Relation to Nutrient Dynamics and Particulate Organic Matter

Table of contents I

Processes and community structure in microbial biofilms of the River Elbe: relation to nutrient dynamics and particulate organic matter

D I S S E R T A T I O N

zur Erlangung des akademischen Grades

doctor rerum naturalium (Dr. rer. nat.)

vorgelegt der Fakultät Mathematik / Naturwissenschaft

der Technischen Universität Dresden

von Dipl. Biol.

F r a n k K l o e p

geboren am 11.06.1972 in Gerolstein

Dresden, 2002 Table of contents II

Dissertation der Technischen Universität Dresden

Datum der mündlichen Prüfungen: 16.08.2001

Referenten: Prof. Dr. Isolde Röske Prof. Dr. Dietrich Uhlmann PD Dr. Werner Manz Table of contents III

Table of contents

1 Introduction ...... 1

1.1 EFFECTS OF DIURNAL CHANGES IN DISSOLVED OXYGEN ON DAILY AND SEASONAL NUTRIENT CONCENTRATIONS IN THE OPEN WATER AND THE HYPORHEIC ZONE OF THE RIVER ELBE...... 3 1.2 TRANSPORT BEHAVIOUR OF ALGAL CELLS IN HYPORHEIC SEDIMENTS ...... 3 1.3 HYPORHEIC BIOFILMS AT TWO CONTRASTING RIVER ELBE SITES...... 5 1.4 ANALYSIS OF MICROBIAL COMMUNITIES OF THE RIVER ELBE...... 6

2 Materials and Methods ...... 8

2.1 DIURNAL CHANGES IN DISSOLVED OXYGEN AND NUTRIENTS ...... 9 2.1.1 Field sampling...... 9 2.1.1.1 Study site and sampling strategy ...... 9 2.1.1.2 Sample treatment and analysis ...... 10 2.1.2 Model description...... 11 2.2 TRANSPORT OF ALGAL CELLS IN HYPORHEIC SEDIMENTS ...... 15 2.2.1 Field sampling strategy ...... 15 2.2.2 Algae and microspheres used for column experiments ...... 15 2.2.3 Column experiments ...... 15 2.2.4 Particle enumeration...... 18 2.3 HYPORHEIC BIOFILMS AT TWO CONTRASTING RIVER ELBE SITES...... 19 2.3.1 Study site and sampling procedure...... 19 2.3.2 Characteristics of the sediment biofilms ...... 20 2.3.2.1 Detrital parameters - sedimentary particulate organic matter...... 20 2.3.2.2 Respiration rate...... 20 2.3.2.3 Denitrification rates...... 21 2.3.2.4 Nitrification rates...... 22 2.3.2.5 Extracellular enzyme activities...... 22 2.3.2.6 Bacterial cell counts ...... 22 2.3.2.7 Sediment total phosphorus...... 23 2.3.3 Chlorophyll a and pheophytin...... 23 2.3.4 Chemical analyses...... 23 2.3.5 Grain size distribution...... 24 2.3.6 Statistical analysis...... 24 2.4 ANALYSIS OF MICROBIAL COMMUNITIES OF THE RIVER ELBE...... 25 2.4.1 Sampling sites...... 25 2.4.2 Preparation of samples ...... 25 2.4.3 Fluorescence in situ hybridization ...... 26 2.4.4 Microscopy ...... 26 2.4.5 Statistical analysis...... 29

3 Results ...... 31

3.1 DIURNAL CHANGES IN DISSOLVED OXYGEN AND NUTRIENTS ...... 31 3.1.1 Temporal variations in discharge and temperature...... 31 3.1.2 Temporal variations in water biochemistry...... 32 3.1.3 Modeling the effects of oxygen on diurnal inorganic nitrogen dynamics in the hyporheic zone ...... 40 3.2 TRANSPORT OF ALGAL CELLS IN HYPORHEIC SEDIMENTS ...... 42 3.2.1 Seasonal variations in algal cell abundance...... 42 3.2.2 Column experiments ...... 43 3.2.2.1 Retention of algal cells ...... 45 3.2.2.2 Retardation of algal cells...... 45 3.3 HYPORHEIC BIOFILMS AT TWO CONTRASTING RIVER ELBE SITES...... 47 3.3.1 Sediment characteristics...... 47 3.3.2 Water chemistry...... 47 3.3.3 Microbial activity and biofilm analysis...... 48 Table of contents IV

3.3.4 Microbial and other sediment variables per streambed surface area...... 53 3.3.5 Relationship between bacterial biomass and activity, sediment composition and structure...... 54 3.4 ANALYSIS OF MICROBIAL COMMUNITIES OF THE RIVER ELBE...... 58 3.4.1 Community composition at the domain-specific level ...... 58 3.4.2 Community composition at the group-specific level...... 60 3.4.3 Community composition at the genus- and species-specific level...... 64

4 Discussion...... 67

4.1 EFFECT OF DIURNAL CHANGES IN DISSOLVED OXYGEN ON DAILY AND SEASONAL NUTRIENT CONCENTRATIONS IN THE OPEN WATER AND THE HYPORHEIC ZONE ...... 67 4.1.1 Effect of oxygen concentration in the open water on hyporheic oxygen dynamics ...... 67 4.1.2 Effect of oxygen on hyporheic nitrate dynamics...... 68 4.1.3 Effect of oxygen on hyporheic ammonium dynamics...... 69 4.1.4 Effect of oxygen on hyporheic nitrite dynamics...... 70 4.1.5 Effect of oxygen on hyporheic phosphate dynamics...... 71 4.1.6 Spatial variability of oxygen and nutrients in the hyporheic zone ...... 72 4.1.7 Nutrient dynamics in the hyporheic zone ...... 72 4.1.8 Modeling effects of oxygen on diurnal inorganic nitrogen dynamics in the hyporheic zone ...... 74 4.2 TRANSPORT OF ALGAL CELLS IN HYPORHEIC SEDIMENTS ...... 75 4.2.1 Occurence of algal cells in hyporheic sediments ...... 75 4.2.2 Experimental retention of algal cells in sediment columns...... 75 4.2.3 Experimental retardation of algal cells in sediment columns ...... 76 4.2.4 Algae as model organisms for particle transport in sediments ...... 77 4.2.5 General interpretation of algal retention in riverine sediments...... 78 4.3 HYPORHEIC BIOFILMS AT TWO CONTRASTING RIVER ELBE SITES...... 79 4.3.1 Spatial differences of the sampling sites...... 79 4.3.2 Spatial differences of the sampling sites on the basis of streambed surface area...... 81 4.3.3 Comparisons with other aquatic sediments in denitrification and nitrification rates...... 82 4.3.4 Denitrification and nitrification at the different sampling sites ...... 84 4.3.5 General interpretation of nitrogen dynamics at the interface between open water and sediments of the river Elbe...... 85 4.4 ANALYSIS OF MICROBIAL COMMUNITIES AT DIFFERENT SAMPLING SITES OF THE RIVER ELBE...... 88 4.4.1 Composition of the microbial communities at domain and group-specific level ...... 88 4.4.2 Composition of the microbial communities at genus- and species-specific level...... 90 4.4.3 Multivariate analysis of microbial communities ...... 91 4.4.4 General interpretation of microbial community analyses...... 93

5 Outlook...... 95

5.1 SYNOPSIS ...... 95 5.2 PERSPECTIVES AND OPEN QUESTIONS...... 97 6 Summary...... 99

7 Zusammenfassung...... 102

8 References ...... 105

9 Acknowledgements / Danksagung ...... 125 Abbreviations V

Abbreviations

µ micro AMWU Ausgesuchte Methoden der Wasseruntersuchung ANOSIM analysis of similarities ANOVA analysis of variance BGLU ß-glucosidase activity Chl a chlorophyll a Co Coswig (near Lutherstadt-Wittenberg) Cy3 5,5´-disulfo-1,1´di(γ-carbopentynyl)-3,3,3´,3´-tetramethyl- inolocarbocyanine (cyanine Cy3.18) d day DD Dresden

D10, D50, D60 10th, 50th (median), and 60th percentile of particle diameter DENI denitrification rate DIN German Technical Standard DOC dissolved organic carbon DO dissolved oxygen DW dry weight EMBL European Molecular Biology Laboratory F flowing water g gramme, gravity constant GFP green fluorescent protein h hour(s) H hyporheic water Hi Hitzacker (near Dannenberg) kf permeability coefficient l liter LAP leucine-aminopeptidase activity m meter, milli M molar MDS multidimensional scaling MFIPC mobile fine interstitial particle carbon MFIPN mobile fine interstitial particle nitrogen Abbreviations VI min minute(s) n.s. not significant NITRI nitrification rate PBS phosphate buffered saline PCA Principal component analysis PCR polymerase chain reaction PHOS phosphatase activity POC particulate organic carbon POM particulate organic material PON particulate organic nitrogen PRIMER Plymouth routines in multivariate ecological research rRNA ribosomal ribonucleic acid RESP respiration rate S sediment s second(s) Tris tris-(hydroxymethyl)-aminomethane Introduction 1

1 Introduction

The hyporheic zone of flowing waters has received increased attention during the last few decades. In general, this subsurface fluvial zone is the transition zone between stream water and groundwater with high hydrological and biological dynamics (Williams 1993, Brunke and Gonser 1997, Gibert et al. 1997). Hyporheic processes determine the distribution and supply of nutrients within lotic systems (Grimm and Fisher 1994, Claret et al. 1997, Valett et al. 1997, Carlyle and Hill 2001), and are important storage zones for organic carbon (Cummins 1974, Wanner and Pusch 2001). Continual water exchange promote a highly active hyporheic microbial metabolism (Findlay 1995), which includes aerobic processes in oxygenated sediments and anaerobic metabolism in hypoxic or anoxic sediments (Dahm et al. 1991, Baker et al. 1999). As a consequence, sharp physical and chemical gradients promote hyporheic systems as subsurface zones of high productivity and diversity of microbial communities (Hendricks 1993, Brockmann and Murray 1997, Baker et al. 1999, Fraser and Williams 1998). The supply of dissolved nutrients and organic matter may originate from surface water or groundwaters (Rutherford and Hynes 1987, Fiebig and Lock 1991, Findlay 1993, Tockner et al. 1999, Fischer et al. submitted). The import of particulate organic matter including authochthonous and allochthonous sources, is mainly controlled by surface water inflow (Sobczak et al. 1998). Hyporheic microbial metabolism is mainly associated to sediment biofilms (Lock et al. 1984, Fiebig and Marxsen 1992, Freeman and Lock 1995) and thus is responsible for the major part of respiration within the system (Pusch 1996). The importance of subsurface zone processes for the implications of river restoration and river bank filtration has been recognized (Brunke and Gonser 1997, Grischek et al. 1998). Further, especially for large rivers, transport of surface water supplies raw water from river bank infiltration for drinking water production. For example, 18% of the drinking water production in Saxony (Germany) based on river bank infiltration and artificial groundwater recharge along the river Elbe (Grischek et al. 1998). Nutrients, organic matter and pollutants has been observed to be efficiently retained during transport through the river bank (Guderitz et al. 1993, Grischek et al. 1998). The river Elbe (Figure 1) is a nutrient rich river receiving treated and untreated wastewater and diffuse inputs. Before 1990, high organic pollution resulted in dissolved oxygen levels approaching 2 - 3 mg l-1 (Guhr et al. 2000). Physical re-aeration and algal photosynthetic activity were insufficient to compensate the oxygen depletion of the river. Since the reduction of industrial inputs (such as paper mill production in Saxony) and construction of many Introduction 2 additional wastewater treatment plants after 1990, the allochthonous dissolved organic loading decreased, but the high secondary organic pollution in terms of phytoplankton production did not change significantly (Chlorophyll a > 100 µg l-1). Thus, the authochthonous particulate organic material content is still high. Previously low nitrification rates led to high mean river water ammonium concentrations of 3.7 mg N l-1, but possibly due to sufficient oxygen supply and increased nitrification the ammonium-nitrogen concentration at present is 0.2 mg l-1 or below (Guhr et al. 2000). Nitrate concentrations remain unchanged and are still high (> 4 mg N l-1). Especially in the context of drinking water supply, nitrate reduction is of particular importance. Present oxygen conditions are governed by autotrophic processes and follow distinct diurnal amplitudes.

Hamburg 100 km

Hitzacker Berlin Poland Magdeburg Coswig

Dresden

Praha Germany Czech Republic

Austria

Figure 1 Study area of the river Elbe. Sampling sites are given in italics.

Introduction 3

1.1 Effects of diurnal changes in dissolved oxygen on daily and seasonal nutrient concentrations in the open water and the hyporheic zone of the river Elbe Primary production in nutrient rich flowing waters can be exceptionally high producing diurnal amplitudes of oxygen (Odum 1956). Infiltration of oxygen-rich water into the hyporheic zone is reported by many authors (Malard and Hervant 1999, and references cited). The dissolved oxygen supply can affect the hyporheic nutrient (N, P) dynamics. Ammonium is oxidized to nitrate in well-oxygenated hyporheic sediments (Triska et al. 1990). If dissolved oxygen in the hyporheic zone becomes depleted, nitrate is reduced by denitrification (Duff and Triska 1990, Claret et al. 1998). Immobilization and release of phosphorus during aerobic and anaerobic conditions, respectively, is considered to be influenced by biotic and abiotic processes (De Montigny and Prairie 1993, Fox 1993). Most of the recent research is focused on low order streams with very low nutrient concentrations (Jones et al. 1995a, Peterson et al. 2001). Some studies describe the seasonal dynamics of nutrients (N, P) in the hyporheic zone with high spatial and low temporal resolution (Hendricks and White 1995, Fraser and Williams 1998) or with low spatial and high temporal resolution (Triska et al. 1990). Diurnal patterns of dissolved oxygen and nutrient concentrations have been observed in several stream ecosystems (Valett 1993, Couling and Boulton 1993). However, there are only few studies of biogeochemical processes in the hyporheic zone of medium sized and large rivers (Hinkle et al. 2001). Recently, Pinay et al. (1999) proposed a concept for large rivers with an important impact of the subsurface zone on the nitrogen cycling of the whole river. It is hypothesized that phytoplankton photosynthetic activity in the open water may influence the nutrient dynamics (inorganic nitrogen compounds and phosphate) in the hyporheic zone of the river Elbe during the growing season by infiltration of dissolved oxygen. This field study estimates the seasonal and diurnal nutrient dynamics of surface water and hyporheic water simultaneously by (i) describing an annual cycle with a high temporal resolution, and (ii) considering diurnal amplitudes. This empirical data basis is used to create a mathematical model to explore how diurnal oxygen variation occuring in the upper sediment layer might affect the inorganic nitrogen in the hyporheic zone.

1.2 Transport behaviour of algal cells in hyporheic sediments Algal cells, both planktonic and benthic, have been found in many groundwater systems (Wasmud 1989, Kuehn et al. 1992, Colwell et al. 1994, Bornette et al. 1996) and hyporheic Introduction 4 zones of streams (Poulíckova 1987, Poulíckova et al. 1998). These empirical findings suggest an advective transport of algal cells in subsurface environments. However, little information is available about the transport behaviour of algal cells. Previous research on interstitial transport of particles has focused on disease-causing bacteria and viruses (Bales et al. 1997, Personné et al. 1998, Simoni et al. 1998, Chen and Strevett 2001) and on bacteria used for bioremediation and degradation of groundwater contaminants (Harvey and Garabedian 1991, Camesano and Logan 1998, Fuller et al. 2000). The transport characteristics of groundwater protozoa in contaminated aquifer systems were described by a few studies (Sinclair et al. 1993, Harvey et al. 1995, Harvey et al. 1997). For shallow sandy marine systems, the advective transport of suspended particles across the water-sediment interface using surrogates for algae defined as microspheres (Huettel et al. 1996, Rusch and Huettel 2000) and algal cells (Pilditch 1998, Huettel and Rusch 2000) depended on sediment permeability (Huettel and Rusch 2000). Investigations of chemical factors controlling organic particulate transport through porous media have identified ionic strength, pH, organic carbon content, and physicochemical cell surface properties of bacteria as major variables (Jewett et al. 1995, O´Melia 1995, McCalau and Bales 1995, Huang et al. 1999, Smets et al. 1999). The import of algae into the hyporheic zone of running waters is of ecological significance. Algae can contribute to the clogging process of the upper sediment layers (i.e. colmation, Uhlmann 1988 and references cited) and thus affect the hydrodynamics and permeability of the subsurface zone (Schächli 1992, Battin and Sengschmitt 1999, Brunke 1999, Bihan and Lessard 2000). Imported algae and their exsudates are degraded by bacteria and stimulate the hyporheic metabolism (Holmes et al. 1998, Wanner and Pusch 2001, Brunke and Fischer 1999, Fischer et al. in press). Furthermore, carbonate precipitation and subsequent chemical clogging in laboratory columns were caused by increased pH resulting from algal photosynthesis (Rinck- Pfeiffer et al. 2000) In order to get a better understanding of algal transport characteristics in porous media, small- scale laboratory tests were performed. This was done by comparing the advective mobility of different morphotypes and sizes of algae and particulate surrogates (microspheres) in flow- through columns at different permeabilities of the river Elbe sediments. It is hypothesized that (i) the advective subsurface transport behaviour of algae through heterogeneous porous media may alter with different size and morphology of algal cells and (ii) that sediment characteristics expressed by the permeability coefficient kf of porous media may influence the amount of algae retained by the sediment and (iii) the retardation of the algae. Introduction 5

1.3 Hyporheic biofilms at two contrasting river Elbe sites Hyporheic sediments are covered by microbial biofilms, influencing the carbon and nutrient cycling in streams and rivers (e.g. Lock 1993, Hendricks 1993, Storey et al. 1999). The microbial assemblages of this ecotone are affected by both the groundwater and the river channel water (Findlay 1995, Jones et al. 1995b, Brunke and Fischer 1999, Baker et al. 1999). The combination of hydrologic exchange and biogeochemical processes such as microbial metabolism, also has the potential to modify the solute composition of groundwaters and channel water in small streams moving through the sediment (Gibert et al. 1990, Vervier 1992, Hendricks and White 1995, Brunke and Gonser 1997). As a result, the oxidation- reduction gradients within the hyporheic zone are required to maintain both nitrification and denitrification processes (Triska et al. 1993a, Hedin et al. 1998, Hill et al. 2001). However, the relationships between microbial activity and the chemical and physical properties of river sediments have received little attention. Hydrodynamic factors, water velocity, and sediment grain size can influence bacterial production, biofilm characteristics and nutrient composition (Claret and Fontvieille 1997, García-Ruiz et al. 1998a, Marxsen 2001). Denitrification processes are strongly related to the carbon content of the upper sediment layers of different physical properties (Hill et al. 1985). The relationships between bacterial abundance and productivity and organic matter quality and quantity was recently measured by Fischer et al. (in press). The authors found substantial differences between stratified and shifting sediments in the river Spree, a sixth order lowland river. Nevertheless, most studies have been associated with low order streams. The functioning of large rivers may be substantially different as the contribution of exchange processes between the streambed and the channel water and therefore the contribution of subsurface metabolism to the ecosystem metabolism is supposed to be of minor importance in high order rivers (Pinay et al. 1999, Vannote et al. 1980). The objectives of this study were (i) to describe and determine differences in bacterial abundances and activity and detrital parameters (particulate organic matter) between two contrasting sites at a large river of both river-bed and river-bank sediments, (ii) to relate the effect of the sediment structure with the denitrification and nitrification rates of the subsurface biofilms, and (iii) to explore the possible contribution of the sediments to the nitrogen processing of the entire river system.

Introduction 6

1.4 Analysis of microbial communities of the river Elbe An important objective in many ecological studies is to understand the causes for natural variability and diversity of microbial communities. In recent years, fluorescent in situ hybridization (FISH) using rRNA-targeted oligonucleotide probes in combination with epifluorescence, and confocal laser scanning microscopy has become a useful tool for analyzing microbial communities, allowing enhanced access to the majority of microorganisms which are not culturable in the laboratory (Amann et al. 1990, Amann et al. 1995). This rRNA-based approach is also linked to the number of target ribosomes in the cell and thus can indicate physiologically active bacteria in environmental samples. Analyses performed to date have been on microbial communities in drinking water (Manz et al. 1993, Kalmbach et al. 1997), soils (Hahn et al. 1992, Assmus et al. 1995), freshwater systems (Hicks et al. 1992, Alfreider et al. 1996, Glöckner et al. 1996, Glöckner et al. 1999), marine systems (Ramsing et al. 1996, Ravenschlag et al. 2000) and wastewater treatment systems (Wagner et al. 1994, Manz et al. 1994, Manz et al. 1998, Kloep et al. 2000). More recently, FISH analysis of river and stream bacterial communities including planktonic and biofilm- associated populations have been performed (Kenzaka et al. 1998, Manz et al. 1999, Lemke and Leff 1999, Böckelmann et al. 2000, Brümmer et al. 2000, Battin et al. 2001). However, microbial community analyses comparing ecological habitats within river systems are comparatively rare. Convective vertical exchange processes between stream water and the subsurface (hyporheic) zone (White 1990, Boulton 1993, Findlay 1995, Brunke and Gonser 1997) provide the connection between the free-flowing planktonic and the sediment- associated river habitat (Hendricks 1993). Brunke and Fischer (1999) previously investigated the relationship between hyporheic bacteria and several invertebrate taxa, emphasizing their relationship to sediment depth and hydrological (infiltration, exfiltration) exchange type. However, studies on the microbial ecology of river ecosystems are not as common (Hendricks 1993, Leff 1994, Zehr and Voytek 1999). The spatial heterogeneity and variability of river habitats requires the application of statistical approaches able to facilitate analysis and interpreting complex data related to microbial populations. Analysis of subsurface microbiological heterogeneity has been discussed at different spatial scales by Brockman and Murray (1997). The current study is an attempt to consider that in general microbial communities are high in diversity and variability due to the many different physiologically active species present. Thus, the output of non-metric multidimensional scaling (MDS) represents an efficient analytical tool for summarizing biotic assemblages to a two-dimensional ordination after comparing pairwise similarities between Introduction 7 natural samples (Clarke and Warwick 1994). In addition, multivariate statistical approaches based on variance analysis between samples have been used very recently to analyze microbial community structures in various samples (McCaig et al. 2001, Röling et al. 2001). The aim of the present study was to characterize and compare the microbial communities in three river habitat types (flowing water, hyporheic water, and sediment communities) at contrasting sites. It is hypothesized that (i) the examination of the free-flowing bacterioplankton community display clear quantitative and qualitative differences in composition as compared to the particle-associated bacteria in the sediment and the hyporheic water communities that comprise the linking intermediate habitat type. Secondly (ii), on a minor spatial scale of an environmental gradient, we hypothesized the discrimination of sediment particle-associated bacteria at two different sediment depths of a moving sand dune of the river Elbe by a comprehensive suite of domain-, group-, and genus/species-specific probes. Materials and Methods 8

2 Materials and Methods

Table 1 A survey of all methods Parameter Method Chapter physico-chemical parameters dissolved oxygen (mg l-1) multiple miniaturized sensor system 2.1.1.2 pH multiple miniaturized sensor system 2.1.1.2 specific conductance (µs cm-1) multiple miniaturized sensor system 2.1.1.2 nitrate (mg l-1) photometrically according to AMWU; 2.1.1.2 and 2.3.4 ion chromatograph nitrite (mg l-1) photometrically according to Jones et al. 2.1.1.2 and 2.3.4 (1984); ion chromatograph ammonium (mg l-1) photometrically according to DIN 2.1.1.2 and 2.3.4 phospate (mg l-1) photometrically according to DIN 2.1.1.2 and 2.3.4 ion chromatograph sulphate (mg l-1) ion chromatograph 2.3.4 chloride (mg l-1) ion chromatograph 2.3.4 chlorophyll a (µg l-1) according to DIN 2.1.1.2 and 2.3.3 pheophytin (µg l-1) according to DIN 2.3.3 dissolved organic carbon according to DIN by a total carbon 2.1.1.2 and 2.3.4 (mg l-1) analyzer particulate organic carbon CHN Analyzer 2.3.2.1 (mg gDW-1) particulate organic nitrogen CHN Analyzer 2.3.2.1 (mg gDW-1) particulate organic matter as loss on ignition 2.2.3 and 2.3.2.1 (mg gDW-1) sediment total phosphorus modified according to DIN 2.3.2.7 (mg gDW-1) D10, D50, D60 (mm) according to DIN 2.3.5 biological parameters algal cell counts (cells ml-1) microscopically 2.2.4 bacterial cell counts fluorescence microscopy using SYTOX 2.3.2.6 (cells gDW-1) Blue as fluoresence dye respiration rate difference in oxygen concentration 2.3.2.2 -1 (mg O2 per liter Sed ) denitrification rate acetylene blockage technique 2.3.2.3 (nmol N gDW-1 h-1) nitrification rate KClO3 inhibition of nitrite oxidation 2.3.2.4 (nmol N gDW-1 h-1) β-glucosidase activity spectrofluorometrically using MUF- 2.3.2.5 (nmol gDW-1 h-1) substrates phospatase activity spectrofluorometrically using MUF- 2.3.2.5 (nmol gDW-1 h-1) substrates leucine-aminopeptidase activity spectrofluorometrically using MUF- 2.3.2.5 (nmol gDW-1 h-1) substrates fluorescence in situ with rRNA- directed oligonucleotide 2.4.3 hybridization probes Materials and Methods 9

2.1 Diurnal changes in dissolved oxygen and nutrients

2.1.1 Field sampling

2.1.1.1 Study site and sampling strategy Samples were taken from the infiltration zone of the water work Dresden-Saloppe at km 416. In this section, the river Elbe has a mean width of about 120 m and the sediment consists mainly of gravel and coarse sand (D10: 0.7 mm, D50: 25.3 mm, D60: 32.3 mm). The river bed of the study reach lacks pool-riffle sequences. At Dresden-Saloppe, discharge ranges from 89.1 to 1740 m3 s-1 (mean 309 m3 s-1, Table 8). A nearshore transect of three depth-profiles (Figure 2) H1 (10, 17 and 25 cm), H2 and H3 (7, 15, 22 and 30 cm) was established in summer 1999.

Meter above sea level (m) 108

Dresden 107 Germany sampling point

600 m3 s-1

106 H3

H2 -7 cm -15 cm 105 -22 cm H1 -7 cm -30 cm -15 cm -22 cm -10 cm -30 cm -17 cm -25 cm 104 0 4 8 (m) 12 16 20

Figure 2. Location and sectional drawing of the sampling site Dresden-Saloppe. H1-3 depth profiles

Materials and Methods 10

Flexible tubes (polypropylene, inner diameter 1.6 mm, length 10 m) connected the hyporheic sampler to a remote sampling location on the river bank. Polyvinylchloride pipes (inner diameter 10 mm, length 5 cm) were used to sample hyporheic water at the depth-profiles, protected by stainless steel mesh (0.2 mm) excluding sand. The sampling site comprising all pipe ends was located above the river bed with the possibility to sample up to a discharge of about 600 m³ s-1. Interstitial water samples were collected using a hand operated vacuum pump. The first 50 ml were used to rinse the tube and were discarded. The subsequent sample of 50 ml was taken prior to the additional withdrawal of 10 ml with a syringe for O2, pH and specific conductance measurements. Another 45 ml were taken after dawn for the sampling of algal cells. River water samples were also collected on each sampling date for the same analyses. To monitor diurnal biochemical dynamics of both surface and hyporheic water, samples were taken weekly at two times of the day (after dawn tn (n night) and one hour after sun maximum td (d day)) between January and December 2000.

