The composition of sediment organic matter (OM) exerts a strong control on biogeochemical processes in lakes, such as those involved in the fate of carbon, nutrients and trace metals. While between-lake spatial variability of OM quality is increasingly investigated, we explored in this study how the molecular composition of sediment OM varies spatially within a single lake and related this variability to physical parameters and elemental geochemistry. Surface sediment samples (0–10 cm) from 42 locations in Härsvatten – a small boreal forest lake with a complex basin morphometry – were analyzed for OM molecular composition using pyrolysis gas chromatography mass spectrometry for the contents of 23 major and trace elements and biogenic silica. We identified 162 organic compounds belonging to different biochemical classes of OM (e.g., carbohydrates, lignin and lipids). Close relationships were found between the spatial patterns of sediment OM molecular composition and elemental geochemistry. Differences in the source types of OM (i.e., terrestrial, aquatic plant and algal) were linked to the individual basin morphometries and chemical status of the lake. The variability in OM molecular composition was further driven by the degradation status of these different source pools, which appeared to be related to sedimentary physicochemical parameters (e.g., redox conditions) and to the molecular structure of the organic compounds. Given the high spatial variation in OM molecular composition within Härsvatten and its close relationship with elemental geochemistry, the potential for large spatial variability across lakes should be considered when studying biogeochemical processes involved in the cycling of carbon, nutrients and trace elements or when assessing lake budgets.
In lake basins, a wide range of factors are known to influence the transport and fate of sedimentary material, such as the location of inlet streams, catchment topography, land-use patterns, fetch, basin morphometry and sediment focusing. Sediment focusing results from a combination of factors such as wind and wave action, basin slope and the settling velocity of different particle sizes, which all contribute to the redistribution of light, fine-grained material rich in clays, organic matter (OM) and associated trace elements from shallower to deeper waters (Blais and Kalff, 1995; Ostrovsky and Yacobi, 1999). While sediment focusing is important, catchment and lake characteristics can be complex and exert a primary influence on spatial patterns in sediment geochemistry, such as in relation to land use in near-shore areas (Dunn et al., 2008; Vogel et al., 2010; Sarkar et al., 2014), complex lake basin morphometries (Bindler et al., 2001; Rydberg et al., 2012) or river inflows (Kumke et al., 2005). The presence of macrophytes or wind-induced water currents have also been shown to affect the spatial distribution of lead (Pb), phosphorus (P) and OM (Benoy and Kalff, 1999; Bindler et al. 2001).
Because trace metals and nutrients are primarily associated with – or are part of – OM, studies focusing on the spatial patterns of metal or nutrient accumulation typically include an analysis of the OM content. The two standard approaches to determine sediment OM content are the analysis of loss on ignition (LOI; Ball, 1964; Santisteban et al., 2004) or the analysis of elemental carbon (C). However, either approach inherently treats OM as a homogeneous sediment component. Recent studies interested in the role of lake sediments as a long-term C sink have likewise mainly treated OM and C as a homogeneous component (e.g., Sobek et al., 2003; Tranvik et al., 2009; Heathcote et al., 2015). Even if this approach is rational from a global perspective of calculating C budgets, treating OM as a homogeneous component is overly simplistic from the perspective of developing insights into the biogeochemical behavior of OM and its influence on C, nutrient, and trace metal cycling and does not take full advantage of the information provided by differences in the OM quality.
In boreal lakes the sediment composition is often dominated by OM, typically ranging from 20 to 60 % on a dry weight basis. Biogenic silica (bSi) may account for as much as 45 % of the sediment dry weight (Meyer-Jacob et al., 2014), and the remaining sediment mainly consists of detrital mineral matter and possibly authigenic minerals. Lake OM is an extremely heterogeneous and complex mixture of molecules that are derived from residues of plants, animals, fungi, algae and microorganisms, which are either transported into the lake from the surrounding catchment (allochthonous) or produced within the lake (autochthonous). Furthermore, these organic compounds may undergo transformations within the water column and the sediment through both biotic and abiotic processes. There have been a few studies where the spatial complexity in OM quality within a lake basin has been assessed using infrared spectroscopy, which yields qualitative information on variations in OM quality (Korsman et al., 1999; Rydberg et al., 2012), or quantitative analyses of photopigments and lipids (Ostrovsky and Yacobi, 1999; Trolle et al., 2009; Vogel et al., 2010; Sarkar et al., 2014). However, little work has been done to detail how the molecular composition of the sediment OM matrix varies spatially within a lake, considering a large number of organic biochemical classes and compounds.
