Science of the Total Environment 506–507 (2015) 353–360

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Quality of dissolved organic matter affects planktonic but not biofilm bacterial production in streams Norbert Kamjunke a,b,⁎, Peter Herzsprung b, Thomas R. Neu a a b

Dept. of River Ecology, Helmholtz-Centre for Environmental Research UFZ, Brückstraße 3a, D-39114 Magdeburg, Germany Dept. of Lake Research, Helmholtz-Centre for Environmental Research UFZ, Brückstraße 3a, D-39114 Magdeburg, Germany

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

• Freshness index and humification index of DOC depend on land use. • Planktonic bacterial production was correlated to DOC concentration and quality. • Production of biofilm bacteria was independent of DOC in stream water. • Biofilm communities were associated with algae, bacteria, and extracellular substances. • Planktonic and biofilm bacteria responded differently to stream water quality.

a r t i c l e

i n f o

Article history: Received 4 September 2014 Received in revised form 11 November 2014 Accepted 11 November 2014 Available online 24 November 2014 Editor: C.E.W. Steinberg Keywords: Bacterial production DOC Freshness index Humification index Biofilm Confocal Laser Scanning Microscopy (CLSM)

a b s t r a c t Streams and rivers are important sites of organic carbon mineralization which is dependent on the land use within river catchments. Here we tested whether planktonic and epilithic biofilm bacteria differ in their response to the quality of dissolved organic carbon (DOC). Thus, planktonic and biofilm bacterial production was compared with patterns of DOC along a land-use gradient in the Bode catchment area (Germany). The freshness index of DOC was positively related to the proportion of agricultural area in the catchment. The humification index correlated with the proportion of forest area. Abundance and production of planktonic bacteria were lower in headwaters than at downstream sites. Planktonic production was weakly correlated to the total concentration of DOC but more strongly to quality-measures as revealed by spectra indexes, i.e. positively to the freshness index and negatively to the humification index. In contrast to planktonic bacteria, abundance and production of biofilm bacteria were independent of DOC quality. This finding may be explained by the association of biofilm bacteria with benthic algae and an extracellular matrix which represent additional substrate sources. The data show that planktonic bacteria seem to be regulated at a landscape scale controlled by land use, whereas biofilm bacteria are regulated at a biofilm matrix scale controlled by autochthonous production. Thus, the effects of catchment-scale land use changes on ecosystem processes are likely lower in small streams dominated by biofilm bacteria than in larger streams dominated by planktonic bacteria. © 2014 Elsevier B.V. All rights reserved.

⁎ Corresponding author at: Dept. of River Ecology, Helmholtz-Centre for Environmental Research UFZ, Brückstraße 3a, D-39114 Magdeburg, Germany. Tel.: +49 391 8109434; fax: +49 391 8109150. E-mail address: [email protected] (N. Kamjunke).

http://dx.doi.org/10.1016/j.scitotenv.2014.11.043 0048-9697/© 2014 Elsevier B.V. All rights reserved.

