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Agriculture has changed the amount and composition of dissolved organic matter inCentral European headwater streams

Daniel Graeber ⁎, Jörg Gelbrecht, Martin T. Pusch, Christine Anlanger 1, Daniel von Schiller 2

Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB), Müggelseedamm 301, 12587 Berlin, Germany

H I G H L I G H T S

► Agriculture can be an important source of dissolved organic matter (DOM) to headwaters.► Export of DOM from agriculture is structurally complex and exhibits a low redox state.► Land-use related patterns of DOM amount and composition are consistent across seasons.► In headwaters, the degree humification of DOM is strongly related to DOC concentration.► Export of DOM from agriculture may affect metabolism and health of aquatic ecosystems.

a b s t r a c ta r t i c l e i n f o

Article history:Received 19 April 2012Received in revised form 20 July 2012Accepted 28 August 2012Available online xxxx

Keywords:FluorescenceParallel Factor AnalysisSeasonalityRedox stateForestWetland

Dissolved organic matter (DOM) is an important part of the global carbon cycle and significantly influencesaquatic ecosystem functions. Recent studies suggest that its amount and composition in freshwaters maybe altered by agricultural land use, whereby the influence of preceding in-stream production and processingis not clear. To assess the land use effect on DOM amount and composition for the export from terrestrial tofreshwater systems at the land–water interface, we sampled headwater streams draining agriculturaland near-pristine catchments (forested and wetland) in the North German plains. To account for spatialand seasonal variation, we conducted a screening of DOM amount (53 sites) and composition (42 sites),and conducted bi-weekly samplings to investigate seasonal variation at eight sites over one year.Concentrations of dissolved organic carbon (DOC) were significantly higher for agricultural and wetlandcatchments than for forested catchments. Moreover, DOC loads exhibited higher seasonal variation foragricultural and wetland catchments than for forested catchments, which was due to higher variation indischarge. Parallel Factor Analysis revealed that the composition of DOM in agricultural catchments wassignificantly different from the other studied catchment types, and was characterized by low redox stateand high structural complexity. Moreover, a gradient from protein- to humic-like fluorescence significantlyseparated forested from agricultural and wetland catchments. The contribution of humic-like DOM wasstrongly and positively related to DOC concentration, suggesting a mechanistic coupling of both. The effectsof land use on patterns of DOC concentration and DOM composition were consistent across seasons, implyingthat land use strongly regulates DOM export. Overall, this study clearly shows the seasonally independentimportance of agricultural land use for the amount and composition of DOM fluxes from the terrestrialzone to surface waters. These altered fluxes may affect ecosystem metabolism and health of agriculturalheadwaters and downstream situated aquatic ecosystems.

© 2012 Elsevier B.V. All rights reserved.

1. Introduction

Dissolved organic matter (DOM) is a largely uncharacterizedamalgam of hundreds of individual compounds with molecularweights varying from simple carbohydrates to highly complexmolecules of different aromaticity (Findlay and Sinsabaugh, 2003;Prairie and Cole, 2009). In surface waters, DOM is an importantdriver of ecosystem functions and constitutes the largest pool oforganic matter in terms of surface water export, with catchmentsoils often being its main source (Wetzel, 1984; Findlay and

Science of the Total Environment 438 (2012) 435–446

⁎ Corresponding author at: Department of Bioscience, Aarhus University, Vejlsøvej 25,8600 Silkeborg, Denmark. Tel.: +45 8715 8562; fax: +45 8715 8901.

E-mail address: [email protected] (D. Graeber).1 Present address: Department of River Ecology, Helmholtz Centre for Environmental

Research GmbH — UFZ, Brueckstr, 3a, 39114 Magdeburg, Germany.2 Present address: Catalan Institute for Water Research (ICRA) H2O Building, Scientific

and Technological Park of the University of Girona, Emili Grahit, 101, 17003 Girona, Spain.

0048-9697/$ – see front matter © 2012 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.scitotenv.2012.08.087

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Sinsabaugh, 2003; Battin et al., 2008; Tank et al., 2010). The source ofDOM defines its amount and composition (Hudson et al., 2007) andthus determines its biological (e.g. enzyme inhibition or respiration;Wetzel, 1992; Fischer et al., 2002; Tank et al., 2010) and physiochemicalfunctions (e.g. attenuation of light or interaction with metals; Morriset al., 1995; Yamashita and Jaffé, 2008).

Around 40% of the earth's land surface is used for agriculture(Foley et al., 2005). Techniques and practices applied in agriculturehave a large potential to alter the amount and composition of DOMin surface waters (Ogle et al., 2005), which is likely to have aneffect on surface water metabolism and health (Young et al., 2008;Tank et al., 2010). To detect such land use driven alterations ofDOM, it is necessary to investigate DOM inputs in headwater streams,which are the first surface water systems in the land to ocean contin-uum with low interference of potential in-stream production andprocessing (Battin et al., 2008). However, studies linking agriculturalcatchment modifications and altered spatio-temporal variability ofDOM amount and composition in streams are scarce, especially foragricultural headwaters.

Previous studies from surface waters have shown that the concen-tration of DOM, measured as the concentration of dissolved organiccarbon (DOC), is independent from (Wilson and Xenopoulos, 2008)or even negatively related to (Cronan et al., 1999) the percentage ofagriculture in the catchment. However, in a review of studies on soilorganic matter (SOM) in agricultural soils, it was shown that in allclimate zones and soil types, tillage and especially plowing physicallydisturb organo-mineral aggregates in soils which results in a loss ofSOM, of which a part potentially is lost as DOM (Ogle et al., 2005;Ewing et al., 2006). These seemingly contradicting results from soiland stream studies imply that only a low percentage of the SOMlost from agricultural soils is transported to the streams as DOMor that DOM originating from agricultural SOM is rapidly taken upin streams.

In contrast toDOMconcentration, subsurface drainage in agricultureprobably increases the catchment area-based export of DOM as it canincrease the proportion of total annual precipitation that is dischargedto surface waters via subsurface flow relative to the amount thatis stored semi-permanently, evaporated, or transpired (Blann et al.,2009). However, until now, no study has compared theDOM catchmentarea-based export from agricultural catchments to the export frompristine or near-pristine catchments for headwater streams in temper-ate climate.

In terms of DOM composition, recent studies have found thatstructural complexity of DOM is affected by the percentage of agricul-tural land use in the catchment, although results of the studies arecontradictory. On one hand, DOM of low structural complexity wasrelated to a high percentage of agricultural area in Canadian catch-ments of varying size (10–963 km2; Wilson and Xenopoulos, 2009).On the other hand, a high proportion of humic-like DOM of highstructural complexity was found in a Danish agricultural streamwith-in an estuary catchment (517 km2; Stedmon and Markager, 2005)and in several sampling stations within two large Australian agricul-tural catchments (149 and 664 km2; Petrone et al., 2011). Similarly,DOM dominated by structurally complex humic substances wasfound in a German agricultural headwater stream with a small catch-ment throughout one year (b10 km2; Sachse et al., 2005). The reasonfor these contrasting findings could be that previous studies wereconducted in catchments of different size, potentially resulting in dif-ferent effects of preceding in-stream production and processing onDOM composition (Battin et al., 2008). Moreover, the different pat-terns found in the studies could be due to actual differences in agri-cultural management and soil types (Petrone et al., 2011), as well asdue to the different history of land cover and use in the catchment,which can have longstanding impacts of DOC concentration andDOM composition (Creed et al., 2003; Maloney and Weller, 2011;Yamashita et al., 2011). Therefore, comprehensive investigations of

DOM composition in agricultural headwater streams of severalsmall, spatially independent catchments with similar land use historyand soil types are necessary to assess the agricultural land use effecton DOM composition, while excluding potential in-stream processingeffects as much as possible due to a low water residence time inthe streams.

