Comparing molecular composition of dissolved organic matter in soil and stream water: Influence of land use and chemical characteristics

Science of the Total Environment 571 (2016) 142–152

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Comparing molecular composition of dissolved organic matter in soil and stream water: Influence of land use and chemical characteristics Anne-Gret Seifert a,⁎, Vanessa-Nina Roth b, Thorsten Dittmar c, Gerd Gleixner b, Lutz Breuer d, Tobias Houska d, Jürgen Marxsen a,e a

University Giessen, Animal Ecology & Systematic Zoology, Heinrich Buff Ring 29, D-35392 Giessen, Germany Max Planck Institute for Biogeochemistry, POB 100164, 07701 Jena, Germany Research Group for Marine Geochemistry (ICBM-MPI Bridging Group), Univ. of Oldenburg, Institute for Chemistry and Biology of the Marine Environment (ICBM), Carl-von-Ossietzky-Str. 9-11, 26111 Oldenburg, Germany d University Giessen, Landscape Ecology & Resources Management, Res Ctr BioSyst Land Use & Nutr IFZ, Heinrich-Buff-Ring 26-32, D-35392 Giessen, Germany e Limnological River Station of the Max Planck Institute for Limnology, Schlitz, Germany b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Environmental parameters as pH and nitrate significantly affect chemical composition of DOM • DOM molecular composition variation was apparent in the vertical stratification of undisturbed soils • DOM became less aromatic with depth inundisturbed soils • Stream DOM is mainly of allochthonous origin and predominantly derived from alongside stream subsoil/groundwater

a r t i c l e

i n f o

Article history: Received 27 May 2016 Received in revised form 1 July 2016 Accepted 5 July 2016 Available online xxxx Editor: Dr. J Jay Gan Keywords: Dissolved organic matter Ion cyclotron resonance mass spectrometry Land use

a b s t r a c t Electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI-FT-ICR-MS) was used to examine the molecular composition of dissolved organic matter (DOM) from soils under different land use regimes and how the DOM composition in the catchment is reflected in adjacent streams. The study was carried out in a small area of the Schwingbach catchment, an anthropogenic-influenced landscape in central Germany. We investigated 30 different soil water samples from 4 sites and different depths (managed meadow (0–5 cm, 40– 50 cm), deciduous forest (0–5 cm), mixed-coniferous forest (0–5 cm) and agricultural land (0–5 cm, 40– 50 cm)) and 8 stream samples. 6194 molecular formulae and their magnitude-weighted parameters ((O/C)w, (H/C)w, (N/C)w, (AI-mod)w, (DBE/C)w, (DBE/O)w, (DBE-O)w, (C#)w, (MW)w) were used to describe the molecular composition of the samples. The samples can be roughly divided in three groups. Group 1 contains samples from managed meadow 40–50 cm and stream water, which are characterized by high saturation compared to samples from group 2 including

⁎ Corresponding author. E-mail addresses: [email protected] (A.-G. Seifert), [email protected] (V.-N. Roth), [email protected] (T. Dittmar), [email protected] (G. Gleixner), [email protected] (L. Breuer), [email protected] (T. Houska), [email protected] (J. Marxsen).

http://dx.doi.org/10.1016/j.scitotenv.2016.07.033 0048-9697/© 2016 Elsevier B.V. All rights reserved.

A.-G. Seifert et al. / Science of the Total Environment 571 (2016) 142–152 Carbon cycle Soil carbon Catchment

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agricultural samples and samples from the surface meadow (0–5 cm), which held more nitrogen containing and aromatic compounds. Samples from both forested sites (group 3) are characterized by higher molecular weight and O/C ratio. Environmental parameters vary between sites and among these parameters pH and nitrate significantly affect chemical composition of DOM. Results indicate that most DOM in streams is of terrestrial origin. However, 120 molecular formulae were detected only in streams and not in any of the soil samples. These compounds share molecular formulae with peptides, unsaturated aliphatics and saturated FA-CHO/FA-CHOX. Compounds only found in soil samples are much more aromatic, have more double bonds and a much lower H/C ratio but higher oxygen content, which indicates the availability of fresh plant material and less microbial processed material compared to stream samples. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Dissolved organic matter (DOM) is an important constituent of terrestrial and aquatic ecosystems. In most eco-regions worldwide, DOM is produced during decomposition and solubilization of either recent plant biomass or from soil organic matter (SOM) and serves as a link between terrestrial and aquatic systems. Since streams are net heterotrophic, they consume more energy than they create, a large proportion of DOM in river ecosystems is derived from terrestrial sources (Rasilo et al., 2015; Houser et al., 2005). Consequently, processes controlling DOM composition and concentration in fluvial systems are largely determined by conditions in the catchment area (Houser et al., 2005; Strayer et al., 2003; Huryn et al., 2002). DOM pools in terrestrial ecosystems are small compared to pools of soil organic matter (SOM) but they are considered to be a key indicator of soil quality (Jones et al., 2014) due to their rapid response to land use and vegetation change. Previous studies showed that land use and vegetation can affect DOM composition e.g. DOM leachates from humified organic soils often are dominated by highly aromatic high-molecularweight compounds (Wickland et al., 2007). Land use changes and vegetation also affect soil properties such as pH, moisture, temperature and nutrient availability (Cohen et al., 2008). DOM is the primary energy source for microorganisms and affects their activity and abundance in soils. Both soil properties and microbial activity interact with DOM composition. Components of DOM differ in decomposability. For example, hydrophobic acids, rich in aromatic structures, are difficult to decompose for microorganisms, whereas leachates from living vegetation and fresh litter have a high contribution of low-molecularweight carbohydrates, which are easy to decompose (Marschner and Kalbitz, 2003). However, fresh plant organic carbon is only available in the upper soil horizons and lost in the upper 20 cm. DOM at greater depths of mineral soils is mainly a product of SOM and only to a minor extent derived from fresh plant organic carbon (Malik and Gleixner, 2013; Steinbeiss et al., 2008; Fröberg et al., 2007). This might be of importance, when the quality of DOM transported to inland waters is considered, because different carbon pools are activated during baseand overland-/interflow. DOM derived from fresh plant material which is restricted to the upper part of the soil is transported to inland waters mainly during overland-/interflow caused by heavy precipitation events, whereas mainly old and highly processed deep DOM are transported to the aquatic systems during baseflow (Fellman et al., 2009; Sanderman et al., 2009). This will result in low concentrations of highly altered DOM in stream water (Sanderman et al., 2009) which reaches streams during most times of the year and thus, is the dominant pool for DOM in streams (Fiebig, 1995). Stream DOM concentration and chemistry reflect the combined influences of terrestrial biogeochemical cycling of organic matter and the discharge regime of the catchment, which is influenced by abiotic factors such as hydrology, precipitation and temperature. Low-order-streams are the first important link in the transporting route of DOM from their soil sources to their sinks, because they integrate chemical compounds from groundwater, surrounding landscape and in-stream processes, whereas the molecular formulae diversity decreases with stream order (Mosher et