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1. Introduction Networks of streams and rivers drain terrestrial environments and couple biogeochemical cycles between land, atmosphere, and oceans (Cole et al., 2007; Battin et al., 2008; Aufdenkampe et al., 2011). The inland waters receive an estimated amount of 2–2.7 billion tons of terrestrial carbon per year on a global scale (Battin et al., 2008; Aufdenkampe et al., 2011). Thus, the carbon turnover in rivers and lakes is of similar magnitude than that in terrestrial ecosystems (Aufdenkampe et al., 2011). Most of the terrestrial organic carbon which enters freshwater systems is respired to CO2 locally or buried in sediments, whereas only a fraction is discharged to the ocean (Aufdenkampe et al., 2011). Streams and rivers are regarded as global hotspots of CO2 evasion (Raymond et al., 2013). Therefore, knowledge of the control of carbon turnover in streams and rivers is of significant importance. Heterotrophic bacteria are the main consumers of DOM whereby DOM is partly removed from water by respiration and partly transferred to bacterial biomass making the carbon available to organisms of higher trophic levels. Thus, bacterial DOM processing is an important ecosystem function in streams, and bacterial biomass production represents a suitable measure for such microbial activity. Besides bacteria suspended in water, bacteria grow also in biofilms which are substratum-associated consortia of microorganisms (including microalgae, bacteria, fungi, protozoans and small metazoans) and their extracellular polymeric substances (EPS). Biofilms are regarded as major sites of carbon cycling in streams (Romani et al., 2004; Battin et al., 2008). Epilithic bacteria on stones may contribute substantially to DOM processing, particularly in small streams where the stream bed is nearly completely covered by stones and fine sediment is scarce. The bacterial processing of organic carbon depends on the quality of DOM which is in turn affected by the degree and type of land use within river catchments. There are shifts in allochthonous input and autochthonous production in streams along agricultural land-use gradients, since nutrients from land use promote autochthonous production (Hagen et al., 2010). Dissolved organic matter (DOM) in agricultural streams was shown to be more labile and thus more accessible for microbes than DOM in wetland streams (Williams et al., 2010). In the river network of the Bode catchment in Germany, a positive relationship was found between the aromatic content of dissolved organic carbon (DOC) and the proportion of forested area in the catchment (Kamjunke et al., 2013). Regarding the relationship between DOM quality and bacterial processing, planktonic bacterial production was positively related to labile DOC concentration in streams in Southern Ontario (Williams et al., 2010), and microbial bioavailability of DOM was negatively related to the proportions of humic-like DOM in streams of Maryland (Hosen et al., 2014). A negative correlation between bacterial metabolism and SUVA (aromatic substance content) and a positive correlation between such metabolism and the reciprocal of molecule size of DOC were detected in lake sediments (Gudasz et al., 2012). In the Bode catchment, CO2 oversaturation of stream water as an inferred indicator of microbial respiration correlated positively with DOC concentration but negatively with parameters of DOC quality (molecular size and aromatic content; Kamjunke et al., 2013). The simultaneous measurement of microbial activity in stream water and epilithic biofilms would enable the differentiation of organic carbon processing between habitats. Thus, the present study focuses on the combination of analysis of metabolic processes and chemical DOC properties. The aim of the present study was to test the hypotheses that (1) the production of stream water bacteria is related to DOC quality which is affected by land-use, and that (2) the production of bacteria in epilithic biofilms which are associated with benthic algae and a slime matrix of EPS is less dependent on DOC in the overlaying water. The study is based on the analyses of bacterial abundance, bacterial production, biofilm structure, and DOC parameters. For the latter, we used the freshness index (ß/α) as a measure of freshly produced organic material, the humification index (HIX)