In headwaters, not only the structural complexity of DOM but alsoits redox state may be affected by agricultural land use. This resultsfrom the fact that microorganisms in soils are able to use humicorganic matter as electron acceptor during oxidation of other organiccompounds or hydrogen, which in turn results in a low redox state ofthe humic organic matter (Lovley et al., 1996). By this mechanism,humic SOM of low redox state could be enriched in soils over longertime scales and the disturbance of organo-mineral aggregates bytillage in agriculture may result in the release of such reduced,humic SOM as DOM. Such a release of humic DOM of low redoxstate was shown for soil leachates in agricultural soils with conven-tional tillage (Bayer et al., 2000, 2002). Moreover, evidence for arelationship between agricultural land use and redox state of DOMin surface water export comes from Canadian catchments, wherethe contribution of reduced DOM has shown a weak positive correla-tion to agricultural land use (Williams et al., 2010). However, in thisstudy, catchments of very different sizes (10–993 km2) were usedand hence a stronger relationship may partly be concealed by preced-ing in-stream processing such as uptake of DOM by microbial organ-isms. This notion which is supported by the strong relationshipsbetween DOM composition and temperature found in the study(Williams et al., 2010). If a strong relationship between agriculturalland use in the catchment and redox state of DOM would be foundconsistently across catchments and seasons, this would have strongimplications for the ecosystem metabolism and health of surfacewaters situated downstream of agricultural catchments. The reasonis that DOM of low redox state was shown to be readily oxidized instreams (Miller et al., 2006) and that high bacterial production iscoupled to low DOM redox state (Williams et al., 2010). However,the consistency of the link between agricultural land use and lowDOM redox state is still unclear. It could thoroughly be assessed bya spatially and seasonally repeated investigation of DOM redoxstate, using fluorescence measurements (Cory and McKnight, 2005;Miller et al., 2006; Williams et al., 2010) in agricultural headwaterstreams close to the terrestrial–water interface.

Drainage of agricultural soils results in altered flow paths and leadsto high discharge after precipitation events (Blann et al., 2009). Loadand concentration of DOM in agricultural streams were shown to bepositively related to discharge and thus have been found to be highlyvariable in time (Royer and David, 2005; Dalzell et al., 2007; Ruark etal., 2009). In contrast, a relatively high stability of the agriculture-specific signal of DOM composition was found across one year for aDanish agricultural stream (Stedmon and Markager, 2005) and in astudy of two relatively large Australian agricultural catchments, butonlywith one sampling date per season (Petrone et al., 2011). Also, sea-sonally stable composition patterns match the findings of a long-termstudy of agricultural soils, which found similar effects on DOM compo-sition and concentration in soil leachates after five and nine years ofconventional tillage (Bayer et al., 2002). It is unclear, why DOM compo-sition in agricultural streams does not exhibit stronger seasonality but apossible cause for this could be that DOM has a long downward travel-ing time in soils, something thatwas shown for SOM in agricultural soils(Ewing et al., 2006; Schmidt et al., 2011). Based on the literature, it islikely that the seasonality of DOM concentration and load is stronglyaffected byhigh seasonality of hydrological regimedue to artificial drain-age of agricultural catchments. In contrast, due to its low seasonality itseems that DOM composition is not ormuch less affected by these alter-ations. However, these differences remain hypothetical since studiescoupling seasonal variation of discharge with DOM amount and compo-sition are still lacking for agricultural headwaters.

436 D. Graeber et al. / Science of the Total Environment 438 (2012) 435–446


The aim of this study was to determine the significance of agricul-tural land use as a source of DOM with altered composition to surfacewaters. We compared the spatial variation in DOM amount and com-position among headwater streams draining agricultural catchmentsand near-pristine catchments (forested and wetland) located in theNorth German plains. We examined the seasonal variation in theland use-related patterns of DOM amount and composition by repli-cated bi-weekly samplings conducted in each catchment type forone year. We assessed DOM quantity by measuring the concentrationand load of DOC and DOM composition by using absorbance andfluorescence measurements and Parallel Factor Analysis (PARAFAC).This multivariate statistical model is used to extract independentfluorophores from fluorescence measurements, which quantitativelyrepresent molecular fractions of DOM (Stedmon et al., 2003). Wehypothesized that, due to the long-term effects of disturbance ofSOM by agricultural practices, agricultural catchments would showseasonally consistent (i) high concentrations of DOC, significantlyhigher than in forested catchments (ii) a structurally complex DOMof low redox state, significantly distinct from near-pristine (forested,wetland) catchments, an effectwhich is probably independent from soiltype. We further hypothesized that, due to the precipitation-eventrelated reactivity and higher water yield of the tile-drained agriculturalsoils, agricultural catchments would exhibit (iii) a larger seasonalvariation in DOM concentration and load, as well as a higher DOCexport, compared to near-pristine catchments. Finally, we used ourdata set to investigate potential correlations between DOC concentra-tion and DOM composition, which may allow to better infer potentialsources of DOM in the catchments.

2. Material and methods

2.1. Study area

The climate in the federal state of Brandenburg (North Germanplains, Germany) is moderately continental with an average annualprecipitation of 555 mm and an average air temperature of 9.4 °C(long-term mean of the years 1991–2009; station Lindenberg,German Weather Service, www.dwd.de). During this time, on aver-age 34 mm of the precipitation occurred at air temperatures b0.5 °Cand thus presumably as snow. The study years 2008 and 2009exhibited an annual mean temperature of 10.3 and 9.8 and an annualprecipitation of 597 and 581 mm, respectively (station Lindenberg,German Weather Service, www.dwd.de). Of the precipitation in2008 and 2009, 10 mm and 60 mm occurred at air temperaturesb0.5 °C. From this data, the study years are representative for thelong-term mean of this region. The landscape of the North Germanplains and other parts of Northern Europe was shaped during thelast glacial maximum (Clark and Mix, 2002). As a result, the soilswere formed by Pleistocene glacial erosion and deposition and areweathered to a low degree (Semmel, 1996). Agriculture (49% of thearea of the Federal State of Brandenburg, mainly crop-based,tile-drained and with conventional tillage) and forests (36%, mainlymixed and coniferous) are the main land use types (Amt fürStatistik Berlin-Brandenburg, 2010). Most wetlands (8%) are lowlandfens, fed by groundwater, and are degraded by drainage and pastureto a varying degree (Zeitz et al., 1998; Zak et al., 2010). Soils in thestudy area are acidic and are dominated by sands and loamy sands(Landesamt für Bergbau Geologie und Rohstoffe, 2012).

According to land use and soil type in the catchment, we dividedour studied catchments in three groups: agricultural, forested andwetland catchments. These cover 93% of the study area and an inves-tigation of these catchment types should allow a good description ofcatchment DOM export to surface waters in the area. Moreover, wechose catchments reflecting the dominant soil types of the studyarea (see Table B1 in Appendix B for details). Additionally, wechecked, if these pre-defined groups are reflecting differences in

DOM composition and DOC concentration data (see Data analysisand statistics for details on the analysis).

For agricultural and forested catchments,we used Corine LandCover(CLC) data to determine land use in the catchments (EuropeanEnvironment Agency, 2007; see Table B2 in Appendix B for details).Agricultural catchments were dominated by pure crop or crop inrotation with green manure, oil-bearing seeds or protein-rich plants(Ministerium für Infrastruktur und Landwirtschaft, 2011). Wetlandswere often used as pasture or were situated within forests and thuswere classified as such in the CLC data. Instead of using CLC data, wedefined wetland catchments as outflows of calciferous, groundwater-fed, lowland fens with forested, pasture or agricultural catchments,drained to a low degree. Moreover, all wetland catchments werecharacterized by >10% of Gley- and Histosols (Landesamt für BergbauGeologie und Rohstoffe, 2012; see Table B1 in Appendix B for details).We delineated the catchment borders using a digital elevationmodel based on the data from the Shuttle Radar Topography mission(Rabus et al., 2002) and topographical maps (Landesvermessung undGeobasisinformation Brandenburg, 2011).