To characterize OM composition at the molecular level, the most commonly used methods are based on liquid or gas chromatography (LC or GC) coupled to fluorescence or mass spectrometry (MS) detection. These methods provide quantitative data on original organic compounds found in the analyzed samples, including highly specific biomarkers of OM sources, for example, and have been successfully employed to study OM composition and reactivity in environmental matrices as well as to reconstruct environmental changes (e.g., changes in vegetation, algal productivity) from peat or sediment cores. However, the associated sample preparation procedures, i.e., extraction–hydrolysis and derivatization, are fastidious and specific to the different biochemical classes of organic compounds such as carbohydrates, proteins or amino acids, lipids, chlorophylls and lignin (e.g., Wakeham et al., 1997; Dauwe and Middelburg, 1998; Tesi et al., 2012). Moreover, sample masses > 10 mg are required. Hence, studies where different OM biochemical classes are targeted using these wet chemical extraction and GC/LC–MS methods are very scarce. However, efforts in characterizing the whole OM composition at the molecular level can bring important insights because the different biochemical classes of OM do not always include specific biomarkers for the different existing sources of OM (e.g., terrestrial plants, macrophytes, higher plants, mosses, algae, bacteria). For example, lignin oligomers are only specific of higher plants (Meyer and Ishiwatari, 1997) and proteins and amino acids mainly provide biomarkers for bacteria and planktonic production (Bianchi and Canuel, 2011). Moreover, the different biochemical classes of OM do not present the same reactivity; for example, proteins, amino acids and neutral carbohydrates have been shown to be among the most reactive organic molecules (e.g., Fichez, 1991; Dauwe and Middelburg, 1998; Amon and Fitznar, 2001; Tesi et al., 2012). Advanced ultra-high-resolution MS techniques, i.e., Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) or linear trap quadruple Orbitrap MS, enable the determination of a large number of organic molecular formulas in liquid samples (> 1000; e.g., Hawkes et al., 2016). These methods have been successfully used to link variability in the molecular composition of dissolved OM (DOM) with different factors and/or processes of environmental ecosystems, such as climate, hydrology and OM degradation in boreal lakes (Kellerman et al., 2014, 2015) or optical properties and DOM photochemical alterations in wetland and seawater (Stubbins and Dittmar, 2015; Wagner et al., 2015). However, in addition to the limited access to these advanced MS techniques due to instrumental costs, extraction–hydrolysis steps are required when studying solid samples, which also make these methods specific to the different biochemical classes of organic compounds.
To study the variability of OM composition in sediments, pyrolysis gas chromatography mass spectrometry (Py–GC–MS) is a good compromise between (i) the quantitative LC/GC–MS or the high-resolution MS methods that target specific compounds and (ii) the qualitative, nonmolecular information provided by high-throughput techniques such as infrared spectroscopy or “Rock-Eval” pyrolysis. Py–GC–MS analysis requires no complex sample preparation but yields semiquantitative data on > 100 organic compounds that are chemical fingerprints of the different OM biochemical classes, which include specific biomarkers for OM sources and degradation status (Faix et al., 1990, 1991; Peulvé et al., 1996; Nierop and Buurman, 1998; Schulten and Gleixner, 1999; Lehtonen et al., 2000; Nguyen et al., 2003; Page, 2003; Buurman et al., 2005; Fabbri et al., 2005; Kaal et al., 2007; Vancampenhout et al., 2008; Schellekens et al., 2009; Carr et al., 2010; Buurman and Roscoe, 2011; De La Rosa et al., 2011; McClymont et al., 2011; Micić et al., 2011; Stewart, 2012).
In the present study, we apply our newly optimized Py–GC–MS method to characterize the molecular composition of natural OM in surface sediments (0–10 cm) from 42 locations within the lake basin of Härsvatten. Härsvatten is a small boreal forest lake in southwestern Sweden that was previously studied for the spatial distribution of Pb and OM contents (Bindler et al., 2001). Our objective here was to comprehensively investigate how the molecular composition of sediment OM varies spatially across a lake with several basins. Our specific research questions were (i) what are the spatial patterns within a single lake for various organic biochemical classes and compounds? (ii) How does the spatial pattern of the OM molecular composition relate to physical parameters (i.e., bulk density and water depth) and elemental, inorganic geochemistry of the sediment material? (iii) Which factors or processes (e.g., provenance, transport pathway and mineralization) appear to explain the in-lake spatial variability of the OM molecular composition?