2.1.1.2 Sample treatment and analysis

O2, pH and conductivity were analyzed on-line using a flow through cell with a multiple miniaturized sensor system (Kurt Schwabe Institut für Mess- und Sensortechnik e.V., Meinsberg, Germany) connected with a data logger (Fluke, Hydra Data Bucket, Karlsruhe, Germany). The oxygen sensor was calibrated weekly, the pH and conductivity sensors monthly. Filtered (0.45 µm, cellulose acetate, Macherey-Nagel, Düren, Germany) water samples (50 ml) were used to determine anions, cations, and DOC. Nitrate, ammonium and phosphate concentrations were determined spectrophotometrically (Hach DR/2000, Loveland, Colorado, USA) by methods modified for small sample volumes according to the German standard methods (DIN, Deutsche Einheitsverfahren 1985). Nitrite was measured spectrophotometrically according to Jones (1984). The results are given for N or P, respectively. DOC samples were quantified using a total carbon analyzer (Rosemount

Analytical Dohrman DC-190, Santa Clara, USA) equipped with a CO2 detector. Chlorophyll a of the water samples was determined after extraction with ethanol according to the German standard method (DIN, Deutsche Einheitsverfahren 1985) and measured with a Spectrophotometer (Hitachi U2000, colora, Lorch, Germany). Materials and Methods 11

2.1.2 Model description The statistical model was developed by Baumert (2002) and examined the diurnal oxygen, nitrate, nitrite, and ammonium dynamics in the hyporheic zone on the basis of the concentrations measured at the above described two sampling times of day: tn and td (after dawn and one hour after sun maximum, respectively). The difference between both concentrations was termed ∆X. For simplicity, the absolute concentrations and differences of the examined sediment depths (10 cm, 17 cm, and 25 cm) of the depth-profile H1 (see chapter 2.1.1.1) were arithmetrically averaged. The equations for each variable in the model are as follows:

Ν µ ∂ []O22∂ ∂ []O (1) −=D Ψ ()...,Tt() ∂ tz∂ ∂ z O2

Ν µ ∂ []NO33∂ ∂ [NO ] (2) −=D Ψ ()...,Tt() ∂ tz∂ ∂ z NO3

Ν µ ∂ []NO22∂ ∂ [NO ] (3) −=D Ψ ()...,Tt() ∂ tz∂ ∂ z NO2

Ν µ ∂ []NH 44∂ ∂ [NH ] (4) −=D Ψ ()...,Tt() ∂ tz∂ ∂ z NH4 where t is the time, z is the sediment depth, D the hydraulic exchange coefficient, µ is the bacterial growth rate, and T is the temperature. The functions ψ... describe the temperature dependent biochemical processes.

The following assumptions were used: - simulated processes for all variables are constant in the averaged sediment depth z. This means, that the upper sediment layer from 0 to 25 cm depth is considered as one model compartment - the hyporheic temperature is only controlled by the open water temperature - the hyporheic temperature controls the metabolism turnover rates - the hydraulic exchange coefficient D is based upon many complex processes as described by Sänger (2001) and Baumert (2001)

Materials and Methods 12

The spatial integration of the transport equations was described as approximation by Baumert (2002).

dX[] 1 3 (5) =−ΡΨ()tX+ dt τ XX[] where X are the diel means of [O2], [NO3], [NO2] or [NH4], P indicates the pelagic variables, and τ = H2/2D is a time constant of the hydraulic exchange as described by Baumert (2001). It is assumed for simplicity that D occurs with spatial constance in the upper sediment layer.

As high resolved temperature data were not available at the sampling site, it is assumed in the model that the variables [O2], [NO3], [NO2] or [NH4] oscillate around daily mean concentrations appeared within sinus-shaped daily amplitudes:

(6) []XP=+XXτ ⋅Ψ

1 (7) Xt()=+X ∆Xsin()2π t/ T24 [] [] 2 []

1 (8) PtXX()=+P ∆ PXsin()2π t/ T24 2

1 d Ψ X (9) ΨΨXX()t =+ []∆Xtsin()2π / T24 2 dX[] where X are the diel means of [O2], [NO3], [NO2] or [NH4], ∆ X are the diel amplitudes of

[O2], [NO3], [NO2] or [NH4], P is the pelagic zone and T24 is the length of a daily amplitude (24 hours).

The equations for the diel means and the diel amplitudes are as follows:

(10) []XP=+XXτ ⋅⋅Ψ

dΨX (11) []∆∆XP=+X τ ⋅⋅ []∆X dX[]

Materials and Methods 13

Additionally, in multiple regression models, the equation for the diel interstitial oxygen amplitudes is as follows:

i p p p i p (12) ∆O 2 = a0 + a1 T + a2 ∆T + a3 O2 + a4 O2 + a5 ∆O 2 p i p i + a6 NO3 + a7 NO3 + a8 ∆NO3 + a9 ∆NO3 p i p i + a10 NO 2 + a11 NO 2 + a12 ∆NO2 + a13 ∆NO 2 p i p i + a10 NH 4 + a11 NH 4 + a12 ∆NH 4 + a13 ∆NH 4 where X and ∆ X are the diel means and the diel amplitudes of the variables, respectively. Indices p and i give the pelagic and interstitial variables, respectively. The model coefficients are shown in Table 2.

Table 2 Model coefficients for multiple regression model (equation 12) model value model value coefficient coefficient a0 0.3166 a9 -0.6937 a1 0.0219 a10 59.9004 a2 -0.2287 a11 -8.3735 a3 0.0401 a12 15.9196 a4 0.4466 a13 -19.3280 a5 0.2320 a14 -4.6027 a6 -0.4090 a15 -1.0720 a7 -0.2450 a16 8.1126 a8 -0.5751 a17 -1.6984

Model parameters based on observations at the sampling site Dresden-Saloppe (km 416.5) during the summer month (29 May 2000 - 02 Okt 2000), when distinct diel oxygen amplitude occurred. In order to estimate the oxygen amplitude at other river Elbe sites in comparison to Dresden-Saloppe, the diel mean concentrations at Dresden-Übigau (km 426.5), and Meißen (km 444.9) were measured (Lange and Kranich, personal communication) (Table 3). As some variables for Dresden-Übigau and Meißen were not available, the multiple regression model for the calculation of the diel interstitial oxygen amplitudes had to be modified (see equation 13) (Baumert 2002).

Materials and Methods 14

Table 3 Model parameters to estimate oxygen amplitude at Dresden-Saloppe (km 416), Dresden-Übigau (km 426.5) and Meißen (km 444.8) (measured by Lange and Kranich, personal communication). Values are the diel mean concentrations. Dresden-Saloppe Dresden-Übigau Meißen flowing hyporheic flowing hyporheic flowing hyporheic water water water water water water Temperature (°C) 18.6 18.4 19.1 Oxygen (mg l-1) 9.9 3.4 9.1 2.6 9.2 0.6 Nitrate-N (mg l-1) 3.92 2.94 4.24 7.65 4.24 2.60 Nitrite-N (mg l-1) 0.022 0.058 0.030 0.020 0.040 0.070 Ammonium-N (mg l-1) 0.076 0.244 0.030 0.020 0.100 0.010

i p p i p i (13) ∆O 2 = a0 + a1 T + a2 O2 + a3 O2 + a4 NO3 + a5 NO3 p i p i + a6 NO 2 + a7 NO 2 + a8 NH 4 + a9 NH 4 where X and ∆ X are the diel means and the diel amplitudes of the variables, respectively. Indices p and i give the pelagic and interstitial variables, respectively. The model coefficients are shown in Table 4.

Table 4 Model coefficients for multiple regression model (equation 13) model value model value coefficient coefficient a0 1.3401 a5 -0.3222 a1 -0.0343 a6 -2.1247 a2 0.0361 a7 -0.5907 a3 0.5913 a8 -6.3924 a4 -0.0760 a9 0.0949

Materials and Methods 15

2.2 Transport of algal cells in hyporheic sediments

2.2.1 Field sampling strategy see 2.1.1.1

2.2.2 Algae and microspheres used for column experiments The algae (Table 5) used for the laboratory investigations were purchased from Culture Collection of Algae at the University of Göttingen, Germany, and cultured on commercially available fertilizer. All cultures were maintained in 500 ml batch cultures at room temperature. Microspheres (Polysciences Inc., Warrington, PA) used as reference particles were carboxylated and had a mean diameter of 6 µm.

Table 5 Characteristics of the algae and microspheres applied in column experiments. Algae Shape Size Chlorella sp. spherical 5 - 10 µm Scenedesmus acuminatus unicellular length 18 - 25 µm width 4 - 6 µm Desmodesmus communis two- cellular 37 - 42 µm four-cellular 47 - 52 µm Pediastrum duplex spherical 20 - 80 µm Microspheres spherical 6 µm

2.2.3 Column experiments The experimental set up consisted of a stock sample tank (Boyle-Mariotte-bottle, 25 l) filled with interstitial water from the aquifer (12 m depth, infiltration zone of the waterwork Dresden-Saloppe) (Figure 3 and Table 6), the filter column and a multiple miniaturized sensor system (Kurt Schwabe Institut für Mess- und Sensortechnik e.V., Meinsberg, Germany) connected with a data logger (Fluke, Hydra Data Bucket, Karlsruhe, Germany) to measure specific conductance. The filter columns (polyvinylchloride, Table 7) were packed with sediment material yielded from near-surface river bottom and from the river bank zone (3 m Materials and Methods 16

Table 6 Mean physico-chemical data of the water from the waterwork Dresden-Saloppe. Averaged data of the year 1998 (water work, personal communication) Variable pH 7.24 -1 NO3-N [mg l ] 2.6 -1 NO2-N [mg l ] 0.052 -1 PO4-P [mg l ] 0.077 Cl- [mg l-1] 43.2 -1 SO4-S [mg l ] 50.9 Ca2+ [mg l-1] 83.5 Mg2+ [mg l-1] 16.3 Fe2+/3+ [mg l-1] <0.067 DOC [mg l-1] 3.3

depth) of the waterwork of the river Elbe in Dresden for each experimental run. Columns were packed for each experiment by differing the levels of compaction of the sediment. Experimental procedures like sterilizing or autoclaving the sediment were not carried out, because this would have affected the sorptive properties of the porous media. For each column, the D50 (median grain size) of the sediment material was determined from the graph of cumulative percentages of each fraction. The flow rate was adjusted to approximately 2 ml min-1 the day before the injection of algae or microspheres. This ensured a constant flow during the experiment. Concentrations of algae were determined just before the experiment. Constant head was achieved by fixing the influent tube (inner diameter 4 mm) to the Boyle- Mariotte-bottle. Algal cell concentrations ranging from 105 to 106 cells ml-1 were injected into the columns suspended in 30 ml algal culture medium (equivalent of 1.0 to 1.5 pore volumes). These injections (15 min) were supplied to the column by a syringe, which was connected to a t-valve. To reduce risk of air entrapment during the experiments, the columns were run in an upflow mode. Effluent from the column was collected in scintillation vials and algae were immediately fixed with formaldehyde solution to an end concentration of 3.7%. Vials were stored at 4°C. The resultant breakthrough curves of the column experiments represented dimensionless data points (C/C0-values), where C is the particle concentration at the column outlet at time t, and C0 is the concentration at the column inlet. The detection limit for the Materials and Methods 17

stock sample tank data logger

Q column L Darcy equation F Q * L kf = tee-valve F * h

Figure 3 Experimental system for monitoring algal cell transport

Table 7 Parameter values for column experiments. Parameter Value Column length (m) 0.1 Column area (m2) 7.07 10-4 Permeability coefficient (m s-1) 2.32 10-5 - 2.02 10-4 Porosity (%) 28 - 41 Pore volume of column (ml) 19.9 - 28.9 Water velocity (ml min-1) 2.0 Filter velocity (m h-1) 0.15 - 0.19

D50 (mm) 0.2 - 0.5 Temperature (°C) 12 - 20 POM (% sediment dry weight) 0.1 - 1.2

Materials and Methods 18

algal cells was approximately a C/C0 of 0.001. To calculate the proportion of cells retained in the column, three to five values of C/C0 taken during the peak region of each breakthrough curve were averaged. The retardation coefficient is defined as the ratio between the time required for the arrival of the particle peak and the theorethical residence time in the column.

The dimensionless data points v/v0 represent the normalized time of an experimental run, where v is the effluent volume measured at each time interval, and v0 is the pore volume. For example, 10 min of the normalized time (v/v0) in each breakthrough curve were measured after one pore volume (mean 20 ml) at the averaged flow rate of 2 ml min-1. The flow rate (Table 7) was determined by measuring the effluent volume collected during each time (2 min interval at the first 30 min of experiment; 5 min interval for the next 50 min of experiment) interval. The pore volume (Table 7) of a column was determined by the difference between saturated and dried (12 h, 80°C) column weight. The algae culture medium was used as a salt tracer. Specific conductance was recorded every 30 s. The pore volume of a column was calculated by the difference between saturated and unsaturated column weight. The permeability coefficient kf of each experimental column was calculated by Darcy´s law (see Figure 3). The vertical distance between the effluent of the Boyle-Mariotte-bottle and the column effluent expressed the hydraulic gradient. Length (L) and area (F) of the column are listed in Table 7. Total particulate organic matter (POM), which was attached to the sediments, was determined as loss on ignition. Column sediment was dried to constant weight at 105°C, and subsequently combusted for 5 h at 550°C in order to determine POM as ash free dry mass.

2.2.4 Particle enumeration The algae and microspheres were counted in duplicate by using a Fuchs-Rosenthal-chamber according to Padisák et al. (1999). The epifluorescence microscope Axioskop (Carl Zeiss, Jena) was at 400× magnification.

Materials and Methods 19

2.3 Hyporheic biofilms at two contrasting river Elbe sites

2.3.1 Study site and sampling procedure Samples were taken during low flow regime in June 2001 from two sites: (i) downstream from Dresden-Übigau (DD) at km 426.5 (51°03Ń,13°40É) and (ii) upstream from Coswig (Co) (near Lutherstadt-Wittenberg) at km 597.5 (51°51Ń,12°28É), where discharge ranged from 89.1 to 1740 m3 s-1 (mean 307 m3 s-1) and 87.5 to 1940 m3s-1 (mean 331 m3 s-1), respectively (Figure 6).

Table 8 Discharge characteristics at the sampling sites Dresden and Coswig Dresden* Coswig** NQ (m3 s-1) 89.1 87.5 MNQ (m3 s-1) 109 126 MQ (m3 s-1) 307 331 MHQ (m3 s-1) 1320 1250 HQ (m3 s-1) 1740 1940 *watergage station Dresden km 59, **watergage station Lutherstadt-Wittenberg km 214; year 1991-2000

For the collection of samples from the middle of the river-bed in Dresden (n = 16) and Coswig (n = 18), a diving bell (base area 6 m2) from the Office of Water and Navigation Magdeburg was employed. The diving-bell was operated under conditions of slight overpressure with the internal pressure being determined by the river Elbe water level. Samples from the river-side in Dresden were collected on the right side (n = 6), where the river-bank is reinforced with large stones. In Coswig, three sampling points in three groyne fields at km 599 were sampled, respectively (n = 18). At each of the sites, piezometers (steel pipes) were inserted into the sediment at two depth layers (0-5 and 20-25 cm). In the following, sediment depths are described as 5 and 25 cm depth. The piezometers (internal diameter = 5 cm) were perforated by a series of 0.5 cm diameter holes along the distal 5 cm of the. For the collection of sediment, 10 l of interstital water was extracted with a hand pump after discarding the first two liters (Bou and Rouch 1967, Brunke and Fischer 1999). The particulate fraction passing a 90µm mesh net was defined as mobile fine interstitial particles (MFIP), the fraction > 90 µm was collected as sediment samples. This sampling approach was successfully applied by Brunke and Fischer (1999) and Fischer et al. (in press). Subsamples Materials and Methods 20 were taken from both layers in order to determine denitrification rate, nitrification rate, β- glucosidase activity, phosphatase activity, leucine-aminopeptidase activity, community respiration rate, dissolved phosphate, carbon, nitrogen, particulate organic matter, and bacterial abundance. For sampling of algal pigments, and for the measurement of physico- chemical parameters, hyporheic water from 25 cm depth was collected by inserting a submersible electric pump immediately after the sediment samples were taken. Samples of the open water were collected from the centroid of the river flow at the sites of sediment collection. Samples were immediately stored on ice until assayed in the laboratory.

2.3.2 Characteristics of the sediment biofilms

2.3.2.1 Detrital parameters - sedimentary particulate organic matter Total particulate organic matter (POM) was determined as loss on ignition. Subsamples of 15-25 g wet weight were dried to constant weight at 105°C, and subsequently combusted for 6 h at 550°C in order to determine POM as ash free dry mass. Particulate organic carbon (POC) and nitrogen (PON) content were determined using a CHN Analyzer NA 1500 Series 2 (Carlo Erba/Fisons Instruments, USA). About 50 g of the dried sediment samples were homogenized with an analytical mill (IKA A 10) for 30 s to achieve grain sizes of < 1 mm. Triplicate subsamples were filled into cylindrical silver capsules (9 mm height, 5 mm diameter; Lüdi AG, Flawil, Switzerland) for analysis. Inorganic carbon was removed with 1 M HCl. The calibration curve was established using acetanilide.

2.3.2.2 Respiration rate Sediment community respiration was measured using sediment cores perfused with river water. Cores were retrieved by hand horizontally from the upper sediment layer (0-5 cm). Sediments were scooped below the water surface into a respiration chamber constructed of clear plastic pipe (18 cm long, 6 cm inside diameter, Volume V = 0.51 l). The chambers were sealed under water with caps covered by a nylon mesh (0.2 mm mesh size) and subsequently stored in ice water for up to three days. In the laboratory, chambers were plumbed to a stirred, high-resolution oxygen probe (Type 4002, Syland Scientific, Germany) an adjustable, electromagnetically clutched precision pump (Type GAM-MA/4-W, Prominent Dosiertechnik, Germany) and installed in a once-through, upward-flow system (Pusch and Materials and Methods 21

Schwoerbel 1994, Jones 1995). The incubation system was run with river water at 20 ± 1°C. Temperature and oxygen concentration were continuously recorded. The incubation was terminated when oxygen concentration was in an equilibrium with the perfusion velocity, which was the case when oxygen concentration in the perfused water was stable for more than 30 min. Perfusion time and flow-through rate (q, L h-1) were recorded. As a control, the oxygen content of river water was measured using a by-pass of the sediment chamber. -1 Community respiration (R, mg O2 per liter Sed ) was calculated using the difference in -1 oxygen concentration between the control and the perfused water (∆O2, mg l ). ∆Oq R = 2 V

2.3.2.3 Denitrification rates

The acetylene (C2H2) block method (Sorensen 1978) was used to determine denitrification rates. The C2H2 block method is based on the linear accumulation of N2O during the incubation period in sediment samples to which C2H2 has been added in order to inhibit the bacterial reduction of N2O to N2. The assays were initiated within 36 h after sampling. About 10 to 20 g (wet weight) of sediment samples were filled into 250 ml bottles sealed by rubber bungs. Headspace was replaced by N2 for 20 min; 50 ml N2-saturated filtered site water with

KNO3 (1 mM), and glucose (1 mM) was added as carbon source by a syringe to offer optimal conditions for denitrification. After 30 min of pre-incubation on a shaker (150 rpm, 23°C), 10 ml N2- and C2H2- saturated site water was injected by a syringe to each sample. C2H2- saturated site water was prepared by bubbling water with C2H2 for 20 min. First N2O analysis was carried out 30 min after C2H2 injection. Samples were incubated for 5 h on a shaker at

23°C and N2O was analyzed using a gas chromatograph (Shimadzu GC-17A) equipped with a 63Ni electron capture detector (oven temperature 50°C, detector temperature 300°C, nitrogen -1 carrier gas flow 30 ml min ). N2O concentrations in the samples were calculated from the measured headspace concentration (Dahlke and Remde 1998). All values for denitrification rates are given in N2O-N. Because the production of N2O was found to be linear, the denitrification rates were estimated by the amount of N2O after preincubation and after 5 h incubation.

Materials and Methods 22

2.3.2.4 Nitrification rates Nitrification rates were obtained by the measurement of the accumulation of nitrite in subsamples containing KClO3 inhibiting the oxidation of nitrite (Wolff and Remde 1998). Assays were initiated within 36 h after sampling. About 30 to 40 g (wet weight) of sediment samples were filled into 50 ml tubes and covered with 20 ml buffer (0.33 mM MgSO4,

0.18 mM CaCl23H2O, 8.5 mM NaCl, 0.01 M phosphate buffer, pH 7.6) and 1 ml KClO3 -1 (3 mM). After 30 min of preincubation on a shaker (200 U min , 25°C), 1 ml NH4Cl (3.7 mM) was added and the first subsample for nitrite measurement was taken. The second subsample was taken after 4 h and nitrite was detected as described below. Pre-investigations showed that the incubation period of 4 h was within the linear phase of nitrite accumulation.

2.3.2.5 Extracellular enzyme activities Fluorogenic substrate analogues (methylcumarinyl (MCA)-substrates and methylumbelliferyl (MUF)-substrates) were used to measure the potential extracellular enzyme activities of leucine aminopeptidase, phosphatase, and ß-glucosidase. Three replicates and one control of each sediment sample were prepared. 3 g of wet sediment were diluted with 8 ml of filtered (0.2 µm) river water. After boiling the controls for 30 min, 1 ml of the substrate analogue (300 mM) was added to replicates and controls. The samples were incubated for 1 to 2 hours at 22°C and fixed by boiling for 3 min. When cooled to room temperature, 1 ml of 0.1 M alkaline glycine puffer (pH 10.5) was added. After centrifugation (5 min at 4000 g), hydrolysis of the substrate analogue was measured by determining the fluorescence of the supernatant (Shimadzu RF-5001 PC spectrofluorometer, 1.5 nm slit, 360 nm (MCA) /365 nm (MUF) excitation, 440 nm (MCA) /450 nm (MUF) emission). Standard MCA (7-amino-4- methyl-coumarin) solutions and standard MUF (4-methyl-umbelliferone) solutions were used for calibration according to Marxen et al. (1998).

2.3.2.6 Bacterial cell counts In order to determine bacterial abundance, subsamples of 1 cm3 sediment were taken from each sampling point and transferred to precombusted 10 ml centrifuge vials containing a sterile filtered solution of 3.5% formaldehyde, 0.85% NaCl, and 1 mM pyrophosphate. Samples were vortexed for 15 s, sonicated for 10 min in a sonication bath (Elma T 710 DH), and vortexed again for 15 s. Bacteria were labelled using SYTOX Blue (Molecular Probes, Materials and Methods 23

Inc., Eugene, Oregon, US) at a final concentration of 0.05 mM. After 20 min of dark incubation, bacteria were filtered onto black polycarbonate filters (Nucleopore, pore size 0.2 µm). Finally, the filters were mounted in Citifluor AF1 (Citifluor Ltd., London, UK) and bacteria were counted using an epifluorescence microscope (Axioskop, Carl Zeiss, Jena, Germany) fitted with a 50 W high-pressure bulb and combinations of specific filters for SYTOX Blue (exciter D436/20, dichroic mirror 455 DCLP, emission filter 480/40).

2.3.2.7 Sediment total phosphorus Subsamples of 1 g wet weighted sediment were extracted by wet oxidation with potassium- peroxodisulphate (0.18 M K2S2O8) to determine total sediment phosphorus. Extracted phosphorus was measured spectrophotometrically (Hach DR/2000, Loveland, Colorado, USA) following the molybdenum blue colorimetric method (DIN, 1985).

2.3.3 Chlorophyll a and pheophytin Hot ethanol (90%, 78°C) was used for the extraction of phytopigments for 12 h at room temperature in the dark. The clear ethanol-pigment-mixture was used to determine chlorophyll a and phaeopigments with a UV-2401 PC Spectrophotometer (Shimadzu) according to DIN (1985).

2.3.4 Chemical analyses Nitrate, nitrite, sulphate, and chloride were analyzed using a ion chromatograph (Shimadzu) equipped with suppressor technique to reduce background conductivity. Ammonium concentrations were determined spectrophotometrically (Hach DR/2000, Loveland, Colorado, USA) modified for small sample volumes according to the German standard methods (DIN). Phosphate was measured spectrophotometrically according to the German standard methods

(DIN). The results are given for NO3-N, NO2-N, NH4-N, PO4-P, or SO4-S. DOC samples were quantified using a total carbon analyzer (Rosemount Analytical Dohrman DC-190, Santa

Clara, USA) equipped with a CO2 detector.

Materials and Methods 24

2.3.5 Grain size distribution From each sampling point, 10 l of sediment were pooled, dried and sieved through a standard set of sieves comprising mesh sizes from 63 µm through 6.3 mm in order to examine the following sediment granulometric data such as the particle size distribution (D10, D50, D60), unequalness U (U = D60/D10), effective grain size De (described by Busch et al. 1993), and the specific surface area of the particles (m2/m3). The specific surface areas can be approximated from the particle surface area, volume, and density

  1   1   2    specificsurfacearea = Σ ()4π ri     43/ π r 3  ρ     i   pi− 

-3 where r is the average particle radius, ρp is the particle density (2650 kg m ), and the subscript i refers to the sieve fractions (described by Kaplan et al. 2000). All particle size variables of the sediment samples were calculated using the software APSM software (Kemmesies 1992). Microbial activities as well as bacterial abundance and detrital variables were determined on the basis of gDW (dry weight) and on the streambed surface area (m2) calculated as the specific surface area of the sediment particles of the considered sediment layers 0-5 and 20-25 cm. As described in chapter 2.3.1, only the sediment particles smaller than 5 mm were retrieved from the river bed. Therefore, the variables on the basis of streambed surface were corrected by the relative coverage determined by the specific surface area of the total sediment.

2.3.6 Statistical analysis Statistical analyses were performed using the statistica/W (ver. 4.5) statistical package (StatSoft Inc., Tulsa, OK, USA). Variables were compared by the nonparametric Kruskal- Wallis test (ANOVA on ranks). This nonparametric test was chosen because of the non- normality and high skewness and curtosis of most variables. For paired depth samples the Wilcoxon matched pairs test and for correlations the Spearman rank correlation coefficient have been used. The relationship of the denitrification rate to other sediment variables was explored using stepwise multiple regression analysis of log transformed data. Step order was forward and minimum tolerance for entry into the model was 0.0001. The principal component analysis (PCA) used for studying the variability of a data set with many variables and for identifying co-varying variables was calculated by using the software SPSS (Release Materials and Methods 25

6.0, SPSS Inc.). Log transformed, standardized data were conducted for an ordination of the different sampling sites. The PCA shows few linear combinations of the variables which can be used to summarize the data, losing as little information as possible (Mardia et al. 1979). Therefore, the original number of variables is contracted into few dimensions, the principal components (PCs). The reduction in dimensions can be used graphically by plotting the samples and variables in coordinate systems defined by the PCs. The comparison of the scores plot (a plot showing the sampling locations along the PCs) and the corresponding parameters plot (a plot showing the contributions of the variables to the same PCs) allows the interpretation and determination of those variables, which explains the highest variability along the PCs of the sampling locations (Mardia et al. 1979). The major advantage of this approach is that a priori knowledge about samples or variables is not required.

2.4 Analysis of microbial communities of the river Elbe

2.4.1 Sampling sites Samples from sites Dresden (DD) and Coswig (Co) were collected from three different habitat types: river Elbe water (F), hyporheic water (H), and sediment (S) as described in 2.3.1. Water and sediment characteristics of the sampling sites are given in Table 8. In addition, five replicate river-bed sediment profiles of a moving sand dune at two depths (5 and 25 cm) were taken from a boat in August 2001 at km 883.5 near Hitzacker (Hi) (53°09´N,

11°06´O). Samples from site (iii) were exclusively from sediment (D10: 0.29 mm, D50: 0.45 mm, D60: 0.52 mm).