2.2. Field sampling

We conducted a screening of 53 headwater streams (0.01–43.9 km2

catchment size, with 91% of the catchments being b10 km2) drainingcatchments covered >76% by crop-based agriculture, tile drained andwith conventional tillage (n=17; hereafter referred to as agriculturalcatchments; see Table B2 in the Appendix for details on the land use),>56% by mixed and coniferous forests (n=19; forested catchments)or by lowland fens (n=17; wetland catchments) from 4 March–9April 2008 (hereafter referred to as spatial-variation dataset). To ana-lyze DOM composition, we measured fluorescence and absorbance in42 of these catchments (agricultural n=15; forested n=16; wetlandn=11). Catchments were spatially independent and we conductedthe sampling within one month to reduce confounding effects of tem-poral variability on DOM quantity and composition. No irrigation wasused during the time of the study in the sampled agriculturalcatchments.

We sampled two of the agricultural (abbreviated as A1 and A2),three of the forested (F1, F2 and F3) and three of the wetland catch-ments (W1, W2 and W3) fortnightly from 16 July 2008–30 June2009 (25 sampling dates; hereafter referred to as seasonal-variationdataset). We measured DOC concentration and discharge at allsampling dates and absorbance and fluorescence at 20 samplingdates (9 September 2008–30 June 2009, excluding 7 October 2008).We determined discharge with a FlowTracker Handheld acousticDoppler velocity meter (F2, W1, W2, W3; SonTek, Yellow Springs,USA) or by NaCl slug additions (A1, F3; Hongve, 1987), dependingon stream morphology and water depth. At two sites (A2 and F1)we measured discharge by collecting the total water volume over aspecified time from a water drainage pipe.

We stored the collected water samples in a refrigerator at 4 °C,brought them to the laboratory, and pressure-filtered (b2 bar)them through pre-rinsed 0.45 μm filters with 8 μm pre-filters(cellulose-acetate membrane filters, Sartorius, Göttingen) within12 h. We acidified the samples for DOC concentrations with HCl topH 2–3 in order to remove dissolved inorganic carbon and mea-sured them immediately. We froze the samples for spectroscopicmeasurements at −28 °C until analysis. Although it can result inchanges of the DOM composition (Fellman et al., 2008), freezingwas necessary because the samples were stored for more thanone year before spectroscopic measurements. We minimized thiseffect by freezing and thawing the samples only once and process-ing them identically. Short-term cold storage in a refrigerator hassimilar effects as single freezing and thawing (Hudson et al.,2009) and we expected a stronger effect of long-term cold storage

437D. Graeber et al. / Science of the Total Environment 438 (2012) 435–446


due to continuous oxidation and microbial activity than for onefreezing/thawing cycle.

2.3. Laboratory analyses

We measured DOC concentration (detection limit=0.5 mg L−1 C)using the High Temperature Catalytic Oxidation — Method (HTCO,multi N/C 3100, Jena Analytik).

We conducted all absorbance measurements on a UV-2401 UV/VISspectrophotometer (Shimadzu, Duisburg, Germany), fluorescencemeasurements for the spatial-variation dataset on a Varian CaryEclipse fluorescence spectrometer (Palo Alto, USA), and those for theseasonal-variation dataset on a Perkin-Elmer LS-50b fluorescencespectrometer (Rodgau, Germany). We measured excitation from 240 to450 nm (5 nm steps) and emission from 300 to 600 nm (2 nm steps)with a slit width of 5 nm to produce Excitation–Emission-Matrices(EEMs; Stedmon and Markager, 2005). We measured the samples atroom temperature and corrected the absorbance spectra (800 to190 nm) for instrument baseline offset (Green and Blough, 1994).Daily measurements of the area under the Raman peak for MilliQwater (Millipore, Schwalbach, Germany) indicated instrument stability(Lawaetz and Stedmon, 2009). Fluorescence and absorbance readingswere within the linear range of the spectrometers and we accountedfor primary and secondary inner-filter effects by inner-filter correction(Lakowicz, 2006). Moreover, for excitation we corrected the spectra byusing the correction provided by the manufacturer, and for emissionwe corrected them by using the BAM fluorescence calibration kit(Pfeifer et al., 2006) and normalized them by the area under theRaman peak at 350 nm excitation wavelength (Lawaetz and Stedmon,2009). These corrections ensured measurement comparability betweenthe two fluorescence spectrometers and provide the best possiblecomparability to other DOM fluorescence studies (Lawaetz andStedmon, 2009). Fluorescence of the samples was not influenced bypH because it was within a small range for the spatial-variation andthe seasonal-variation datasets (mean±SD=7.7±0.6 and 7.6±0.4,respectively; Patel-Sorrentino et al., 2002).

2.4. Spectroscopic indices and PARAFAC modeling

From absorbance spectra we calculated two indices: a) SUVA254

(L mg−1 m−1), a surrogate for DOM aromaticity (Weishaar et al.,2003), and b) spectral slope for 275–295 nm (S275–295), a surrogate foraverage molecular size (Helms et al., 2008). From EEMs we calculatedthree indices: a) humification index (HIX; Zsolnay et al., 1999), for

which values ~1–2 are associated with non-humified material andvalues >10 are typical for fulvic acid extracts (Zsolnay et al., 1999), b)fluorescence index (FI;McKnight et al., 2001), forwhich values ~1.3 sug-gest a dominant terrestrial, higher-plant DOM source and values ~1.8suggest a predominant microbial DOM source (McKnight et al., 2001),and c) freshness index (β:α; Parlanti et al., 2000), for which values >1correspond to DOM freshly released into water, whereas values ~0.6–0.7 correspond to lower DOM production in natural waters and higherinput of terrestrial origin (Wilson and Xenopoulos, 2009).

To analyze the independent fluorophores of the EEMs with theaim to assess the composition of DOM, we created a PARAFACmodel with the DOM Fluor Toolbox (version 1.7; Stedmon and Bro,2008) for Matlab (version R2009b, MathWorks, Ismaning, Germany)using all measurements from the spatial and seasonal variationdatasets (n=194). We identified and split-half validated (see Fig.A1 in Appendix A for split-half and final plots of the components)seven PARAFAC components (hereafter referred to as fluorophoresC1 to C7) and established the best model fit by random initialization(Stedmon and Bro, 2008). We express DOM fluorescence of thePARAFAC components as percent contribution of each component tothe fluorescence. We assigned potential sources and functions toeach PARAFAC component by comparing them to componentsreported in the literature (Table 1).

2.5. Data analysis and statistics

Prior to the data analyses, we log-transformed not-normally dis-tributed variables (visually checked with q–q plots and histograms)of the spatial-variation dataset (SUVA254, C7) and of the seasonal-variation dataset (C4, C5, C7).

We assessed the effects of catchment size, land use and soil type onDOMcomposition andDOCconcentration by using redundancy analysis(RDA; rda function of vegan package, Oksanen et al., 2011), variancepartitioning (varpart function of vegan package) for R (version 2.15.1;R Development Core Team, 2012). We used permutational ANOVAs ofthe RDA models of catchment size, land use and soil type to assess ifthey significantly explain the variance of the dataset (99,999 iterations,anova.cca function of vegan package). For all analyses, we used thespatial-variation dataset. We did not analyze the seasonal variationdataset, since it includes only eight catchments, which results in alow number of replicates for the explanatory variables. In addition,the catchments of the seasonal-variation dataset are also includedin the spatial-variation dataset, which makes the analysis of theseasonal-variation dataset redundant. For the analyses, the spatial

Table 1Emission and excitation peaks as well as assignment of potential sources and functions of components found by PARAFAC analysis according to literature. Primary peaks are givenwithout brackets, secondary peaks in brackets.