for humic material, and the specific UV absorption as an indicator for aromatic compounds. 2. Material and methods The investigations were done in the Bode catchment area in the Harz Mountains (maximum altitude 1142 m, total area 2000 km2), Germany (Fig. 1) which is part of an observation network called Terrestrial Environmental Observatories (TERENO) of the German Helmholtz association (Zacharias et al., 2011). Sampling was performed at 17 sites: six small streams with one upper reach site and one middle reach site each, and five lower reach sites along the river Bode as well as a lowland tributary were selected (Table 1). The rivers Hassel and Rappbode are impounded (Rappbode reservoir). The land use of the Bode catchment area shows a clear gradient from forested parts of the National Park to agricultural and urbanised areas (Fig. 1; CORINE data; EEA, 2012). In the headwaters, the proportion of forest and semi-natural area was close to 100%, whereas the proportion of agricultural land use increased at downstream sites (Table 1). Sampling was performed during the vegetation period on 27th and 28th August 2013 under conditions of summer base flow (see Table 1 for discharge amounts). There was no rain in the week before sampling. For DOC analysis, water samples were transferred into acid-rinsed and combusted brown glass bottles, kept at 4 °C for maximum of 24 h, and filtered through glass fibre filters (Whatman GF/F). DOC concentrations were measured using high temperature combustion (DIMATOC 2000, Dimatec Analysentechnik GmbH, Essen, Germany). The absorption at 254 nm was measured using a Hach Lange DR 5000 spectrophotometer. The specific UV absorbance at 254 nm (SUVA), which correlates with per cent DOC aromaticity, was calculated from the UV absorbance at 254 nm divided by DOC concentrations (Weishaar et al., 2003). Fluorescence excitation emission matrices (EEMs) were collected using a spectrofluorometer (AQUALOG, HORIBA Jobin Yvon, USA). Fluorescence intensity was measured during emission scans (240 nm– 600 nm every 3.27 nm, 8 pixel) at set excitation wavelengths in 3 nm increments from 240 nm to 600 nm. A 5 nm bandpass for excitation and emission wavelength and 1 s integration time were used. Fluorescence data were corrected (including blank subtraction) using HORIBA internal software before the correction of inner-filter effects (Kothalawa et al., 2013) which was used as usual method in many studies. Afterwards, the EEMF spectra were Rayleigh-scattering masked and corrected for inner-filter effects using an AQUALOG internal recorded UV–Vis absorbance spectrum in the same quartz cell of the fluorescent sample. The freshness index ß/α (Fellman et al., 2010; Halbedel et al., 2013) was computed as the ratio of the emission intensity at 380 nm divided by the highest detected emission intensity between 420 and 435 nm, all obtained for an excitation at 310 nm. The humification index HIX was calculated by dividing the peak area under the emission spectra between 435 and 480 nm with the peak area under the emission spectra between 300 and 345 nm, both at 255 nm excitation wavelength (Zsolnay, 2003). Production of stream water bacteria was measured using the leucine technique (Kirchman et al., 1985; Simon and Azam, 1989) as described by Kamjunke et al. (2005) since much more bacteria incorporate leucine into protein in comparison to thymidine incorporation into DNA (Perez et al., 2010). After storage of samples at 4 °C until the next day, triplicate 5 ml aliquots and one formalin-treated control (3.7%, final concentration) were spiked with 14C-leucine (10.8 MBq mmol−1, Sigma, 50 nM final concentration). Samples were incubated in the laboratory at in situ temperature for 1 h in the dark on a shaker. Incorporation was stopped with formalin, and 0.6 ml 50% trichloracetic acid (TCA) was added. Proteins were extracted for 15 min and filtered onto 0.2 μm Nuclepore membranes. Filters were rinsed twice with 1 ml 5% TCA and once with 80% ethanol. After dissolving the filters in 0.5 ml Soluene (Packard) and adding 2.5 ml Hionic Fluor (Packard) to each scintillation vial, radioactivity was measured using a Liquid Scintillation Analyzer

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Fig. 1. Sampling sites and land use in the Bode catchment area.

Abundance of suspended bacteria was estimated from formalin-fixed samples (3.7% final concentration) after staining with acridine orange and counting using an epifluorescence microscope (Axioskop2, Zeiss) as according to Kamjunke et al. (2005). Biofilm bacteria on stones were counted after detachment from small stones (about 1 cm in length) fixed with formalin in sterile-filtered stream water by ultrasonication. Staining and counting was performed as described above. Detailed structural analysis of the microbial community attached to the stones was conducted by Confocal Laser Scanning Microscopy (CLSM) using a TCS SP5 X (Leica). Imaging was done as described in Lawrence et al. (1998) and Neu et al. (2004). For visualisation of extracellular polymeric substances (EPS), i.e. glycoconjugates, samples were stained with lectin AAL (Aleuria aurantia) conjugated with the fluorochrome Alexa568 for 30 min at room temperature in darkness (Neu et al., 2001). After rinsing with tap water three times, the samples were counter-stained with the nucleic acid specific fluorochrome Syto9 for 5 min for visualisation of bacteria. A 63× NA 0.9 water-immersible lens was employed for recording image series. From the white laser,

(2300 TR, Packard). The external standard ratio method was used for quenching. Carbon production was calculated using the equations of Simon and Azam (1989). To obtain the daily production rate, measured BP per hour was multiplied by 24 (Kirchman and Hoch, 1988; Pace and Cole, 1994). Production of biofilm bacteria was also estimated with leucine incorporation as according to Espeland et al. (2001). Stones of about 1 cm length were transferred to scintillation vials and covered with 4 ml sterile-filtered stream water. Triplicate aliquots and one formalintreated control (3.7%, final concentration) were spiked with 14C-leucine (5 mM final concentration; Fischer and Pusch, 1999). After incubation for 1 h under continuous shaking (Romani and Sabater, 1999) and extraction with TCA on ice, biofilms were removed from stones by ultrasonication for 1 min (20 kHz, 20%; HTU Soni130, Heinemann, Germany). Stones were removed and rinsed, and the supernatant was filtered and measured as described above. To estimate the surface area of the rocks, they were wrapped with tin foil, and the weight of that foil was related to the weight of one cm2 foil (Scott et al., 2008).