al., 2015). Spatial and seasonal patterns which affect soil biogeochemical cycling are supposed to determine the concentration and composition of DOM found in streams and rivers. The objective of this study was to examine the composition of terrestrial DOM and its influence on stream water DOM in immediate vicinity. We compared DOM composition from different vegetation sites and two different depths within a small area of the catchment and at two nearby points in the streams. DOM was characterized using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). Analytical techniques such as FT-ICR-MS provide information about the molecular composition of DOM. Due to the high mass accuracy, molecular formulae can be assigned to individual detected masses within the complex DOM mixture (Koch et al., 2005; Kujawinski et al., 2004). Characteristic fingerprints, consisting of many thousand molecular formulae, allow the distinction of DOM of different origins (Koch et al., 2005) and to identify unique molecular formulae for different ecosystems including rivers (Roth et al., 2014). 2. Materials and methods 2.1. Study site and sampling The study site is a small area inside the 23.4 km2 Schwingbach catchment. Samples were taken close to the junction of the Vollnkirchner Bach and the Schwingbach (SI-Figure). The Schwingbach and its tributary the Vollnkirchner Bach are low-mountainous creeks located in Hüttenberg (50°30′0″ N, 8°37′0″ E, Hesse, Germany). The Schwingbach is 10.9 km long, with an altitude range from 233 to 415 m a.s.l. The Vollnkirchner Bach is 4.7 km long and drains a 3.7 km2 subcatchment. Both creeks are part of the “Study landscape Schwingbachtal” of the Justus Liebig University Giessen (Orlowski et al., 2014; Lauer et al., 2013). Land use is dominated by forested sites (36.9%) and arable land (39%), whereas grassland sites (10.5%) are mainly distributed along the stream. Soils are forested Cambisols as well as agricultural Stagnosols with thick loess layers (Stagnic Luvisols). Gleysols can be found predominantly under grassland sites along the streams. Meteorological data were recorded at a climate station at the catchment outlet of the Vollnkirchner Bach including air temperature and precipitation. The climate station is equipped with an automated weather station (Campbell Scientific Inc., AQ5, UK) including a CR1000 data logger. Soil moisture and temperature were measured in 10 cm depth and 40 cm depth in grassland, 20 cm depth and 40 cm depth in agriculture soils and in 5 cm depth in deciduous forest soils (only soil moisture). Soil moisture and temperature was measured with permanently installed EC-5 sensors at forested and 10 cm grassland sites and 5TM-sensors at agriculture and 40 cm grassland sites equipped with EM50 data logger. The climate is classified as temperate with a mean annual temperature of 10.5 °C and annual precipitation sum of 588 mm for the hydrological year 01.11.2013–31.10.2014 (fb09-pasig.umwelt.unigiessen.de:8081). Data are shown in Fig. 1. Soil samples were taken twice a month in February, April, June, August and October from a managed meadow (0–5 cm, 40–50 cm), deciduous forest (0–5 cm), mixed-coniferous forest (0–5 cm) and

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Fig. 1. Meteorological data recorded at the climate station at the catchment outlet near the test site and soil temperature and soil moisture measured with permanently installed sensors on the test site.

agricultural sites (0–5 cm, 40–50 cm). Meadow was cut once a year (01.05.2014) and grazed by sheep (02.–03.02. 2014, 01.09– 02.09.2014). On the agricultural area the following activities took place: tillage 08.09.2014, manure 22.09.2014, planting 01.10.2014. Samples were taken at three different locations at each vegetation/ depth within the specific area and at the specific sampling date and bulked together. Main plant species are given in Table 1. Each sample was kept in a labelled plastic bag and transported to a refrigerator where it was stored at 4 °C for no longer than 2 days until further processing. Green plant material was removed and soil samples were sieved through a 4.0 mm mesh and used for following analysis and experiments. Carbon content was determined from ball-milled subsamples using an elemental analyzer (Vario EL III″, elementar, Germany). 2.2. Analyses of water extractable carbon The water-extractable organic carbon fraction (WEOC) was isolated from soil samples as follows: 300 g (200 g from coniferous forest) of soil Table 1 Main plant species on different test sites. Land use