Härsvatten is a boreal forest lake located in southwestern Sweden
(58
Maps of Härsvatten showing
The basin of Härsvatten can be divided into four general areas (Bindler et al., 2001): (1) the main south basin, which represents about half of the lake area (sample sites S1–S24; maximum depth, 24.3 m) and includes the lake's small outlet stream; (2) a north basin (sample sites N1–11; maximum depth, 12 m), which includes a small inlet stream draining from the headwater lake Måkevatten that enters Härsvatten through a small wetland; (3) an east basin, which has a maximum depth of nearly 10 m (sample sites E1–E6) and is separated from the main north–south axis of the lake by a series of islands and shallow sills (< 3 m water depth); and (4) a generally shallow (< 3 m water depth) central area separating the north, east and south basins (sample sites M1–M6).
In total, we analyzed 44 surface sediment (0–10 cm) samples that were collected in winter 1997–1998 (Fig. 1) for a study of Pb and spheroidal carbonaceous particles (Bindler et al., 2001). These samples were collected as follows: short sediment cores (0–25 cm) were taken with a gravity corer from the ice-covered lake in winter 1997 and 1998 and were sectioned on-site into an upper sample (0–10 cm) and a lower sample (10–25 cm; not studied here). In the laboratory, the samples were weighed, freeze-dried, and reweighed to determine the water content and dry mass of the sediment. The freeze-dried samples have been stored in plastic containers within closed boxes shielded from light and at room temperature since winter 1997–1998. Before further analysis in this study, the samples were finely ground at 30 Hz for 3 min using a stainless steel Retsch swing mill.
The concentrations of major (Na, Mg, Al, Si, K, Ca, P, S, Mn, Fe) and trace
elements (Sc, Ti, V, As, Br, Y, Zr, Ni, Cu, Zn, Sr, Pb) were determined using
a wavelength-dispersive X-ray fluorescence spectrometer (WD-XRF; Bruker S8
TIGER) and a measurement method developed for powdered sediment samples
(Rydberg, 2014). Accuracy was assessed using sample replicates, which were
within
Total mercury (Hg) concentrations were determined using thermal desorption
atomic absorption spectrometry (Milestone DMA-80) with the calibration curves
based on analyses of different masses of four certified reference materials
(CRMs). Analytical quality was controlled using an additional CRM and
replicate samples included with about every 10 samples. The CRM was within
the certified range, and replicate samples were within
We also included the OM content (in % dry mass), determined as
LOI after heating dried samples at 550
Biogenic silica (bSi) was determined by Fourier transform infrared (FTIR) spectroscopy following the approach described in Meyer-Jacob et al. (2014). In brief, sediment samples were mixed with potassium bromide (0.011 g sample and 0.5 g KBr) prior to analysis with a Bruker VERTEX 70 equipped with a HTS-XT accessory unit (multi-sampler). The recorded FTIR spectral information was used to determine the bSi concentrations employing a partial least squares regression (PLSR) calibration based on analyses of synthetic sediment mixtures with defined bSi content ranging from 0 to 100 %.
We calculated the mineral Si fraction (Si
The molecular composition of OM was determined by Py–GC–MS following the method developed by
Tolu et al. (2015). In brief, 200
In the sediments of Härsvatten, 162 pyrolytic organic compounds were identified, and peak
areas were normalized by setting the total identified peak area for each
sample to 100 %. A detailed list of the 162 identified organic compounds
is provided in the supplementary information along with information on their
molecular mass and structure, references for the theoretical mass spectra and
calculated or reference retention index values (Table S1 in the Supplement).
Although the pyrolysis temperature we employed, i.e., 450
We performed all statistical analyses using SPSS software package PASW,
version 22.0. Two separate principal component analyses (PCA) were performed,
one for the elemental geochemistry (i.e., dry bulk density (B.D.) and
contents of OM (LOI), major and trace elements, and bSi) and the other for
the OM molecular composition. Prior to the PCA, all data were converted to
Z-scores (average
Summary statistics of the elemental geochemical properties of the surface
sediments from Härsvatten are presented in Table 1 and the detailed data
are given in Table S2. The sediments from sites M4 and S15 are two outliers
because they have B.D., bSi, OM and elemental contents (e.g., Na, Mg, Al,
K) that deviated by more than 4 standard deviations from the average
values of all analyzed sediment samples (Table 1). Moreover, these sediment
samples are too coarse (predominantly sand) for Py–GC–MS analysis according
to our method based on 200
Summary statistics for sediment elemental geochemistry.