Table 1 Location (us: upstream, ds: downstream), discharge rate (Q), stream reach (SR; up: upper reach, mi: middle reach, lo: lower reach), and land use characteristics of sampling sites. Discharge data from Flood Prediction Centre of the state Saxony-Anhalt (http://www.hochwasservorhersage.sachsen-anhalt.de/), ameasured at Elend, bmeasured at Tanne, cmeasured at Thale, dmeasured at Wegeleben. Land use gathered from colour infrared (CIR) digital aerial photograph data (agricultural area, forest and seminatural area). No.

Water

Location

N

E

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Kalte Bode Kalte Bode Warme Bode Warme Bode Rappbode Rappbode Hassel Hassel Selke Selke Holtemme Holtemme Bode Bode Groß. Graben Bode Bode

us Schierke us Mandelholz res. us Braunlage us Sorge us Benekenstein ds Trautenstein Birkenmoor Hasselfelde Silberhütte Hausneindorf Steinerne Renne Mahndorf Wendefurth us Gröningen Oschersleben Hadmersleben Staßfurt

51.77026 51.74214 51.73853 51.69650 51.66534 51.69296 51.63923 51.69238 51.63035 51.83896 51.81783 51.88842 51.73640 51.93181 52.02351 52.00542 51.85306

10.64522 10.72593 10.61169 10.69231 10.71065 10.79079 10.88493 10.85888 11.09648 11.27007 10.72939 10.97336 10.91753 11.20196 11.22371 11.31930 11.59773

Q (m3 s−1) 0.124a 0.170b 0.023 0.019 0.297 0.028 0.366 2.48c 3.25d 0.98 4.10 4.59

SR

Agric. (%)

Forest (%)

up mi up mi up mi up mi up mi up mi lo lo lo lo lo

0.0 0.0 0.0 0.0 0.3 5.7 0.0 18.5 16.1 72.5 0.0 91.0 4.5 70.0 76.9 62.6 67.3

99.9 97.5 100.0 100.0 95.3 93.8 100.0 76.8 80.4 25.1 98.2 7.6 92.3 26.7 21.5 31.9 28.6

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the lines at 500, 578 and 633 nm were selected for excitation. Emission signals were collected sequentially for reflection (495–505 nm), and in the green (515–560 nm), orange (590–650 nm) and far red (650–720 nm) parts of the spectrum. Three stones per site were used for imaging; three images were recorded from the top and bottom side each. The results of image analysis, i.e. the quantification of digital signals (voxels) recorded by laser microscopy, were used for the calculation of semi-quantitative biovolume values (Pawley, 2000). The data for bacteria, EPS-glycoconjugates as well as autofluorescence of cyanobacteria and algae were extracted by the software JImageAnalyzer (Staudt et al., 2004; Buchholz et al., 2012). Biovolumes were estimated after manual thresholding at 40 (bacteria, EPS) or 50 (chlorophyll a) pixel intensity which is a common procedure necessary for the discrimination between measured signal and background. In order to separate the data on EPS-glycoconjugates, cyanobacteria and algae present in two channels, the co-localisation tool of Imaris (Bitplane) was used. Finally the semi-quantitative data from all three parameters could be calculated. Statistical analyses, i.e. the calculation of regression coefficients and p values for relationships between DOC parameters, bacterial production and biovolume data of CLSM, were performed using the software IBM SPSS Statistics 21. For the pairwise comparison of the upper and middle reaches of the six streams, a Wilcoxon signed ranks test was used. The data of the DOC fluorescence spectra (excitation emission matrices) were used for the calculation of indices only but not for a parallel factor (PARAFAC) decomposition. For statistical reasons, such an analysis would need at least 50 samples, a data set of 17 samples is not sufficient. 3. Results The concentration of DOC ranged between 0.95 and 4.7 mg C L−1. It was relatively low in upper reaches and middle reaches of rivers Kalte and Warme Bode (sampling sites 1–4) and in the upper reaches of rivers Rappbode and Selke (sampling sites 5, 9), whereas the highest concentration was measured in the middle reach of river Hassel (sampling site 8; Fig. 2). The specific UV absorption (SUVA) tended to be highest