2.4.2 Preparation of samples For fixation of samples, approximately 10 cm3 of sediment was transferred into 50 ml centrifuge bottles and immediately covered with 3.7% formaldehyde solution. River water samples and hyporheic water samples (45 ml, respectively) were transferred into 50 ml centrifuge bottles containing 5 ml of 37% formaldehyde solution. All samples were stored at 4°C prior to analyses. Bacteria in river water and hyporheic water samples were collected by centrifugation at 7,000 g for 20 min (4°C). Sediment associated bacteria were detached and homogenized by sonication (Sonoplus GM70, Bandelin Electronics, Berlin, Germany). All bacterial samples Materials and Methods 26 were subsequently washed with phosphate buffered saline (PBS, 130 mM NaCl and 10 mM

NaHPO4/NaH2PO4, pH 7.4) resuspended in 200 µl and stored in a mixture with equal parts of PBS and ice-cold absolute ethanol at -20°C as desribed by Amann et al. (1990). Prior to counting, approximately 4- to 6-µl of the samples were spotted onto Teflon-coated slides (Paul Marienfeld, Germany), dried for 20 min at 46°C, and dehydrated.

2.4.3 Fluorescence in situ hybridization All hybridizations were performed as described by Manz et al. (1992) at 46°C for 3 hours in hybridization buffer containing 0.9 M NaCl and formamide (percentage of formamide as described in cited references), 20 mM Tris-HCl, 0.01% SDS, and the oligonucleotide probe at a concentration of 20 - 50 ng ml-1. Following hybridization, slides were subjected to a stringent washing step for 20 min. In the washing solution (20 mM Tris-HCl, pH 8; 0.01% sodium dodecyl sulfate), the stringency was maintained by lowering the sodium chloride concentration according to the formamide concentration used in the hybridization buffer (see cited references). Samples were stained with 5 µg/ml SYTOX Blue (Molecular Probes, Inc., Eugene, Oregon, US) for 15 min at 4°C. Finally, after washing with distilled water, the slides were mounted in Citifluor AF1 (Citifluor Ltd., London, UK). All probes presented in Table 9 were labeled with the indocarbocyanine dye Cy3. Unlabeled competitor oligonucleotides for BET42a, GAM42a, beta8a, beta6c, Neu23a were used to improve the specificity of the hybridization as described previously (Manz et al., 1992). The oligonucleotides were synthesized by MWG Biotech or Interactiva.

2.4.4 Microscopy For microscopical examination, an epifluorescence microscope Zeiss (Carl Zeiss, Jena, Germany) Axioskop fitted with a 50-W high-pressure mercury bulb was used. The combinations of specific filters used were for SYTOX Blue (exciter D436/20, dichroic mirror 455 DCLP, emission filter 480/40) and for Cy3 (exciter HQ 535/50, dichroic mirror Q 565 LP, emission filter HQ 610/75). For domain- and group-specific probes, each sample was enumerated by choosing 10 microscopic fields and counting up to 1000 SYTOX Blue- stainedcells. For genus- and species-specific probes, each sample was investigated for the presence or absence of hybridization signals on the whole slide section.

Materials and Methods 27

Table 9 Probe sequence used for FISH. Target organisms Oligonucleotidea Sequence (5´-3´) Reference Common name Domain Bacteria S-D-Bact-0338-a-A-18 GCTGCCTCCCGTAGGAGT Amann et al.,1990 EUB338 Refer to Daims et al. 1999 S-D-Bact-0338-b-A-18 GCAGCCACCCGTAGGTGT Daims et al. 1999 EUB338 II Refer to Daims et al. 1999 S-D-Bact-0338-c-A-18 GCTGCCACCCGTAGGTGT Daims et al. 1999 EUB338 III Domain Archaea S-D-Arch-0915-a-A-20 GTGCTCCCCCGCCAATTCCT Amann et al., 1990 ARCH915

α-subclass of Proteobacteria S-Sc-aProt-0019-a-A-17 CGTTCGYTCTGAGCCAG Manz et al.,1992 ALF1b ß-subclass of Proteobacteria L-Sc-bProt-1027-a-A-17 GCCTTCCCACTTCGTTT Manz et al.,1992 BET42a γ-subclass of Proteobacteria L-Sc-gProt-1027-a-A-17 GCCTTCCCACATCGTTT Manz et al.,1992 GAM42a Cytophaga-Flavobacteria-group S-P-CyFla-0319-a-A-18 TGGTCCGTGTCTVAGTAC Manz et al. 1994 CF319a Planctomycetales S-P-Pla-0046-a-A-18 GACTTGCATGCCTAATCC Neef et al. 1998 PLA46 genus Amaricoccus S-G-Amar-0839-a-A-22 CTGCGACACCGAACGGCAAGCC Maszenan et al. Amar839 2000 genus Sphingomonas S-G-Lsa-0644-a-A-18 CCAGGATTCAAGCAATCC Schweitzer et al. LSA644 2001 genus Methylobacterium S-G-Mybm-1171-a-A-18 ATCCACACCTTCCTCGCGGC Manz, unpublished MYBM1171 Paracoccus sp. S-G-Par-0615-a-A-18 ACCTCTCTCGAACTCCAG Neef et al.,1996 PAR651 Zoogloea ramigera S-St-Zra-0647-a-A-18 CTGCCGTACTCTAGTTAT Rosselló-Mora et ZRA al. 1995 genus Azoarcus S-G-Azo-644-a-A-18 GCCGTACTCTAGCCGTGC Hess et al., 1997 Azo644 Genus Aquabacterium S-G-Aqua-0841-a-A-21 GCTTCGTTACTGAACAGCAAG Manz et al., 2002 AQUA Member of the genus S-St-IsoA3-0069-a-A-18 TGCGAATCCCCCCCTTTC Manz, unpublished Aquabacterium A3 Member of the genus S-St-IsoB4-0633-a-A-19 GCCGTGCAGTCACAAGTGC Kalmbach et al., Aquabacterium beta4 1997b Member of the genus S-St-IsoB6-0633-a-A-22 CTAGCCTTGCAGTCACAAAGGC Kalmbach et al., Aquabacterium beta6c 1997b Member of the genus S-St-IsoB8-0633-a-A-19 GCCTTGCAGTCACAAATGC Kalmbach et al., Aquabacterium beta8a 1997b Member of the genus S-St-IsoB8-0069-a-A-19 CCAGGTTGCCCCGCGTTAC Kalmbach et al., Aquabacterium beta8b 1997b β1- Proteobacteria S-*-Bone-0663-a-A-17 GAATTCCATCCCCCTCT Amann et al. 1996 BONE23a β2- Proteobacteria S-*-Btwo-0663-a-A-17 GAATTCCACCCCCCTCT Amann et al. 1996 BTWO23a Burholderia L-G-Bce-1406-a-A-18 CCCATCGCATCTAACAAT Schleifer et al., Bce 1992 Leptothrix discophora S-St-Ldi-0649-a-A-18 CTCTGCCGCACTCCAGCT Wagner et al., LDI23a 1994 Members of the genus S-*-Neu-0653-a-A-18 CCCCTCTGCTGCACTCTA Wagner et al., Nitrosomonas sp. Neu23a 1995 Nitrosococcus mobilis S-S-Ncmob-0174-a-A-18 TCCTCAGAGACTACGCGG Juretschko et al. NmV 1998 Materials and Methods 28

Nitrosomonas europaea-lineage S-*-Nse-1472-a-A-18 ACCCCAGTCATGACCCCC Juretschko et al., NSE1472 1998 Members of the genus S-G-Nsm-0156-a-A-19 TATTAGCACATCTTTCGAT Mobarry et al., Nitrosomonas sp. Nsm156 1996 Freshwater Nitrospira spp. S-G-Nsr-1156-a-A-18 CCCGTTCTCCTGGGCAGT Schramm et al. NSR1156 1998 Freshwater Nitrospira. spp. S-G-Nsr-0826-a-A-18 GTAACCCGCCGACACTTA Schramm et al. NSR826 1998 genus Nitrospira S-G-Ntspa-0662-a-A-18 GGAATTCCGCGCTCCTCT Schramm et al. Ntspa 1998 Phosphate accumulating organisms S-*-Pao-0846-a-A-21 GTTAGCTACGGCACTAAAAGG Crocetti et al. 2000 PAO846 S. natans and relatives S-St-Sna-0656-a-A-18 CATCCCCCTCTACCGTAC Wagner et al., SNA23a 1994 Eikelboom Typ 021N S-St-21N-0652-a-A-18 TCCCTCTCCCAAATTCTA Wagner et al., 21N23a 1994 Genus Acinetobacter S-G-Aca-0652-a-A-18 ATCCTCTCCCATACTCTA Wagner et al. 1994 ACA23a Metylomonas album S-G-Mmb-1121-a-A-18 CATCACGTGTTGGCAACTAA Bourne et al., 2000 Mmb1121 Pseudomonas spp. PP986 Manz, unpublished Pseudomonas spp. L-G-Ps-056 GCTGGCCTAGCCTTC Schleifer et al. Ps56aX 1992 Pseudomonas stutzeri species S-G-Pstb-0738-a-A-17 GTCAGTATTAGCCCAGG Bennasar et al. Pstb 1998 Thiothrix nivea S-St-Tni-0652-a-A-18 CTCCTCTCCCACATTCTA Wagner et al., TNI23a 1994 Desulfobulbus propionicus S-G-Dsb-0660-a-A-20 GAATTCCACTTTCCCCTCTG Devereux et al., 660 1992 Desulfarculus, sp., S-G-Dsma-0488-a-A-20 GCCGGTGCTTCCTTTGGCGG Manz et al., 1998 Desulfomonile sp., Syntrophus spp. DSMA488 Desulforhopalus vacuolatus S-G-Dsr-0651-a-A-18 CCCCCTCCAGTACTCAAG Manz et al., 1998 DSR651 Desulfosarcina variabilis S-G-Dss-0658-a-A-18 TCCACTTCCCTCTCCCAT Manz et al., 1998 DSS658 Desulfovibrio vulgaris S-G-Dsv-1292-a-A-18 CAATCCGGACTGGGACGC Manz et al., 1998 DSV1292 Desulfovibrio desulfuricans. S-G-Dsv-0698-a-A-20 GTTCCTCCAGATATCTACGG Manz et al., 1998 DSV698 Desulfobacter postgatei S-G-Dsb-0985-a-A-19 CACAGGATGTCAAACCCAG Manz et al., 1998 DSB985 Flavobacterium isolate S-St-Iso8-1004-a-A-18 GGTCTGTTTCCAAACCGG Böckelmann et al. Flavo1004 2000 Haliscomenobacter hydrossis S-St-Hhy-0655-a-A-18 GCCTACCTCAACCTGATT Wagner et al., HHY23a 1994 rDNA clones in the green S-*-GNSB-0633-a-A-20 TAGCCCGCCAGTCTTGAACG Sekiguchi et al. nonsulfur bacteria GNSB633 1999 Legionellaceae S-F-Leg-0705-a-A-18 CTGGTGTTCCTTCCGATC Manz et al. 1995 LEG705 Planctomyces maris group Pla1 Manz, unpublished Planctomyces maris group Pla2 Manz, unpublished Planctomyces maris group Pla3 Manz, unpublished Pirellula sp. Pir Manz, unpublished aProbe nomenclature as described by Alm et al. (1996)

Materials and Methods 29

2.4.5 Statistical analysis Univariate statistical analyses of specific hybridization values was performed using the StatSoft software package 5.0 (StatSoft Inc., Tulska, US). Statistical significance of differences were tested with the Mann-Whitney U test. For multivariate analysis, we performed the non-metric multidimensional scaling (MDS) as described by Clarke (1993) and Kruskal and Wish (1978) on the FISH group-specific probe data and the presence/absence FISH data. For determination of similarities between any pair of samples the similarity coefficient S (Bray and Curtis, 1957) was used as proposed by Clarke and Warwick (1994):

p  yy−   ∑i=1 ij ik  S jk =−1001 p   yy+   ∑i=1()ij ik  where Sjk is the similarity between the jth and the kth samples, yij represents the abundance or presence/absence for the ith oligonucleotide probe signal in the jth sample, and yik are the data for the ith signal in the kth sample. The similarity coefficient ranged between 0 and 100%, where S = 100% constitutes two identical samples (Clarke and Warwick 1994). To prove the assumption that the multivariate analysis also reflects less abundant phylogenetic groups, square root transformation was applied for the group-specific oligonucleotide data. The presence or absence of a hybridization signal in the river Elbe samples was revealed by the qualitative interpretation, where the data matrix is replaced by 1 (presence) or 0 (abscence), and the Sorenson similarity coefficient identical to the Bray-Curtis similarity coefficient is computed. Similarities were calculated by the program PRIMER-E vers. 5.2.4, a computer software developed at the Plymouth Marine Laboratory (UK). The iterative numerical MDS algorithm attempts to place n samples in a 2-dimensional ordination in a way that their relative distances reflect the corresponding pairwise similarities of the community composition. Non-metric MDS plots were represented without axis scaling, as sample information is given in relative distances apart. Calculation of the stress value was performed as described by Clarke and Warwick (1994) and computed by the PRIMER-E program MDS. Stress values less than 0.05 correspond to a nearly perfect ordination with no prospect of misinterpretation. Levels of stress greater than 0.3 indicate totally random positions of the samples. To determine differences between or within river Elbe habitat types or sediment depths, the one-way ANOSIM (analysis of similarities) permutation test statistic procedure of PRIMER-E was used as described by Clarke and Warwick (1994).

Materials and Methods 30

Rank similarities were calculated by

Rr=−r/(nn−1)/4 ()BW( ) where rB is defined as the average similarity between different sites (in this study: sampling sites, habitats or depths), rW as the average similarity among replicates within sites, and n representing the total number of samples. R usually ranges between 0 and 1, with approximately zero when similarities between and within sites are comparable and the null hypothesis (H0, that there are no statistical differences between sites) is not falsified. A rank value of 1 means, that all replicates within sites are more similar to each other than samples from different sites.

Results 31

3 Results

3.1 Diurnal changes in dissolved oxygen and nutrients

3.1.1 Temporal variations in discharge and temperature Discharge of the river Elbe at Dresden during the study period ranged between 102 m3 s-1 in June and 1690 m3 s-1 in March following snowmelt (Figure 4 and Table 8). During spring flood (day 60 to 104), sampling was impossible due to river bank flooding. Baseflow conditions occurred from June to the end of November with discharges ranging from 102 to 237 m3 s-1. Short discharge peaks in July and August were caused by low-flow augmentation from dams in the Czech Republic to maintain navigation. Sampling at depth profile H1 was possible during the whole annual cycle. Thus results described in the following in particular obtain the patterns and trends of this profile. Depth profile H2 could be sampled at nearly any time at the depths of 22 and 30 cm. The following described patterns and trends were also observed for H2, but correlations were not significant. Caused by the unexpected long base flow period during the study period, sampling at depth profile H3 was only possible on a few days. Due to the pumpage by the water work in the study reach, the vertical hydraulic gradient was positive (water work Dresden, personal communication).

1800 25 ) -1 temperature (°C)

s 20 3 1200 15

10 600

water discharge (m 5

0 0 Jan 00 Jan 00 Mrz 00 Apr 00 Mai 00 Jun 00 Jul 00 Aug Sep Okt 00 Nov Dez Jan 01 J F M A M J J 00 A 00 S O 00 N00 D

Figure 4. Seasonal variation of water discharge, minimum diurnal temperature (open squares), and maximum diurnal temperature (closed squares) of the river Elbe at Dresden in year 2000. Results 32

Water temperature followed a typical seasonal pattern from a minimum of 3°C in December to maximum of 20-25°C in summer. There were significant diel fluctuations in temperature within the summer season with amplitudes higher than 5°C. Specific conductance in the river Elbe tended to increase from river to hyporheic water. Mean values for river water and hyporheic water at 10, 17, and 25 cm depths were 456, 483, 486, and 484 µSm cm-1, respectively. Previous tests revealed that no river water leaked into the hyporheic zone down to the sampling depth locations of the profiles.

3.1.2 Temporal variations in water biochemistry As had to be expected, the autotrophic biomass in the surface water of the river Elbe measured as chlorophyll a followed a seasonal trend. It attained a maximum value of chlorophyll a of 61 µg l-1 in the first decade of May and averaged 30 µg l-1 in summer. In the colder season consistent with shorter day-length, chlorophyll a was significantly lower and ranged from 1 to 5 µg l-1. Dissolved oxygen (DO) was distinctly lower in hyporheic water than in river water (Figure

5A). Time series on DO (tn) (tn night; measured after dawn) of river water and hyporheic water revealed distinct patterns of seasonal variation. Before spring highwater, hyporheic DO ranged between 4.3 and 7.3 mg l-1 (41% and 70% saturation, respectively) and ranged in summer only between 1.1 and 3.9 mg l-1 (12% and 43% saturation, respectively). Completely oxygen free water was never collected. After water temperatures were below 11°C in

September minimum daily DO (tn) concentrations in the sediment layers were higher at the same water discharge. Maximum diel river and hyporheic DO (td) (td day) concentrations were measured after sun maximum, where river water reached 16.2 mg l-1 (180% saturation) in May and averaged 11.8 mg l 1 (134% saturation) in summer. Independent on the seasonal pattern, the depth profiles of DO exhibited three principal patterns: (i) decreasing DO concentrations with increasing depth, (ii) similar concentrations in all sediment layers and (iii) DO increase with increasing sediment depth. River water and hyporheic water DO concentrations differed significantly for samples taken after dawn (tn night) and one hour after sun maximum (td day). In Dresden, dawn begins about 40 - 50 min before sunrise. The time between the two samplings was about 5.5 h in December and 10 h in June. These diurnal patterns are presented in terms of temporal DO differences

(td-tn) between the day/night samplings. Differences (td-tn) may not be the same as total diel amplitudes, as the defined sampling times were usually not corresponded to the extreme Results 33

stream water DO 12 11 (mg l-1) -10 10 9 8 -15 7

6 sediment depth (cm) 5 4 -20 3 2 1 -25 0 A J F M J J A S O N D

temporal stream water 6 DO- -10 5 difference (mg l-1) 4

3 -15 2 sediment depth (cm) 1 -20 0 -1 -2 -25 B J F M J J A S O N D

Figure 5. Sampling profile H1 (see Fig. 1); Seasonal variation of (A) DO concentration (tn) after dawn (B) DO difference (td-tn) between the DO concentration measured after sun maximum (td) and after dawn (tn) on the same day. In March and April 2000, sampling was impossible because of flood. Samples were taken weekly in year 2000.

Results 34 points of day/night amplitudes. All the data mentioned in the following only refer to this described pattern. According to the increased DO differences (td-tn) in the river water in -1 summer ranging between 0.9 and 5.5 mg l , the hyporheic DO differences (td-tn) were also positive ranging between 0.3 and 3.8 mg l-1 (Figure 5B). Maximal pH values were observed in the river water, followed by a significant decline in the hyporheic water. Stream water pH ranged from 7.0 to 8.3 measured after dawn and from 7.1 and 9.1 after sun maximum. Hyporheic pH varied between 6.3 and 7.4 with a mean of 6.8. Significant correlations existed between pH and DO in both stream and hyporheic water. Nitrate was the major form of inorganic nitrogen in the water body and in the hyporheic -1 water. Hyporheic nitrate concentrations NO3-N (tn) before spring flood (4.0 - 5.9 mg l ) were relatively similar at all depths and slightly higher than in the river water (4.2 - 5.0 mg l-1) (Figure 6A). With lower discharge and increasing temperatures in summer, concentrations of

NO3-N in river and hyporheic water became lower. The average summer concentration of -1 NO3-N in surface water was 3.9 mg l , whereas in 10, 17, and 25 cm depth, NO3-N concentration was significantly lower (3.0, 2.7., and 2.2 mg l-1, respectively). The three vertical distribution patterns described above for hyporheic DO were also found for hyporheic nitrate-N. Over the annual cycle, water temperature and nitrate concentrations in stream water and hyporheic water were significantly and negatively correlated (Pearson correlation coefficient, open water, 10 cm, 17 cm, 25 cm r = -0.55, -0.47, -0.62, -0.74, respectively, p < 0.005, n = 38). The diurnal nitrate dynamics were as follows: hyporheic water nitrate-N

(td) concentrations measured after sun maximum were higher than hyporheic nitrate-N (tn) measured after dawn. This diurnal hyporheic nitrate-N pattern in terms of NO3-N differences

(td-tn) was significantly correlated to the DO differences (td-tn) (Figure 8A). For instance, -1 when absolute DO (td) concentration in each depth was more than 1 mg l higher than DO (tn) -1 concentration after dawn (diel DO differences (td-tn) > 1 mg l ), nitrate-N (td) after sun maximum were above nitrate-N (tn) concentration after dawn (diel NO3-N differences (td-tn) > -1 -1 0 mg l ). This becomes evident from a maximal nitrate-N difference (td-tn) of 1.26 mg l in 17 cm sediment depth. However, absolute nitrate-N concentrations in the hyporheic water were not correlated to absolute hyporheic DO concentrations. Results 35

stream water NO3-N 7.0 (mg l-1) -10 6.0

5.0

-15 4.0 sediment depth (cm) 3.0 -20 2.0

1.0

-25 0.0 A J F A M J J A S O N D

NO -N stream water 2 (mg l-1) 0.24 -10 -10.00 0.20

0.16 -15 -15.00 sediment 0.12 depth (cm)

-20 -20.00 0.08

0.04

-25 -25.00 0.00 J F A M J J A S O N D B

Figure 6 Sampling profile H1 (see Figure 2); Seasonal variation of (A) nitrate concentration (tn) after dawn, (B) nitrite concentration (tn) after dawn. In March and April 2000, sampling was impossible because of flood. Samples were taken weekly in year 2000.

Results 36

stream water NH4-N 2.0 (mg l-1) -10 1.8 1.6 1.4

-15 1.2 sediment 1.0 depth (cm) 0.8 -20 0.6 0.4

0.2 -25 0.0 J F A M J J A S O N D A

PO4-P stream water (mg l-1) 0.40

-10 0.35

0.30

-15 0.25 sediment 0.20 depth (cm) 0.15 -20 0.10

0.05 -25 0.00

J F A M J J A S O N D B

Figure 7 Sampling profile H1 (see Figure 2); Seasonal variation of (A) ammonium concentration (tn) after dawn, and (B) phosphate concentration (tn) after dawn. In March and April 2000, sampling was impossible because of flood. Samples were taken weekly in year 2000.

Results 37

The annual mean nitrite-N concentration in river Elbe water was 0.034 mg l-1 and varied from 0.014 to 0.068 mg l-1 (Figure 6B). The highest nitrite-N concentration in the hyporheic water occured during summer, and decreased afterwards with decreasing water temperature, ranging from a maximum of 0.22 mg l-1 in June to below the detection limit in January. There were distinct seasonal and diurnal patterns of nitrite in hyporheic water. Negative correlations between hyporheic nitrite-N and nitrate-N concentrations (not shown) were observed for all sediment depths.

1.41.4 0.4 AB-10-10 c cmm -1-100 c cmm 0.0 0.70.7 -0-0.4.4 0.00.0 -0-0.8.8 -0.7-0.7 r = 0.72 r = 0.68 r = 0.72 -1-1.2.2 r = 0.68 p < 0.05 p < 0.05 p < 0.05 p < 0.05 -1.4-1.4 -1-1.6.6 --1101230123456456 -10123234455

) ) 0.4

-1 1.4 -1 -17-17 ccm -1-177 ccmm 0.0 0.7 -0.4 0.0 -0.8 -0.7 r r= =0.83 0.83 -1.2 r r= = 0.58 0.58 -N- difference (mg liter -N- difference (mg liter 3 p p< <0 .000.0055 4 n.s.n.s. -1.4 -1.6 NO -10123456NH -1012345

1.4 0.4 -2-255 ccmm -2-255 ccmm 0.0 0.7 -0.4 0.0 -0.8 r = 0.69 -0.7 r = 0.69 r = 0.70 p < 0.05 -1.2 p < 0.05 p < 0.05 -1.4 -1.6 -10123456 -1012345

DO- difference (mg liter-1)

Figure 8 Comparison between diurnal (A) nitrate and (B) ammonium differences (td-tn) to diurnal dissolved oxygen DO difference (td-tn) in depth profile H1 (see Fig. 1) in summer (>15°C). Temporal differences (td-tn) indicate the increase or decrease of nitrate or ammonium, respectively, from the sampling after dawn (tn) to the sampling after sun maximum (td) in relation to the diurnal DO dynamics; n = 15. Dashed lines indicate the 95% confidence intervals. Horizontal lines at zero represent the situation when there was no difference between the day (td) - night (tn) sampling. Results 38

Similarly to the diurnal nitrate pattern, the nitrite-N difference (td-tn) in summer (>15°C) was significantly and negatively correlated to the DO difference (td-tn) (Figure 9A). The values of -1 the differences ranged from -0.15 to 0.10 mg l . For instance, negative differences (td-tn) were calculated, when NO2-N concentrations after dawn (tn) were above the values measured after sun maximum (td) on the same day. In summer, the diurnal patterns of NO3-N differences

(td-tn) were related to the diurnal pattern of NO2-N differences (td-tn) (Figure 9B).

0.12 AB0.06 -10 cm -10-1 c0m cm 0.06 0.00 0.00 -0.06 -0.06

-0.12 rr == --00.88.88 -0.12 r r= = - 0-0.83.83 pp << 00..000011 pp < < 0 0.00.0055 -0.18 -0.18 -10123456 -1.4 -1.0 -0.6 -0.2 0.2 0.6 1.0 1.4 ) )

-1 -1 0.12 0.060.06 -1-177 ccmm -1-17 7c mcm 0.06 0.000.00 0.00 -0.06-0.06 -0.-0.0606 r = -0.89 -0.12-0.12 r = -0..8989 -0.-0.1212 r = -0.89

-N- difference (mg liter -N- difference (mg liter p < 0.001 2 p < 0.001 2 p < 0.001 -0.-0.1818 NO -0.18-0.18 NO --11010123234566 --1.41.4 --1.01.0 --0.60.6 --0.20.2 0.2 0.6.6 1.0 1..4

0.120.12 0.060.06 -25-25 c cmm -2-25 5cm cm 0.060.06 0.000.00 0.000.00 -0.06-0.06 -0.06-0.06 r = -0.68 -0.12-0.12 r = -0.27.27 -0.12-0.12 r = -0.68 n.s.s. pp << 00.05.05 -0.18-0.18 -0.18-0.18 --110101232345456 -1.4-1.4 -1.0-1.0 -0.6-0.6 -0.2-0.2 0.20.2 0.6 11.0.0 1.4

-1 -1 DO- difference (mg liter )NO3-N- difference (mg liter )

Figure 9 Comparison between diurnal nitrite differences (td-tn) to diurnal (A) dissolved oxygen DO and (B) nitrate differences (td-tn) in depth profile H1 (see Fig. 1) in summer (>15°C). Temporal differences (td-tn) indicate the decrease of nitrite from the sampling after dawn (tn) to the sampling after sun maximum (td) in relation to the diurnal DO and nitrate dynamics; n = 15. Dashed lines indicate the 95% confidence intervals. Horizontal lines at zero represent the situation when there was no difference between the day (td) - night (tn) sampling. Results 39

During the annual cycle, ammonium-N (tn) levels (Figure 7A) in the hyporheic water (0.02 - 1.87 mg l-1) were higher than in river water (0.01 - 0.88 mg l-1) and decreased with increasing water temperatures. Ammonium-N (tn) concentrations in surface and hyporheic water were significantly and negatively correlated to NO3-N (tn) concentrations. Distinct diurnal ammonium concentration patterns resulted in negative diel NH4-N differences (td-tn) correlated to the corresponding DO differences (td-tn) (Figure 8B). For instance, ammonium-N (td) concentrations after sun maximum were generally lower than the NH4-N concentrations measured after dawn (tn) on the same day, when DO (tn) concentrations were low. Inorganic phosphate concentrations in river and hyporheic water were generally higher

(Figure 7B) in the cold season. Mean PO4-P concentrations in river water after dawn and after sun maximum were 0.105 and 0.112 mg l-1, respectively (Table 10). In the hyporheic water, the concentrations ranged from 0.07 on 28 August to 0.40 mg l-1 on 17 April. Although no correlation existed between PO4-P (both as in terms of absolute concentrations and calculated diel differences) and the corresponding DO, hyporheic PO4-P concentrations after sun maximum were significantly lower than after dawn in the summer period (Table 10).