Component Emissionpeak

Excitationpeaks

Assignment of potential sources and functions

C1 420 b240 (315) Humic-like fluorophorea,b; absent in wastewater, terrestriala; not correlated with land use or bacterial productionb

C2 510 260 (390) Humic-like fluorophorea–c; terrestrial or autochthonous, fulvic acid-like, present in all environmentsa; reduced terrestrial semi-quinonefluorophore (SQ1)c; positively related to agriculture and bacterial productionb

C3 432 255 (370) Humic-like fluorophorea–c; terrestrial/autochthonous, fulvic acid-like, present in all environmentsa; reduced aquatic semi-quinonefluorophore (SQ2)c; positively related to agriculture and bacterial productionb

C4 428 245 (325) Humic-like fluorophorea,b; absent in wastewater, terrestrial; negatively related to percentage of forage crop agriculture, positivelyrelated to percentage of wetland in the catchment and negatively to bacterial productiona

C5 360 b240 (290) Protein- and tryptophane-like fluorophore, microbial-produceda,b; derived from aquatic production or correlated with terrestrialmaterial in forested catchmentsa; negatively related to wetland catchmentsb; positively to leucine-aminopeptidase activityb and tobioavailabilityd

C6 396 340 (250) Humic-like fluorophorea; dominates the fluorescence of wastewater DOM, correlated with DOM exported from agriculturalcatchmentsa; reduced semi-quinone fluorophore (SQ3)c

C7 302 270 (b240) Protein- and tyrosine-like fluorophore, produced and removed by the same processes as tryptophan-like fluorescencea; positivelyrelated to bioavailabilityd

a Stedmon and Markager (2005).b Williams et al. (2010).c Cory and McKnight (2005).d Fellman et al. (2008).



variation dataset was z-standardized and not-normally distributedvariables (visually checked with q–q plots and histograms) were log-transformed (SUVA254, C7). As explanatory variables, we used catch-ment size, aswell as percentages of catchment area covered by differentland use (arable land, forest, pasture, urban, other land use) and soiltypes (Albeluvisols, Albeluvi-/Luvisols, Albeluvi-/Planosols, Arenosols,Areno-/Albeluvisols, Areno-/Gley- and Luvisols, Areno-/Podsols,Gleysols, Histosols, Histo-/Gleysols, Luvisols and Podsols; see Tables B1and B2 in Appendix B for details). Before RDA and variance partitioning,we forward selected the variables of land use and soil type (ordistepfunction of vegan package) to get the most parsimonious explanationof the data and to avoid collinearity between the explanatory variables.As selection criteria for the variables included into the forward selection,we used the adjusted coefficient ofmultivariate regression (R2

adj), signif-icance of the variables (pb0.05) and the Akaike Information Criterion(Borcard et al., 2011). As stopping criteria for the forward selection, weused the significance of the variables (pb0.05) and R2

adj of a globalmodel containing all potential explanatory variables of either land useor soil type (Blanchet et al., 2008; Borcard et al., 2011).

To assess, whether our pre-defined catchment types are significantlydifferent from each other in terms of DOC concentration and DOM com-position, we used a permutational MANOVA (99,999 iterations, adonisfunction of veganpackage) for R. Prior to the test, we checked that homo-geneity of variance–covariance matrices was given (betadisper andpermutest functions of vegan package).

We reduced the number of DOMcomposition variables (i.e. spectro-scopic indices and PARAFAC components) by using two separatedprincipal component analyses (PCAs), one for the spatial-variationdataset (spatial-PCA) and one for the seasonal-variation dataset(seasonal-PCA) in SPSS 17 (SPSS Inc., New York, USA). We usedz-standardized variables for the PCAs, and the weight of a variable ona PCA component was considered significant when its loading was>0.7. We extracted the scores of the principal components by theBartlett–Andersen-method. We excluded DOC concentration fromPCA to analyze DOM composition and DOC concentration separately.

To compare DOC concentration, spectroscopic indices, PARAFACresults and scores of spatial-PCA components among catchment typesof the spatial-variation dataset, we conducted a Dunnett–Tukey–Kramer pairwise multiple comparison test (DTK-Test) using the DTKpackage (Lau, 2009) for R. To compare DOC concentration and scoresof seasonal-PCA among the eight catchments of the seasonal-variationdataset, we conducted a one-way repeated-measures ANOVA (nmlepackage for R; Pinheiro et al., 2010) and a Tukey-HSD pairwisemultiplecomparison post-hoc test (multcomp package for R; Hothorn et al.,2008).

For the seasonal-variation dataset, we calculated the DOC loads ofeach site and date by the periodic method (Kauppila and Koskiaho,2003). To compare the DOC loads and their variability among thecatchments, we normalized the DOC loads by their annual sum foreach of the catchments. To assess the temporal stability of DOCloads and concentrations and their dependence on discharge, wecalculated the annual coefficient of variation (CV) of all three vari-ables for each site. To assess the dependence of DOC on discharge,we calculated the annual discharge-weighted mean concentration(DWMC) by dividing the annual sum of DOC load by the annualsum of discharge.

To assess the annual DOC export on a per area basis, we summedup the DOC loads and divided them by the catchment size. To assessuncertainty of the DOC export, we used Monte Carlo (MC) simula-tions (mean of 50 repetitions of a MC simulation with 100,000iterations) in R to estimate the uncertainty of the export calculations.This was necessary, since determination of catchment sizes yieldshigh uncertainties due to small catchment size and moderate surfaceslopes (Miller and Morrice, 1996) in addition to the uncertaintiesintroduced by discharge measurements (e.g. Pelletier, 1988). Basedon Miller and Morrice (1996) and on the catchment size, we assumed

the following relative errors of catchment size for our catchments:A1=10%; A2=20%; F1=30%; F2=30%; F3=10%; W1=6%; W2=6% and W3=2%. For F2 we assumed a high error because determina-tion of the catchment boundaries was especially difficult. In additionto the error of catchment size, we introduced a relative error for thedischarge measurements into the MC simulation, based on the typeof measurement. For the velocity meter-based discharge measure-ments (F2, W1, W2, W3), we assumed an error of 20% (Pelletier,1988), for salt dilution-based measurements (A1 and F3) we assumedan error of 5% (Hudson and Fraser, 2002) and no error was assumedfor the discharge measurements based on total water collection (A2and F1). We assumed normal distribution for all input variables ofthe MC simulation.

To investigate if DOM composition was related to DOC concentra-tion, we fitted linear models for DOC and Spatial-PC1 and PC2, as wellas Seasonal-PC 1 and PC 2 across all sites (lm function, stats packagein R; R Development Core Team, 2012). For the entire seasonal-variation dataset, DOC concentrations were not-normally distributed,which is in contrast to the DOC concentrations for the single catch-ments. We reached normal distribution by log transformation. Despitethe inner-filter correction applied to the data (see above, Lakowicz,2006), reduced inner-filter effects could still occur for samples withsample absorbances >0.3 cm−1 at 254 nm (Ohno, 2002). To check ifthis influenced potential correlations between DOC concentrationsand DOM composition, we repeated the correlations for only sampleswith absorbances b0.3 cm−1 at 254 nm.

3. Results

3.1. Predictors of DOM amount and composition

We used variance partitioning to test whether land use or soil typewere explaining more variance of the spatial-variation dataset andused RDA to investigate the relationships in detail. We excludedcatchment size as an explanatory variable from these analysesbecause it was not significantly explaining the variability of thespatial-variation dataset (RDA and permutational ANOVA, F=0.9,p=0.37). As explanatory variables for land use, the percentagesof arable land and forest in the catchments were forward selectedand as explanatory variables for soil type, the percentages ofArenosols, Areno-/Podsols and Histosols in the catchments wereforward selected.