Main vegetation

Agriculture Triticum Managed meadow Dactylis glomerata, Bromus hordeaceus, Aleopecurus pratensis, Ranunculus acris, Ajuga reptans, Rumex acetosa, Plantago lanceolata, Stellaria holostea, Deciduous forest Quercus petraea, Carpinus betulus, Melica uniflora Mixed coniferous Pinus sylvestris, Picea abies, Carpinus betulus, Melica uniflora, forest Oxalis acetosella, Convallaria majalis

were weighed into 2 L glass flask. 600 mL deionized water was added and the mixture was shaken for 2 h using an overhead shaker. The extracts were decanted and centrifuged (10 min, 3000 rpm) to accelerate the subsequent filtration through a fritted filter funnel with porosity 2 (16–40 μm) and 0.7 μm glass microfiber filters (GF/F, Whatman, England). This extraction method follows the procedure from Embacher et al. (2007). It is considered as a “relatively” weak procedure with minimized artifacts. Therefore, the extracted WEOM should reflect a water (or soil solution) soluble carbon fraction which is relatively close to the potential WEOM in situ (Embacher et al., 2007). − 3− The soil solutions were analyzed for DOC, NO− 3 , NO2 , PO4 and pH. The DOC concentration was measured using a TOC-5000A (Shimadzu). Soil solution pH was measured with a multi 3430 Set F (WTW, Germany). An ion chromatograph ICS-2000 (Dionex) was used to determine NO− 3 , 3− concentration. As separating column AS18 2 × 250 mm NO− 2 , PO4 with KOH as eluent was used. Statistical differences between soils and soil solutions were analyzed using Kruskal–Wallis following Mann-Whitney U test. Additionally, we tested correlation between environmental data with Spearman's rank rs correlation. These tests were performed with SPSS 17.0 (SPSS Inc., Chicago, Illinois, United States). To identify the molecular composition electrospray ionization Fourier transform ion cyclotron resonance was applied. For ESI-FT-ICR-MS, samples taken within a month were mixed. Thus, in total we had 30 soil samples, five within each sampling plot. Additionally, we had eight stream water samples taken at the same time as soil samples (except for February). The sampling plots are abbreviated with A0–5, A40–50, M0–5, M40–50, C0–5, D0–5 whereas A - agricultural area, M – managed meadow, C – mixed coniferous forest and D – deciduous forest. The numbers 0–5 and 40–50 indicate soil depth [cm]. The streams are

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indicated with S = Schwingbach, V = Vollnkirchner Bach. The months are abbreviated with F – February, Ap – April, J – June, Ag – August and O – October. For ESI-FT-ICR-MS, in a first step, DOM was desalted and concentrated with solid phase extraction (SPE) (Varian PPL (0.1 g), Dittmar et al., 2008). The SPE cartridges were activated with methanol (MS grade) over night and conditioned with ultrapure water, methanol and finally with pH 2 ultrapure water. Specifically, DOC solutions were acidified with HCl (p.a.) to pH 2. Samples were passed through the cartridge by gravity, and were rinsed with pH 2 ultrapure water to remove any salt. The cartridge was dried using nitrogen, and DOC was eluted with 800 μL methanol. For the ESI-FT-ICR-MS measurements the DOC concentrates were diluted with ultrapure water and methanol to 1:1 methanol/water (v/v) and 15 mg DOC/L. ESI-FT-ICR-MS measurements were accomplished in negative ionization mode at the Bruker Solarix 15 T FTICR-MS at the University of Oldenburg (Germany). The analytic solution was injected to the ESI source with 120 μL/h and ESI needle voltage was set to − 4 kV. 500 broadband scans were accumulated for each sample run. Spectra were calibrated using an in-house mass reference list. Matlab R2012 (The MathWorks, Inc.) was used for data preparation. Further data handling was performed with Excel 2007 and Canoco 5 (Microcomputer Power, Ithaca, New York, United States). We used molecular formulae with a mass between 150 and 800 Da. Molecular formula assignment was only performed for detected masses having a signal/noise ratio (S/N) above 5. Molecular formulae assignment considered the elements C, H, O, N, S, P (N ≤ 4, S ≤ 2, P ≤ 1) (Herzsprung et al., 2012; Riedel et al., 2012). Further, ultrapure water was treated similar to samples and was used as a procedural blank for extraction and FT-ICR-MS. Masses detected in blank samples were removed and only molecular formulae detected in more than one sample were used for data interpretation. For statistical analyses the dataset with normalized peak intensities and their corresponding elemental composition was used. From this basic data set, a set of magnitude-weighted parameters were calculated based on each formula and relative intensity including aromaticity index (AI-mod, Koch and Dittmar, 2006), oxygen to carbon ratio (O/ C), hydrogen to carbon ratio (H/C), nitrogen to carbon ratio (N/C), double bond equivalents (DBE), double bond equivalents to carbon ratio (DBE/C), double bond equivalents to oxygen ratio (DBE/O), the difference between DBE and number of oxygen (DBE-O), molecular weight (MW) and number of carbon atoms (C#) to characterize each sample. The magnitude-weighted parameters were calculated for each measurement as the sum of all of the products of chemical information and relative intensity (e.g., (C#)w = ∑(C#n ∗ Mn) with n for each individual elemental formula and M for the relative peak intensity (Roth et al., 2013; Sleighter et al., 2010). According to their O/C and H/C ratio individual molecular formulae can be plotted in van Krevelen (VK) diagrams and grouped based on established molar ratios, AI-mod and heteroatom content into BC (black carbon) with C b 15 or C ≥ C15 (AImod N 0.666, N, S or P b 1), BC-CHOX (AI-mod N 0.666, N, S or P ≥ 1), polyphenols (0.5 b AI-mod ≤ 0.666) (Dittmar and Koch, 2006), highly unsaturated compounds (AI-mod b 0.5, H/C b 1.5) (Stenson et al., 2003), unsaturated aliphatics (1.5 ≤ H/C ≤ 2, N = 0) (Seidel et al., 2014) saturated FA (fatty acids)-CHO (H/C N 2, O/C b 0.9, N, S or P b 1), saturated FA-CHOX (H/C N 2, O/C b 0.9, N, S or P b 1), sugars CHO (O/C ≥ 0.9, N, S or P b 1), sugars CHOX (O/C ≥ 0.9, N, S or P ≥ 1) and peptids (1.5 ≤ H/C ≤ 2, O/C b 0.9; N ≥ 1) (Seidel et al., 2014). Further polyphenols, highly unsaturated compounds and unsaturated aliphatics were divided according to their O/C ratio in O-poor and O-rich compounds groups (O-rich: 0.5 b O/C b 0.9, O-poor: O/C ≤ 0.5). It is important to note that with FT-ICR-MS we can only describe the molecular composition of formulae and not their molecular structure. Thus, one formula can represent numerous isomeric compounds. We used principal component analysis (PCA) and redundancy analysis (RDA) with forward selection to compare our data and determine the relationship between samples, magnitude-weighted parameters