in small streams of the mountains (Kalte and Warme Bode, Rappbode, Hassel, upper reach of Holtemme; sampling sites 1–8, 11). Regarding the six streams of sampling sites 1–12, SUVA was always higher in the upper reaches (odd numbers) than in the middle reaches (even numbers; p = 0.028). The humification index (HIX) showed a similar pattern as the SUVA, with highest values in the upper reaches. It was negatively related to the proportion of agricultural area in the catchment (r2 = 0.62, p b 0.001; Fig. 4). In contrast, the index of freshly produced organic material (β/α) showed the opposite trend: always lower values in the upper reaches (odd numbers) for sampling sites 1–12 (p = 0.028) and high values in the lower reaches of larger lowland streams (Fig. 3). It showed a positive relationship to agricultural land use (r2 = 0.83, p b 0.001; Fig. 4). Since the proportion of agricultural land use was only weakly correlated to the sub-catchment area of each site (r2 = 0.35), the correlation coefficients between the subcatchment size and freshness index (r2 = 0.32) as well as humification index (r2 = 0.26) were lower than those between agricultural area and the respective indices. Humification and freshness index were negatively correlated (r2 = 0.77, p b 0.001). The abundance of planktonic bacteria differed by one order of magnitude between sites (0.22–3.86 · 109 L−1). They were low in upper and middle reaches of the rivers Kalte Bode, Warme Bode and Holtemme (sampling sites 1–4, 11–12) and in the upper reaches of the rivers Rappbode, Hassel and Selke (sampling sites 5, 7, 9; Fig. 4). Higher abundances were found in the middle reaches of the latter three rivers (sampling sites 6, 8, 10) and in lower reaches of the larger streams in the lowland (Bode, Großer Graben; sampling sites 14–17). The abundance of biofilm bacteria ranged between 1.1 and 5.1 · 109 dm−2. It was highest in Großer Graben, but no clear pattern regarding upper, middle and lower reaches was observed. Bacterial production of planktonic bacteria ranged between 3 and 193 μg C L−1 d−1 and showed a pattern similar to that of their abundance: high values in the middle reaches of the rivers Rappbode, Hassel and Selke as well as in the lower reaches of larger lowland streams. Production of biofilm bacteria amounted to values between 139 and 794 μg C dm−2 d−1. It was highest at one Bode site (sampling site 16), but no systematic pattern was observed. Production of stream water bacteria was positively

5

6

a 3 2 1

SUVA (L mg-1 m-1)

DOC (mg L-1)

b Upper reaches Middle reaches Lower reaches

4

0

4

2

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1

16

c

0.8

12

d

β/α

HIX

0.6 8

0.4 4

0.2 0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Fig. 2. DOC concentration (a), specific UV absorption (SUVA; b), humification index (HIX; c), and freshness index (ß/α; d) at sampling sites.

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Semi-quantitative analysis of Confocal Laser Scanning Microscopy data revealed that the biofilms consisted of EPS-glycoconjugates, eukaryotic algae, cyanobacteria and bacteria (see Fig. 6a as an example). In most cases the biovolume of the biofilms was dominated by EPS-glycoconjugates (range 30–95%, average 57%) followed by the biovolume of bacteria (22%), eukaryotic algae (17%) and cyanobacteria (4%; Fig. 6b). Biovolumes of EPS and algae did not correlate to bacterial production of biofilms.

4. Discussion

Fig. 3. Freshness index (a) and humification index (b) as a function of the proportion of agricultural area in the catchment.

related to DOC concentration of surface water (r2 = 0.25, p = 0.042; Fig. 5). Furthermore, planktonic BP was positively related to the freshness index (r2 = 0.55, p = 0.001) but negatively to the humification index (r2 = 0.42, p = 0.005) and SUVA (r2 = 0.28, p = 0.029). In contrast, biofilm bacterial production did not show relationships to DOC parameters (open symbols in Fig. 5; r2 = 0.00–0.12, p: 0.17–0.81).