The variance partitioning of the forward selected explanatoryvariables of land use and soil type resulted in an adjusted coefficientof multivariate regression (R2

adj) of 0.41 (permutational ANOVA,F=6.6, pb0.001). The unique contribution of land use was R2

adj=0.12 (F=4.8, pb0.001) and the unique contribution of soil typewas R2

adj=0.06 (F=2.3, p=0.008). The joint contribution of landuse and soil type was higher than the unique contributions (R2

adj=0.23) but could not be tested for significance as in variance partitioningthe significance of joint contributions is generally not testable (Borcardet al., 2011).

In the RDA, the forward selected explanatory variables wellexplained the variability of DOC concentration and DOM composition,as well as our pre-defined groups (Fig. 1). The percentage of arableland was best related to C3, C6 and HIX, as well as the pre-definedagricultural catchments. Percentages of Histosols and Areno-/Podsolsin the catchments were best related to C4 and S275–295 and thepre-defined wetland catchments (Fig. 1). Percentages of forest coverand Arenosols in the catchments were best related to log C7 and C5and the pre-defined forested catchments (Fig. 1).

The pre-defined catchment types (agricultural, forested and wet-land) of the spatial-variation dataset exhibited a significantly differentcomposition of DOM (permutational MANOVA, F=7.5, pb0.001).Thus, our pre-defined catchment types are a good predictor for DOM



composition and DOC concentration, and can be used for furtheranalyses.

3.2. Effects of catchment type and season on DOM amount

The spatial-variation dataset from the screening during spring2008 revealed that DOC concentrations were similarly high for theagricultural and wetland catchments but low for the forested catch-ments (Table 2). This pattern held only partially across seasons,because the annual mean DOC concentration was highest for twoout of the three investigated wetland catchments and lowestfor two out of the three forested catchments (Table 3). Mid-levelannual mean DOC concentrations were observed for the agriculturalcatchments (Table 3).

The annual discharge weighted mean concentrations (DWMC) ofDOC were slightly deviating from the simple mean concentrationsfor the agricultural and wetland catchments, but no clear directionof the deviation and no strong changes in the above reported overallpatterns of DOC concentrations were found (Table 3). No deviationbetween DWMC and the simple mean DOC concentrations wasfound for the forested catchments (Table 3).

Seasonal variation in DOC concentrations was low for most of thecatchments (Table 3; Fig. 2 a., b., c.). A strong pattern was only foundat W1, where the DOC concentration peaked (58.7 mg CL−1) andthen directly fell to its annual minimum (14.1 mg CL−1) in March(Fig. 2 c.). In contrast, the seasonal variation in DOC load was highfor all but the forested catchments (Table 3; Fig. 2 d., e., f.), with W1again showing the highest CV and exporting half of its annualload on one occasion. Most catchments also showed high seasonalvariation in discharge (Table 3).

The estimated annual area-based export of DOC was 8–875 kg km−2 yr−1 (Table 3). It was always higher in the agriculturalthan in the forested catchments and the wetland catchmentsexhibited the highest variability of annual export (Table 3). Based onthe Monte Carlo simulations, the uncertainty of the estimated annualexport was high (15–80%), especially in F1 and F2 (80%, Table 3).

3.3. Effects of catchment type and season on DOM composition

The first two principal components (PC) of the PCA explained mostof the variance for the spatial-variation dataset (PC 1: 46.6%; PC 2:19.5%, overall 66.1%) and the seasonal-variation dataset (PC 1: 54.9%;PC 2: 21.7%, overall 76.1%) and revealed clear differences in DOM com-position among catchment types (Fig. 3). Agricultural catchmentsexhibited high relative fluorescence of fluorophores C3 and C6, as wellas high HIX and low C7 (Fig. 3, Table 2). In addition, agricultural catch-ments were related to low S275–295 for the spatial-variation dataset(Fig. 3a, Table 2) and high FI for the seasonal-variation dataset(Fig. 3c). Forested catchments were characterized by higher percent-ages of the fluorophore C7 than the agricultural or wetland catchments(Fig. 3, Table 2). In contrast, wetland catchments were characterized byhigh percentages of C1 (Fig. 3, Table 2). High relative fluorescence of thefluorophore C2 characterized both agricultural andwetland catchments(Fig. 3, Table 2). No clear patterns of seasonal variationwere discerniblefor the DOM composition, except for a slight peak of Seasonal-PC 1 forthe agricultural catchments and two of the wetland catchments inMarch–April (W2, W3, Fig. 4 a., c.).

According to the literature (see also Table 1), fluorophores C1–C4and C6 are similar to humic-like fluorophores and hence probablyrepresent humified DOM (Stedmon and Markager, 2005), wherebyC2, C3 and C6 are probably redox sensitive as these are similar toreduced semiquinone substances (Cory and McKnight, 2005). Thefluorophores C5 and C7 are similar to protein-like fluorophores(Stedmon and Markager, 2005).

3.4. Correlations between DOC concentration and DOM composition

Spatial- and Seasonal-PC 1 exhibited a strong positive correlationwith DOC concentration, regardless of catchment type or season(Fig. 5. a., b.). In contrast, no correlation was found for Spatial-PC 2(Fig. 5 c.), and a negative, weak correlation was found for Seasonal-PC 2 (Fig. 5 d.). As PC1 represents a gradient from protein- tohumic-like fluorescence, the strong positive relationship to DOCconcentration suggests that higher concentrations of DOC aregenerally more humified (Figs. 3, 5).

Even after removing samples with absorbance >0.3 cm−1 at254 nm (potential inner-filter effects), strong positive correlationswere found for Spatial- (r2=0.62, pb0.001, n=40) and Seasonal-PC 1(r2=0.63, pb0.001, n=114). The correlation remained insignificantfor Spatial-PC 2 (r2=0.001, p=0.85, n=40). In contrast to the

Fig. 1. Results from the redundancy analysis (RDA) of theDOMcompositiondata andDOCconcentration of the spatial variation dataset and the forward-selected explanatoryvariables for land use and soil type (n=42).

Table 2Mean values (±standard deviation) of DOC concentration (agricultural n=17, forestedn=19, wetland n=17) and composition (agricultural n=15, forested n=16, wetlandn=11) from the screening of catchments with different land-use types in spring 2008(spatial-variation dataset). Superscript letters indicate significant differences (Dunnett–Tukey–Kramer pairwise multiple comparison test) among land-use types.

Catchment type

Agricultural Forested Wetland

DOCa 7.6±2.8A 1.5±0.9B 12.0±8.8A

Spatial-PC 1b 0.8±0.7A −0.9±0.7B 0.2±0.6A

Spatial-PC 2b 0.8±0.8A 0.0±0.6B −1.2±0.4C

S275–295c 0.011±0.003A 0.014±0.003B 0.017±0.001C

SUVA254c,d 2.5±0.3A 2.5±0.5A 2.7±0.5A

FIe 1.7±0.2A 1.8±0.2A 1.6±0.3A

β:αe 0.8±0.1A 0.7±0.1A 0.7±0.1A

HIXe 6.3±1.5A 2.7±0.9B 4.8±1.1C

C1f 18.3±1.6A 12.9±3.7B 19.3±3.0A

C2f 7.8±1.3A 6.2±1.7B 7.6±0.8A

C3f 11.2±0.9A 9.1±1.4B 9.4±0.7B

C4f 25.2±3.1A 20.5±2.9B 26.2±2.6A

C5f 16.2±1.6A 18.2±2.0B 17.7±1.4AB

C6f 12.7±1.9A 9.0±1.4B 6.9±1.2C

C7f 8.5±3A 24.2±8.1B 12.8±4.3C

a DOC concentration in mg C L−1.b Scores of principal components extracted for each of the sites from PCA on spatial

dataset (Fig. 3).c Indices derived from absorbance measurements.d Unit is L mg C−1 m−1.e Indices derived from fluorescence measurements.f Components created by PARAFAC analysis.



full dataset, a weak positive correlation was found for Seasonal-PC 2(r2=0.14; pb0.001; n=114).