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and/or environmental data. To describe the relative importance of different groups of environmental variables and their joint effects a variation partitioning analysis was performed. These tests were performed with Canoco 5. Differences in DOM composition between both depths of meadow samples were evaluated by comparing the relative intensities in both depths. To determine which molecular formulae are more abundant, only molecular formulae which were detected in all five samples of each depth were used and the average values were compared. We considered only molecular formulae which increased/decreased their relative intensities by N 25% by comparison to the other soil depth. We also examined molecular formulae which were detected in all five samples of M0–5 and in none of M40–50 samples and vice versa. 3. Results and discussion 3.1. Differences in molecular composition between samples In all 38 investigated soil and stream samples 6194 different molecular formulae were identified and used to describe the molecular composition (SI, Table 1). 1196 molecular formulae were found in all 38 soil and stream samples (SI, Table 2). Most of these common molecular formulae were highly unsaturated compounds (O-poor, 53.7%, O-rich, 16.8%) and formulae indicating O-poor polyphenols (18.6%, SI, Table 3). These ubiquitous compounds are basics for lignin, a class of complex biopolymers in the support tissues of vascular plants, and tannins, astringent plant polyphenolic compounds. Besides this overlap between samples, the chemical composition of DOM displays some site specific variation as shown in the PCA biplot (Fig. 2). The distribution of samples within the PCA is based on all detected molecular formulae. Differences between samples were more pronounced between mineral soils (agriculture and managed meadow) on one hand and organic soils (forest) on the other hand. Differences

Fig. 2. Principal component analysis biplot of PC1 and PC2 based on all 6194 molecular formulae. Magnitude-weighted parameters that were calculated from the relative intensities of all 6194 formulas were added as supplementary variables. A0–5, A40–50, M0–5, M40–50, CO-5, DO-5 are abbreviations for A - agricultural area, M - managed meadow, C - mixed coniferous forest and D - deciduous forest. The numbers 0–5 and 40–50 indicate soil depth [cm]. The streams are indicated with S = Schwingbach, V = Vollnkirchner Bach. The months are abbreviated with F - February, Ap - April, J - June, Ag - August and O - October.

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between surface and subsoil samples were found only in managed meadow samples, but not in the agricultural samples. Only modest differences were found between surface samples from managed meadow compared to arable soils as described also by Purton et al. (2015). To interpret the variation of molecular formula composition of the samples, magnitude-weighted parameters (Table 3) were used as supplementary variables and plotted in Fig. 2. The supplements are not included in the PCA calculation and only show the direction of each gradient for all magnitude weighted parameter. PC1 and PC2 explain together 61.3% of the variance among the 38 samples. The samples can be roughly divided in three groups. Group 1 contains samples from M40–50 and stream water that are characterized by high saturation. To group 2 belong samples from A0–5, A40–50 and M0–5, which comprise more nitrogen containing and aromatic compounds indicated by higher (DBEO)w, (DBE/O)w, (AI-mod)w and (DBE/C)w values. Both forest sites cluster as group 3 and are characterized by higher molecular weight and oxygenation ((C#)w, (MW)w, (O/C)w). The first principal component (PC1) explains 32.2% of the variation. Group 2 and 3 are divided by PC1. Samples of group 1 are divided from group 2 and 3 by PC2, which explains further 29.1% of the variation, but partly also by PC1. Within group 2, all M0–5 samples are separated along PC1 from both A0–5 and A40–50 samples, whereas M0–5 and M40–50 are mainly separated along PC2. (C#)w, (MW)w and the (N/C)w ratio explain the sample correlation with PC1 (Fig. 2), whereas PC2 is related to saturation/aromaticity ((H/C)w, (AI-mod)w and (DBE/C)w. Samples with high (H/C)w values have positive PC2 scores and have higher values in stream water and M40–50 samples, whereas within group 1 the managed meadow samples are separated from stream samples by PC1 indicating higher molecular weights for stream samples. M40–50F differs from other samples in this group by having a lower (DBE-O)w value, which indicates a higher carbon saturation. Samples with high (AI-mod)w, (DBE/C)w, (DBE/O)w, (DBE-O)w and (N/C)w values have negative PC1 and PC2 scores and are from A0–5, A40–50 and M0–5. Within this group, agricultural samples have a higher (N/C)w ratio compared to M0–5 samples. Forest samples are described by a relative higher number of (C#)w, higher (MW)w and higher (O/C)w ratios compared to meadow and

agricultural samples. Within group 3, deciduous and coniferous forest samples are separated by PC1 with higher molecular weight and oxygenation for coniferous forest. Analysis by RDA in combination with forward selection of magnitude-weighted parameters revealed that all magnitude-weighted parameters explain together 78.8% of the variability of the samples, whereas most variability was explained by (N/C)w ratio and aromaticity index. Our findings of the variability between forest at one hand and meadow/agriculture on the other hand can be only partly related to above ground vegetation and are in accordance with studies from Bu et al. (2010) and Traversa et al. (2008), where DOM differs as a function of plant litter and its degradation processes. We found only minor differences between managed meadow and agricultural samples. That agrees with investigations by Guggenberger et al. (1995) and Purton et al. (2015) on SOM who observed that broad land use categories are poor predictors of SOM chemistry, having only minor affects and that soil-scale processes have greater control over chemical composition. 3.2. Environmental data and their influence on DOM composition The environmental variables used in the study consist of data to describe soil carbon content (soil C, DOC), nutrient input (nitrate, nitrite, phosphate), climate (soil temperature, soil moisture) and acidity (pH). Data are summarized in Table 2 and Fig. 1. If all six sites were compared, these environmental parameters vary significantly between sites. Soil C was significantly higher at both forest sites (p = 0.008 to all other sites/ depths) and significantly lower at A40–50 and M40–50 compared to other sites/depths (p = 0.008 to all other sites/depths). There were no differences in soil C between M0–5 and A0–5 (p = 0.56) as well as between M40–50 and A40–50 (p = 0.690). At both forested sites pH was significantly lower compared to all other sites/depths (p = 0.008 to all other sites/depths), with no difference between the forested sites (p = 0.56). There was also a weak significant difference between A0–5 and M0–5 (p = 0.32). Soil C and pH as well as DOC and pH were significantly negatively correlated (rs = 0.733, p ≤ 0.001 for soil C and rs = 0.601, p = 0.001 for DOC, Fig. 3). Differences in soil pH as measured in our