The concentration of DOC in the Bode catchment (0.95–4.7 mg C L−1) was lower than at the same time two years previously (2.2– 14.3 mg C L − 1; Kamjunke et al., 2013) when discharge was higher by a factor of two to three. DOC concentration was affected by land use; lower concentrations were found in the headwaters, higher values were measured at downstream sites. Graeber et al. (2012) observed higher DOC concentrations in agricultural than in forest catchments too. Particularly the DOC quality was dependent on land use in the Bode catchment. The negative relationship between the humification index and the proportion of agricultural area in the catchment (i.e. the positive relationship to the proportion of forest) confirms previous results of Kamjunke et al. (2013), who found a positive correlation between the aromaticity of DOC (SUVA) and the proportion of forest. Furthermore, these observations are in agreement with the positive relationship between the freshness index and agricultural land use. Freshness and humification index were negatively related, as also found for streams in Southern Ontario (Williams et al., 2010). We performed our investigation in summer only, but the effect of land use on patterns of DOC concentration and DOM composition were reported to be seasonally independent (Graeber et al., 2012; Kamjunke et al., 2013). In contrast to our findings, Graeber et al. (2012) measured a higher humification index in agricultural than in forest catchments in German lowlands which might be caused by differences in hill slope, soil type and tillage.

4

250

d-1) -1

Upper reaches Middle reaches Lower reaches

3

Bact. Prod. in Water

Bacteria in Water (109 L-1)

a

2

1

0

150

100

50

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1000

c

Bact. Prod. in Biofilm

6

gC dm-2 d-1)

8

Bacteria in Biofilm (109 dm-2)

b

200

4

2

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

d 800

600

400

200

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Fig. 4. Abundances of planktonic (a) and biofilm bacteria (c) and production of planktonic (b) and biofilm bacteria (d) at sampling sites (mean ± SD of triplicate water samples for planktonic production and of three stones for biofilm abundance and production).

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Fig. 5. Production of planktonic bacteria (filled circles, regression lines and equations) and biofilm bacteria (open circles, r2 values) as a function of DOC concentration (a), freshness index (b), humification index (c) and specific UV absorption (d).

The abundance of planktonic bacteria (0.22–3.86 · 109 L− 1) approximated those found in Amazonian clear water streams (0.58–1.38 · 109 L−1; Farjalla et al., 2002), but were lower than abundances in the lowland river Spree in Germany (4.0–7.8 · 109 L− 1; Fischer and Pusch, 2001). The minimum values of production of planktonic bacteria in the Bode catchment (3–193 μg C L−1 d−1) were similar to those measured in Amazonian streams (3–6 μgC L−1 d−1; Farjalla et al., 2002), and all values were in the same range as those of the river Spree (22–94 μgC L−1 d−1; Fischer and Pusch, 2001), in the river Biobio in Chile (3–168 μg C L−1 d−1; Vargas et al., 2013) and in streams in Southern Ontario (1–429 μg C L−1 d−1; Williams et al., 2010). The production in the Bode catchment was dependent on DOC quality. A previous study observed a positive relationship between the DOC concentration and the CO2 oversaturation of stream water (Kamjunke et al., 2013). The typical CO2 supersaturation in streams may be caused by inflow of supersaturated groundwater and/or by in-stream decomposition of organic matter (Jones et al., 2003). The CO2 oversaturation in the Bode catchment was negatively related to oxygen saturation and was significantly lower in the headwaters than at downstream sites (Kamjunke et al., 2013). Since the relative contribution of groundwater inflow to stream discharge should be higher in mountainous areas of small headwater streams than in the lowland areas of larger streams, we might assume that the high CO2 oversaturation in the downstream reaches was due to in-stream processes. Thus, microbial decomposition of organic matter may be one explanation, and CO2 oversaturation may be regarded as an inferred indicator of microbial respiration in the streams (Kamjunke et al., 2013). The present study endorses these findings with a real measure of microbial carbon turnover and a positive relationship between bacterial production and DOC concentration. Furthermore, Kamjunke et al. (2013) described a negative correlation between CO2 oversaturation and the aromaticity (SUVA) as well as molecular size (slope ratio) of DOC. Also these findings could be verified by our measurement of production of stream water bacteria, which was positively related to the freshness index and negatively to humification index and SUVA. This is in agreement with results from 43 streams in Southern Ontario in which planktonic