Table 10 Mean concentration of inorganic phosphate in the river water and in the interstitial water of depth profile H1 (see Fig. 1) measured after dawn (tn) and after sun maximum (td) in summer (>15°C). Differences (td-tn) indicate the decrease of phosphate from the sampling after dawn (tn) to the sampling after sun maximum (td) (n=18, Mean±SD).

Stream -10 cm -17 cm -25 cm PO4-P (tn), after dawn 0.105 0.126 0.134 0.159 (mg l-1) ±0.044 ±0.049 ±0.065 ±0.090

PO4-P (td), after sun maximum 0.112 0.116 0.108 0.139 (mg l-1) ±0.039 ±0.034 ±0.032 ±0.071

PO4-P (td-tn) difference -0.010 -0.025 -0.020 (mg l-1) ±0.027 ±0.049 ±0.041

PO4-P (td-tn) difference (Min) -0.096 -0.142 -0.138

PO4-P (td-tn) difference (Max) 0.021 0.053 0.026

Results 40

Total DOC in the surface water during the whole period of investigation ranged from 4.1 to 7.8 mg l-1 with a mean of 6.1 mg l-1. Hyporheic DOC in the sediment depths 10, 17, and 25 cm decreased down to 0.8, 0.9, and 1.1 mg l-1, respectively, and averaged 4.6 mg l-1. No relationships between DOC and both DO and nitrate could be observed.

3.1.3 Modeling the effects of oxygen on diurnal inorganic nitrogen dynamics in the hyporheic zone The diurnal hyporheic dissolved oxygen amplitude was estimated with two multiple regression models: one in which all parameters (listed in equation 12, chapter 2.1.2) of the sampling station Dresden-Saloppe were regarded, and one shortened model (listed in equation 13, chapter 2.1.2) with the parameters measured at Dresden-Übigau and Meißen by Lange and Kranich (personal communication, 2002). In all cases, dissolved oxygen, and inorganic nitrogen compounds of the open water and the hyporheic zone were considered. The degree and symmetry of point scatter in Figure 10 provides a general indication of the validity of using equation 12 to estimate the hyporheic oxygen amplitude.

4 3 2 observed i 2 O 01

01234 O i calculated 2

Figure 10 Comparison of the measured interstitial oxygen amplitudes with values calculated by equation (12), see chapter 2.1.2 Results 41

The coefficient of determination r2 was 0.979, that means, that calculated and observed diurnal hyporheic oxygen amplitude were found to agree well. Principal component analysis of all variables (data not shown, Baumert 2002) allowed a simplified regression model with only the variables describing over 90% of the variation of the first three factors of the PCA.

p p p i i 2 Those variables were T , ∆T , O2 , O2 , and NO3 . The coefficient of determination r was 0.659.

4 3 2 observed i 2 O 01

0123 O i calculated 2 Figure 11 Comparison of the measured interstitial oxygen amplitudes with values calculated by equation (13), see chapter 2.1.2

Using equation 13 according to the variables provided in Table 3 of the sampling sites Dresden-Übigau and Meißen, the coefficient of determination r2 was relatively high (r2 = 0.560). Calculated and measured diurnal hyporheic oxygen amplitude values are shown in Figure 11. According to this statistical model, the diurnal hyporheic oxygen amplitudes of Dresden-Saloppe, Dresden-Übigau, and Meißen were 1.3, -0.5, and -0.6 mg l-1, respectively. That means for the negative values, that dissolved hyporheic oxygen after sun maximum is lower than after dawn. This is in contrast to the observed oxygen patterns, where hyporheic oxygen after sun maximum is distinctly higher as compared to the concentrations after dawn (chapter 3.1.2). Results 42

3.2 Transport of algal cells in hyporheic sediments

3.2.1 Seasonal variations in algal cell abundance Algal abundance in stream water and the hyporheic sediment depth profile varied during the year (Figure 12). After the spring flood, phytoplankton abundance increased to a maximum in late May (1.2 104 cells ml-1).

algal abundance (103 cells ml-1)

12.0 350 solar radiation (W m stream 300 8.0 250 200 150 4.0 100 50

0.0 0 -2 ) 3.1 6.3 8.5 24.1 14.2 27.3 17.4 29.5 19.6 10.7 31.7 21.8 11.9 2.10 4.12 23.10 13.11 7.0 -10 cm 3.5

0.0 03.01.00 24.01.00 14.02.00 06.03.00 27.03.00 17.04.00 08.05.00 29.05.00 19.06.00 10.07.00 31.07.00 21.08.00 11.09.00 02.10.00 23.10.00 13.11.00 04.12.00 7.0 -17 cm 3.5

0.0 03.01.00 24.01.00 14.02.00 06.03.00 27.03.00 17.04.00 08.05.00 29.05.00 19.06.00 10.07.00 31.07.00 21.08.00 11.09.00 02.10.00 23.10.00 13.11.00 04.12.00 7.0 -25 cm 3.5

0.0 03.01.00 24.01.00 14.02.00 06.03.00 27.03.00 17.04.00 08.05.00 29.05.00 19.06.00 10.07.00 31.07.00 21.08.00 11.09.00 02.10.00 23.10.00 13.11.00 04.12.00 J F M A M J J A S O N D

Figure 12 Seasonal variation of solar radiation and abundances of algal cells in surface water and three hyporheic depth of the river Elbe. In March and April 2000, sampling was not possible because of high water conditions.

Results 43

The phytoplankton composition changed from a diatom-dominated community in spring (dominating genus: cylindric Aulacoseira sp.) to a community dominated to 60% by green algae (dominating genus: Scenedesmus sp.) in summer. In autumn, diatoms again dominated the phytoplankton composition. High algal cell counts were recorded for all sediment depths only in late May, when only minor changes in discharge occurred (Figure 4). At a depth of 25 cm below the river bottom increasing algal counts were recorded for the first two weeks in June, thereafter algal abundance was generally higher in 25 cm than in 10 and 17 cm sediment depth. The size and varied from small coccoid algae to large species such as Pediastrum sp.

3.2.2 Column experiments Laboratory experiments were designed to assess magnitude of advective algal cell transport through water-saturated hyporheic river Elbe bed sediments. Conditions for column experiments are listed in Table 7. The experiments were replicated several times at different -5 -4 -1 permeabilities with kf- values ranging from 2.3 10 to 2.0 10 m s . As the experiments were stopped after 3 hours, it can be assumed that there were no losses by algal degradation. The shapes of the breakthrough curves from different algal morphotypes differed remarkebly (Figure 2) All algal morphotypes were strongly retained by the sediment within the columns, whith the highest retention at low permeabilities. At a permeability of 2.6 10-5 m s-1, 93.2% of -4 the Chlorella sp. cells were retained by the column sediment (Figure 13A). At kf = 1.3 10 , (Figure 13B) retention was lower. Chlorella sp. could not longer be detected in the effluent after a flow through of about 6 pore volumes (60 min). Scenedesmus acuminatus abundance in the column effluent showed strongest increase with increasing permeability (Figure 13 C and D; 98.6% and 83.6%, respectively). The 2- and 4-cellular Desmodesmus communis cells were subjected to a nearly complete retention at low permeability (Figure 13 E and G; 98.4 and 99.5%, respectively). For these morphotypes, about 4.5 pore volumes, which corresponded to ca. 45 min of flow through, were required for a complete breakthrough. The P. duplex cell concentrations in the effluent were always below the detection limit for all permeabilities investigated. Breakthrough curves of the microspheres were similar to those of the investigated algae (data not shown).

Results 44

0.3 A 0.3 B

Chlorella sp. Chlorella sp. 0.2 -5 0.2 -4 kf = 2.6 10 kf = 1.3 10

0.1 0.1

0.0 0.0 01234567 01234567

0.3 C 0.3 D

S. acuminatus 0.2 S. acuminatus 0.2 -5 -4 kf = 2.7 10 kf = 1.4 10 ) . 0 0.1 0.1

0.0 0.0 01234567 01234567

0.3 E 0.3 F

relative cell density (c/c D. communis D. communis 0.2 0.2 (2-cellular) (2-cellular) k = 4.8 10-5 k = 2.0 10-4 0.1 f 0.1 f

0.0 0.0 01234567 01234567

0.3 G 0.3 H

D. communis D. communis 0.2 0.2 (4-cellular) (4-cellular) -5 -4 kf = 5.4 10 k = 2.0 10 0.1 0.1 f

0.0 0.0 01234567 01234567

normalized time, pore volumes (v/v0)

Figure 13 Dimensionless concentration courses in the effluent of flowthrough experiments for different green algae. Columns were packed with sediments from the river Elbe. C/C0 is (cells/ml in the effluent)/(cells/ml in the influent). v/v0 is the normalized time calculated as (effluent volume in ml measured at each time interval)/(pore volume in ml). Breakthrough curves are examples at low and high permeabilities expressed by permeability coefficient kf, respectively.

Results 45

3.2.2.1 Retention of algal cells Each transport experiment with a different algal morphotype was characterized by a calculated retention rate corresponding to the measured permeability coefficient kf. The percentage of retained spherical-shaped Chlorella sp. cells decreased significantly (Pearson correlation coefficient, r = -0.84, p < 0.005, n = 12) from 96.1 to 86.4% at permeabilities ranging from 2.3 105 to 1.1 104 m s-1, respectively. The strongest relationship between algal transport patterns and permeability was measured for S. acuminatus (Figure 14, Pearson correlation coefficient, r = -0.88, p < 0.0001, n = 18). Only 77.5% of the injected cells were 4 -1 retained by the column sediment at kf- value of 2.0 10 m s . Retention of 2- and 4- cellular D. communis decreased significantly from 98.6 to 89.5% and 99.5 to 94.9%, respectively (Figure 14).

3.2.2.2 Retardation of algal cells For all morphotypes, the first cells were detected in the column effluent after the collection of one pore volume, with the exception of S. acuminatus, of which cells were detected prior to the theoretical residence time of the water in the filter column. The peak of algal cells arrived more delayed compared to that of water with increasing diameter of the algal cells (Figure 13). The retardation factor for Chlorella sp. was highest (2.54) (Figure 15), when 5 -1 permeability was lowest (kf- value 2.3 10 m s ). Retardation decreased to a minimum of 1.53 corresponding to increasing permeabilities (Pearson correlation coefiicient, r = -0.76, p < 0.01, n = 12, Figure 15). The relationship between retardation and permeability of S. acuminatus was also highly significant (Pearson correlation coefficient, r = -0.85, p < 0.00001, n = 18). The relationships between retardation and permeability were not significant for 2- and 4- cellular D. communis cells (Pearson correlation coefficient, r = -0.53, p < 0.11, n = 13 and r = -0.53, p < 0.11, n = 13, respectively) (Figure 15). Results 46

100 Chlorella sp. 100 S. acuminatus 95 95 90 90 85 85 r = -0.84 r = -0.88 80 p < 0.005 80 p < 0.0001 75 75 0.0E+00 5.0E-05 1.0E-04 1.5E-04 2.0E-04 0.0E+00 5.0E-05 1.0E-04 1.5E-04 2.0E-04

100 100 retention (%) ] 95 95 90 90 D. communis D. communis 85 85 r = -0.78 (2-cellular) r = -0.84 (4-cellular) 80 p < 0.005 80 p < 0.0005 75 75 0.0E+00 5.0E-05 1.0E-04 1.5E-04 2.0E-04 0.0E+00 5.0E-05 1.0E-04 1.5E-04 2.0E-04

-1 permeability coefficient kf (m s )

Figure 14 Relationships between retention of different morphotypes of green algae and the -1 sediment permeability coefficent kf (m s ) of river Elbe sediments.

4 Chlorella sp. 4 S. acuminatus 3 3

2 2 r = -0.76 1 1 r = -0.85 p < 0.01 p < 0.00001 0 0 0.0E+00 5.0E-05 1.0E-04 1.5E-04 2.0E-04 0.0E+00 5.0E-05 1.0E-04 1.5E-04 2.0E-04

4 D. communis 4 D. communis retardation (-) ] (2-cellular) (4-cellular) 3 3

2 2 r = -0.53 1 r = -0.53 1 p < 0.11 p < 0.11 0 0 0.0E+00 5.0E-05 1.0E-04 1.5E-04 2.0E-04 0.0E+00 5.0E-05 1.0E-04 1.5E-04 2.0E-04

-1 permeability coefficient kf (m s ) Figure 15 Relationships between retardation of different morphotypes of green algae and the -1 sediment permeability coefficent kf (m s ) of river Elbe sediments. Results 47

3.3 Hyporheic biofilms at two contrasting river Elbe sites

3.3.1 Sediment characteristics Grain size distribution of Dresden (DD) and Coswig (Co) river Elbe sampling sites was considerable different between sites (Table 11). All granulometric variables of the sediments were significantly different (H-test, Kruskal-Wallis-test, p < 0.0005) between the two sites. The sediments at Dresden were stable and mainly consisted of gravels and stones, which resulted in a high degree of unequalness U (25.4-76.9). Averaged D10 and D50 values were 1.2 mm and 13.6 mm at DD river-bed, and 0.6 mm and 44.0 mm at DD river-side sampling points, respectively (Table 11). Sediments in Coswig were more homogenous (U ranging from 5.0 to 10.0, mean 3.7) and exhibited a typical structure of moving sand dunes in the middle of the Elbe. The sediment specific surface area (m2/m3) at Coswig was approximately twice as high as in Dresden.

Table 11 Sediment characteristics of the sampling sites Dresden and Coswig (Mean±SD). Dresden, km 426.5 Coswig, km 597.5 river-bed river-sidea river-bed groyne fields

D10 (mm) 1.1±0.4 0.6 0.8±0.3 0.6±0.1

D50 (mm) 13.6±3.2 44.0 2.4±0.9 3.4±0.9

D60 (mm) 19.0±3.2 47.6 2.9±1.0 4.4±1.0 U 19.2±5.5 77.0 3.7±0.8 7.5±1.6 effective grain size (mm) 3.0±0.6 2.7 1.7±0.7 1.4±0.3 specific surface (m2/m3) 2058±450 2204 3886±1234 4244±700 a DD-river-side, n = 1.

3.3.2 Water chemistry Biochemical properties at the sampling sites are listed in Table 12. Interstitial nitrate-N concentrations in Dresden were higher than in Coswig. Ammonium-N in the open water of -1 -1 Dresden (120 µg l ) was distinctly higher than in Coswig, where NH4-N was below 10 µg l .

In contrast, interstitial NH4-N concentrations were higher in Coswig than in Dresden and revealed a significant negative correlation with nitrate-N (Spearman rank coefficient, Results 48

rS = -0.68, p < 0.005, n = 18) in the interstitial water. Chlorophyll a was markedly higher in Coswig interstitial water than in Dresden. The autotrophic biomass (Chl a) detected in the interstitial water of Coswig was strongly correlated with the mobile fine interstitial particle carbon (MFIP-C, Spearman rank coefficient, rS = 0.64, p < 0.005, n = 18) and nitrogen

(MFIP-N, Spearman rank coefficient, rS = 0.86, p < 0.001, n = 18,) content, whereas these correlations were not significant in samples from Dresden .

Table 12 Water chemistry and biological data of the open water and interstitial water at the sampling sites Dresden and Coswig (mean±SD).

Dresden Coswig open river-bed river-side open river-bed river-side water 25 cm 25 cm water 25 cm depth 25 cm depth depth depth n = 1 n = 8 n = 3 n = 1 n = 9 n = 9 -1 NO3-N (mg l ) 3.9 3.34±0.71 5.33±0.96 3.7 2.82±1.13 1.34±1.84 -1 NO2-N (µg l ) 40 30±20 30±20 20 20±10 30±10 -1 NH4-N (µg l ) 120 40±20 90±20 <10 130±210 190±24 total P (µg l-1) 270 450±310 1900±1280 282 380±130 270±130 -1 PO4-P (µg l ) 132 110±20 70±10 92 110±10 150±80 Chloride (mg l-1) 30.2 26.8±2.91 31.4±0.60 33.6 31.6±0.80 29.5±3.94 -1 SO4-S (mg l ) 26.5 72.0±9.5 120.8±27.2 29.7 88.1±2.1 83.2±26.3 DOC (mg l-1) 3.5 3.55±0.35 4.63±0.85 6.6 5.32±1.58 3.10±0.85 Chl a (µg l-1) 61 102.4±48.8 58.9±18.4 84 288.2±195.7 27.3±22.2 Phaeophytin (µg l-1) 23 95.8±69.9 97.0±63.1 63 70.6±41.4 18.5±12.1

3.3.3 Microbial activity and biofilm analysis The denitrification rates per gDW of sediments in Dresden decreased significantly (Kruskal- Wallis test, p < 0.01, df = 1, H = 10.52, n = 22; Table 15) from the river-side to the river-bed (Figure 16). The vertical decrease from 5 to 25 cm sediment depth was strong in the sediment sample pairs of Dresden (Wilcoxon matched paired test, Z = 2.9, p < 0.005, n = 11). Results 49

Denitrification rate (nmol N gDW-1 h-1) Dresden Coswig 0 100 200 300 400 500 0 50 100 150 200 250

river-bed 5 5 river-side/ groynes

river-bed

25 25 river-side/ groynes

Denitrification rate (µmol N m-2 h-1) Dresden Coswig

ment depth (cm) . 0 5000 10000 15000 20000 0 5000 10000 15000 20000

sedi river-bed 5 5 river-side/ groynes

river-bed

25 25 river-side/ groynes

Nitrification rate (nmol N gDW-1 h-1) Dresden Coswig

0123450.0 0.2 0.4 0.6 0.8 1.0

river-bed 5 5 river-side/ groynes

river-bed

25 25 . river-side/ groynes

Nitrification rate (µmol N m-2 h-1) Dresden Coswig

0 20 40 60 80 100 120 140 160 180 0 20 40 60 80 100 120 140 160 180

ment depth (cm) . river-bed 5 5 sedi river-side/ groynes

river-bed

25 25 river-side/ groynes

Figure 16 Denitrification and nitrification rates with respect to N per gDW h-1 and N per m-2 h-1 of the sampling sites Dresden and Coswig and the river-bed (white boxes) and river-side/groyne field (hatched boxes) samples. Boxes mark the 25th percentile, the 50th percentile (median, closed square) and the 75th percentile. The left and the right margins mark the minimum and the maximum values exculding outliers. Outliers and extremes are defined as values lying more than 1.5 and 4.5 box sizes from the box margins and are shown separately as closed circles and obelisks, respectively. For the DD river-side samples (n = 3), all absolute values are shown as minimum, median, and maximum. Results 50

Coswig sediments exhibited lower denitrification rates per gDW (Figure 16) than Dresden sediments (Kruskal-Wallis test, p < 0.001, df = 1, H = 11.15, n = 57; Table 15). Differences between river-bed and groyne fields were not detected (Kruskal-Wallis test). Differences between the 5 cm and 25 cm sediment layers were significant in the groyne fields (Wilcoxon matched paired test, p < 0.05, Z = 2.4, n = 9), but not in the river-bed sample pairs. In multiple regression models of log-transformed data, the combination of nitrate and organic carbon sources (POC, DOC, and MFIP-C) to the denitrification rates was explored (Table 13).

Table 13 Multiple regression between denitrification rates and nitrate-N, DOC, POC, and MFIP-C in Dresden and Coswig. In the second multiple regression for Coswig sediments, pheophytin is calculated in exchange for MFIP-C. DOC dissolved organic carbon, POC particulate organic carbon, MFIP-C, mobile fine interstitial particle carbon. Dresden Term entered partial r p Model r2 NO3-N 0.95 <0.001 0.86 POC 0.65 0.06 0.89 MFIPC 0.55 0.12 0.92 multiple r2 F-ratio df p N 0.92 28.3 3 <0.001 11

Coswig Term entered partial r p Model r2 NO3-N -0.66 0.02 0.17 DOC 0.47 0.12 0.42 MFIPC 0.33 0.29 0.48 multiple r2 F-ratio df p N 0.48 3.1 3 0.08 14 Term entered partial r p Model r2 Pheophytin 0.71 0.006 0.31

NO3-N -0.63 0.02 0.59 multiple r2 F-ratio df p N 0.59 7.92 2 0.007 14

Results 51

Stepwise regression entered nitrate-N, POC, and MFIP-C to explain 92% of the variance of the denitrification rates in Dresden sediments (Table 13). In contrast, in Coswig, where interstitial nitrate-N concentrations were lower and the partial correlation coefficient between denitrification rate and nitrate was significantly negative, multiple regression detected no significance. However, pheophytin content instead of mobile fine interstitial particulate carbon (MFIP-C) in combination with nitrate-N, POC, and DOC explained 59% (multiple regression, p < 0.01, F = 7.92, n = 14) of the denitrification rate (Table 13). Pheophytin explained most of the variability.

Table 14 Bacterial abundance and detrital variables at the sampling sites Dresden and Coswig (mean±SD). Dresden Coswig depth (cm) river-bed river-side river-bed groyne fields Bacterial abundance 5 1.89 109 4.00 109 2.55 108 8.24 108 (cells gDW-1) ±1.09 109 ±4.45 108 ±9.97 107 ±3.16 108 25 1.92 109 2.36 109 2.20 108 6.30 108 ±8.56 108 ±4.50 108 ±8.82 107 ±1.86 108 POM (mg gDW-1) 5 0.45±0.09 0.92±0.07 0.34±0.16 0.57±0.32 25 0.66±0.30 1.10±0.48 0.35±0.17 0.41±0.11 POC (mg gDW-1) 5 0.11±0.04 0.28±0.08 0.10±0.08 0.27±0.15 25 0.16±0.16 0.44±0.33 0.11±0.14 0.22±0.11 C/N ratio 5 10.4±1.7 17.3±4.7 12.4±4.3 27.1±7.1 25 13.5±6.4 24.2±10.5 15.0±10.6 31.9±12.8

Mean nitrification rates in Dresden sediments tended to increase from the river-bed to the river-side (Figure 16). The rates measured at Dresden were significantly higher than determined for Coswig sediments (Kruskal-Wallis test, p < 0.001, df = 1, H = 38.53, n = 57, Table 15). The vertical decrease in activity from 5 to 25 cm sediment depth was significant in the sediments from Dresden and from the Coswig groyne fields (Wilcoxon matched paired test), but not in the Coswig river-bed samples. Nitrification rate was closely correlated with the respiration rate (Spearman rank coefficient, rS = 0.74, p < 0.0001, n = 28) and the denitrification rate (rS = 0.61, p < 0.0001, n = 54). Results 52

Extracellular enzymatic activities per gDW exhibited no differences between sites (Table 15). Total bacterial abundance per gDW was highest at Dresden river-side compared to the DD river-bed and in the Coswig samples (Table 14 and Table 16). Total particulate organic carbon content was strongly related to the particulate organic matter (Spearman rank coefficient, rS = 0.77, p < 0.001, n = 58). All detrital parameters were significantly related to the D50 of the sediments (Spearman rank correlation, p < 0.01, n = 58). The differences between 5 and 25 cm were not significant for the detrital parameters POM, POC, and PON from all sites (Wilcoxon matched pairs test). However, the C/N ratio of the sediment depth 5 and 25 cm increased significantly in Dresden the sediments (Wilcoxon matched paired test, Z = 2.9, p < 0.005, n = 11), which was not the case in the Coswig samples, where no significant differences could be observed.

Table 15 Kruskal-Wallis test (ANOVA on ranks) summary on microbial variables and bacterial abundance per dry weight and specific streambed surface area of considered sediment layers 0-5 and 20-25 cm. Levels of significance are * p < 0.05, ** p < 0.01, *** -1 -1 p < 0.001 (df = 1). RESP in mg O2 l h DENI, NITRI, BGLU, PHOS, LAP in nmol gDW-1 h-1 and µmol m-2 h-1, respectively. Bacterial abundance in cells gDW-1 -2 and mS , respectively. RESP respiration rate, DENI denitrification rate, NITRI nitrification rate, BGLU β-glucosidase activity, PHOS phosphatase activity, LAP leucine-aminopeptidase activity, n.s. not significant. Differences between Dresden (DD), Coswig (Co), river-bed, river-side, and groyne fields gDW-1 m-2 site n H highest H highest value value RESP Dresden-Coswig 29 19.40*** Dresden - - DD (river-bed-river-side) 22 0.67 n.s. - - Co (river-bed-groyne fields) 35 1.42 n.s. - - DENI Dresden-Coswig 57 11.15*** Dresden 0.34 n.s. DD (river-bed-river-side) 22 10.52** river-side 2.63 n.s. Co (river-bed-groyne fields) 35 2.41 n.s. 4.14* river-bed NITRI Dresden-Coswig 57 38.53*** Dresden 19.32*** Dresden DD (river-bed-river-side) 22 7.44** river-side 2.17 n.s. Co (river-bed-groyne fields) 35 0.06 n.s. 2.52 n.s. BGLU Dresden-Coswig 58 2.08 n.s. 24.36*** Coswig DD (river-bed-river-side) 22 0.66 n.s. 7.85** river-bed Co (river-bed-groyne fields) 36 0.32 n.s. 1.37 n.s. PHOS Dresden-Coswig 58 0.37 n.s. 33.65*** Coswig DD (river-bed-river-side) 22 3.13 n.s. 12.52*** river-bed Co (river-bed-groyne fields) 36 6.57* groyne fields 0.48 n.s. LAP Dresden-Coswig 58 0.54 n.s. 27.29*** Coswig DD (river-bed-river-side) 22 8.70** river-side 12.52*** river-bed Co (river-bed-groyne fields) 36 2.71 n.s. 8.84** river-bed Bacterial Dresden-Coswig 58 36.69*** Dresden 13.45*** Dresden abundance DD (river-bed-river-side) 22 7.04** river-side 2.87 n.s. Co (river-bed-groyne fields) 36 24.36*** groyne fields 21.22*** groyne fields Results 53

3.3.4 Microbial and other sediment variables per streambed surface area Denitrification rates per streambed surface area (m2) of sediment (0-5 cm and 20-25 cm depth layers considered) were not significantly different between the Dresden river-bed and river- side sediments (Kruskal-Wallis test). In Coswig, the denitrification rates per m2 streambed were slightly higher in the river-bed than in the groyne fields (Kruskal-Wallis test, p < 0.05, df = 1, H = 2.41, n = 36).