4. Discussion

4.1. Predictors of DOM amount and composition

Catchment size had no significant effect on DOC concentration andDOM composition. This indicates that for the small catchments usedin our study (0.1–50 km2) no potentially catchment-size relatedprocesses (e.g. different water flow paths and in-stream processing)have altered DOM composition. Moreover, this means that we caninvestigate land use and soil type related effects without the interfer-ence of such processes.

The unique effects of land use and soil type were significantlyexplaining DOC concentration and DOM composition but the varianceuniquely explained by land use (R2

adj=0.12) was twice as high as forsoil type (R2

adj=0.06). However, the variance jointly explained by landuse and soil type was much higher than their unique effects (R2

adj=0.23). This indicates that a large part of the effects explained by landuse is inextricably linked to the effects explained by soil type in termsof DOC concentration and DOM composition but that a part of the varia-tion is only explained by land use. A closer look at the relationships ofland use and soil type reveals that both strongly affect DOC concentrationand DOM composition in the forested and wetland catchments (Fig. 1).However, for the agricultural catchments, we found that DOC concentra-tion and DOM composition are solely explained by the percentage of ar-able land cover in the catchment (Fig. 1). Together with the varianceexplained uniquely by land use, it is clear that land use especially arableland use has a soil type-independent effect on DOC concentration andDOM composition (Fig. 1).

4.2. Effects of catchment type and season on DOM amount

In support of our first hypothesis, agricultural catchments consis-tently exported high DOC concentrations, significantly higher thanfor forested catchments and similar to those from wetland catch-ments. In our study area, most of today's agricultural catchmentsare situated in former forested catchments (Hofmann and Pommer,2005). Thus, higher DOC concentrations in streams draining agricul-tural catchments may indicate that a previously inert pool of DOC offormer forest soils is mobilized by tillage and exported to surfacewaters (Ogle et al., 2005), a process that may be intensified by directlinkage of agricultural soils and streams by tile drains (Blann et al.,2009) due to less retention of DOC in the soil. Also, application oforganic fertilizers to the agricultural fields could result in higherDOC concentrations in the streams; however, such an effect was notobserved in a previous North American study (Royer et al., 2007).

Our findings of high DOC concentrations in agricultural catch-ments are in contrast to the results from previous studies in Canadaand the USA, in which DOC concentration was not related to thepercentage of agriculture in the catchment (Wilson and Xenopoulos,2008) and in some cases was even lower in agricultural streamsthan in forested streams (Cronan et al., 1999). As in our study, areasof the former studies are located in the temperate climate zone andcrop-based agriculture is the main type of agricultural land use.However, differences in actual and historical land use as well as soiltypes may be responsible for the different findings. Soil types ofprevious studies are comparable to those from our study (Cronanet al., 1999) or are not reported (Wilson and Xenopoulos, 2008),thus differences in soil type cannot explain the differences betweenthe studies partly due to lacking information, but differences in landuse history may. The catchments from Wilson and Xenopoulos

Table 3Annual mean values (±1 SD) of DOC concentrations, discharge-weighted mean (DWM) DOC concentration, annual estimated DOC export (±1 SD) and coefficients of variation forannual mean DOC concentration, discharge, DOC load and DOC export and scores of seasonal principal components (PC) 1 and 2, as well as SUVA254, HIX, FI and β:α for each of thecatchments sampled fortnightly from July 2008 to June 2009 (±1 SD, seasonal-variation dataset). A = agricultural, F = forested, W = wetland catchments. N=25 for each catch-ment, excluding Seasonal PC 1 and 2 (n=20). Superscript letters indicate significant differences (pb0.05) of a Tukey HSD following a one-way repeated measures ANOVA for DOCconcentration (n=200, F=196.9, pb0.0001), Seasonal PC 1 (n=160, F=145.5, pb0.0001) and PC 2 (n=160, F=107.1, pb0.0001).

Catchment DOC conc. DWM DOC conc. Estimated DOC export DOC conc. Discharge DOC load DOC export Seasonal-PC

mg CL−1 kg C km−2 yr−1 Coefficient of variation (%) 1 2

A1 6.7±1.4AF 7.1 645±111 21 73 84 17 0.2±0.5A 1.3±0.4A

A2 6.9±1.7AF 6.2 394±107 24 82 59 27 0.7±0.4B 1.1±0.5A

F1 2.1±0.6B 2.1 68±54 28 8 28 80 −1.3±0.5C 0.3±0.7B

F2 1.3±0.5B 1.3 295±235 39 31 48 80 −1.5±0.4C −0.2±0.5C

F3 3.8±0.7AB 3.8 54±9 17 14 24 17 −0.2±0.5D −0.7±0.5D

W1 35.3±8.4C 24.2 8±2 24 303 292 19 0.7±0.3B −1.6±0.4E

W2 14.1±5.4D 16.9 875±140 38 93 108 16 1.1±0.5E −0.2±0.4C

W3 9.4±4.4EF 12.8 344±52 47 85 128 15 0.4±0.5AB −0.1±0.4C

Fig. 2. Time series of DOC concentration and percentage of annual DOC load for theagricultural catchments (panels a., d.), forested catchments (panels b., e.) and wetlandcatchments (panels c., f.).



(2008) have been taken into agricultural land use during the 20thcentury, which is in contrast to our agricultural catchments, whichhave been more intensively used for agriculture since approximately1750 A.D. (Hamel, 1999). This difference in history of agriculturalland use may explain the different effects of agriculture on DOCconcentration. However, according to a meta-analysis from Guo andGifford (2002), there is no generally applicable rule for the timingof SOM loss from soils after land use conversion. An alternativeexplanation may be the different catchment sizes of our study com-pared to the previous studies. We conducted our study in relativelysmall catchments (0.01–43 km2, with 91% of the catchments beingb10 km2; see Table B2 in Appendix B for details), whereas the NorthAmerican studies were conducted in small to large (10–993 km2;Wilson and Xenopoulos, 2008) or medium to large catchments(129–1307 km2; Cronan et al., 1999). The larger catchment sizesindicate that the sampling sites in those studies were situated in higherstream orders. The different effects of agriculture on DOC concentra-tions reported from ours and the previous studies could therefore be aresult of DOM production and processing along the stream networkwhen moving from low order to higher stream orders (Battin et al.,2008). However, since no study has investigated the processing of agri-cultural DOM in different stream orders yet, this remains hypotheticaland needs to be addressed in future studies.

In contrast to the findings for the agricultural catchments, theconcentrations of DOC in wetland catchments were within the rangereported in other studies (Wiegner and Seitzinger, 2004; D'Amoreet al., 2010). Thus, the similarity of the DOC concentrations found foragricultural and wetland catchments is probably not due to low DOCconcentrations in the wetland catchments but rather due to high DOCconcentrations in the agricultural catchments. Considering the largeextent of agricultural land cover in our study area, our findings implythat, in this area, agricultural soils may be an important terrestrialDOM source to headwater streams and aquatic ecosystems.

The seasonal variation in DOC concentrations was similarly low inagricultural and forested catchments but was partly higher in wet-land catchments, which is in contradiction to our third hypothesis.In contrast to previous studies (Royer and David, 2005; Dalzellet al., 2007; Ruark et al., 2009), our results imply an independenceof DOC concentration from the hydrology of agricultural catchments.Furthermore, the high variation of DOC concentrations in wetlandcatchments may indicate that the DOC concentration is affectedby a process (e.g. high variability in soil water table, evaporation,photodegradation) which does not exist or is less pronounced in theother catchment types in addition to the potential variation in DOCconcentrations due to drainage. Similarly, seasonal variation in DOCloads was higher in agricultural catchments than in forested catch-ments, but lower than in wetland catchments.