Table 2 Overview over available soil characteristics. Land use

Depth

Month

C [%]

DOC [mg/L]

pH

NO3-[ppm]

NO− 2 -[ppm]

PO3− 4 -[ppm]

Agriculture

0–5 cm

February April June August October February April June August October February April June August October February April June August October February April June August October February April June August October

1.69 2.26 2.01 2.01 1.73 0.75 1.01 0.74 1.14 0.97 2.17 2.58 2.14 2.15 2.57 0.97 0.81 0.88 0.92 0.76 6.31 5.67 6.53 11.70 7.93 34.23 16.95 43.26 19.88 29.55

8.92 11.40 10.30 8.44 10.47 6.67 6.69 5.77 4.89 6.02 9.02 11.53 7.29 5.18 7.27 3.13 3.04 4.22 2.89 2.37 55.49 34.33 64.48 82.16 63.89 115.24 93.94 124.44 120.56 ‐

6.47 6.76 6.72 6.86 6.97 6.43 6.71 7.01 6.62 7.07 6.47 6.43 6.54 6.50 6.20 6.22 6.39 6.71 6.48 6.67 4.53 5.12 5.44 4.76 5.56 3.90 4.50 4.51 4.78 4.69

11.12 13.66 7.07 10.01 7.24 9.71 12.57 4.61 5.36 7.10 7.54 5.83 8.08 5.37 5.35 2.36 1.26 1.86 1.66 1.21 0 0 0 0 0 0 0.03 0 0 0.02

0.08 0.03 0.05 0.15 0.26 0.19 0.10 0.04 0.09 0.16 0.23 0.24 0.06 0.03 0.05 0.11 0.03 0.01 0.02 0.04 1.72 0.86 0.21 0.12 1.09 0.13 1.23 2.75 0.64 0.36

0.43 0.56 0.40 0.89 2.93 0.13 0.34 0.09 0.05 0.47 0.22 0.03 0 0 0.02 0 0 0 0 0 0.15 0.33 0.32 0.16 0.29 3.32 0.13 1 0.04 0.21

40–50 cm

Managed meadow

0–5 cm

40–50 cm

Deciduous forest

0–5 cm

Mixed coniferous forest

0–5 cm

A.-G. Seifert et al. / Science of the Total Environment 571 (2016) 142–152

Fig. 3. Principal component analysis biplot of PC1 and PC2 shows the approximated correlation of environmental variables. A sharp angle indicates a positive correlation. The length of the arrow is a measure of fit for the environmental variable.

study can be related to aboveground vegetation since the initial pH varies among plant litter (Cornelissen et al., 2006). Especially woody debris has been shown to be more acidic than litter from leave bodies, due to the production of organic acids (Ma et al., 2014). The release of these organic acids during accumulation in soils alters the pH of soil organic matter (Finzi et al., 1998), eventually leading to low pH values as measured in our forest samples. Moreover, our results reveal a negative correlation between pH and DOC concentration that might be explained by lower sorption at decreasing pH due to decreasing negative charge on the DOM itself (Kalbitz et al., 2000a). Nitrate concentration was significantly higher at both agricultural soil depths and M0–5 compared to forested sites and M40–50 (p = 0.008 to all other sites/depths) and significantly higher in M40–50 samples compared to forest samples (p = 0.008 to both forested sites). Our results suggest that DOM composition depends to a large degree on environmental parameters. Testing chemical parameters showed that only pH and nitrate had significant effects (Fig. 4). Together they explain 75.3% of the variability. If pH is not selected using interactive forward selection test also DOC has significant effects. However, due to the negative correlation (Fig. 3; De Troyer et al., 2014; Roth et al., 2013) and thus, contradictory effects between pH and DOC in soil solutions, DOC content alone gives no further explanation to the variation in DOM composition if both environmental variables (pH and DOC) are considered. Soil pH is positively correlated to (N/C)w and negatively correlated to (C#)w, (O/C)w ratio and (MW)w. Similar results were found by Roth et al. (2015). The nitrate content is positively correlated to all parameters related to aromaticity and (N/C)w and is negatively correlated to (H/C)w (Fig. 4). Influence of nutrient status on SOM composition was also described by Vancampenhout et al. (2012). We performed variation partitioning, which explains the variation by two groups of environmental variables (pH and nitrate). According to this analysis most of the explained variation in magnitude-weighted parameters was related to shared effects of pH and nitrate (46.4%). In addition, the independent effect of pH was considerable (20.7%), whereas nitrate alone explained only 6.3% of the variation. These changes in environmental parameters namely pH and nitrate might be largely affected by land-use. The effects of forest ecosystems on pH and agriculture on nutrient status was shown in investigation by Rigueiro-Rodriguez et al. (2012) and Frink et al. (1999). Several studies investigated the effect of environmental variables like pH and nitrate concentration but also DOC content on carbon degradation and transport processes in soils (Andersson et al., 2000; Kalbitz et al., 2000b; Guggenberger et al., 1994). In general, inorganic nutrients like nitrate and phosphate e.g. stimulate microbial DOC consumption. We measured moderate nitrate concentration in the arable soil and in the surface samples of managed meadow soils in our study. Consequently, the relative higher abundance of aromatic compounds in these samples might be related to the depletion of easy degradable low-molecular