bacterial production was positively related to labile DOC concentration and tended to be higher in streams with increased anthropogenic land use (Williams et al., 2010). Accordingly, microbial bioavailability of DOM was related to decreasing proportions of humic-like DOM in streams of Maryland (Hosen et al., 2014). The abundance of biofilm bacteria (1.1–5.1 · 109 dm−2) was similar to densities observed in streams in New Hampshire (1–15 · 109 dm−2; Findlay and Howe, 1993). The biofilm bacterial production in biofilms (139–794 μg C dm−2 d−1) corresponds to values of 70–511 μg C dm−2 d−1 1 measured for epilithic bacterial production in small streams in Texas (Scott et al., 2008). On average, biofilm bacterial production per dm2 was as high as that in 4 L stream water. That means, biofilm production was as high as planktonic production if the water depth is 40 cm and, consequently, biofilm production dominates in small, more shallow streams whereas planktonic production dominates in large, more deep streams. Biofilm production was less variable between sites than the production of planktonic bacteria was. In contrast to stream water bacteria, the production of biofilm bacteria was not a function of DOC quantity or quality. Primary production of benthic algae seems to be the major driver for carbon supply of biofilm bacteria. Furthermore, bacteria in biofilms seemed to be partly protected by the EPS matrix of the biofilm (see Fig. 6a). It is known from other studies, that benthic bacterial production may be coupled to algal production in oligotrophic streams but is decoupled by nutrient enrichment (Scott et al., 2008), and that there was no tight trophic connection between benthic bacterial and primary production in streams (Findlay and Howe, 1993). The cycles of carbon and nutrients within the biofilm matrix were described to be independent of stream water in oligotrophic streams, bacteria facilitate algae by nutrient regeneration and algae facilitate bacteria via increased carbon generation (Scott et al., 2008). Furthermore, microbes in biofilms may be remarkably resilient to changes in DOC, as there was no change in bacterial numbers and production after the addition of DOC (Freeman and Lock, 1995). The matrix is regarded as a buffer against changing organic substrate supply; the EPS and any absorbed or incorporated organic matter serve as energy source during DOC deprivation (Freeman and Lock, 1995). This might explain the lack of

CO2 oversaturation

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a

r² = 0.33

Microbial respiration

DOC

b

Planktonic bacteria Biofilm bacteria

DOC Fig. 7. (a) Positive relationship between CO2 oversaturation as an indicator of total microbial respiration and DOC concentration in the Bode catchment (modified after Kamjunke et al., 2013). (b) Conceptual figure on microbial respiration as a function of DOC separated for independent biofilm bacteria and affected planktonic bacteria.

Fig. 6. (a) Maximum Intensity Projection of a CLSM data set of an epitithic biofilm from river Bode (site number 14). Colour allocation: blue — autofluorescence of chlorophyll a, violet — cyanobacteria, green — bacteria, red — lectin-specific EPS glycoconjugates. (b) Relative proportion of biovolumes of algae, cyanobacteria, bacteria and EPS glycoconjugates from top-view CLSM images at sampling sites.

a relationship between biofilm bacterial production and DOC parameters in the stream water in the Bode catchment area. However, bacterial production of biofilms did not correlate to biovolumes of algae or EPS in our streams. Instead, biofilm BP was positively related to biofilm phosphorus (μg P cm−2) which was measured from larger, fist-sized stones sampled on the same days and at the same sites (data not shown; r2 = 0.60). Thus, biofilm BP might not be limited exclusively by organic carbon but also a shortage of nutrients eventually occurs. 5. Conclusion Overall, planktonic bacterial production was correlated to the total concentration of DOC and parameters of DOC quality (i.e. freshness index, humification index) which depend on land use. In contrast to planktonic bacterial production, the production of biofilm bacteria was independent of DOC in stream water, a fact which may be explained by the association of biofilm bacteria with benthic algae and an extracellular matrix which represent additional substrate sources. The data show that planktonic and biofilm bacteria responded differently to stream water quality. Bacteria in streams seem to be regulated at very different scales which are controlled by very different processes: planktonic bacteria at a landscape scale controlled by land use, and biofilm bacteria at a biofilm matrix scale controlled by autochthonous production. Fig. 7 illustrates a conceptual view of bacterial production in streams. The observed positive relationship between DOC and CO2

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