Also in contradiction to our third hypothesis, we found a highseasonal variation in DOC loads in both agricultural and wetlandcatchments, which arises from high variability in the dischargevalues. The reason for the high variation in agricultural catchmentsis probably that drainage of agricultural fields results in short flowpaths from the soil to the stream via tile drains so that precipitationand evaporation affect the stream flow more directly (Dalzell et al.,2007). For wetland catchments, the high seasonal variation in DOCloads was also related to the high temporal variability of discharge.This is surprising since wetlands are often thought to reduce flowvariability (Preston and Bedford, 1988; Tockner and Stanford, 2002).A potential explanation could be that anthropogenic drainage ofthe wetlands results in higher variability of flow which adds to thealready high variability in DOC concentration. In contrast to theother catchment types, the forested catchments exhibited lowseasonal variation in DOC load, likely due to a stable groundwatersupply of water and DOC (Inamdar et al., 2012).

The second and third highest estimated annual export of all eightcatchments of the seasonal-variation dataset was found for the

Fig. 3. Results from the principal component analysis (PCA) of the DOM composition data (spectroscopic indices and PARAFAC results). Panel a. shows the loadings of the DOMcomposition data and panel b. the scores for the catchments of the spatial-variation dataset in spring 2008 (n=42). Panel c. shows the loadings of the seasonal-variation datasetfrom the catchments sampled fortnightly from June 2008 to June 2009 (n=160). Panel d. shows annual mean (symbols) and standard deviation (whiskers) of the scores from theseasonal-variation dataset. In panels a. and c., the closed symbols denote significant variables (loading >0.7).



agricultural catchments. Only W2, a wetland catchment, exhibited ahigher annual export. Thus, we can partly accept the third hypothesisthat agricultural land use combined with subsurface tile drainageresults in high DOC export. The range of annual DOC exportfrom tile drained agricultural catchments in our study (394 and645 kg km−2 yr−1) was within the range reported for tile drainedagricultural catchments in mid-western US streams (Royer andDavid, 2005), but lower than the range reported for the Danishagricultural catchments (Stedmon et al., 2006). The main reasonfor the comparably low annual export reported in our study couldbe the low precipitation characteristic (long-term annual mean:555 mm yr−1) of the study area which resulted in low runoff andhence, low DOC export.

4.3. Effects of catchment type and season on DOM composition

In support of our second hypothesis, agricultural catchmentsexhibited a composition significantly different from that of forestedand wetland catchments due to the high relative fluorescence ofthe fluorophores C2, C3 and C6, as well as high HIX and low C7 inagricultural catchments. Moreover, this distinct DOM composition ofagricultural catchments was seasonally consistent. The fluorophoresC2, C3 and C6 are similar to reduced semiquinone-like fluorophores(Cory and McKnight, 2005) indicating a low redox state of theagricultural DOM and the high HIX indicates a high degree ofhumification (Fellman et al., 2010) which corresponds to a highstructural complexity.

Our findings suggest that the most plausible source of altered DOMcomposition in agricultural catchments is physical disruption of humicSOM by tillage and release as DOM (Ogle et al., 2005). This is supportedby studies showing that the amount of reduced semiquinones ispositively related to humification of SOM (Martin-Neto et al., 1998),to tillage and therefore disturbance of organo-mineral aggregates inagricultural soils (Bayer et al., 2000, 2002). The mechanism probablyresponsible for the accumulation of semiquinones in soils is the reduc-tion of quinones by microbial oxidation of other organic compounds orhydrogen under anaerobic conditions (Lovley et al., 1996) combinedwith the stabilization of semiquinones by a network of aromatic ringswithin the terrestrial humic substances (Scott et al., 1998). Moreover,the assumption that SOM is the main source of agricultural DOM issupported by its high structural complexity, as implied by the highHIX (Fellman et al., 2010). However, with tillage as the source of alteredagricultural DOM it remains unclear, why no distinct seasonal changesexist in DOM composition that may be linked to tillage. A possiblecause for this could be that DOM has a long downward traveling timein soils, where it undergoes microbial reduction until the products ofthis process (reduced semiquinones) reach the tile drain and finallythe stream. For SOM, it was shown that, with increasing soil depth,the contribution of microbially-derived organic matter increases andthat it can have a long residence time in agricultural soils (Ewinget al., 2006; Schmidt et al., 2011). If the same mechanisms apply forDOM, this would explainwhy part of the DOMwas exported as reducedsemiquinones and no immediate tillage effect on DOM composition instreams was found.

Application of organic fertilizers or pesticides can, with high prob-ability, be excluded as alternative source of agricultural DOM. If theDOM in the agricultural catchments of our study was derived fromorganic fertilizers, it would have been less humified and hence havea lower HIX and higher percentages of fluorophores similar to trypto-phan (C5) and tyrosine (C7; Baker, 2002; Naden et al., 2010). More-over, none of the fluorescence spectra reported for pesticides in theliterature resembles the ones found for the fluorophores in ourstudy (Mueller et al., 1992; Porini and Escandar, 2011). Altogether,our data strongly suggests that the relatively high complexity andreduced state of DOM in agricultural catchments are due to thedisturbance of SOM by tillage and the subsequent release of DOMfrom SOM.

As a result of this release, DOM with a higher contribution ofreduced semiquinone moieties is exported to streams. Withinstreams, fluorophores similar to the reduced semiquinone-likefluorophores of our study were found to be readily oxidizedeither abiotically or by microorganisms (Miller et al., 2006), and afluorophore similar to C3 was found to be positively related tobacterial production (Williams et al., 2010). The higher amount ofreduced semiquinone-like fluorophores in agricultural catchmentsfound in our study may therefore be coupled to higher bacterial pro-duction and biotic or abiotic oxidation in the downstream situatedsurface waters (Miller et al., 2006; Williams et al., 2010). Moreover,an increased microbial respiration or abiotic oxidation (Miller et al.,2006; Williams et al., 2010) could alter the tightly coupled biogeo-chemical cycles (Schlesinger et al., 2011) in aquatic ecosystemsreceiving water from agricultural catchments. For example, a higheravailability of oxidizable carbon could directly enhance assimilativemicrobial nitrogen uptake in streams and other surface waters(Bernhardt and Likens, 2002).

The composition of DOM in forested and wetland catchmentswas significantly distinct from that in agricultural catchments. Thefluorescence of DOM for the wetland catchments was mainly charac-terized by high percentages of the fluorophores C1 and C2. C1 is ahumic-like fluorophore of probably terrestrial origin and C2 is areduced semi-quinone like fluorophore (Cory and McKnight, 2005).Moreover, DOM from wetland catchments had a lower molecularsize than the DOM exported from agricultural catchments as

Fig. 4. Time series of Seasonal-PC 1 and Seasonal-PC 2 for the agricultural catchments(panels a., d.), forested catchments (panels b., e.) and wetland catchments (panels c., f.).The dashed horizontal lines indicate zero scores for better comparability. Spatial- andSeasonal PC1 (panels a., b. and c.) indicate the gradient from protein-like to humic-likefluorescence, with higher percentages of humic-like DOM at higher values of PC1. Highervalues of Spatial- and Seasonal PC2 (panels d., e. and f.) indicate higher percentages of thereduced semiquinone fluorophore C6.



indicated by higher S275–295 (Helms et al., 2008). This compositionindicates that in our study DOM in wetland catchments was derivedfrom less humified SOM than in agricultural catchments. Moreover,the wetland catchments were less characterized by reducedsemiquinone-like fluorophores than the agricultural catchments,although for anoxic peat layers of wetlands the production of suchreduced semiquinone-like fluorophores could be expected. Our find-ing implies that either most DOM in the outflow of the sampled wet-land catchments originates from oxic peat layers or that the longerresidence time in the wetland catchments results in the removal ofa part of the reduced semiquinone-like fluorophores. However, thedetailed mechanisms leading to low production or high removal ofthese fluorophores in wetlands need to be further investigated.