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weight compounds like carbohydrates due to available nutrients. In contrast, inorganic nutrient and DOC content was low in M40–50 samples. Due to these limitations degradation processes might decrease in subsoil. Furthermore, according to Rumpel et al. (2004) the aromatic portion of partially degraded lignin-and tannin-derived compounds might be gradually adsorbed by mineral soil components, and thus are partly inaccessible to microbial degradation. These effects would further limit the degradation process in subsoils. It has been shown that decreasing pH negatively affect DOM degradation through its effect on the activity and the composition of the microbial community (Marschner and Kalbitz, 2003) and the inhibition of phenol oxidase resulting in the accumulation of phenolic substances that constrain other enzymes (Freeman et al., 2001). Organic compounds like organic acids, inhibitory to bacterial activity may accumulate in the DOC pool and further lower biodegradation processes. We find that higher molecular size, higher oxygenation and carbon content and less nitrogen containing compounds occur with lower pH values as described also by Roth et al. (2015, 2013). Decreasing pH negatively affects net nitrogen mineralization (Szlavecz et al., 2006), whereas the higher degree of oxygenation might be partly related to the occurrence of phenolic tannin compounds in forest samples (Roth et al., 2015). 3.3. Vertical differences in DOM composition The clustering of A0–5 and A40–50 in one region of the PCA (Fig. 2) suggests relatively homogenous DOM chemistry and may arise from agricultural use (e.g. tillage, ploughing). If environmental data were compared, both depths differ only in soil C/DOC concentration. In contrast, M0–5 and M40–50 samples differ significantly in soil C/DOC, nitrate concentration and soil moisture content, which is accompanied by different DOM composition. Fig. 5a shows all molecular formulae, which had a N 25% higher relative intensity in M0–5 compared to M40–50 samples and vice versa (Fig. 5b). Fig. 6a, b shows molecular formulae which were either detected in all M0–5 samples, but in none of the M40–50 samples (Fig. 6a) or in all M40–50 samples and in none of the M0–5 samples (Fig. 6b). In general, molecular formulae with a relative higher intensity in M0–5 compared to M40–50 samples, where characterized by lower m/z values (M0–5; 375.3; M40–50: 417.6), higher aromaticity (AI-mod: M0–5: 0.55; M40–50: 0.27), lower H/C ratio (M0–5: 0.91; M40–50: 1.32) and higher O/C ratio (M0–5: 0.40; M40–50: 0.35). Similar trends were observed when evaluating depths trends in mineral soils (unpublished data by Roth et al.). These values indicate the input of fresh plant material, which is partly less microbially processed. M40–50 samples contain relatively more lipids, proteins, aminosugars and carbohydrates than M0–5 samples. These substances serve as main substrates and constituents of microorganisms. It is important to note that with ESI-FT-ICR-MS only trends and relative changes between samples can be detected. With this method we are not able to quantify the abundance of compounds because ionization efficiencies depend on the structure of molecules and the ionization efficiency can be suggested to be low for carbohydrates compared to many other component groups detected by FT-ICR-MS. Moreover, only few carbohydrates were detected, because carbohydrates are highly hydrophilic and hence not well recovered by solid phase extraction. The availability of these compounds in the subsoil can be attributed to decreased microbial activity as described also by Purton et al. (2015). Limited microbial activity might be partially attributable to low nutrient availability, but also to increased influence of organo-mineral associations, as the proportion of mineral-bound organic matter typically increases with depth (KögelKnabner et al., 2008). Besides decreasing microbial activity, the availability of easy degradable compounds in the subsoil might be also an indication of SOM recycling in deeper soil horizons as an important process in DOC production. After temporal immobilization by sorption to the mineral soil matrix or co-precipitation, DOC re-released into soil water might get altered or newly produced by microbial synthesis (Malik and Gleixner, 2013; Gleixner, 2013; Kaiser and Kalbitz, 2012)

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Table 3 Magnitude-weighted parameters.

A0–5F A0–5Ap A0–5J A0–5Ag A0–5O Average Stddev A40–50F A40–50Ap A40–50J A40–50Ag A40–50O Average Stddev M0–5F M0–5Ap M0–5 J M0–5Ag M0–5O Average Stddev M40–50F M40–50Ap M40–50J M40–50Ag M40–50O Average Stddev DF DAp DJ DAg DO Average Stddev CF CAp CJ CAg CO Average Stddev SAp SJ SAg SO Average Stddev VAp VJ VAg VO Average Stddev

(H/C)w

(O/C)w

(C#)w

(DBE/O)w

(DBE/C)w

(DBE-O)w

(AI-mod)w

(N/C)w

(M/W)w

1.07 1.03 1.05 1.06 1.11 1.06 0.03 1.10 1.08 1.09 1.10 1.10 1.09 0.01 1.11 1.10 1.12 1.12 1.12 1.11 0.01 1.19 1.22 1.20 1.22 1.23 1.21 0.02 1.07 1.13 1.15 1.16 1.14 1.13 0.04 1.11 1.12 1.10 1.13 1.11 1.11 0.01 1.69 1.27 1.24 1.23 1.36 0.22 1.51 1.23 1.27 1.22 1.31 0.14

0.38 0.40 0.41 0.41 0.44 0.41 0.02 0.44 0.40 0.42 0.41 0.40 0.41 0.02 0.41 0.42 0.41 0.42 0.40 0.41 0.01 0.42 0.40 0.41 0.40 0.39 0.40 0.01 0.41 0.44 0.42 0.43 0.41 0.42 0.01 0.47 0.46 0.46 0.45 0.41 0.45 0.02 0.47 0.38 0.39 0.39 0.41 0.04 0.35 0.39 0.37 0.39 0.38 0.02

