous trees to soil and
sedimentary archives might be underestimated when the alkanoic acid pattern
is not corrected for degradation. The same applies for the deciduous forests
and probably also for the grass sites.
Overall, our results show that
n-alkanoic acid patterns are significantly different between
the investigated dec, con and grass sites;
the specific CDG indices might be valuable proxies for
palaeovegetation;
degradation affects the homologue patterns and CDG indices, at least
in the dec samples, so that procedures to correct for degradation need to be
developed and tested.
Conclusions
We have systematically investigated leaf-wax-derived long-chain
n-alkane and n-alkanoic acid patterns in litter and top
soils along a European transect. Our findings are as follows:
Both compound classes show distinct differences depending on the type of
vegetation. The vegetation signal is not only found in the litter; it can
also be preserved to some degree in the topsoil. The grass sites contain
more n-C31 and n-C33 alkanes than the dec sites
but less n-C27. The ratio
(n-C31+n-C33) / (n-C27+n-C31+n-C33)
seems to be most suitable to distinguish between those two vegetation types
in our study area. Litter and soil samples in coniferous forests are
probably biased by the understorey, so vegetation reconstructions solely
based on the n-alkane pattern are blind for coniferous trees.
Nevertheless, the n-alkanes show a great potential for
palaeovegetation reconstruction along our transect, but the species-specific
absolute and relative variability in the homologue abundances need to be
taken into account.
We propose three n-alkanoic acid indices to distinguish
contributions from the three investigated vegetation types: index C is the
relative abundance of the C24n-alkanoic acid and represents
the input of coniferous trees. Index D is the relative abundance of the
C28n-alkanoic acid and is particularly high in litter and in
topsoil of deciduous forests. The relative abundance of the C32 and
C34n-alkanoic acids is expressed as index G and shows the
contribution from grasses and herbs.
The homologue patterns of leaf waxes change from litter to Ah1 and Ah2.
Although we cannot completely rule out effects related to possible land use
and vegetation change in the past, the overall consistent trends imply that
degradation plays an important role. Degradation not only lowers the OEP
and EOP of n-alkanes and n-alkanoic acids, respectively, but also reduces the vegetation-specific differences of the homologue
patterns. An updated endmember model is suggested to account for degradation
effects on n-alkanes, but similar procedures still need to be
developed and tested for the n-alkanoic acids before their
potential for palaeovegetation reconstructions can be fully exploited.
Overall, our findings suggest that combined investigations of
n-alkane and n-alkanoic acid distributions on a regional
scale have great potential for palaeovegetation reconstruction, although
degradation effects need to be taken into account. In particular, with regard
to the n-alkanoic acids, more research is needed to gain a better
understanding of those effects.
Data availability
The dataset we used in this paper is accessible via the Supplement. For
the endmember model we combined our dataset with the dataset published in Zech
et al. (2013b) at 10.1016/j.palaeo.2013.07.023.
The Supplement related to this article is available online at doi:10.5194/soil-2-551-2016-supplement.
Acknowledgements
We thank P. Neitzel, who contributed in large
part to the work in the field and in the laboratory at ETH Zurich, and
Q. Lejeune for support in the field, as well as C. Magill for scientific
discussions. C. Diebold helped with the laboratory work at the University of
Bern. We also acknowledge L. Wüthrich and M. Bliedtner for helpful
discussions. The research was funded by the Swiss National Science
Foundation (PP00P2 150590).
Edited by: R. Zornoza
Reviewed by: two anonymous referees
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