Table 16 Kruskal-Wallis test (ANOVA on ranks) summary on microbial variables and particulate organic material per dry weight and specific streambed surface area of considered sediment layers 0-5 and 20-25 cm. Levels of significance are * p < 0.05, ** *** -1 -2 p < 0.01, p < 0.001 (df = 1). POM, POC, PON, total-P in mg gDW and mg mS , respectively. MFIP-C and MFIP-N in % gDW. POM particulate organic matter, POC particulate organic carbon, PON particulate organic nitrogen, total-P total-phosphorus, MFIP-C mobile fine interstitial particulate carbon, MFIP-N mobile fine interstitial particulate nitrogen, D10 tenth percentile of grain size distribution, D50 median particle size, n.s. not significant. Differences between Dresden (DD), Coswig (Co), river-bed, river-side, and groyne fields gDW-1 m-2 site n H highest H highest value value POM Dresden-Coswig 58 19.99*** Dresden 19.42*** Coswig DD (river-bed-river-side) 22 7.85** river-side 4.57* river-bed Co (river-bed-groyne fields) 36 11.46*** groyne fields 1.37 n.s. POC Dresden-Coswig 58 0.41 n.s. 21.60*** Coswig DD (river-bed-river-side) 22 7.85** river-side 0.35 n.s. Co (river-bed-groyne fields) 36 15.89*** groyne fields 13.23*** groyne fields PON Dresden-Coswig 58 16.35*** Dresden 18.44*** Coswig DD (river-bed-river-side) 22 8.54** river-side 7.85** river-bed Co (river-bed-groyne fields) 36 3.93* groyne fields 0.03 n.s. C/N ratio Dresden-Coswig 58 5.18* Coswig - - DD (river-bed-river-side) 22 8.70** river-side - - Co (river-bed-groyne fields) 36 19.06*** groyne fields - - total-P Dresden-Coswig 58 36.52*** Dresden 0.89 n.s. DD (river-bed-river-side) 22 9.85** river-side 11.50*** river-bed Co (river-bed-groyne fields) 36 0.26 n.s. 6.73** river-bed MFIP-C Dresden-Coswig 58 0.86 n.s. - - DD (river-bed-river-side) 22 3.96* river-bed - - Co (river-bed-groyne fields) 36 22.52*** river-bed - - MFIP-N Dresden-Coswig 58 0.04 n.s. - - DD (river-bed-river-side) 22 9.59** river-bed - - Co (river-bed-groyne fields) 36 26.27*** river-bed - - D10 Dresden-Coswig 29 14.10*** Dresden - - DD (river-bed-river-side) 11 12.83*** river-side - - Co (river-bed-groyne fields) 18 6.77** river-bed - - D50 Dresden-Coswig 29 40.35*** Dresden - - DD (river-bed-river-side) 11 12.83*** river-side - - Co (river-bed-groyne fields) 18 9.65** groyne fields - - Results 54

The nitrification rates in the considered depth layers (0-5 cm and 20- 25 cm) were significantly higher at Dresden than in the Coswig samples (Kruskal-Wallis test, p = 0.001, df = 1, H = 19.32, n = 36). No differences could be detected between the river-bed and river- bank samples. The extracellular enzyme activities (Table 15) and the detrital parameters (POM, POC, and PON, Table 16) per streambed surface area were significantly higher in Coswig than in Dresden. Furthermore, the DD and Co river-bed samples showed higher proportions in most variables per streambed surface than the DD river-side and Co groyne field samples, respectively (Kruskal-Wallis test).

3.3.5 Relationship between bacterial biomass and activity, sediment composition and structure In order to provide an overview of the relationships between the measured parameters at the contrasting sample sites, principal component analysis (PCA) was applied. The factorial map (Figure 17A) of the PCA of the sampling points is based on bacterial abundance and the microbial activities. The ordination plot separates the two sampling sites Dresden and Coswig. Coswig river-bed and groyne field samples have a small overlap. The river-bed samples from Coswig were clustered very closely to each other, whereas Dresden river-bed and river-side samples could be separated more distinctly. The first two factors of the PCA accounted for 79.4% of the variation of the microbial variables (Figure 17B). Factor 1 (PC 1) is strongly correlated with β-glucosidase activity (r = 0.89), phosphatase activity (r = 0.86), and leucine-aminopeptidase activity (r = 0.87). Factor 2 (PC 2) is determined by bacterial abundance (r = 0.90) and nitrification rate (r = 0.92). The Dresden sediment samples arranged on the upper part of the ordination were characterized by an especially high bacterial abundance. The ordination of the abiotic parameters (Figure 18A) by principal component analysis reflects similar patterns that separate Dresden and Coswig, but also distinctly the river-bed of DD and Co from the DD river-side and Co groyne field samples, respectively. The first two factors accounted for 74.9% of the total variability of the abiotic variables (Figure 18B). PC 1 correlated strongly with total-P (r = 0.90), D50 (r = 0.86), POM (r = 0.78), and PON (r = 0.77), whereas PC 2 was determined by POC (r = 0.89), C/N ratio (r = 0.88), and MFIP-N (r = 0.75). Results 55

3.0 A

2.0 DD25s DD5s

DD5b 1.0 DD25b

Factor 2 0.0 Co5g

-1.0 Co25g Co5b Co25b

-2.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 Factor 1

1.0 bacterial NITRI B abundance DENI 0.5 BGLU

PHOS 0.0 LAP Factor 2 (28.2%) -0.5

-1.0 -1.0 -0.5 0.0 0.5 1.0 Factor 1 (51.3%)

Figure 17 PCA ordination plot of microbial variables. (A) Factorial scores of sampling positions. DD Dresden, Co Coswig, b river-bed, s river-side, g groyne fields, 5 and 25 indicated the 0-5 cm and 20-25 cm sediment layers, respectively. The arrow indicates a gradient of microbial biomass (B) Ordination of the microbial variables. Values in brackets along axes are the amounts of variation explained by the principal component. DENI denitrification rate, NITRI nitrification rate, BGLU β-glucosidase activity, PHOS phosphatase activity, LAP leucine-aminopeptidase activity. Results 56

2.0 A 1.5 Co25g Co5g DD25s 1.0

0.5 DD5s a 0.0 DD25b b Factor 2 -0.5 Co25b

-1.0 DD5b Co5b -1.5

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 Factor 1

1.0 B POC C/N ratio POM 0.5 PON D

3%) 50 . 5 total P (2 0.0 or 2 t

D10 Fac -0.5 MFIP-C

MFIP-N -1.0 -1.0 -0.5 0.0 0.5 1.0 Factor 1 (49.6%) Figure 18 PCA ordination plot of environmental variables. (A) Factorial scores of sampling positions. DD Dresden, Co Coswig, b river-bed, s river-side, g groyne fields, 5 and 25 indicated the 0-5 cm and 20-25 cm sediment layers, respectively. Arrow "a" and "b" indicate a gradient of microbial biomass (B) Ordination of the microbial variables. Values in brackets along axes are the amounts of variation explained by the principal component. POM particulate organic matter, POC particulate organic carbon, PON particulate organic nitrogen, total-P total-phosphorus, MFIP-C mobile fine interstitial particulate carbon, MFIP-N mobile fine interstitial particulate nitrogen, D10 tenth percentile of grain size distribution, D50 median particle size. Results 57

Bacterial abundance was significantly correlated to the respiration rate (Spearman rank correlation, rS = 0.67, p < 0.001, n = 28), the denitrification rate (rS = 0.43, p < 0.05, n = 56), and the nitrification rates (rS = 0.72, p < 0.001, n = 56) (Table 17). The β-glucosidase activity was only weakly correlated with bacterial abundance, while phosphatase and leucine- aminopeptidase activity were not correlated with bacterial abundance. The correlations of particulate organic variables with bacterial abundance were also significant (Table 17). Leucine-aminopeptidase activity in Coswig was significantly correlated to chlorophyll a

(Spearman rank coefficient, rS = 0.56, p < 0.05, n = 18) and pheophytin (rS = 0.60, p < 0.01, n = 18), whereas β-glucosidase and phosphatase activity in Dresden and Coswig did not correlate with bacterial abundance.

Table 17 Spearman rank correlation coefficients between bacterial abundance and microbial as well as detrital and granulometric data. Levels of significance are * p < 0.05, ** p < 0.01, *** p < 0.001. Correlation values marked with asterices hold a level of significance of p < 0.01 after sequential Bonferroni correction (Holm 1979, Peres- Neto 1999). RESP respiration rate, DENI denitrification rate, NITRI nitrification rate, BGLU β-glucosidase activity, PHOS phosphatase activity, LAP leucine- aminopeptidase activity, POM particulate organic matter, POC particulate organic carbon, PON particulate organic nitrogen, total-P total-phosphorus, MFIP-C mobile fine interstitial particulate carbon, MFIP-N mobile fine interstitial particulate nitrogen, D10 tenth percentile of grain size distribution, D50 median particle size. Bacterial abundance n r RESP 28 0.67*** DENI 56 0.43*** NITRI 56 0.72*** BGLU 58 0.33** PHOS 58 n.s. LAP 58 n.s.

POM 56 0.76*** POC 56 0.50*** PON 56 0.68*** C/N ratio 56 n.s. total-P 58 0.72*** MFIP-C 58 -0.52*** MFIP-N 58 -0.43*** D10 58 n.s. D50 58 0.85***

Results 58

3.4 Analysis of microbial communities of the river Elbe

3.4.1 Community composition at the domain-specific level The morphotypes of the Sytox Blue-stained cells in all three river Elbe habitat types (river water, hyporheic water, and sediment) at Dresden and Coswig, and in the two investigated sediment depths (5 and 25 cm) near Hitzacker were diverse and consisted of cocci, rods, vibrios, and filamentous bacteria of various sizes. The average detection yields with the eubacterial probe mixture EUB338 I-III relative to Sytox Blue stained total cell counts within

Table 18 Relative abundance of major phylogenetic groups in different river Elbe habitats determined by fluorescence in situ hybridization.

Location Sampling sediment Fraction (%) of total cells (mean±SD) a site depth detected with probeb (cm) EUB338 ALF1b BET42a GAM42a CF319a PLA46 DD F river water 68±7 10±2 14±2 14±4 16±5 <2 DD H 2 river-bed 20-25 35±7 9±2 9±1 7±1 5±2 <2 DD H 4 river-side 20-25 39±4 9±2 12±1 7±1 6±2 <2 DD S 1 river-bed 0-5 49±8 13±5 10 7±1 5±1 16±4 DD S 2 river-bed 20-25 44±8 10±3 10±1 7±1 5±1 13±2 DD S 3 river-side 0-5 42±11 9±2 8±2 8±1 5±1 11±1 DD S 4 river-side 20-25 48±15 8±1 9±2 7±1 5±1 11±1

Co F river water 62±8 10±2 13±1 12±2 13±3 <2 Co F g river water 63±6 10±3 12±1 12±3 10±3 <2 Co H 2 river-bed 20-25 53±8 5±2 13±3 13±2 7±3 <2 Co H 4 groynes 20-25 48±9 8±1 10±2 10±2 4±1 <2 Co S 1 river-bed 0-5 62±7 8±3 11±1 10±2 4±2 14±3 Co S 2 river-bed 20-25 42±6 8±1 11±1 9±1 7±1 12±4 Co S 3 groynes 0-5 50±10 8±2 11±1 9±2 6±2 15±7 Co S 4 groynes 20-25 46±7 8±2 10±2 9±1 5±1 14±3 a DD Dresden; Co Coswig; g groynes; F river water; H hyporheic water; S sediment; 1-4 sample numbering b Percent detection compared to SYTOXBlue. Means and standard deviation were calculated from the counts of 10 arbitrarily selected on each microscopic field section.

Results 59

Table 19 Relative abundance of major phylogenetic groups in different river Elbe sediment depths near Hitzacker determined by fluorescently in situ hybridization.

Locationa Sampling sediment Fraction (%) of total cells (mean±SD) site depth detected with probeb (cm) EUB338 ALF1b BET42a GAM42a CF319a PLA46 Hi S 1 river-bed 0-5 49±5 14±3 16±3 17±2 16±1 <2 Hi S 2 river-bed 20-25 42±7 9±2 8±3 10±2 15±4 <2 Hi S 3 river-bed 0-5 55±5 15±3 11±2 15±1 13±2 <2 Hi S 4 river-bed 20-25 41±6 12±2 8±2 10±1 12±1 <2 Hi S 5 river-bed 0-5 51±3 17±4 11±1 15±2 15±2 <2 Hi S 6 river-bed 20-25 44±2 9±1 9±2 9±2 14±1 <2 Hi S 7 river-bed 0-5 51±3 13±2 12±2 15±2 17±1 <2 Hi S 8 river-bed 20-25 44±4 9±1 8±1 13±1 13±1 <2 Hi S 9 river-bed 0-5 52±4 17±3 16±3 14±1 15±1 <2 Hi S 10 river-bed 20-25 44±4 11±1 9±2 11±1 13±2 <2 a S sediment; 1-10 sample numbering b Percent detection compared to SYTOXBlue. Means and standard deviation were calculated from the counts of 10 arbitrarily selected fields on each microscopic field section.

the bacterioplankton of the river Elbe in June 2001 ranged from 63% in Coswig to 68% in Dresden (Table 18). In contrast to this, the numbers of EUB338-stained cells in hyporheic water and associated with the sediment were significantly lower (43% and 47%, respectively, Mann Withney U-test, p < 0.05, n = 15). Hyporheic and sediment probe-active Bacteria did not differ significantly (p = 0.49, n = 12). In August 2001, the proportion of EUB338 hybridized bacteria associated to the sediment of the moving dune near Hitzacker differed with respect to the sediment depth (Table 19). The highest percentage of EUB338-hybridized Bacteria within five replicates was found at 5 cm depth (51%) and decreased significanly to an average of 43% at the 25 cm sediment depth (Mann Withney U-test, p < 0.01, n = 10). Although probe Arch915 (specific for most members of the Archaea) was applied to all samples, no hybridized cells were detected in any habitat. Phytoplankton cells were detected microscopically in river and hyporheic samples, but never in sediment samples.

Results 60

3.4.2 Community composition at the group-specific level The relative in situ abundances of the main subclasses of bacterial groups within the different river Elbe habitat types and sediments depth are shown in Tables 3 and 4, respectively. Within habitat type samples (Table 18), the proportions of the proteobacterial bacterial groups were

FISH-group

Stress: 0,05 DD-S-1 Co-S-3 river water DD-S-2 Co-S-2 sediment Co-S-4Co-S-1 DD-F DD-S-4 DD-S-3 Co-F Co-F-g

hyporheic water Co-H-2 DD-H-4 Co-H-4 DD-H-2

Figure 19 Non-metric multidimensional scaling ordination for the group-specific FISH data of three habitat types of the river Elbe at two sites. The ordinations are based on Bray- Curtis similarities from square-root transformed abundances with the resulting stress value. Non-metric MDS plots are represented without axis scaling. Overlapping sediment samples were DD-S-3, DD-S-4 and Co-S-1, Co-S-4, respectively.

generally similar with a slight dominance of the β- and γ-Proteobacteria (between 7 and 14% of total). Interestingly, the abundance of Planctomycetales detected with probe Pla46, comprising the genera Planctomyces, Pirellula, Gemmata and Isosphaera, was especially high (between 11 and 16%) in the sediment samples in June at both river sites. Hybridized planctomycetes were usually large cocci with diameters > 1 µm. In hyporheic and river water, the relative abundance of Pla46- cells was detected < 2%. Analysis of differences between the river water and sediment samples by univariate statistical analysis showed significant discrimination at the group-specific level for all probes (Mann-Withney U-test, p < 0.05, n = 11) with the exception of α-Proteobacteria (Mann-Withney U-test, p = 0.153, n = 11). The α- Results 61

Proteobacteria and the Cytophaga-Flavobacterium group represented a significantly higher proportion of the bacterial population in the river water compared to the hyporheic water (Mann-Withney U-test, p < 0.05, n = 7), but not for other group-specific oligonucleotide probes. Hyporheic water and sediment samples only significantly differed with respect to the planktomycetes (Mann Whitney U-test, p < 0.01, n = 12). The proportions of the α-, β-, and

FISH-R-group

Stress: 0,01 9 5 3 5 cm depth 1 7

10 26 25 cm depth 8

Figure 20 Non-metric multidimensional scaling ordination for the group-specific FISH data of two sediment depths of the river Elbe near Hitzacker. The ordinations are based on Bray-Curtis similarities from square-root transformed abundances with the resulting stress value. Non-metric MDS plots are represented without axis scaling. 1,3,5,7,9: 5 cm, 2,4,6,8,10: 25 cm depth.

γ- Proteobacteria and the Cytophaga-Flavobacterium group were not significantly different among the habitats. In August 2001, the proportion of planctomycetes detected in the 5 and 25 cm sediment depths of the moving dune near Hitzacker was below 2%. The mean percentage of specific cell counts within the α-, β-, and γ-Proteobacteria and the Cytophaga- Flavobacterium group (Table 19) decreased significantly from 5 cm depth to 25 cm depth (Wilcoxon matched pairs test, p < 0.05, n = 10). The ordination plot of the square-root transformed group-specific FISH-data computed on the basis of non-parametric multidimensional scaling (MDS) revealed clustering of both the three habitat type samples (Figure 19) and the two sediment depth samples (Figure 20) with low stress values of 0.05 and 0.01, respectively. Results 62 DD-S-1 DD-S-2 DD-S-3 DD-S-4 Co-S-1 Co-S-2 Co-S-3 Co-S-4 DD-H-2 DD-H-4 Co-H-2 Co-H-4 DD-F Co-F Co-F-g AMAR839 11111111001 0000 LSA644 000000000000000 MYBM1171 111111111111111 PAR651 111100000000000 ZRA 000000000000000 Azo644 111111001010111 Aqua 000000000000000 A3 000000000 0 0 0 000 B4 000000000000000 B6c 00001 000001 0 111 B8a 1100000000001 00 B8b 11111110001 0 111 BONE23a 000011111111111 BTWO23a 1 0 1 0 11110000000 Bce 000000000000000 LDI23a 1 0 1 0 11000000000 Neu23a 00000000001 0011 NmV 000000000000000 Nse1472 000000000000000 Nsm156 0000111100001 00 Nsr1156 0 1 001110 1 000000 Nsr826 0 1 0000001 000000 Ntspa 000000000000111 PAO846 001111111111111 SNA23a 111111111111111 21N23a 110000000000100 ACA23a ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## Mmb1121 11110000110 0111 PP986 111111111111111 Ps56aX 000000000000000 Pstb ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## TNI23a 0000000000000 00 660 000000000000000 DSMA488 000000000000000 DSR651 111111111111111 DSS658 001111110011111 DSV1292 111111111111111 DSV698 000011110011011 DSV985 110000000000100 FLAVO1004 111111111111001 HHY23a 0000111100110 11 GNSB633 000000000000000 LEG705 111111111111111 PIR 000011110000000 PLA1 111111110000000 PLA2 110000000000000 PLA3 000000000000000

Figure 21 Presence/absence FISH signal pattern of genus- and species-specific probes at different river Elbe habitat types. Black and white squares indicate present and absent FISH signal, respectively. DD Dresden; Co Coswig; g groin; F river water; H hyporheic water; S sediment; 1-4 sample numbering. Results 63 1 2 3 4 5 6 7 8 9 10 AMAR839 1111111111 LSA644 0000000000 MYBM1171 1111111111 PAR651 0 1 0001 0001 ZRA 0000000000 Azo644 1111111111 Aqua 1110 110 110 A3 0000000000 B4 0000000000 B6c 0000000000 B8a 1 001 000000 B8b 1111111111 BONE23a 0 1 0001 0000 BTWO23a 1111111111 Bce 0000000000 LDI23a 0000000000 Neu23a 0 1 00111000 NmV 0000000000 Nse1472 0000000000 Nsm156 0000000000 Nsr1156 1 0 1 0001111 Nsr826 0000000000 Ntspa 0000000000 PAO846 1110001100 SNA23a 1111111111 21N23a 1001000000 ACA23a ## ## ## ## ## ## ## ## ## ## Mmb1121 1111 1111 11 PP986 1111111111 Ps56aX 0000000000 Pstb ## ## ## ## ## ## ## ## ## ## TNI23a 1110110110 660 0000000000 DSMA488 0000000000 DSR651 1111111111 DSS685 1111111111 DSV1292 1111111111 DSV698 1111111111 DSV985 1111111111 FLAVO1004 110 1110 111 HHY23a 1111111111 GNSB633 0000000000 LEG705 1111111111 PIR 1111111111 PLA1 1100000000 PLA2 0000110000 PLA3 00001 0 1100 Figure 22 Presence/absence FISH signal pattern of genus- and species-specific probes at different river Elbe sediment depths. Black and white squares indicate present and absent FISH signal, respectively. S sediment; 1,3,5,7,9: 5 cm, 2,4,6,8,10: 25 cm depth. Results 64

Pairwise testing by the analysis of similarities (1-way ANOSIM) yielded significantly different results for sediment-associated and river water bacteria (analysis of similarities, r = 1, p < 0.01, n = 15), and for sediment-associated and hyporheic water bacteria (analysis of similarities, r = 0.99, p < 0.005, n = 15). Although the ordination plots suggest differences in position of the points between river and hyporheic water samples (Figure 19), discrimination at the 0.05 level was not significant (analysis of similarities, r = 0.8, p = 0.06, n = 15). Significant discrimination among the investigated sites Dresden and Coswig was not observed (analysis of similarities, r = 0.08, p = 0.17, n = 15). The clear pattern of clustering among sediment depth samples near Hitzacker (Figure 20) showed a high similarity coefficient (analysis of similarities, r = 1, p < 0.01, n = 15) on the group specific level.

3.4.3 Community composition at the genus- and species-specific level FISH analysis using an comprehensive panel of nearly 50 genus- and species-specific oligonucleotide probes targeting several main subclasses of bacteria were analyzed through a qualitative presence or absence hybridization signal approach. Presence was defined as the presence of at least three hybridized cells in the whole investigated sample slide section. The majority of the genus-and species-specific probes used was affiliated with the Proteobacteria, especially the ß-Proteobacteria (Table 9). Additional probes targeted against members of the Cytophaga-Flavobacterium-Bacteroides-group, the Planctomycetales-group, Legionellaceae (LEG705), and members of green nonsulfur bacteria (GNSB633) were also employed. Seven oligonucleotide probes yielded positive hybridization signals in all river water, hyporheic water, and sediment samples (Figure 21). For example, probe MYBM1171 affiliated to the genus Methylobacterium, probe SNA23a affiliated to Sphaerotilus natans and relatives, probes PP986 and Ps56aX affiliated to Pseudomonas spp., respectively, and probe LEG705 affiliated to Legionellaceae detected cells in all habitat types. Probe SNA23a hybridized to a high number of rod-shaped bacteria in all river water samples. However, only few cells in the hyporheic water and sediment samples hybridized with this probe. Hybridization with probe LEG705 resulted in high numbers of hybridized cells in all river and hyporheic water samples and in sediment samples from Dresden, but only few cells were detected in Coswig sediment samples. A total of 16 oligonucleotide probes did not react with any cell in all habitat type samples (Figure 21). The numerical dominance of 26 probes out of the 47 probes indicated presence/absence hybridization signal differences between and within the habitat types of the two sites Dresden and Coswig (Figure 21). For example, probes Results 65

PLA1, DSR651, and, with one exception AMAR839, hybridized only to cells in sediment samples, but not in river and hyporheic water samples. Probes Nsm156 and PIR detected cells only in Coswig sediments, but not in Dresden sediments. Ntspa and B6c hybridized cells were almost exclusively observed in river water samples. In most cases, these probes showed widely distributed presence/absence data with dominances in different habitat types or sites. In both the 5 cm and 25 cm sediment depths, hybridization of 17 out of the 47 oligonucleotide probes including a broad phylogenetic distribution among the bacterial subclasses indicated probe-positive cells in all samples (Figure 22). The majority of 20 out of the 47 probes detected no hybridization signal in all samples. In contrast to the habitat type samples, only 12 probes showed differences in the pattern of presence/absence hybridization signals with random distribution. Few probes showed signals in 5 cm depth, but not in 25 cm depth (B8a) and vice-versa (PAR651, MmA).

FISH

Stress: 0,14 Co-S-4 Coswig Co-S-3 Co-H-4 Co-S-2Co-S-1

Co-H-2 Co-F Dresden Co-F-g DD-S-3DD-S-4

DD-S-1 DD-F DD-H-4 DD-S-2 DD-H-2

Figure 23 Non-metric multidimensional scaling ordination for the genus- and species-specific FISH data of three habitat types of the river Elbe at two sites. The ordinations are based on Bray-Curtis similarities from presence/absence FISH signal patterns with the resulting stress value. Non-metric MDS plots are represented without axis scaling. Overlapping Coswig sediment samples were Co-S-1, Co-S-2.

Multidimensional scaling of the original set of presence/absence FISH-data for the habitat type samples clearly separated the two sites Dresden and Coswig (Figure 23). The similarity coefficient indicated significant discrimination on the basis of the sites (analysis of Results 66 similarities, r = 0.37, p < 0.01, n = 15). The stress value (0.14) was considerably higher than for the group-specific data ordination plot. Analysis of similarity also distinguished between sediment-associated and hyporheic water bacteria (analysis of similarities, r = 0.65, p < 0.005, n = 15) and sediment-associated and river water bacteria (analysis of similarities, r = 0.80, p < 0.01, n = 15). It was remarkable, that the Coswig sediment samples revealed closer clustering in contrast to the Dresden sediment samples (Figure 23). As for group-specific FISH data, the similarities within habitats of the river and hyporheic water samples were lower than the similarities between habitats indicated by nonsignificant discrimination (analysis of similarities, r = 0.51, p = 0.57, n = 15). No clear patterns of clustering were observed for the 5 cm and 25 cm sediment depth (Figure 24; stress value 0.13). Pairwise testing by the analysis of similarities (1-way ANOSIM) of all points showed no significant difference relative to each other regardless of the sediment depths (analysis of similarities, r = 0.03, p = 0.44, n = 10).

Reststrecke

Stress: 0,13 7 (5)

3 (5) 5 (5) 8 (25)

6 (25) 1 (5) 9 (5) 2 (25) 10 (25)

4 (25)

Figure 24 Non-metric multidimensional scaling ordination for the genus- and species-specific FISH data of two sediment depths of the river Elbe near Hitzacker. The ordinations are based on Bray-Curtis similarities from presence/absence FISH signal patterns with the resulting stress value. Non-metric MDS plots are represented without axis scaling. Numbers in parentheses are sediment depths. 1,3,5,7,9: 5 cm, 2,4,6,8,10: 25 cm depth. Discussion 67

4 Discussion

4.1 Effect of diurnal changes in dissolved oxygen on daily and seasonal nutrient concentrations in the open water and the hyporheic zone

4.1.1 Effect of oxygen concentration in the open water on hyporheic oxygen dynamics The variations in dissolved oxygen and nutrients (N, P) during seasonal and diurnal observations indicate complex local interactions of biotic and physico-chemical processes in the hyporheic water of the river Elbe at Dresden-Saloppe (km 416). In the river Elbe, daily cycles of phytoplankton primary production and respiration are strongly evident during summer months (Böhme and Eidner 2001). High hyporheic oxygen demand occurred in late spring through autumn accompanied by large diel oxygen amplitudes. Samples taken in the cold season indicated higher hyporheic oxygen concentrations and saturation with little diel variation. The strong diel DO patterns allow conclusions about the degree of exchange between the surface water and the subsurface water as well as to the direction and magnitude of interstitial flow (Valett 1993). In many studies interstitial temperature patterns (White 1990, Silliman et al. 1995) or vertical hydraulic gradient determined by means of piezometers (Gibert et al. 1990, Valett et al. 1994, Hill and Lymburner 1998) have been used as indicators of hydrologic exchange. In field investigations on diel DO changes over periods of 24 h (data not shown), about two hours were required for the surface water to enter the sample locations in the depth profile. Only a short phase shift of the hyporheic DO amplitude could be detected as compared to the surface water DO amplitude. Residence times of water below 1 h were observed by measuring interstitial oxygen amplitudes (Grimm and Fisher 1984). Thus, in this study a high hydraulic conductivity of the sediment became evident and the data suggest that negative vertical hydraulic gradients cause the positive correlation between river water DO and interstital DO concentrations. In spring, when water level was high and strong water level variations occurred, highly aerobic habitats dominated the hyporheic zone and lateral mixing of oxygen-saturated interstitial water was likely to be extensive. During base flow conditions without distinct fluctuations in water level, exchange of river and hyporheic water is thought to be minimal (Baumert, personal communication) and thus poorly-oxygenated interstitial water dominated even during low temperature periods. Alternations of surface water levels due to short rainstorm events or discharge control by dams in the upstream region during base flow conditions could result in variable surface water-subsurface water exchange rates and infiltration of DO and solutes. Well defined patterns of DO and nutrient concentrations are Discussion 68 associated to areas of downwelling and upwelling water in pool-riffle sequences (Valett et al. 1994, Hill et al. 1998). The sampling location near Dresden provides a typical streambed morphology of a high order stream, but typical pool-riffle sequences are lacking due to flow regulation measures dating back to the 18th century. The mechanisms of exchange between channel and hyporheic water are presumably small-scale advective currents of oxygen- saturated water from the stream driven by unequally localized pressure distributions on the sediment along the riverside of the study reach as well as short-time water level fluctuations. Although there is lack of quantitative data on the exchange of surface water and hyporheic water, there is evidence that the water exchange with the upper sediment layers may be high. Diurnal patterns in surface water and hyporheic water DO and nutrient patterns were measured after dawn and one hour after sun maximum. This time span was 10 h and 7.5 h in June and September, respectively. According to fluctuating parameters such as diel stream primary production and respiration (e.g. Naegeli and Uehlinger 1997, Uehlinger et al. 2000), exchange processes between the surface-groundwater interface (Triska et al. 1990, Valett et al. 1994), transport time to interstitial water sites through the hyporheic zone (Findlay 1995,

Valett et al. 1996), the weekly differences (td-tn) measured in this study by no way can reflect the actual diel amplitudes, but provide information about two contrasting daily situations.