18.97 17.84 18.21 18.58 19.13 18.55 0.53 19.48 18.58 18.61 18.96 18.57 18.84 0.39 19.74 19.98 19.72 19.84 19.64 19.78 0.13 20.41 20.70 20.18 19.42 19.76 20.09 0.51 20.73 21.00 20.22 21.24 20.39 20.72 0.42 21.28 21.41 21.84 21.74 21.08 21.47 0.32 20.49 20.44 20.09 19.98 20.25 0.25 18.62 20.50 20.24 20.26 19.91 0.86

1.81 1.77 1.70 1.66 1.42 1.67 0.15 1.47 1.69 1.63 1.61 1.67 1.61 0.09 1.53 1.48 1.50 1.47 1.55 1.51 0.03 1.33 1.36 1.38 1.46 1.42 1.39 0.05 1.56 1.33 1.47 1.32 1.51 1.44 0.11 1.28 1.26 1.29 1.31 1.53 1.33 0.11 0.70 1.39 1.37 1.41 1.22 0.35 1.27 1.39 1.39 1.41 1.37 0.06

0.55 0.58 0.56 0.56 0.52 0.55 0.02 0.53 0.55 0.54 0.53 0.54 0.54 0.01 0.52 0.52 0.51 0.52 0.52 0.52 0.00 0.47 0.46 0.47 0.46 0.46 0.46 0.01 0.53 0.50 0.49 0.48 0.49 0.50 0.02 0.50 0.50 0.50 0.49 0.50 0.50 0.00 0.21 0.43 0.45 0.46 0.39 0.12 0.32 0.45 0.43 0.46 0.42 0.06

3.01 2.97 2.72 2.60 1.46 2.55 0.63 1.67 2.57 2.15 2.17 2.30 2.17 0.33 2.01 1.81 1.88 1.73 2.10 1.91 0.15 0.99 1.13 1.16 1.35 1.30 1.19 0.14 2.30 1.08 1.50 0.97 1.69 1.51 0.53 0.78 0.75 1.02 0.98 2.01 1.11 0.52 −4.92 1.10 1.16 1.27 −0.35 3.05 −0.25 1.21 1.19 1.30 0.86 0.74

0.44 0.47 0.45 0.44 0.38 0.44 0.03 0.39 0.43 0.41 0.40 0.41 0.41 0.01 0.39 0.39 0.38 0.38 0.39 0.39 0.01 0.33 0.32 0.33 0.33 0.32 0.33 0.01 0.40 0.35 0.35 0.33 0.36 0.36 0.03 0.34 0.34 0.35 0.34 0.37 0.35 0.01 0.14 0.29 0.31 0.32 0.27 0.08 0.20 0.31 0.29 0.32 0.28 0.05

0.06 0.06 0.06 0.06 0.05 0.06 0.00 0.05 0.06 0.05 0.05 0.06 0.05 0.01 0.04 0.04 0.04 0.04 0.05 0.04 0.00 0.03 0.03 0.03 0.04 0.04 0.03 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.00 0.01 0.01 0.01 0.01 0.02 0.01 0.00 0.01 0.03 0.03 0.03 0.03 0.01 0.02 0.03 0.03 0.03 0.03 0.01

377.28 360.65 372.78 369.62 396.42 375.35 13.26 386.20 375.64 402.55 380.69 376.97 384.41 10.93 389.02 394.52 403.67 404.73 397.07 397.80 6.53 397.38 394.74 413.95 414.29 406.34 405.34 9.09 442.42 440.74 446.60 437.98 415.30 436.61 12.31 428.35 427.36 410.11 403.43 402.51 414.35 12.68 436.47 401.18 398.32 396.19 408.04 19.06 371.48 406.00 397.85 402.45 394.44 15.67

and thus, can support a large microbial community (Gabor et al., 2014). Additionally, M40–50 are partly influenced by groundwater, thus water-logging might be also responsible for decreased degradation and the availability of easy degradable compounds. Even though we found low-molecular-weight compounds like lipids, proteins, aminosugars and carbohydrates in the subsoil of M40– 50 samples, in general, the averaged relative intensity of present lowmolecular-substances (200 b m/z b 300) was higher in surface than in subsoil samples and beyond that higher in agricultural than in meadow samples (A0–5 = 3.45, SD = 0.72, A40–50 = 2.81, SD = 0.41, M0–5 = 2.42, SD = 0.15, M40–50 = 1.95, SD = 0.37, SI – Table 4). There were two exceptions (A-October and M-August), which might be related to related to tillage, manure and planting of Triticale on arable land in September/October and influence of high rainfall intensity in August (Fig. 1) and ensuing increased leaching in meadow samples during this month. In contrast, the averaged relative intensities of substances with

m/z values between 350 and 450 were similar in surface soils and subsoils and differed only slightly between arable soil and managed meadow soils (A0–5 = 2.48, SD = 0.10, A40–50 = 2.42, SD = 0.03, M0–5 = 2.55, SD = 0.05, M40–50 = 2.55, SD = 0.11). For m/z N 500, the averaged relative intensity was similar in subsoil and surface samples of agricultural samples and higher in subsoil compared to surface samples if the managed meadow soils were compared. Generally, the averaged intensity for m/z N 500 compounds was higher in managed meadow than in agricultural samples (A0–5 = 0.88, SD = 0.10, A40–50 = 0.94, SD = 0.11, M0–5 = 1.19, SD = 0.05, M40–50 = 1.42, SD = 0.04). 3.4. Soil water vs. stream water We found 120 molecular formulae that were only detected in stream water and in none of the 30 soil samples. These stream compounds are mainly aliphatic, have a high H/C ratio and contain less oxygen (Fig. 7a).