The lowest B.D. is observed among the three deepest sampling locations
(23.5–24.5 m) in the main south basin, where we also find the lowest bSi
content and the highest contents in organically bound elements, including S,
Br, P and certain trace metals, i.e., Cu, Ni, Hg and Zn. These sediments have
high OM content (> 50 %), but the highest [OM] (57–58 %)
are observed among isolated sites that are located close to the shoreline
(N1–N2, E3, S5, S23; 3.1–7.4) and that also include the lowest [Al], [P],
[K], [Si
For the elemental geochemistry dataset, five principal components were
retained. We present only the first four PCs, which together explain 74 %
of the total variance (PC1–4
Combined loading and score plots for PCs 1–4 of the elemental geochemistry dataset. For the PC loadings, filled circles correspond to active variables. Other variables (empty circle and italics) were added passively. Sediment samples are colored according to the results of the cluster analysis.
Positive loadings on PC3
For the cluster analysis of the elemental geochemistry dataset, we selected a
solution of six clusters (cluster
In the south basin, the sediments found at shallower water depth
(cluster
The sediments found at shallow water depth between the north and east basins
and in the central area (cluster
The pyrolytic products identified in the surface sediments of Härsvatten
were classified into 13 OM classes, i.e., carbohydrates, N compounds,
chitin-derived pyrolytic products, phenols, lignin, chlorophylls,
Summary statistics for the molecular composition of sediment OM given as relative abundances (expressed in %) of the 41 groups of pyrolytic organic compounds, which belong to 13 classes of OM as indicated in bold (to be continued).
Continued.
Continued.
Summary statistics of these 41 groups of organic compounds are presented in
Table 2 and the detailed data are given in Table S4. The
coefficients of variation for the abundances of the different organic
compound groups range from 15 to 106 % with an average of
38
Whole-lake and cluster averages for a selection of elemental geochemical parameters and of ratios indicative of OM source types and their degradation status.
Most of the N compounds, which usually derive more from algae than from
higher plants and mosses (Bianchi and Canuel, 2011), have the highest
abundances among the three deepest sampling locations (23.5–24.5 m) in the
main south basin (S12, S18 and S24). These three deepest sampling locations
also present the highest abundances of (i) pyrolytic compounds containing an
acetamide functional group previously shown to be a good indicator of the
presence of chitin, a component of fundi cell walls and arthropod
exoskeletons, in biological and geological samples (Gupta et al., 2007);
(ii) phytadienes, i.e., pyrolytic products of chlorophylls (Nguyen et al.,
2003); (iii) short-chain alkan-2-ones (2K C13–C17); and (iv) steroids. In
contrast, most of the carbohydrates, which usually derive mostly from higher
plants and mosses (Bianchi and Canuel, 2011), have the highest abundances
among the sediments situated close to the shoreline (N1–N2, E3, S5, S23)
such as for the abundances of phenols, guaiacyl- and syringyl-lignin
oligomers, long-chain
Among the shallow sites (2.5–3.0 m) located between the north and east
basins (N10, M1) and the shallow and intermediate water depth (4–20 m) sites
of the south basin (S1–S4, S6–S11, S13–S17, S19–S22), we find the highest
abundances of degradation products of carbohydrates (i.e., (alkyl)furans and
furanones and hydroxyl- or carboxy-furans and furanones); of proteins,
amino-acids and/or chlorophylls (i.e., pyridines_O, (alkyl)pyrroles,
pyrroles_O, pyrroledione and pyrrolidinedione, pristenes); and of lipids
(i.e., short-chain
For the OM molecular composition dataset, six principal components
(PC1–6
Combined loading and score plots for PCs 1–6
PC2
PC3
On PC4
For PC5
PC6
As with the elemental geochemistry dataset, a solution of six clusters
(cluster
In the south basin, the majority of sites found at shallower and intermediate
water depths group in cluster
The majority of sites in the northern half of the lake group within
cluster
The cluster
The surface sediments used in this study comprise the uppermost 10 cm. Given the inherent variation in sedimentation rates across a lake basin, each bulk sample represents material deposited over different timescales. We know from the developmental work for our Py–GC–MS method using annually laminated sediments that there are transformations in OM composition within the uppermost few centimeters, i.e., the first few years following deposition (Tolu et al., 2015). Thus, these bulk sediment samples provide initial insights into the spatial variability in molecular OM composition within a lake basin resulting from longer-term sedimentation processes (including those within the sediment), reflecting years to decades.