4.1.2 Effect of oxygen on hyporheic nitrate dynamics Downwelling zones supply oxygen to the hyporheic zone and thus support aerobic respiration (Findlay et al. 1993, Jones 1995, Jones et al. 1995b). Furthermore, both nitrification (Triska et al. 1990, Holmes et al. 1994, Jones et al. 1995a) and denitrification (Duff and Triska 1990, Triska et al. 1993a, Holmes et al. 1996) are influenced. As the diel minimum hyporheic -1 DO (tn) concentration measured after dawn was always above 1.0 mg l , no anaerobic conditions had to be expected for the hyporheic zone during the study year 2000. As nitrate losses were related to low DO concentrations, anaerobic microniches may have still provided an environment in which reducing conditions (possibly supporting denitrification) could prevail (Faafeng and Roseth 1993, Holmes et al. 1996). The oxidation-reduction gradient was probably stronger than reflected by the presented data. This was indicated by the smell of H2S in some samples especially after dawn. Although processes such as sulfate reduction or methanogenesis were not measured, anaerobic microbial metabolism may have been likewise significant. Sulphate reducing bacteria could be found in hyporheic sediments from the river Elbe at Dresden (see chapter 4.4). Anaerobic processes have also been documented for Discussion 69 hyporheic waters by many other authors (Dahm et al. 1991, Baker et al. 1999, Rulík 2000) and several microbial redox processes may simultaneously take place (Ludvigsen et al. 1998). The field data suggest that the river Elbe oxygen dynamics affected hyporheic nitrate reduction. This effect was especially pronounced after spring flood, when oxygen production from phytoplankton photosynthesis occured in clear diel patterns. Increased diel nitrate-N differences (td-tn) during summer were significantly correlated with diel DO differences (td-tn) (Figure 8A) indicating that aerobic respiration and nitrate reduction are strongly coupled. In laboratory assays, Christensen et al. (1990) and Nielsen et al. (1990) related the photosynthetic DO production to an increase of the oxic sediment depth and a reduced denitrification activity. Examination of pool-riffle sequences principally demonstrated similar hyporheic nitrate patterns as shown in the diurnal variations of this study. In downwelling zones, DO and nitrate decreased with prolonged subsurface residence time (Valett et al. 1996, Tesoriero et al. 2000). Therefore, alternating oxic-anoxic conditions may alter dissimilatory reduction of nitrate. Triska et al. (1989a,b) observed low diel nitrate concentrations followed by high DO concentrations. The authors attributed this pattern to photoautotrophic assimilatory nitrogen demand. Seasonal nitrate patterns in the hyporheic water of the upper sediment layers of river Elbe are assumed to be primarily a function of temperature. This is consistent with studies of Grischek et al. (1998), who reported low nitrate concentrations in the aquifer at a bank filtration site along the river Elbe during periods of high water temperature, which is an indication of higher biological activity in the river bed sediments.

4.1.3 Effect of oxygen on hyporheic ammonium dynamics Nitrification may be one source of nitrate in the hyporheic zone. Infiltration supplies the oxygen required for hyporheic nitrification. The field data suggest that the observed temporal hyporheic ammonium patterns were affected by the daily shift of the oxic-anoxic interface (Figure 8B). Hyporheic oxygen concentrations after sun maximum probably were sufficient to support the oxidation of ammonium at all depths reached by stream water, but obviously the nocturnal activity of nitrifying bacteria was limited by diminished DO concentrations. Thus ammonium concentrations measured after dawn were significantly higher. Valett (1993) found that nitrate in a pool-riffle sequence exhibited distinct diel patterns caused by nitrification. Like other studies in Sycamore Creek, a strong positive correlation between DO and nitrate reflected hyporheic nitrification (Grimm et al. 1991, Jones et al. 1995a). Based on the diel net changes in DO, nitrate-N, and ammonium-N, it was not possible to determine the Discussion 70 proportion to which nitrate was controlled by nitrification and by dissimilatory nitrate reduction. However, as ammonium concentration in the river Elbe was low in comparison with nitrate, it is assumed that nitrification is of minor importance for the total inorganic nitrogen concentration in the hyporheic zone.

4.1.4 Effect of oxygen on hyporheic nitrite dynamics Nitrite is a common intermediate in different nitrogen converting processes that occur in aquatic sediments. Nevertheless, data on hyporheic nitrite in the subsurface water are found to be very scarce in the literature (Stief and Neumann 1998). In the presented observations, hyporheic nitrite was especially high in summer and diel nitrite-differences correlated significantly with the diel differences in oxygen and nitrate (Figure 9A,B). Highest nitrite-N concentrations occured after dawn when anaerobic microhabitats in interstitial biofilms can be expected. Stief and Neumann (1998) also observed high hyporheic nitrite concentrations under DO concentrations ranging between 0.5 and 1.5 mg l-1. Elevated interstitial nitrite concentrations have been attributed to inhibition of nitrification by oxygen deficiency (von der Wiesche and Wetzel 1998) or by high levels of free ammonia (Smith et al. 1997). But the latter seems improbable for the river Elbe due to neutral pH-values and relatively low ammonium concentrations in interstitial water. Rather it is suggested that dissimilatory nitrate reduction especially in summer leads to nitrite accumulation in the river sediment (Kelso et al. 1997). Denitrification is favored when carbon supply is limited, while dissimilatory nitrate ammonification is preferred when nitrate is limiting (Kelso et al. 1999). These authors reported effects of different dissolved organic substrates to changes between dissimilatory nitrate ammonification and denitrification depending on bacterial species composition. The competition for varying electron donors (e.g., acetate, propionate, and butyrate) in the intracellular bacterial electron flow system may be the principal cause of the nitrite accumulation in Pseudomonas stutzeri (e.g., van Rijn et al. 1996). It was shown that dissolved organic carbon distribution and concentration in the hyporheic zone can change quantitatively (Findlay et al. 1993, Clay et al. 1996) and qualitatively (Sun et al. 1997) in the diel and seasonal cycle. This may influence the diurnal activity of bacteria (Kaplan and Bott 1982, 1989). However, due to significant differences in the temporal concentration patterns between nitrite and on the one hand DO and on the other side nitrate, nitrite is suggested as a suitable indicator of the surface-groundwater interface in nitrate rich rivers. Consistently, seasonal fluctuations of nitrite were determined in a sand and gravel aquifer in the lower Rhine region Discussion 71 by denitrification processes (Oswald et al. 1999). In conclusion from these results, denitrification is suggested to be the major hyporheic nitrate reducing process as diel nitrate differences were often higher than the differences in hyporheic ammonium concentrations measured on both diurnal sampling times. Further, the reduction of nitrate to ammonium is inhibited by oxygen (Omnes et al. 1996).

4.1.5 Effect of oxygen on hyporheic phosphate dynamics Anaerobic microhabitats might also influence the diurnal phosphate dynamics. A sequence of oxic-anoxic conditions is relevant to heterotrophic microbial phosphorus transposing processes for luxary uptake and release of phosphate into the bulk water (Gächter et al. 1988, Gächter and Meyer 1993, Davelaar 1993) and physico-chemical immobilization and mobilization of phosphate (Fox 1993, Roden and Edmonds 1997). Diurnal redox changes in river Elbe sediment probably influence phosphate concentrations, but in contrast to diurnal nitrogen dynamics, the diurnal phosphate patterns were not significantly coupled to DO differences (td-tn). However, hyporheic phosphate concentrations after dawn were higher than measured after sun maximum, when diurnal oxygen concentrations had increased (Table 10). Previous studies on the basis of diurnal, seasonal and spatial observations emphasized, that DO concentrations may control the dynamics of phosphorus in river sediments (Valett 1993, Mulholland 1992, Valett et al. 1990, Jones et al. 1995b, Hendricks and White 1995). These authors reported increasing interstitial phosphate concentrations concurrent with decreasing DO, but did not discuss biological processes. Phosphate accumulating microorganisms were detected in the river Elbe surface water and sediment samples (see 4.4). This may indicate the capability of river sediments for biological phosphate uptake and release. Further, in river Elbe sediments differing in aerobic-anaerobic conditions Wiltshire (1990) found significant different proportions of bio-available phosphorus compounds. Microbial reduction of Fe3+ to soluble Fe2+ at anaerobic conditions is also suggested to influence subsurface phosphate concentrations (Gächter et al. 1988, Montigny and Prairie 1993, Carlyle and Hill 2001). Diurnal phosphate dynamics in river Elbe hyporheic water (Figure 25) is controlled by both physico-chemical processes (e.g. Fe3+/Fe2+ sorption/desorption) and biotic uptake and release, but the quantitative proportions between both processes are still unknown. Discussion 72

4.1.6 Spatial variability of oxygen and nutrients in the hyporheic zone Three distinct principal patterns in concentrations of DO, nitrate, nitrite, ammonium, and phosphate along the depth profiles were observed. The temporal and spatial variation in concentration presumably have to be attributed to the longitudinal transport of infiltrated water along discrete hyporheic water flow paths. Hendricks and White (1991) reported that anoxic and oxic patches can alternate over short distances in the vertical and the longitudinal axis. Several reasons for the different temporal and spatial patterns were proposed suggesting intense variations of the pore channels in the hyporheic zone. In general, decreasing oxygen concentrations in downwelling zones were expected due to respiratory activity as described above. Deviations from this trend were most likely due to local variations in hyporheic oxygen demand. Similar amounts of oxygen in all sediment layers or elevated amounts in deeper layers especially in the summer season (Figure 5A) may be attributed to a small-scale patchiness of the interstices. Valett et al. (1994) proposed that upwelling and downwelling zones in pool-riffle sequences appear to be spatially distinct at any time, but the magnitude of exchange also varies temporally. Heterogeneity at the sampling location is presumably increased by temperature variations in terms of breakdown of diurnal and seasonal thermal hyporheic gradients (Hendricks and White 1991) and variations in water level producing a spatial and temporal pattern. The hypothetical net effect is that well-oxygenated water and cooler surface water sink downward into the sediments, displacing the warmer, but oxygen- poor interstitial water (Whitman and Clark 1982) which rises to the surface water. At a larger scale, the magnitude of the fluctuations in oxygen, nitrogen and phosphorus dynamics in river Elbe probably reflect the varying flow regime in the interstices produced by biotic changes of the hyporheic microbial biomass and community. Sinke et al. (1998) demonstrated that the redox gradients and subsequently the microbial processes in sand columns were subject to a fluctuating water table. Increased oxygen introduced by lowering the water table caused a partial and temporal oxidation of previously reduced species.

4.1.7 Nutrient dynamics in the hyporheic zone Changes in inorganic nitrogen and soluble reactive phosphorus concentrations indicative of microbial metabolism show that oxygen consumption and production was an important factor in nitrogen and phosphorus dynamics in the hyporheic zone of the river Elbe. The observation that diel DO amplitudes affected nitrogen and phosphorus amplitudes supports the hypothesis that the oxidation-reduction gradient (e.g. nitrification-denitrification interface) towards Discussion 73 deeper sediment layers varies diurnally in vertical and lateral direction. Further, anaerobic patches within the hyporheic zone of the upper sediment layers are also subject to diurnally changes. The observations of these temporally influenced couplings are presented in a conceptual model for nitrogen and phosphorus cycling in downwelling zones of the hyporheic zone of the river Elbe (Figure 25). Dissolved oxygen derived from the surface water is transported into hyporheic sediments through infiltration processes. Oxygen concentrations are decreased due to respiration. During the day, nitrifying bacteria oxidize the ammonium in the day-time to nitrate and simultaneously decrease the concentration of hyporheic oxygen.

day night flow water surface

O O stream 2 2 flow

O NO O2 NO3 2 3 nitrification NH 4 denitrification NH4 hyporheic zone nitrification denitrification

immobilization PO4 PO4 release

aerobic less aerobic/more anaerobic

Figure 25 Conceptual model of hyporheic nutrient dynamics in an idealized downwelling zone of a river with strong diurnal amplitudes in dissolved oxygen. The sizes of the boxes indicate the relative magnitudes in dissolved oxygen, nitrogen and phosphate during the day/night cycle. The arrows show the downwelling zone where stream water infiltrates. When interstices are aerobic, nitrate is produced by nitrification or not utilized by denitrification and the oxic/anoxic boundary layer (e.g. nitrification/denitrification) shifts down to deeper sediment layers. Phosphate is immobilized by biological or physico-chemical processes. During the night, dissolved oxygen decreases. Thus, the effect is reversed.

Discussion 74

Nitrate is decreased by dissimilatory nitrate reduction and the nitrification/denitrification interface is elevated to the upper sediment layers. Phosphate is mobilized and/or immobilized by chemical and biological processes. During night, the downward mass flow of surface water oxygen is lower and subsequently nitrate reduction increases. The nitrification/denitrification interface is shifted downwards into deeper sediment layers. Phosphorus is released into the bulk water. In conclusion, photosynthetic activity in the river Elbe by autotrophic biomass may control the diurnal cycle of hyporheic aerobic and anaerobic respiration in downwelling zones. This results in diurnal hyporheic inorganic nitrogen and phosphate dynamics concomitant with a shifting oxic-anoxic gradient in different sediment layers.

4.1.8 Modeling effects of oxygen on diurnal inorganic nitrogen dynamics in the hyporheic zone The statistical model based on the conceptual model of the hyporheic nutrient dynamics in downwelling zones with strong diurnal dissolved oxygen amplitudes (Figure 25). The model should allow to test the consistency of the observed oxygen and inorganic nitrogen patterns at other river Elbe sampling sites. A deterministic analysis of the data of Dresden-Saloppe sampling site was not possible (Baumert 2002). Therefore, multiple regression models were provided to calculate diurnal variations of dissolved hyporheic oxygen. The consideration of all variables measured at Dresden-Saloppe (km 416) provided a high correlated model. However, at Dresden-Übigau (km 426.5) and Meißen (km 444.8), the sampling strategy was applied only with the mean concentrations of temperature, dissolved oxygen, nitrate, nitrite, and ammonium in the open water and the hyporheic zone. The diurnal amplitudes were not measured. Thus, the multiple regression was highly restricted and only possible with the available variables (Figure 11). This restriction explained the results of the calculation, that at Dresden-Übigau and Meißen a negative diurnal hyporheic oxygen amplitude was found. This is in contrast to the conceptual model provided in Figure 25. Thus, model calculations suggest that more emprical data are needed on temperature, dissolved oxygen and inorganic nitrogen compounds of more sampling sites, specifically on values of entire 24 h sampling periods. More empirical information on hydrologic data would also increase the understanding of the system (personal communication, Baumert 2002). Discussion 75

4.2 Transport of algal cells in hyporheic sediments

4.2.1 Occurence of algal cells in hyporheic sediments The abundance of algae in both surface and hyporheic waters of the river Elbe changed seasonally. Continuous deposition and retention of particulate organic particles onto the benthic sediment surface across the water-sediment interface led to infiltration into the pore spaces. This has been shown by several authors (Poulícková 1987, Hendricks and White 1995, Poulícková et al. 1998). Temporal and spatial variations of suspended particle input into subsurface sediments can be associated to changes in water level or to heterogenous pressure fields. In case of the Dresden-Saloppe site, temporal variations in algal cell abundance within the different sediment layers seemed to be caused primarily by variations in pore channel flow or permeabilities in the hyporheic zone. Sediment grains of the upper part of the river Elbe are heterogeneous and less sorted, emphasizing a wide spectrum of pore sizes. For instance, the high algal accumulation in May 2000 in all sediment layers for additional two weeks continued especially in 25 cm depth, suggesting an underflow by stream water. Furthermore, particle concentration changes or patchy distribution of particles in the hyporheic zone are also reasonable explanations for the observed dynamics. Moreover, the species composition of the infiltrated algae consisted of phytoplankton and benthic algae and even the deeper sediment layers were reached by large species (e.g. Pediastrum sp.) despite long transport times through the sediment. Bacterial transport in saturated porous media has been investigated by several authors (Harvey and Garabedian 1991, Bolster et al. 2000, Holben and Ostrom 2000). The patterns recorded by them support the interpretation of the field data, that streamwater-derived input of organic particles occurred. Therefore, one objective of this study was to compare the different morphotypes of advectively transported algal cells in column experiments to estimate algal retention and retardation by the sediment.

4.2.2 Experimental retention of algal cells in sediment columns It is evident that all morphotypes of algae interacted with the sediment of the hyporheic zone of the river Elbe. The results of the laboratory experiments were the combined effect of (i) settlement due to low hydrodynamics, (ii) mechanical straining resulting in filtration of particles within the interstices, (iii) chemical attachment, (iv) detachment/redeposition Discussion 76 processes, (iv) advective flow of particles, (v) dispersion owing to pore sizes, (vi) variations of velocities within pore channels, and (vii) flow patterns within the grain framework. The column experiments showed how particle transport in the porous media of the hyporheic zone of the river Elbe depends on the permeability of the sediment. The permeability is influenced by the grain size of the sediment material, but also by its sorting and degree of consolidation (Krumbein et al. 1994). In the column sediments with a low permeability -5 -5 -1 coefficient kf (2.3 10 - 2.7 10 m s ), the highest amount of each algal morphotype was retained by the sediment. In the experiments presented, significant transport of D. communis by advective pore water flows was restricted to sediments with permeabilities greater than 4.8 10-5 m s-1. Further decrease of the permeability coefficient fell under the detection limit of D. communis cells. The diameter of D. communis cells is difficult to determine due to their stings. The stings increase the cell shape, but due to their flexibility, the cell shape and morphology is variable. Furthermore, the stings are assumed to promote the bridging of pores and thus clogging the voids. The spherical-shaped Chlorella sp. cells moved more readily through river Elbe sediments than S. acuminatus or D. communis cells. The retention rates of the column experiments in the low permeability coefficient range (2.3 10-5 - 2.7 10-5 m s-1) indicated that not more than 96% were retained in the porous media contrasting to nearly complete retention of S. acuminatus and D. communis. Generally, the sediment was sufficiently permeable for advective pore water transport for the tested algae except of P. duplex. The large cells of P. duplex (20 - 80 µm) were completely entrapped within the interstitial pore space in the investigated range of permeabilities, assuming that the cell diameter was larger than the pore sizes of the sediment.

4.2.3 Experimental retardation of algal cells in sediment columns In general, the recorded retardation of all algal morphotypes decreased with increasing permeability coefficient (Figure 15). The timing of the residence time of water and the peak of the algae indicated, that the velocity of the water was greater than transport of the algae. Comparison of the retardation factors of Chlorella sp. and D. communis two- and four- cellular cells investigated at different permeabilities showed that the coefficient kf had only low impact on peak arrival of algal cells (Figure 15). The relatively high permeabilities are expected to facilitate extensive advective flows. Laboratory studies with sandy aquifer sediments reported comparable retardation factor of 3 for stained flagellates with 4 - 5 µm in diameter (Harvey et al. 1995). In studies investigating bacterial transport, the peak arrival of Discussion 77 bacterial cells and that of the conservative tracer appeared to be coincident (Harvey and Garabedian 1991, Fuller et al. 2000). In contrast, peak arrival of S. acuminatus cells showed a highly significant correlation to the permeability of the porous media. The peak of S. acuminatus was observed to precede, lag, or coincide with the hydraulic residence time of water. The early appearance of the initial large numbers of algae shortly after injection (Figure 13 D) suggests that the pore volume available for the algae was smaller than for the water. This can be caused by (i) interstitial voids, in which water, but not algae can penetrate, (ii) by algal cells that were pushed along the pathway of advective pore water flow by the advancing front of the water, or (iii) by preferential flow paths in the sediment cores. The algal cells and the water probably moved through the cores following flow lines and accessed only a fraction of the measured pore volume.

4.2.4 Algae as model organisms for particle transport in sediments Although microsphere transport behaviour in sediments differed from that of algae, there were a number of similarities between the transport characteristics of both particulate types. Both seem to be subject to substantial retention and retardation. The use of the chemically stable and easy to handle microspheres as transport analogs for algal cells may be the abiotic control of spherical-shaped cells with different diameters. The disadvantage of using microspheres e.g. is that they cannot provide information on different morphotypes of algal cells and the varying buoyant density of algal cells and net charge of surface etc. Little information is available on the range of buoyancy densities of algae in aquatic systems. Buoyant densities for groundwater bacteria and microflagellates from a sandy aquifer ranged from 1.043-1.081 g cm-3 and 1.017-1.039 g cm-3, respectively (Harvey et al. 1997). Phytoplankton cells have the ability to adjust their water content floating freely in the suspension, so their buoyant density is variable. Algae with high buoyant density due to reserve products would contact grain surfaces with greater frequency caused by a stronger influence of settling. However, we assume near neutral buoyancy of algae. Generally, the transport behaviour of algal cells in porous media in laboratory studies make them ideal model particles assessing biotic controls of particle motility in natural sediments They are easy to handle in laboratory cultures and it is easy to monitor them by microscopy or other techniques. The ability to reliably count algal cells in the context of transport characteristics of algal cells is a key to understand the behaviour of polymorphic and poly-shaped particles in subsurface environments. Further detection strategies could be based on stable isotope Discussion 78 enrichment of cells as described by Holben and Ostrom (2000) for bacteria. A more suitable approach to enumerate and track algal cells is the use of flow cytometry (e.g. Olson and Zettler 1995, Simon et al. 1995, Porter et al. 1997).

4.2.5 General interpretation of algal retention in riverine sediments The spatial and temporal distribution of algal cells in the hyporheic zone of the river Elbe showed that the intrusion of particles is a significant input source of organic matter into the subsurface zone of eutrophic rivers. Taking into account that (i) more than 90% of the algal cells were retained by the sediment as determined in the laboratory experiments and (ii) the interstitial sampling procedure in the field study only covered the loosely associated and free- flowing algae, the majority of the penetrated algae is sediment-associated. Therefore, hyporheic sediments are an important sink for algal cells and may promote nutrient cycling. However, algae in the aphotic regions of aquifers could remain viable for extended periods of time by utilizing stored energy reserves, limiting cellular metabolism and employing facultative heterotrophic metabolism. Marine diatoms are reported to survive more than 90 d of darkness (Smayda and Mitchell-Innes 1974). Although Scenedesmus sp. can survive 3 months of darkness, a large proportion undergoes cell lysis in the short term of some days (Dehning and Tizler 1989, Berges and Falkowski 1998). The subsurface accumulation of particulate organic matter reduces the permeability of the sediment layers (Vanek 1997). Thus, the pore water flow could be decelerated and the deposition of further particles enhanced. Further, the accumulation of algal biomass into the hyporheic zone may influence the organic matter dynamics of the aphotic subterranean zone. Enhanced organic matter turn over may give rise to the formation of subsurface microbial biofilms, which in turn decrease permeability in river sediments (Vandevivere and Baveye 1992, Battin and Sengschmitt 1999). Summer water temperatures increase the growth rate of bacteria promoting interstitial biofilm growth. The input of algae into the sediment and bacterial activity could result in the release of extracellular polymeric substances (Freeman and Lock 1995, Smith and Underwood 1998, Romani and Sabater 2000). Thus, this may increase the rate of hyporheic clogging and decrease the pore channel flow (Battin and Sengschmitt 1999). Emphasizing the short-time variation of the hyporheic mass-flow patterns, Goedkoop et al. (1997) reported immediate response of benthic bacterial production after a diatom pulse within only two hours. Furthermore, occasional bed erosion in flood situations destroys the biofilms in the upper sediment layers and therefore may be an important process in reconstituting the Discussion 79 sediment structure. This means that the stochastic small-scale patchiness in grain size and interstitial flow is enlarged by the adhesion of algae and bacteria and the unequal distribution of biofilm growth. Contrasting to the laboratory studies, the permeability in natural river sediments is reported to be horizontally and vertically heterogeneous, resulting in low permeabilities downwards to the sediment and higher permeabilities in longitudinal direction (Saenger 2000). Thus, once the algal cells have penetrated through the surface, the advective transport in longitudinal direction is determined otherwise. The results presented in this study have important ecological implications. The seasonal succession of the algal distribution from diatom dominance in spring to a chlorophyte domination in late summer changed the species-specific algal adherence spectrum to solid surfaces after penetration of the particles into the subsurface zone. The experiments allow the reasonable establishment of trends in transport behaviour of particles through porous media. Considering the different cell volumes of algal cells (Pohlmann and Friedrich 2001) as basis for the calculation of their specific carbon contents, the advective transport through the sediment layers and the varying hydrodynamics in the pore interstices of the hyporheic zone may co-determine the input of particulate organic carbon into the hyporheic zone.