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Fig. 4. a. Ordination biplot based on redundancy analyses of magnitude-weighted parameters and environmental parameters. Nitrate and pH were identified as significant factors and included in the model. b. Partitioning of the variation explained by the different sets (pH or nitrate) or combinations of sets (pH and nitrate) of explanatory variables.

In contrast, we detected 223 molecular formulae which were not detected in any stream sample, but were found in samples from all six sampling plots (M0–5, M40–50, A0–5, A40–50, D0–5, C0–5) and were additionally available at least in 50% of all soil samples. These

compounds are much more aromatic, have more double bonds and a much lower H/C ratio, which indicates the availability of fresh plant material and less microbial processed material, but higher oxygen content. There were only two compounds which were present in all 30 soil

Fig. 5. The van Krevelen diagram shows the distribution of chemical components with a relative higher (N25%) intensity in a) all averaged MO-5 samples compared to averaged M40–50 and b) in all averaged M40–50 compared to averaged M0–5.

Fig. 6. The van Krevelen diagram shows the distribution of chemical components which where abundant either in a) all M0–5 samples and in non of the M40–50 samples or b) in all M40–50 samples and in non of the M0–5 samples.

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horizons/groundwater from managed meadow samples and stream surface water assuming as well that water from these vegetation site and horizons are primarily transported to the stream (Orlowski et al., 2015). It has been shown that stream DOC concentration is similar to concentrations found in soil water from deeper soil horizons despite high concentrations observed in leachate from surface soil (McDowell and Wood, 1984). However, organic-rich surface soils can contribute substantially to stream DOC when hydrologic conditions ensure connectivity of these DOC sources to the streams (Pacific et al., 2010; Hood et al., 2006; Prokushkin et al., 2005). Results of overland flow into the stream were not obvious from our measurements. Similar results were detected by Orlowski et al. (2015). Here, even during peak flow, isotopic signals of rainfall caused by increased runoff to the streams, did not contribute substantially to stream water isotopic composition although fast rainfall–runoff behaviors were observed (Orlowski et al., 2014). These observations are often made in small catchments were increased rainfall results in the discharge of “old” groundwater (Kirchner, 2003). The lack of evidence for overland- and/or interflow might further be related to only punctual measurements, because inputs to the streams are rapidly transported downstream. Our results show that chemical compounds which were available only in stream water, but not detected in any soil sample were much more aliphatic (Roth et al., 2015). Large molecules, condensed H/C and tannins were not found in streams. As already discussed, the complex soil structure adsorbed substances or they were degraded and thus, prevented them from being transported down the soil profile and further into the stream. 4. Summary and conclusion

Fig. 7. Van Krevelen diagram showing the distribution of chemical components only present either in a) streams or in b) soil water. The components which represent streams were detected only in stream water but in none of the 30 soil samples. The soil components were found in samples from all six sampling plots (MO-5, M40–50, A0–5, A40–50, DO-5, C0–5) and were additionally available at least in N50% of all soil samples.

samples and in none of the eight stream samples (Fig. 7b). Large differences between compounds found either in streams or in soils are the lack of peptides, unsaturated aliphatics and saturated FA-CHO/FACHOX which were exclusively found in streams and the lack of highly unsaturated O-rich compounds and O-rich polyphenols which were exclusively found in soils. Other compounds like BC-CHOX and highly unsaturated O-poor compounds are detected in very low abundances in streams (Fig. 7a, b). We assume that most compounds found in streams, are not produced in the stream itself, but are of allochthonous origin. Besides flow from adjacent soils (including base flow, interflow and overland-flow, artificial drainage systems) allochthonous compounds can enter the stream via direct leachate of litter. The molecular formulae indicating O-poor polyphenols and highly unsaturated compounds might be a result of direct leachate and further modification and transformation processes in streams (Lu et al., 2013). In contrast to these allochthonous input, we assume that most peptides might be water-borne, because they were found in all streams and not in soils. Algal remains, for example, comprise a high percentage of peptide-like compounds (Knicker et al., 2004). We found the largest overlap and similarity in chemical composition between stream and M40–50 cm (Fig. 2) assuming that soil water from deeper horizons and/or groundwater predominantly feeds the flow to the stream. This observation is well in agreement with another study recently performed in the Schwingbach catchment that showed similar isotopic composition for soil water from deeper

We linked soil and stream DOM composition that were investigated with a method that allows us to resolve a high spectrum of compound classes on a molecular level. We compared molecular formulae of 30 soil samples from different sites characterized by different land use, vegetation cover and soil depth and eight stream samples. Most molecular formulae are not soil-ecosystem and/or season specific and were present at all sites and depths and during most seasons. System specific molecular formulae for soil samples were found in low number and low relative intensity and thus, could not be attributed to a specific land-use or vegetation scenario, even if only surface soil samples were compared. However, differences between soil samples were observed and seem to be more pronounced between mineral and organic soil surfaces and if vertical soil samples in non-disturbed profiles were compared. Managed meadow samples from surface and subsoil suggest a decline of aromatic structures with soil depth, which might indicate less fresh plant material in subsoils. Land use is a key driver for changes in environmental conditions. These induced changes especially in pH and nitrate content are reflected in DOM composition of soil samples and might be more important than vegetation itself. Decreasing pH is linked to higher carbon and oxygen content of molecules and thus, greater molecular weight, which is probably related to inhibited microbial and enzymatic activity at low pH. Nitrate content is positively correlated to all parameters related to aromaticity and (N/C)w and negatively affects (H/C)w, what probably reflects easy decomposable aboveground vegetation and thus, more fresh plant material in surface soils. Unique molecular formulae were only found when streams and overall soil samples were compared. Thus, results might either reflect the anthropogenic influence in this landscape what obliterates differences in DOM composition or only broad ecosystem categories like streams and soils contain noticeable unique molecular formulae. Since environmental characteristics and vertical soil profiles are important for DOM composition, future studies should focus on the influence of environmental data and consider vertical soil stratification and groundwater influence.

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