The distribution of both clusters
As shown previously for OM (as % LOI) and Pb (Bindler et al. 2001), there
is a physical and inorganic geochemical gradient from shallower to deeper
waters reflecting sediment focusing in the south basin of Härsvatten.
B.D. and bSi decrease from shallower (cluster
In this main basin of Härsvatten, OM originates from a combination of
autochthonous algal production and allochthonous input (Sect. 3.2.2). The
dominance of benthic diatoms in acidified lakes and the declining bSi content
with depth would indicate that the algal material in deeper areas of the
basin should mainly derive from resuspended benthic algal production.
However, this benthic algal production is not reflected in the OM molecular
composition. The sediments from shallow and intermediate water depths
(cluster
Overall, our molecular characterization of OM in the south basin suggests an enrichment in algal versus allochthonous OM (e.g., higher N compound : carbohydrate ratio) in the deeper areas of a deep, simple lake basin, in line with previously reported sediment C : N ratios along lake-basin transects (Kumke et al., 2005; Dunn et al., 2008; Bruesewitz et al., 2012). Given our data on the degradation status of algal and allochthonous OM, we believe that this trend in OM quality results from preferential degradation of algal versus allochthonous OM in sediments at shallower–intermediate water depth in addition to the known focusing of living, and residues of, autochthonous OM towards deeper sites (Ostrovsky and Yacobi, 1999).
In the northern half of the lake, 11 of 19 locations fall within
cluster
The sediments found across the north and east basins and at the deeper
sampling site of the central area (clusters
The shallow sites located between the north and east basins and between the
central area and the south basin (i.e., cluster
Among the sediments found in a small number of near-shore locations
(cluster
The molecular composition of natural OM has been shown to exert a strong influence on key biogeochemical reactions involved in in-lake and global cycling of C, nutrients and trace metals, such as C mineralization or nutrients–trace metal sorption and transformations into mobile and/or bioavailable species (Drott A et al., 2007; Sobek et al., 2011; Gudasz et al., 2012; Tjerngren et al., 2012; Kleeberg, 2013; Bravo et al., 2017). Our work demonstrates that OM molecular composition can vary significantly within a single lake system in relation to basin morphometry, lake chemical and biological status (e.g., presence of macrophytes, which is influenced by, acidification, for example) and the molecular structure and properties of the different OM compounds (e.g., higher resistance of allochthonous versus autochthonous OM upon degradation). Our results further show that it may be problematic to extrapolate data on OM composition from only a few sites or one basin when scaling up to a whole lake. Thus, investigating sedimentary processes and the resulting fate of C and trace elements using sampling strategies focused on the deepest area of a lake or on single transects from shallower to deeper sites, may not fully capture the variation in either elemental geochemistry or OM composition.
Overall, this study underlines that the OM molecular composition and its spatial heterogeneity across a lake are two factors that should be considered to better constrain processes involved in the fate of C, nutrients and trace metals in lake ecosystems to improve whole-lake budgets for these elements and to better assess pollution risks and the role of lakes in global elemental cycles.
The supporting information includes the raw data for sediment elemental geochemical parameters and for the 41 groups of organic compounds (resulting from the identification of 162 pyrolytic organic compounds) used for the statistical analysis and discussion. Raw data for the 162 pyrolytic organic compounds will be provided upon request from the authors.
Julie Tolu and Richard Bindler designed the research. Julie Tolu performed Py–GC–MS analyses with help from Lorenz Gerber and did the data treatment with the data processing pipeline of Lorenz Gerber. Julie Tolu and Johan Rydberg performed XRF and mercury analyses. Julie Tolu and Carsten Meyer-Jacob performed FTIR measurements and Carsten Meyer-Jacob determined the inferred bSi. Julie Tolu, Johan Rydberg, Carsten Meyer-Jacob and Richard Bindler interpreted the data. Julie Tolu prepared the manuscript with consistent contributions from Johan Rydberg, Richard Bindler and Carsten Meyer-Jacob.
The authors declare that they have no conflict of interest.
We would like to thank the university of Umeå (Sweden) for the funding of this work, which was supported by the environment's chemistry research group as well as the Umeå Plant Science Centre for making the Py–GC–MS available to us and Junko Takahashi Schmidt for the technical support in the Py–GC–MS laboratory. We also thank the two anonymous referees and the editor for their relevant comments. Edited by: M. van der Meer Reviewed by: two anonymous referees