4.3 Hyporheic biofilms at two contrasting river Elbe sites

4.3.1 Spatial differences of the sampling sites This study showed the influence of detrital and granulometric variables on microbial acitivity within the sediment at two contrasting sites at the large River Elbe. At both sites, sediments from different river-compartiments (river-bed and river bank) and sediment depths (0-5 cm and 20-25 cm) were examined, therefore including a broad range of biofilm composition and structures. The sediments from the Upper Elbe at Dresden were characterized by high D50 values and heterogenous grain size distribution, whereas the sediments from the Middle Elbe at the Coswig site were characterized by low D50 values and a more homogeneous grain size distribution. Bacterial abundance was highest in the river-side samples of the Dresden sediments and was lower in the Coswig river-bed. This high variability is indicated by the arrow in the PCA plot of all microbial data (Figure 17A). This relationship is also in good accordance with the comparisons of the sampling sites on the basis of gDW (Table 15) and the Spearman rank Discussion 80 correlations (Table 17). The high percentage of coarse sand, gravel and stones at Dresden provides high river bottom roughness and increases the stability of the sediment. Thus, the high organic content accumulated within the interstitial voids can be explained by the high stability of the sediment. In contrast, Coswig sediments reveal a moving, loosely packed sandy sediment type especially in the river-bed, where the upper sediment layers were constant moving due to homogenously small grain sizes. Consequently, much lower contents of POM and POC were found in the Coswig river-bed sediments (Table 14). This is also corroborated by the positive relationship between the detrital parameters and the D50. The higher mobility of sand particles, and thus the instability of sediment structure favours the abrasion of the biofilm and downstream transport of organic matter. Interstitial organic matter thus may get lost, permanently or episodically, from the sediment surface layer. The correlation between bacterial abundance and the detrital parameters suggests a direct relationship as also reported in other studies (Middelboe et al. 1995, Claret and Fontvieille 1997, Fischer et al. in press). Organic particles constitute a substratum for bacterial attachment and growth. This is demonstrated by the two arrows in Figure 18A indicating the correlation along Factor 2, which is mainly described by the detrital parameters. The D50 separated the sampling sites and the detrital parameters separated the river-bed from the river- bank. The high POC values and C/N ratios in the Coswig groyne field samples were probably an artifact resulting from coal pieces contained in the samples. However, this can be explained by the retentive feature of groyne fields as the preferential zone for sedimentation of fine particles. The microbially most active zone in river sediments is restricted to the upper sediment layers (García-Ruiz et al. 1998, Battin and Sengschmitt 1999, Fischer and Pusch 2001). This was also obvious in this study, as the highest bacterial abundance and activity were found in the layer from 0-5 cm depth. The vertical depth gradient of denitrification and nitrification activity was much steeper in the Dresden sediments and the Coswig groyne field samples than in the constantly moving Coswig river-bed sediments. Here, the upper 0-5 cm sediment layer was not different in microbial activity and organic matter storage compared to the sediment layer from 20-25 cm sediment. Generally, the Coswig river-bed sediments had a low spatial variability within all parameters as shown by the close clustering in both PCA ordination plots (Figure 17 and Figure 18). These findings are consistent with results of a previous study on the lowland river Spree (Fischer et al. in press) and of microbial community analyses using oligonucleotide probes at the same sites (see chapter 4.4). Discussion 81

Some extracellular enzymes, particularly phosphatase, are produced by both algae and bacteria (Chróst 1991, Chappell and Goulder 1994, Romani and Sabater 2000). However, β- glucosidase and leucine-aminopeptidase activities are generally associated with heterotrophic bacteria (Chróst 1991), but may also be promoted by algal exsudates and algal lysis products due to the supply of carbon sources (Romani and Sabater 2000, Ainsworth and Goulder 2000). That means, that the extracellular enzyme activities were of minor importance to detect differences between river Elbe sites. This could be explained by the apparent uncoupling between bacterial abundance and the extracellular enzyme activities (Table 17). However, on a more narrow spatial scale, extracellular enzyme activities at Dresden showed vertical differences between different sediment depths. The close relationship between algal pigments and leucine-aminopeptidase activity may explain the high scattering within the groyne field samples (Figure 17A) along PC 1. The interstital water samples from the groyne fields showed the highest concentrations of chlorophyll a and pheophytin and also high a variability between the samples. This can be attributed to the high spatial variability of the groyne fields. The sample locations within the three groyne fields along the transects from the river-bank to the main flow exhibited a high degree of local heterogeneity. Further, the sequence of the three sampled groyne fields was positioned in a river channel curve leading to different mixing patterns between the main flow of the river and the groyne fields. The deposition of suspended material varied considerably depending on groyne field morphology (Sukhodolov et al. 2002) and thus leads to highly variable different storage of autotrophic biomass within the sediment. (see also 4.2).

4.3.2 Spatial differences of the sampling sites on the basis of streambed surface area The comparison of the relationships between microbial, detrital, and granulometric variables were applied on the basis of gDW. This allows the estimation of the influence of the grain size distribution on the sediment retention of particulate organic matter. As described in chapter 2.3.1, only particles smaller than 5 mm were retrieved from the river bed. Nevertheless, especially the Dresden sediments were larger and thus the coarse deposits were not regarded in total. Therefore, to consider the entire sediment from an ecological point of view, it is required to calculate the microbial and detrital variables on the basis of the streambed surface area (m2). For simplicity it is assumed that the specific activity per bacterial cell on the surface of the sand grains, gravels and stones of the different sediments and their adsorption capability to organic matter was similar. However, differences in Discussion 82 microbial activity on the particle surface of different types of sediments have been discussed previously (Fiebig and Marxen 1992, Marxen 2001). Microbial and detrital variables measured per unit streambed area were found to be considerably higher at Coswig than at Dresden. The surface area of fine grained sediments at Coswig was large, which probably enhanced the adsorption of dissolved and particulate organic matter and the establishment of microbial biofilms. In contrast, the sediments at Dresden containing gravels and stones provided lower specific surface area (Table 11). Furthermore, the area-based rates were particularly high in the river-bed sediments compared to the river-bank. This suggests that the river-bed sediments of large rivers are the most important subsystems for material turnover, though the river-bank had a more intensive hydrologic exchange between the groundwater and the open water (Baumert, personal communication).

4.3.3 Comparisons with other aquatic sediments in denitrification and nitrification rates Although it is not possible to make an exact comparison between the results of this study and those from other aquatic sediments, the rates of denitrification and nitrification observed in other studies were in the same orders of magnitude as the rates measured in the river Elbe (Table 20). Most studies on denitrification used the acetylene blockage technique, except Nielsen (1992) and Pind et al. (1997), who applied the 15N- isotope pairing technique. Denitrification rates per g DW ranged over more than 5 orders of magnitude. Denitrification was distinctly low at many of the fifty sites examined in north-east England (García-Ruiz et al. 1998a), because they comprised headwater sites low in nutrients. The downstream, more eutrophic locations, had higher denitrification rates (García-Ruiz et al. 1998a) comparable to rates measured in our study. Studies which measured denitrification rates on the basis of streambed surface area used sediment cores and injected the acetylene into the core without constant shaking. This is in contrast to the slurry technique conducted here and the subsequent calculation based on the relative surface area of the grain size distribution of the sediment. Although results of this study were in the same order of magnitude as for example rates reported from the river Wiske (García-Ruiz et al. 1998b), rates of river Elbe (Dresden and Coswig) were higher. Dobbs et al. (1989) detected lower activities in core experiments as compared to the slurry technique. However, the river Elbe is a nitrate-rich large river with high organic content. Thus, the potential for denitrification is higher than in low-order streams. The factors influencing the denitrification rates are particularly nitrate concentration, organic carbon, and temperature (Bradley et al. 1995, Pfenning and McMahon 1996, García- Discussion 83

Table 20 Denitrification and nitrification rate measurements in rivers, streams, and estuaries. For better comparison, original data in the table were recalculated to the same basis. Soil and wetland samples are presented in brackets due to putatively comparability. Site Parameter Characteristics Authors Denitrification rates µmol g-1 h-1 South Platte River, 0.03-0.04 fine grained surficial sediments Pfenning and nitrate rich, USA McMahon 1996 South Platte River, 0.013-0.063 fine grained surficial sediments Bradley et al. 1995 nitrate rich, USA Fifty stream sites in north-east 5.0 10-6-0.26 0-5 cm depth García-Ruiz et al. England, UK sediment slurry samples 1998a (Riparian Atlantic Coastal 0.027±0.007 ponded surface soil horizon Pavel et al. 1996 Plain wetlands, USA) River Elbe, Germany 0.019-0.591 Dresden this study 0.012-0.304 Coswig µmol m-2 h-1 Three Ontario rivers, different 107-1230 0-5 cm depth Hill and Sanmugadas in nutrient loading, USA sediment core 1985 Gelbaek, nitrate rich lowland 100-1400 0-3 cm depth Christensen et al. river, Denmark sediment cores 1990 Swale-Ouse river system, 36 to 324 river Swale, García-Ruiz et al. several sites along a river 758 to 1194 river Wiske, 1998b continuum, UK fine grained sediments Swale-Ouse river system, 20 headwater site, Pattinson et al. 1998 several sites along a river 659 downstream site, continuum, UK fine grained sediments River Wiske, eutrophic 140±28 coarse sand García-Ruiz et al. 1998c lowland river 134±61 sand and gravel 99±5 fine sand and clay Lowland stream, Denmark 20.8 fine grained surficial sediments Nielsen 1992 Lowland stream, Denmark 0-750 fine grained surficial sediments Pind et al. 1997 River Dorn, agricultural 800 0-2.5 cm depth catchment, England sediment cores River Elbe, Germany 600-5890 Dresden this study 1270-6039 Coswig

Nitrification rates µg l sediment-1h-1 Sycamore Creek, Sonoran 0.93±0.31 upwelling zone Jones et al. 1995a desert stream, USA 0.12±0.21 downwelling zone 2-17 cm depth sediment (Flooded soils, Australia) ca. 0.3 nitrite inhibition by KClO3 Chen et al. 1995 River Elbe, Germany 1.68±0.75 Dresdena this study 0.63±0.20 Coswiga µmol m-2 h-1 Estuary, Denmark 97-108 Vilhelmsborg Slotsso Sloth et al. 1992 25 Kysing Fjord 0-12 cm sandy sediment River Elbe, Germany 32-152 Dresden this study 20-51 Coswig a For comparison, original data were recalculated to nitrification rates per liter sediment-1 Discussion 84

Ruiz et al 1998a), but also sediment composition (Pattinson et al. 1998). The river Elbe study relates high denitrification rates to high surface areas of sandy sediments available for microbial attachment. Methodological discussion about different approaches measuring denitrification in sediments is provided by Steingruber et al. (2001). There are only few studies available on nitrification rates in the sediments of flowing waters. These studies have examined net nitrification as change in nitrate over time (Jones et al 1995) or accumulation of ammonium in sediment cores after addition of acetylene to inhibit nitrification (Sloth et al. 1992). As in the study presented here, Chen et al. (1995) measured nitrification rate in flooded soils by inhibition of nitrite oxidation using potassium chlorate. The authors compared 10 techniques used for estimating nitrification rates. The authors concluded that techniques based on the inhibition of one step of nitrification could underestimate nitrification due to ineffective inhibitors of the specific enzymes. Nevertheless, nitrification rates of the river Elbe were in the range of the other studies (Table 20).

4.3.4 Denitrification and nitrification at the different sampling sites The variability of the denitrification rates within and between Dresden and Coswig was explained by multiple regression analysis including interstitial nitrate and all carbon sources (POC, DOC, and MFIP-C) (Figure 12). The estimates obtained from the regression model differed significantly between both sites. Three factors were recognized to influence denitrification in Dresden sediments. One was interstital nitrate, which explained most of the variation. This is consistent with regression models applied by García-Ruiz et al. (1998a). The other factors were POC and MFIP-C. Especially in Dresden sediments the POC content in terms of dry weight was high, providing organic carbon as electron donator for the denitrification. However, the results do not allow to conclude whether denitrification at the Dresden site was limited by the availability of nitrate or of organic carbon. No significant regression models could be developed for denitrification rates in Coswig sediments. Surprisingly, interstitial nitrate was slightly negatively related to denitrification. The MFIP-C in exchange for interstitial pheophytin content included into the multiple regression (both variables were closely related) resulted in significant estimation of denitrification rate in the Coswig sediments. The detected relationship suggests considerable algal participation to the denitrification process and corresponds to other studies on denitrification (García-Ruiz et al. 1998a). Algal cells can directly contribute to the carbon pool with their biomass and with exudates released during growth and senescence (Romani and Sabater 2000, Yallop et al. Discussion 85

2000). Due to low organic carbon concentrations, the denitrification at Coswig were presumably carbon limited. It is suggested that phytoplankton accumulation in river sediments has substantial effects on denitrification rates. The open water samples from both sites differed strongly in ammonium-N concentrations. At Dresden, the high concentrations may explain the higher nitrification rates on the basis of gDW in the upper sediment layers than those observed in Coswig. The rates based on streambed surface area were also higher at Dresden despite of higher specific sediment surface area (m2/m3) in Coswig. The sampling site at Dresden was upstream from an effluent of a wastewater treatment plant without denitrification step, suggesting increasing rates downstream. Nitrification rates in the surface layer sediments were strongly related to respiration. The oxygen demand is simultaneously measured by this approach. The correlation between nitrification and denitrification rates may indicate the strong relationship between both processes. Nitrate is produced by nitrification and subsequently decreased by denitrification (Hinkle et al. 2001, Peterson et al. 2001, chapter 4.1). Furthermore, the strong negative correlation between ammonium and nitrate in Coswig also reflects the strong relation between both processes.

4.3.5 General interpretation of nitrogen dynamics at the interface between open water and sediments of the river Elbe Studies on stream nutrient dynamics have recently addressed links between the mainstream flow and adjacent storage environments, such as the hyporheic zone (Triska et al. 1993b, Dahm et al. 1998, Mulholland et al. 2000). These studies mainly focused on small streams and headwater sites. Studies on nitrogen cycling in large rivers are very scarce in the literature (Pinay et al. 1999). These authors discussed denitrification and the capacity of riparian floodplain soils contributing to the cycling of nitrogen in large rivers. The processing of matter in running waters was first described by the nutrient spiraling concept (Newbold et al. 1982). This approach provides a theorethical framework to be able to estimate the influence of instream processes on nutrient retention on condition that the streams examined were uniform, homogeneous, and have no tributaries. Longitudinal patterns of dissolved nutrient concentrations are functions of flux, abiotic and biological processing and can be expressed quantitatively as processing lengths (i.e. distance the average molecule travels as dissolved ion after microbial metabolism) (Fisher et al. 1998). Long processing lengths reflect low rates of material cycling. High denitrification rates in the sediments decrease processing lengths. Discussion 86

The rate k (s-1) of denitrification and nitrification is calculated by dividing the measured rate per unit stream width (gN m-2s-1) of the streambed by the nitrate and ammonium concentrations (g m-3) in the open water, respectively. The nitrogen processing length S (m) of denitrification and nitrification is determined by dividing the mean downstream loading of nitrogen (g s-1) (nitrate and ammonium, respectively) by k (Newbold et al. 1982, Fisher et al. 1998). It is assumed for simplicity, that the river channel profile was trapezoidal regardless of the groyne field morphology in Coswig, where a complex flow regimes occurs. The different proportions of the river-bed and river-bank (DD river-side and Co groyne fields) on the whole river widths of the two sites are considered in the calculation. A mean width of 100 m for the Dresden and Coswig river-bed plus 20 m for the river-side at Dresden (120 m total width at Dresden) and plus 80 m for the groyne fields at Coswig (180 m total width at Coswig) is estimated. Discharge MNQ (see Table 8) was 109 and 126 m3 s-1 at Dresden and Coswig, respectively. Microbial metabolism down to a sediment depth of 25 cm was included into the calculations at both sites. This is corroborated by high chlorophyll a concentrations measured in these depths. Based on nitrate concentrations in the open water of 4 g m-3 at both sites (see Table 12 and Guhr et al. 2000), denitrification processing lengths were 83 and 58 km for Dresden and Coswig, respectively. Only few data exist on denitrification processing length in other streams and rivers. Martí et al. (1997) measured the nitrate uptake lengths of < 120 m in Sycamore Creek, a desert stream, before and after flood. In this stream, uptake was attributed to assimilation by phytobenthos. However, this headwater stream is hardly comparable with the river Elbe. Generally, the processing length is strongly related to discharge (Fisher et al. 1998). This may explain the very long processing lengths in the river Elbe although the absolute rates of nitrogen metabolism were relatively high (see Figure 16). Further, the proportion of the sediment surface in the cross-sectional area of large rivers decreases in comparison with low-order streams. However, despite higher discharge at Coswig the shorter spiralling length compared with Dresden is due to the higher specific surface area of the sediments and the greater width of the river. This agrees with the study of Cooke and White (1987) on the river Dorn, England, where the highest denitrification was associated with the deposition of fine-grained sediments. If we assume that the hydrologically connected depth between the open water and the sediment was 10 cm deeper in the Coswig river-bed (based on the results that the river-bed at Coswig showed only weak gradients in all variables; see chapter 4.3.1), denitrification length is reduced to 51 km and thus is 39% shorter than calculated for Dresden. This confirms the suggestion that the river-bed sediments were important subsystems for nitrogen turnover. The calculation shows that denitrification may Discussion 87 become more important at the downstream sites of the river Elbe, although denitrification at Dresden measured on the basis of gDW was distinctly higher than at Coswig. As the result of the distinctly lower ammonium-N concentrations in the open water at Coswig, the nitrification processing length was only 11 km compared to 80 km in Dresden. A comparison with other studies is not possible. Most studies focused on NH4-N- uptake lengths in low-order streams (Mulholland et al. 2000, Peterson et al. 2001), where lengths in the range of 10 to 1000 m were measured. Butturini and Sabater (1999) measured ammonium retention efficiency in hyporheic sediments and calculated an uptake length of less than 3.3 cm. The authors of this study suggested that almost all ammonium uptake was the result of heterotrophic activity rather than nitrification or abiotic sorption. Nevertheless, Newbold (1992) noted that nitrification in streams is mainly associated with sediments. As reported by

Peterson et al. (2001) for several headwater streams in North America, 70-80% of NH4-N removal was due to uptake on the stream bottom such as assimilation by autotrophic (phytoplankton) and heterotrophic organisms as well as physical sorption onto fine sediments or organic detritus. Only 20-30% was due to nitrification. The proportions for the river Elbe might be different, but the processes are likely the same. In conclusion, the results of this study demonstrate that spatial variation of microbial metabolism in sediments of the river Elbe mainly depended on the sediment grain size and bacterial abundance. Sediments of high mechanical stability and high grain size due to gravel and stones (Dresden) contained more organic carbon and thus had higher microbial activity (per gDW) than the moving sandy sediments of Coswig (low grain size). In contrast, microbial metabolism at Coswig was considerably higher if considered per unit streambed area due to the specific surface area of the sediment available for microbial attachment and growth. Therefore, denitrification processing lengths at Coswig were shorter than measured at Dresden. There is a need for a better understanding of the nitrogen cycling, before reliable prediction of the nitrogen dynamics and mass balance calculations of the whole river system can be made. However, the present study provides insight into the role of sediment structure and bacterial abundance as related to denitrification and nitrification in sediments of a large river.

Discussion 88

4.4 Analysis of microbial communities at different sampling sites of the river Elbe

4.4.1 Composition of the microbial communities at domain and group-specific level The aim of this study was the disrimination of the microbial communities of three river ecological habitat types at two different river Elbe sites and the examination of sediment particle-associated bacteria at two different sediment depths. The investigation was analyzed at different phylogenetic scales by FISH with 16S- and 23S-rRNA-directed oligonucleotide probes. This top-to-bottom approach was performed with existing probes at different phylogenetic levels ranging from domain- and group-specific down to genus- and species- specific level of resolution. In this study, river water bacterioplankton was summarized as both the free-flowing and the particle-attached bacteria colonizing on microbial aggregates. The percentage of bacteria detectable with the Bacteria-specific EUB338 probe mixture (Daims et al. 1999) in the bacterioplankton was in the range of 65%, indicating that the majority of the microbial cells can be detected in principle. These findings are consistent with previous studies on the microbial community structure in river water samples (Kenzaka et al. 1998, Manz et al. 1999, Lemke and Leff 1999, Böckelmann et al. 2000) and other pelagic environments (Alfreider et al. 1996, Weiss et al. 1996, Glöckner et al. 1999). Böckelmann et al. (2000) detected more than 70% of lotic microbial river snow aggregates in the river Elbe with probe EUB338. Decreased abundance of Bacteria-detected EUB338 cells indicated that hyporheic water samples were presumably more similar to sediment samples than to river bacterioplankton with respect to the proportion of physiologically active cells. Interpretation of analytical results from hyporheic water community samples is difficult due to the complexity of hydrological exchange processes within the hyporheic zone (Findlay 1995, Brunke and Gonser 1997). Considering the large number of phytoplankton cells detected in the hyporheic water samples, it is suggested that there may be a dominant downwelling of river water into the hyporheic zone. The hydrological exchange processes of all of the sites are influenced by exfiltration of low-nutrient groundwater or infiltration of high-nutrient stream water and this may influence the percentage of EUB338-detected cells. In June 2001, sediment samples from the river-bed and river-side at 5 and 25 cm sediment depth were regarded as replicate sediment samples of Dresden and Coswig, respectively. The detection yields ranging from 42 to 62% Eubacteria (EUB338) in the sediments are comparable to those obtained for other microbial sediment communities (Llobet-Brossa et al. 1998, Ravenschlag et al. 2000). The number of sediment sample replicates in August allowed statistical analysis of the two depths Discussion 89 investigated. The observation that the proportion of EUB338 positive cells significantly decreased down to deeper sediments depths is in accordance with vertical profiles obtained from Wadden Sea sediments of the North Sea (Llobet-Brossa et al. 1998). However, EUB338-detected cells at 25 cm depth in sediments only decreased to 41% in this study, suggesting active bacteria were even present in deeper sediment layers. The finding that Archaea (as assessed by probe Arch915) were not detected is consistent with other river studies (Manz et al. 1999, Böckelmann et al. 2000), but one cannot exclude the possibility that Archaea are present, particularly in deeper sediment layers. Planctomycetales are reported to have widespread distribution in aquatic environments (Fürst 1995, Neef et al. 1998). However, one of the most surprising results of the FISH data in this study was the high abundance of Pla46-hybridized cells in the sediment samples compared to the low abundance of Planctomycetales in the river and hyporheic water samples. High values of 16% Planctomycetales were detected in the sediments at both sites in June, when phytoplankton abundance is usually high in the river Elbe. The relative abundance was low in late August with values of less than 2%. Since previous studies of microbial communities in the river Elbe have reported a seasonal trend in Planctomycetales abundance with in summer (Böckelmann et al., 2000, Brümmer et al. 2000), it is suggested that biofilm-associated Planctomycetales have an important physiological function in the river Elbe, especially during the summer months. The application of probes specific to the α-, β-, and γ-subclasses of Proteobacteria and the Cytophaga-Flavobacterium group allowed the detection of differences in the microbial composition of the different habitat types and sediment depths. Interestingly, in this study β-Proteobacteria did not constitute the predominant component of the river Elbe community structure as was previously reported by Böckelmann et al. (2000) and Brümmer et al. (2000), neither in the bacterioplankton nor in the hyporheic nor sediment communities. Differences in the number of Cytophaga-Flavobacteria hybridized cells between pelagic river water (Böckelmann et al. 2000) and biofilm-associated bacteria (Brümmer et al. 2000) of the river Elbe were also indicated by samples obtained near Dresden and Coswig. Members of the Cytophaga-Flavobacterium group are known as mainly aerobic bacteria, which are specialized on the degradation of complex macromolecules (Reisenbach 1991). The river water aggregates probably constitute the most active habitat type of the river Elbe for the Cytophaga-Flavobacterium bacteria. Consistent with the domain-specific differences between habitat types, all group-specific probes, with exception of the α- Proteobacteria probe, indicated differences between the river water and the sediment. Since the relative proportions between the members of the subclasses did not strongly change, the Discussion 90 difference between the habitat types presumably only reflected a decrease in the detection rates of the bacteria. Similarly, with increasing sediment depth, bacteria of all subclasses occurred in decreasing proportions with a high level of significance. This observation indicates that nearly all of the cells identified at the group-specific level decreased by a similar degree with sediment depth. These gradients may be related to the metabolic activity of the bacteria. The present study has shown descriptive data of microbial community distributions with low phylogenetic resolution comparable to most studies of different aquatic ecosystems so far. Distributions of the relative abundance at the domain- and group-specific scale based on habitat type and sediment depth indicated some differences between samples, but by means of univariate statistical analysis, the structural distribution of the communities using the group-specific probes allowed only a rough characterization of the microbial populations. Thus, this analysis is not entirely appropriate for the analysis and comparison of multi-species communities. Obviously this is the key constraint of analyses at the group- specific phylogenetic scale.

4.4.2 Composition of the microbial communities at genus- and species-specific level Experiments utilizing a high number of probes directed to bacterial cells on the genus or species level may more accurately reflect differences in the resolution of the most active members of the microbial assemblage. The study presented here is an attempt to analyze microbial composition on different spatial scales by a comprehensive suite of nearly 50 genus- and species-specific oligonucleotide probes for the major phyla within the domain Eubacteria. The typical basis for the interpretation of FISH results in environmental microbiology is to score only probe-positive metabolic active cells showing bright hybridization signals. Meanwhile, if bacterial cells stained by fluorescent DNA intercalating dyes (usually DAPI, here Sytox Blue) cannot be hybridized with rRNA-targeted probes, the constraints of FISH are well-known. Cells may remain undetectable due to: (i) impermeability to the probes caused by structural restrictions such as cell wall thickness; (ii) low cellular ribosomal content below the FISH detection limit; (iii) the absence of the rRNA region targeted by the probe (Lee and Kemp 1994, Amann et al. 1995). Within the habitat types of the Dresden and Coswig sites, 7 probes showed positive signals for all investigated samples, whereas 16 genus-and species-specific probes did not show any signal at all. Probe-positive bacteria common to all river Elbe habitat types and sites studied, seem to be common environmental bacteria. For example, Legionellaceae and Pseudomonas spp. are Discussion 91 known to be widely distributed in soil, sediments, and water (Colbourne and Dennis 1989, Cavallo et al. 1999). In contrast to this, the microbial communities at different sediment depths were characterized by a lower degree of spatial variability. This is reflected by only 12 out of 47 probes showing a presence/absence signal pattern versus 17 probes positively reacting to hybridization probes and 20 probes which showed no signals within any sample. Even on the basis of the large number of genus- and species-specific probes showing presence/absence FISH signal patterns, the community structure of the river habitat samples was found to be more complex than the communities detected in the sediment depths samples. More than 50% compared to nearly 25% of all applied probes were accounted for presence/absence differences in the microbial communities, respectively. Although in some samples no positive hybridization signals were detected, this does not necessarily exclude the presence of any rRNA signature or genotype in those habitat or depth samples due to the above mentioned potential limitations of the rRNA approach. Obviously, bacteria directed to probes showing no hybridization signal in any sample were rather suggested to be absent or at least have a low total amount of the corresponding target sequence within the nucleic acid pool of the microbial community of the river Elbe.

4.4.3 Multivariate analysis of microbial communities Multivariate analysis was employed to reduce the complexity of high-dimensional community data to a low dimensional (e.g., 2-dimensional) space (Clarke 1993). The MDS plots produced by group-specific data revealed lower "stress" values than the ordinations of the genus- and species-specific probes. The accuracy of an MDS plot may decrease with increasing numbers of probes which could lead to distortion or stress between the similarity distances (Clarke 1993), but in this study stress values provided sufficient ordination for accurate comparison of samples. Multidimensional scaling analysis provided clear evidence that the application of the comprised group-specific FISH data was more informative for comparing communities from the river Elbe habitat types than univariate statistical approaches. The MDS ordination cluster of the sediment samples was clearly separated from the other two habitat types (Figure 19). Although the data were square-root transformed, the dissimilarity with the hyporheic water samples could be explained by the high relative abundance of the Planctomycetales. In contrast, the separation between river water and sediment samples was even significant without consideration of the Planctomycetales data. The similarity of the FISH data prevented identification of significant discriminating clusters Discussion 92 between the river and hyporheic water samples. From the ecological point of view, it is proposed that the above described inflow of river water into the hyporheic zone explains the similarity between the community structures in these two habitat types. While discrimination between habitat types was observed, the different geographical sites (Dresden and Coswig) could not be separated indicating that the habitat type is a dominant controlling factor of community structure. On the basis of these results, it is suggested that phylogenetic resolution on the group-specific scale is restricted to discriminating microbial community variations on this ecological scale. In this study, the application of several genus- and species-specific probes combined with multivariate analysis revealed the greatest degree of discrimination in the statistical analyses. The presence/absence data analysis gives equal weighting to all taxa detected irrespective of their abundance in a specific microenvironment (Clarke and Warwick 1994). The analysis of similarities between the position of points in the MDS plot significantly discriminated among the microbial communities in the three river habitat types and further between the Dresden and Coswig sites. The differences in microbial community structure between Dresden and Coswig are perhaps not surprising because of the unique and specific response of different bacterial species to the physico-chemical and sediment-characteristic differences among the sites. This may reflect a functional succession in the bacterial assemblage in the river Elbe between Dresden and Coswig downstream, where several tributaries with different levels of pollution flow into the Elbe. On the basis of these results, conclusions are drawn regarding the higher similarity between the Coswig sediment samples in contrast to the higher dissimilarity within the Dresden sediment samples (Figure 23). This resulted presumably from differences in the abiotic structure of the river. At Coswig, the grain size distribution in the river-bed and the groyne sediment samples revealed a high level of uniformity (Table 11) and were only loosely stratified (see 4.3.1). The sediment structure at Dresden was heterogenous in grain size and showed a high degree of mechanical stability, potentially providing a higher number of microhabitats within the sediments. This may explain the more heterogeneous microbial community structure corresponding to the relative greater distances in the MDS ordination than for the Coswig sediment samples. Although the MDS ordination plot (Figure 23) was derived from probe data reflecting the presence/absence pattern, only a small subset of probes may give a result similar to that of the full set of genus- and species-specific probes. Thus, there appears to be considerable structural or functional redundancy. Only those probes without interchangeable replacement Discussion 93 in presence or absence signals within and between habitat types and sites were considered to be ´discriminative probes´. For example, probe Nsm156 and PIR signals were present in Coswig sediments, but absent in Dresden sediments. Probes showing nearly random distribution of positive signals for the target taxa or only presence or absence for all samples were accordingly defined as ´non-discriminative probes´. On this basis one could identify particular active bacterial taxa by FISH that contributed to systematic and statistically significant differences and similarities between community structure in different samples. The analysis of similarities between communities from different sediment depths with genus- and species-specific probes revealed no differences between the depths as is evident from the heterogenous distribution in the ordination plot (Figure 24). This was possibly due to a high degree of spatial heterogeneity of the sediments that comprised the resolution at this fine phylogenetic scale. Therefore, defining structural redundancy in the sets of oligonucleotide probes and thus all probes were characterized as non-discriminative. The application of the group-specific probes at the two sediment depth communities was more sensitive and discriminative among the community structure (Figure 20). The group-specific scale reduced the phylogenetic resolution to few large subclasses of the domain Bacteria. This approach was able to detect differences which more narrow phylogenetic scales would not have been able to detect.