Percentages of the fluorophores C5 (tryptophane-like) and C7(tyrosine-like) were higher for forested than for agricultural catch-ments. In a study of potential DOM sources in a forested catchment,the occurrence of these fluorophores was clearly related to groundwa-ter sources, probably resulting from the adsorption of humic,hydrophobic DOM to mineral soils, thereby increasing the relative im-portance of the non-humified fluorophores (C5, C7) in the fluorescencesignal (Inamdar et al., 2012). A low degree of humification is supportedby the low HIX found for the forested catchments (Zsolnay et al., 1999).

The humic-like fluorophore C4 exhibited no consistent relation toland use, as it was positively correlated with the other humic and fulvicfluorophores on the spatial scale and highly negatively correlatedwith the same fluorophores on the temporal scale. We propose thepolycondensation of polyphenols as the cause for this pattern. Shortlyafter leaching, polyphenol fluorescence is similar to tyrosine- andtryptophane-like fluorescence and becomes more similar to humic-likefluorescence with increasing oxidative polycondensation (Maie et al.,2006). The fluorophore C4 couldmark the endpoint of this development.

4.4. Correlation between DOC concentration and DOM composition

We found a strong, significant correlation between DOC concen-tration and Spatial- as well as Seasonal-PC 1 across all catchmenttypes, suggesting that the fluorescence of DOM becomes morehumic-like at higher DOC concentrations. The most reasonable expla-nation for the found correlation is that humified DOM is exportedfrom a source which at the same time exports high amounts ofDOM, irrespective of the land-use type. The high contribution ofhumic-like DOM at high DOC concentrations suggests that the pro-posed source is humified terrestrial organic matter, a notion whichis supported by other studies (del Giorgio and Pace, 2008; Williamset al., 2010). Moreover, in a study of water sources in a forested catch-ment, it was found that groundwater exported more protein-likeDOM, whereas sources at the soil surface and the more SOM-richupper soil layers export more humic-like DOM (Inamdar et al.,2012). According to this, the main water source of our forested catch-ments was probably deeper groundwater, whereas for our wetlandand agricultural catchments it probably has been soil layers closerto the surface and rich in SOM. Another possible explanation for thiscorrelation could be inner-filter effects, which have been shown toincrease HIX by higher sample absorbance at shorter wavelengths(Ohno, 2002). However, we can assume that inner-filter effectswere not the cause of the observed correlation because we foundsimilar correlations for samples with an absorbance b0.3 cm−1 at254 nm for which our inner-filter correction was proven to removeall inner-filter effects (Ohno, 2002). A third potential explanation isthat higher DOM concentrations directly result in the generation ofDOMwith a more humic-like fluorescence due to a higher probabilityof intermolecular interactions (Zsolnay, 2003). However, such a cor-relation between DOM concentration and fluorescence characteristics

−3

−2

−1

01

23

Spa

tial−

PC

1

AgriculturalForestedWetland

y = 0.24 x − 1.2r² = 0.63p < 0.001n = 42

a

−3

−2

−1

01

23

Sea

sona

l−P

C 1

A1A2F1F2

F3W1W2W3

y = 1.77 x − 1.39r² = 0.65p < 0.001n = 160

b

2 4 6 8 10 12

−2

−1

01

23

4

Spa

tial−

PC

2

DOC conc. (mg C L−1)

y = −0.02 x + 0.11r² = 0.01p = 0.651n = 42

c

−0.5 0.0 0.5 1.0 1.5

−2

−1

01

23

4

Sea

sona

l−P

C 2

log DOC conc. (mg C L−1)

y = −0.63 x + 0.5r² = 0.08p < 0.001n = 160

d

Fig. 5. Correlations between DOC concentration and the principal components Spatial-PC 1 or Spatial-PC 2 and between log DOC concentration and Seasonal-PC 1 or Seasonal-PC 2(panels b., d.). The black line indicates the correlation and the dashed red and blue lines indicate lower and upper 95% confidence and prediction intervals, respectively. Spatial- andSeasonal PC1 (panels a., b.) indicate the gradient from protein-like to humic-like fluorescence, with more humic-like DOM at higher values of PC1. Higher values of Spatial- andSeasonal PC2 (panels c., d.) indicate higher percentages of the reduced semiquinone fluorophore C6.



was not observed for humic and fulvic acids, even when humic acidconcentrations of 5 and 100 mg L−1 were compared (Mobed et al.,1996).

Based on this relationship it could be that the actual increase ofDOC concentrations in many European and North American surfacewaters (Roulet and Moore, 2006; Monteith et al., 2007) results in amore complex, humic-like DOM composition in headwater streamsand downstream aquatic systems. Moreover, this shift in DOM com-position may affect the microbial availability of DOM in these systems(Amon and Benner, 1996).

5. Conclusions

Compared to forested and wetland catchments, agricultural catch-ments exported high concentrations and loads of structurally com-plex dissolved organic matter (DOM) with a high contribution ofreduced semiquinone-like fluorophores, a difference that was signifi-cant and stable across seasons. Moreover, differences in DOC concen-tration were directly correlated to humification of DOM regardless ofland use, implying an ubiquitous terrestrial source of DOM, probablyupper soil layers rich in soil organic matter. Finally, we found thatthe effect of agricultural land use is unrelated to soil type whichimplies that the found alterations in DOM amount and compositionare related to physical alterations and management of agriculturalcatchments. High amounts of DOM of low redox state potentiallyavailable to oxidation (Miller et al., 2006; Williams et al., 2010) prob-ably result in reduced ecosystem health by increased ecosystemrespiration (Young et al., 2008), but could also indirectly enhancecoupled processes, as well as transport of organic nutrients to surfacewaters. However, the mechanisms which induce these alterations ofDOM, as well as their effects on aquatic ecosystems are still largelyunknown but can be addressed by coupling knowledge and methodsfrom soil and freshwater sciences.

The role of agricultural land use as a significant, constant source ofDOM is mostly neglected in the literature due to the focus on exportof inorganic nutrients and other pollutants. However, our resultsand the high spatial extent of agriculture in the study area (Amt fürStatistik Berlin–Brandenburg, 2010) suggest that agricultural landuse needs to be considered as an important source of structurallyaltered DOM to surface waters in the Pleistocene landscape of CentralEurope. However, it is still not clear why studies in other regions withsimilar climate and similar agricultural land use partly come to differ-ent results in terms of exported DOM amount and composition(Cronan et al., 1999; Stedmon et al., 2006; Wilson and Xenopoulos,2008, 2009; Williams et al., 2010). Potential differences in catchmentcharacteristics need to be addressed in future studies to better under-stand the general effect of agricultural land use on DOM in aquaticecosystems.

Acknowledgments

This study was conducted within the PRESTO-Catch projectfunded by the German Federal Ministry of Education and Research(01SFF0710). D. von Schiller was supported by a postdoctoralfellowship of the German Academic Exchange Service and “la Caixa”Foundation. We thank J. Schreiber, B. Schütze and T. Rossoll for fieldassistance, and E. Zwirnmann, A. Lüder, S. Schell and H.-J. Exner forlaboratory assistance. We thank T. Mehner and the participants inthe course on Scientific Writing at the Leibniz-Institute of FreshwaterEcology and Inland Fisheries for helpful discussions on an earlier draftof this article. We thank the editor and the anonymous reviewers fortheir helpful comments and we thank K. Özkan and A. M. Poulsenfrom the Department of Bioscience, Aarhus University, for statisticaland linguistic assistance, respectively.

Appendix A. Supplementary data

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.scitotenv.2012.08.087.

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