4.4.4 General interpretation of microbial community analyses It is suggested that microbial communities analyzed by FISH on different phylogenetic scales of resolution revealed particular patterns reflecting spatial differences. These findings are summarized into a generalized scheme given in Figure 26. Genus- and species-specific resolution can provide insights into both the habitats and sites with m and km scales, respectively, and putatively into the biofilm (µm) scale by discriminative probes, which are typically derived from FISH signals of individuals of specialized bacterial species. This microscale distribution of a microbial community was investigated in nitrifying biofilms by Schramm et al. (1996, 1999). In the depths or environmental strata suggested as mm/cm scale, it is difficult to discriminate communities at the level of genus- and species probes. However, this spatial scale was effectively analyzed and discriminated using group-specific assemblage analysis (Figure 26). A similar pattern is revealed at the domain-specific phylogenetic level, but with minor relative resolution. In conclusion, quantitative and qualitative analysis by FISH and application of multidimensional scaling enabled discrimination between microbial Discussion 94 community structures from different river habitat types and sites and river sediment depths through resolution at different phylogenetic scales. The need is recognized to integrate microbiological data based on physiological activity with environmental parameters to increase the knowledge of spatial patterns and the relationship between population structure and function. The hitherto known constraints of FISH analysis restricted by the insufficient resolution of microbial communities on the basis of domain- and group-specific probes can perhaps be compensated for by the pre-selection of probes and appropriate sampling strategies for the different spatial scales. Group-specific probes should only be used together with other probes reflecting the highly-dimensional feature of microbial communities. Therefore, multivariate approaches may be useful in further studies to compare the complex and diverse structure of natural microbial communities.

phylogenetic scale

domain

group

relative resolution genus-species

biofilm depth habitat sites µm mm/cm m km

Figure 26 Generalized scheme reflecting the relative resolution of microbial community structure examination at different phylogenetic scales. Circle size reflects the degree of relative resolution: extremely low, low, medium, and high, respectively. The different spatial and ecological scales were modified and extended according to Brockman and Murray (1997). Relative resolution in biofilm (µm) are suggested (open circles).

Outlook 95

5 Outlook

5.1 Synopsis The aim of this thesis was to provide new information on microbial processes and communities within sediments of a large river. These so called "hyporheic zone" is still almost a "black box" in river ecology. Thus, an experimental set up was chosen comprising a broad spectrum of aspects, and subsequently the results of this research and informations provided by the literature were summarized. Many limnological and microbiological methods have been used on the framework shown in Figure 27.

organic material - free flowing Bacteria - aggregates DOM POM Algae streamflow O material export 2 inorganic nitrogen streamflow PO 4 NH NO NO material import 4 2 3 N2

flowing water hydrological exchange at the sediment/water interface hyporheic zone

-advective transport organic pool for -retention/retardation DOM POM Algae -(colmation) - in hyporheic water -(EPS production) Bacteria - sediment associated -aerobic respiration -(N assimilation) -anaerobic respiration e.g. denitrification -desulfurication O2 hyporheic flow to groundwater

nitrification denitrification seepage

from catchment (adsorption) NH4 NO2 NO3 N2 (mineralization) (clogging) uptake (assimilatory uptake) PO4 immobilization (nitrate ammonification) release

Figure 27 Conceptual model of the material dynamics in the river Elbe regarding the topics of this present Ph.-D. thesis. Processes in brackets were not measured in this study, but suggested by other studies. All processes showed patterns at different spatial and temporal scales. DOM dissolved organic material, POM particulate organic carbon.

Outlook 96

Microbial biofilms are efficient sites for the uptake, immobilization, transformation, and metabolism of both dissolved and particulate organic substances and nutrients (Findlay et al. 1993, Lock et al. 1984, Fischer et al. in press, Grimm et al. 1984, Hendricks and White 1995). Especially in summer, the oxygen dynamics of the river Elbe near Dresden appeared in substantial day/night cycles due to high photosynthetic activity of the phytoplankton. The infiltrated oxygen affected the nitrogen and phosphorus dynamics in the hyporheic zone resulting in clear diurnal nitrate, nitrite, ammonium, and phosphate patterns (see chapter 4.1). Thus, the phytoplankton indirectly affected the nutrient dynamics in the river Elbe. The findings of large amounts of phytoplankton cells in the hyporheic water of the river Elbe sediments supports the idea that there exists substantial advective flow of different morphotypes of algae through the interstitial pores. In column experiments, Additionally could be shown that the retention and retardation of algal cells in hyporheic sediments depends on the permeability of the porous media (see chapter 4.2). In cooperation with project partners of the Research Association Elbe-Ecology (Bundesanstalt für Gewässerkunde BfG, Berlin; Institute of Freshwater Ecology and Inland Fisheries IGB, Berlin; Ecosystem Saxonia GmbH, Dresden; Institute of Hydrobiology, University of Jena; Centre of Environmental Research UFZ, Magdeburg), the unique opportunity was offered to work within a diving bell on the river-bottom of the river Elbe. This offered the unique possibility to investigate contrasting sites of a large river (Upper part and Middle part of the river Elbe; river-bed and river-bank sites) by the same techniques. It could be shown that sediments located at Dresden were highly consolidated and thus the biofilms which were very different in microbial and detrital properties in comparison to the moving sandy sediments at Coswig. Nitrogen metabolizing processes were estimated for the river Elbe by means of the nutrient spiralling concept (Newbold et al. 1982, Fisher et al. 1998). Further, there was strong evidence that algal cells were involved in denitrification as carbon source (see chapter 4.3). Microbial communities can be distinguished at various phylogenetic and spatial scales. Recently studies using fluorescence in situ hybridization (FISH) with rRNA-targeted oligonucleotide probes only revealed descriptive aspects of different microbial communities. In this study FISH was used to investigate microbial communities at different river habitat types (free flowing bacteria and aggregates; bacteria in the hyporheic water; sediment biofilm bacteria) and sediment depths (5 and 25 cm). In the present case, the combination of FISH

Outlook 97 and multivariate statistical analysis provided new aspects on examining microbial communities at different scales and extends the utility of this approach to facilitate more comprehensive ecological studies of microbial habitats (see chapter 4.4).

5.2 Perspectives and open questions The effect of dissolved oxygen on the nitrate and nitrite dynamics in the hyporheic zone was shown by this thesis, suggesting an influence on denitrification in riverine microbial biofilms. This was observed at the spatial scale of an ecological stratum in hyporheic sediments. It could be of interest to test this observation at the biofilm scale. One possibility is the application of microsensors based on immobilized denitrifying bacteria (Larsen et al. 1996) or on liquid ion-exchanging membranes (Schramm et al. 1998). Another promosing tool is a bacterial single cell indicator based on the green fluorescent protein (GFP)-based reporter system, that has been realized in part within the scope of this thesis. The GFP from the jellyfish Aequorea victoria can be used as a reporter for the visualization of gene expression and protein subcellular localization (Chalfie et al. 1994). Advantages such as species independence and the lack of a requirement for substrates and cofactors makes GFP unique as a reporter gene. One study of GFP-marked E. coli cells observed their persistence in stream water (Leff and Leff 1996). The GFP-approach was also applied to other gram-negative bacteria than E. coli (Matthysse et al. 1996). The application of GFP as a molecular marker in environmental microorganisms is reviewed by Errampalli et al. (1999). The next step in developing the use of GFP was the construction of fusions to genes encoding unstable gfp proteins (Andersen et al. 1998). Now it was possible to create the fusion of GFP and the Escherichia coli rrnBP1 promotor to monitor the rates of rRNA synthesis and thus to observe for example the cellular growth activity at the single cells level of P. putida (Sternberg et al. 1999, Ramos et al 2000). Although synthesis of GFP requires molecular oxygen (Tsien 1998), GFP was detected by Vollack (personal communication) under low oxygen conditions required for denitrification. This was the basis for the idea to create a single-cell indicator for microbial denitrification in aquatic biofilms as a part of the study reported here. The denitrification enzymes of Pseudomonas stutzeri were best examined among denitrifying bacteria and the promotor of the nirS gene (nitrite reductase) was found to be the most strongest in comparison to other regulating enzymes (Zumft 1997). The expression of denitrification enzymes of P. stutzeri in response to the dissolved oxygen level was investigated by Körner and Zumft (1989). The nirS promotor region of P. stutzeri was Outlook 98 described by Härtig and Zumft (1999). A Streptomycin resistant Pseudomonas stutzeri strain and pUT-miniTn5-Km transposon vector (DeLorenzo et al. 1990) were kindly provided by K.-U. Vollack and W. G. Zumft (Institute of Microbiology, Karlsruhe, Germany). To construct the primers amplifying the nirS promotor region by PCR, the whole nirS gene was obtained from the EMBL databank. Unstable GFP´s were kindly provided by J.B. Andersen (Department of Microbiology, Lyngby, Denmark). Plasmid construction of cloning and transposon vectors was performed as described by Andersen et al. (1998). The aim of this study was to create genetically engineered P. stutzeri strains as fluoresceing single cell reporter systems in microbial biofilms in response to spatially and temporally varying oxygen conditions within the biofilm. Unfortunately, the promising work could not be finished within the scope of this thesis. However, the survival and behaviour of the Streptomycin resistent P. stutzeri strain at environmental conditions was measured in a diploma thesis during this project and P. stutzeri was found to be a suitable indicator for studies natural aquatic waters such as river water and groundwater. A similar system for nitrification based on luciferase-encoding operons in Nitrosomonas sp. was performed by Ludwig et al. (1999). The luminescence was dependent on the pH of the medium and the concentration of the ammonium.

In chapter 4.1, nitrate reduction and nitrite accumulation in the hyporheic water is discussed with subject to the occurrence of denitrification and nitrate ammonification processes. The simultaneous occurrence of both pathways has been described by some studies (e.g. Bianchi et al. 1994, Omnes et al. 1996, Bonin et al. 1998). Both processes are of interest considering the nitrogen budget of the hyporheic zone of the river Elbe and also the mass balance calculations for the water quality model QSIM (Quality SIMulation). The most promising approach to measure nitrogen cycling may be the 15N tracer technique (Mulholland et al. 2000).

Another open area for study is based on results discussed in chapter 4.2. Advective algal transport was related on the sediment permeability. But algae also affect the permeability of the hyporheic zone and are reported to participate in clogging of river sediments (Battin and Sengschmitt 1999, Underwood and Smith 1998). It could be of interest for hydrologic exchange studies and for the water quality model QSIM (algae are obtained in this model (Kirschesch and Schöl 1999) to which proportion different algal morphotypes and shapes are involved in the clogging process of the upper sediment layers. Summary 99

6 Summary

1. The conceptual subject of this study was to investigate the effects of microbial biofilms of the hyporheic zone of the river Elbe on nutrient dynamics and elimination. The spatial and temporal distribution of oxygen, inorganic nitrogen (nitrate, nitrite, and ammonium), organic particles, and microbial processes and community structures were examined. Due to the complexity of microbial communities, multivariate statistical tools were applied.

2. The input of dissolved oxygen (DO) into the hyporheic zone was measured on the basis of diurnal oxygen variations in the pore water of the river Elbe in the infiltration zone of a water work near Dresden. DO was reduced in the hyporheic environment, but revealed distinct diel fluctuations. Thus, it is suggested that infiltrated oxygen effects the diurnal hyporheic nitrogen and phosphorus dynamics. Diurnal differences on DO concentrations were measured after dawn and after sun maximum in weekly intervals at depth-profiles (10, 17, and 25 cm) over 12 months. Nitrate patterns showed significant temporal variation. Low nitrate -1 concentrations occurred after dawn (< 3 mgNO3-N l ) consistent with low DO concentrations. Differences between the two contrasting diel sampling times were highest in -1 summer (1.26 mgNO3-N l ) and significantly correlated with diel DO pattern. Distinct diel variations for ammonium detected lowest concentrations measured after sun maximum, when DO was highest. Nitrite exhibited strongest correlations with DO and nitrate, suggesting hyporheic nitrite as suitable indicator of the surface-water-groundwater interface in nitrate rich rivers. Hyporheic phosphate concentrations were lowest after sun maximum and increased significantly within a day. This is the first field study showing that diurnal patterns of nitrate, nitrite, ammonium, and phosphate concentrations in the interstitial water indicate that the oxic-anoxic interface was subject to diurnal shifting in vertical and presumably in lateral direction as well as within hyporheic anaerobic patches. On the basis of these results a general model is proposed for the diurnal patterns of the hyporheic nitrogen and phosphorus dynamics downwelling zones of a river.

3. The advective transport of algal cells into the interstices of the hyporheic zone of the river Elbe was spatially and temporally heterogenous. Variations in algal cell abundance within different sediment layers (10, 17, and 25 cm) appeared to be primarily caused by temporal hydraulic fluctuations in pore channel flow and spatial variations in sediment permeability. Despite long transport times through the sediments, even the deep sediment layers (25 cm) Summary 100 were reached by large phytoplankton species (e.g. Pediastrum sp.). Therefore, it is hypothesized that (i) the advective interstitial transport patterns vary between different algal sizes and morphotypes and (ii) sediment characteristics, expressed by the permeability coefficient kf of porous media, affect retention and retardation of surface algae during subsurface transport. The transport behaviour of different green algae (Chlorella sp., Scenedesmus acuminatus, Desmodesmus communis, and Pediastrum duplex) and algal sized microspheres was tested in flow-through column experiments with hyporheic sediments. The algal cell transport was directly related to the permeability of the column sediments. An -5 -4 increasing permeability kf from 2.3 10 to 2.0 10 caused a significant decrease in retention and retardation of algae and microspheres.

4. Characteristics of microbial biofilms such as bacterial abundance and activities and detrital parameters in sediments of large rivers are still poorly investigated. Therefore, this study focused on two contrasting sites of the river Elbe to estimate the contribution of the sediment structure on microbial variables. The Dresden sediments (river-bed and river-side) of the Upper Elbe were characterized by high grain size and high mechanical stability and consisted of gravel and stones. In contrast, Coswig sandy sediments (river-bed and groyne fields) of the Middle Elbe were more homogeneous in grain size distribution and loosely associated. The highest bacterial abundances and particulate organic matter contents could be found in the stable sediments of the river-side in Dresden resulting in high microbial activities per g dry weight. Potential denitrification rates in the 0-5 cm sediment depths averaged 69.3 and 308.4 µmol N gDW-1 h-1 in the river-bed and river-side, respectively. Substantially lower values were measured in the river-bed of Coswig. Denitrification rates varied from 28.8 to 52.6 µmol N gDW-1 h-1 in the Coswig river-bed and groyne fields, respectively. Nitrification rates showed similar activity patterns between sites. However, on the basis of streambed surface area (m2), microbial activities were considerably higher in the Coswig sediments. This is attributed to the increased internal surface area of the sandy sediments available for microbial attachment. The findings demonstrate that the sediment structure significantly affected denitrification and nitrification within the whole river ecosystem. While both sites revealed the same river water nitrate concentrations (4 mg l-1), it was estimated that the denitrification processing length based on the spiralling length concept was distinctly lower in Coswig.

Summary 101

5. In this study, the microbial communities of three different river habitat types and two sediment depths at different sites of the river Elbe has been assessed by fluorescently in situ hybridization (FISH) using a broad panel of rRNA-targeted oligonucleotide probes. Multivariate statistical analysis was performed on FISH data at different scales of population composition and complexity. Differences between free flowing river water, hyporheic water, and sediment-associated bacteria at two sites were detected using group-specific FISH data by calculation of similarity coefficients and ordinations of non-metric multidimensional scaling (MDS). FISH signal patterns representing the community structure at both sampling sites (Dresden and Coswig) could be clearly separated by analyses using genus- and species- specific probes in qualitative presence/absence assays. This level of spatial resolution at a narrow phylogenetic scale was revealed by discriminative probes, which hybridized to bacterial cells unique to different habitat types or sites. Fine scale spatial analysis furthermore showed significant differences between sediment depths at the group-specific level. However, at the genus and species-specific level, high levels of spatial heterogeneity in sediment community composition masked the resolving power of genus- and species-specific probes. The results allowed the proposal of a general model for the relative resolution of microbial communities that emphasizes the complexity at different spatial scales that may explain previously known constraints of FISH analysis. Hence, the combination of FISH data and multivariate statistical analysis extends the utility of this approach to facilitate more comprehensive ecological studies of natural microbial habitats. Zusammenfassung 102

7 Zusammenfassung

1. Das Ziel der vorliegenden Arbeit war die Untersuchung von mikrobiellen Biofilmen in hyporheischen Sedimenten der Elbe sowie deren Auswirkungen auf die Stoffdynamik und die Nährstoffelimination. Es sollte die räumliche und zeitliche Verteilung von Sauerstoff, anorganischem Stickstoff, organischen Partikeln und mikrobiellen Prozessen untersucht werden. Aufgrund der Komplexität der mikrobiellen Gemeinschaften wurden multivariate statistische Methoden angewendet.

2. Die Sauerstoffkonzentrationen in der Elbe schwanken im Tagesverlauf aufgrund photosynthetischer Aktivität des Phytoplanktons. Im hyporheischen Interstitial wurde Sauerstoff durch respiratorische Prozesse reduziert. Allerdings konnte dort ebenfalls der diurnale Rhythmus festgestellt werden. Daraus läßt sich schließen, dass der ins Hyporheal eingetragene Sauerstoff einen Einfluß auf die Stickstoff- und Phosphordynamik im hyporheischen Interstitial ausübt. In einem wöchentlichen Meßintervall wurden ufernahe Probenahmestellen bis in 30 cm Tiefe an zwei unterschiedlichen Tageszeitpunkten (nach Morgendämmerung und eine Stunde nach Sonnenhöchststand) beprobt. Nitrat zeigte eine eindeutige zeitliche Abhängigkeit. Niedrige Nitratkonzentrationen im Hyporheal wurden nach -1 Dämmerung gemessen (< 3 mgNO3-N l ), als die Sauerstoffkonzentrationen gering waren. Die Konzentrationsdifferenzen (Tagesamplitude), die zwischen den beiden - Probenahmezeitpunkten ermittelt wurden, waren im Sommer am höchsten (1.26 mgNO3-N l 1) und korrelierten signifikant mit den Tagesamplituden von Sauerstoff. Die Ammoniumkonzentrationen im Hyporheal waren nach Sonnenhöchststand am geringsten, als Sauerstoff die höchsten Konzentrationen zeigte. Das im Hyporheal gemessene Nitrit korrelierte signifikant mit Sauerstoff und Nitrat. Aus diesem Grund wurde geschlossen, dass Nitrit ein geeigneter Indikator für die Stickstoffdynamik im Hyporheal von stickstoffreichen Fließgewässern ist. Die Phosphatkonzentrationen waren nach Sonnenhöchststand am geringsten und stiegen bis zur Morgendämmerung an. Dies ist die erste Felduntersuchung, in der die tageszeitlichen Schwankungen von Sauerstoff, Nitrat, Nitrit, Ammonium und Phosphat über einen langen Zeitraum gemessen wurden. Es konnte festgestellt werden, dass die oxisch/anoxische Phasengrenze in der oberen Sedimentschicht einer tageszeitliche Rhythmik unterliegt, die sich in vertikaler und lateraler Richtung auswirkt. Auf der Grundlage der Ergebnisse wurde ein generalisierendes Modell vorgestellt, welches die diurnalen Zusammenfassung 103

Veränderungen der Stickstoff- und Phosphordynamik im hyporheischen Interstitial beschreibt.

3. Der advektive Transport von Algenzellen in die Porenräume des hyporheischen Interstitials unterlag einer räumlichen und zeitlichen Heterogenität. In Felduntersuchungen variierte die Zell-Abundanz in verschiedenen Sedimenttiefen (10 cm, 17 cm, 25 cm) aufgrund fluktuierender hydrologischer sowie sedimentologischer Bedingungen. In tieferen Sedimentschichten konnten trotz langer Fließwege größere Algen (z.B. Pediastrum sp.) nachgewiesen werden. Daraus läßt sich schließen, dass 1. Algen unterschiedlicher Morphologie und Größe ein spezifisches Transportverhalten zeigen und 2. dass die

Permeabilität der Sedimente, ausgedrückt durch den Durchlässigkeitsbeiwert kf, die Retention und die Retardation von Algenzellen beeinflußt. Getestet wurden verschiedene Grünalgen (Chlorella sp., Scenedesmus acuminatus, Desmodesmus communis, und Pediastrum duplex) sowie 6 µm ∅ große Kunststoffkügelchen ("microspheres"). Die Versuche wurden in Durchflußsäulen mit ufernahem Sediment aus der Elbe durchgeführt. Der advektive Transport der Algen konnte direkt mit der Durchlässigkeit der Sedimente in Beziehung gesetzt werden. -5 -4 Die Zunahme des Durchlässigkeitsbeiwertes kf von 2,3 10 to 2,0 10 hatte eine eindeutige Abnahme der Retention und Retardation zur Folge. Die Ergebnisse der Feld- und Laboruntersuchungen des advektiven Transports von Algen wurden hinsichtlich der hydrodynamischen Prozesse im Interstitial und der Stoffdynamik von partikulärem organischen Material diskutiert.

4. Mikrobielle Biofilme von Sedimenten großer Flüsse sind bezüglich ihrer bakterieller Abundanz und Aktivität sowie der Detritusparametern bisher nur wenig untersucht worden. In der vorliegenden Arbeit wurden zwei unterschiedliche Flußabschnitte der Elbe untersucht. Die Sedimente von Dresden (Flußmitte und Ufer der Oberen Elbe) enthielten steinige und kiesige Bestandteile und zeigten einen hohen Grad an Stabilität. Im Gegensatz dazu waren die sandigen Sedimente von Coswig (Flußmitte und Buhnenfelder der Mittleren Elbe) in ihrer Größenzusammensetzung recht homogen und locker geschichtet. Die höchste bakterielle Abundanz und Konzentration an partikulärer organischer Substanz konnte in den Ufersedimenten in Dresden gefunden werden. Die bakteriellen Aktivitäten (Denitrifikation, Nitrifikation, β- Glukosidase, Phosphatase und Leucin-Aminopeptidase) waren folglich dort auch am stärksten. Die Denitrifikationsrate in Dresden betrug in der Flußmitte und am Ufer (0-5 cm Sedimenttiefe) 69,3 and 308,4 µmol N gDW-1 h-1. In den Sedimenten von Coswig Zusammenfassung 104 lagen die Raten in der Flußmitte und in den Buhnenfeldern bei 28,8 und 52,6 µmol N gDW-1 h-1. Die Unterschiede der Nitrifikationsraten zwischen den Standorten waren ähnlich. Die berechneten bakteriellen Aktivitäten auf der Basis der Sedimentoberfläche pro m2 waren hingegen in Coswig höher. Dies liegt in der großen internen Oberfläche der sandigen Sedimente begründet. Obwohl an beiden Standorten die Nitratkonzentrationen ähnlich (4 mg l-1) waren, konnte auf der Basis des "Nutrient Spiralling" Konzeptes von Elwood et al. (1982) eine wesentlich stärkere Denitrifikation ermittelt werden. Die Untersuchungen verdeutlichten, dass die Struktur der Sedimente einen starken Einfluß auf die mikrobiellen Prozesse in Flußökosystemen haben.

5. Die mikrobielle Biozönose von drei Habitattypen (Flußwasser, Interstitialwasser, Sediment) und zwei Sedimenttiefen (0-5 cm und 20-25 cm) wurde mit Hilfe der in situ Hybridisierungstechnik (FISH - fluorescence in situ hybridization) untersucht. Es wurde ein breites Spektrum von Oligonukleotidsonden eingesetzt. Die Ergebnisse wurden auf unterschiedlichen räumlichen und phylogenetischen Ebenen mit multivariaten statistischen Methoden (nicht-metrische multidimensionale Skalierung, MDS) ausgewertet. Auf der Art- und Genus-spezifischen Ebene waren die untersuchten Standorte (Dresden und Coswig) signifikant unterschiedlich. Dies konnte mit der qualitativen Presence/Absence-Auswertung detektiert werden. Mit gruppenspezifischen Sonden und der Eubakterien-Sonde (EUB338), die quantitativ ausgewertet wurden, konnten allerdings keine Unterschiede festgestellt werden. Die Anwendung der gruppenspezifischen Sonden trennte die Habitattypen als auch die Sedimenttiefen deutlich auf. Die mikrobiellen Biofilmpopulationen in den beiden Sedimenttiefen konnte durch die art- und genus-spezifischen Sonden nicht signifikant unterschieden werden. Vermutlich war die räumliche Struktur der Sedimente zu heterogen, um eine Auftrennung auf dieser phylogenetischen Ebene zu ermöglichen. Auf der Basis der Ergebnisse wurde ein generalisierendes Modell erstellt, welches die relative Auflösung von mikrobiellen Populationen auf unterschiedlichen räumlichen und phylogenetischen Ebenen verdeutlicht und damit bisher bekannte Limitierungen der Hybridisierungstechnik erklärt. References 105

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9 Acknowledgements / Danksagung

Frau Prof. I. Röske danke ich herzlich für das Ermöglichen dieser Dissertation, jede Unterstützung und Diskussionsbereitschaft im Rahmen meiner Arbeit sowie für die Übernahme des Referates.

Herrn Prof. D. Uhlmann danke ich sehr für konstruktive Kritik an Manuskripten, Anregungen während der praktischen Phase der Arbeit sowie für die Übernahme des Referates.

Herrn PD Dr. W. Manz danke ich besonders für viele anregende und wertvolle Diskussionen und fortdauernde Unterstützung meiner Arbeit, für die konstruktive Kritik an Manuskripten, die Übernahme des Referates sowie für die Möglichkeit eines Aufenthaltes auf dem Forschungsschiff "Sonne".

Frau I. Schachtschabel danke ich für das Bearbeiten von Probenbergen und die unermüdliche Hilfsbereitschaft bei allen Arbeitsabläufen. Die Zusammenarbeit mit ihr war für mich nicht nur eine fachliche Bereicherung.

Helmut Fischer trug besonders während der Taucherschachtaktion zum Gelingen der Arbeit bei und war vor allem in der Endphase resistent gegen klingelnde Telefone. Dank auch für die Hilfe beim Erstellen und der Überarbeitung eines Manuskriptes.

Matthias Brunke, Thomas Grischek und Norbert Kamjunke danke ich sehr für umfangreiche konstruktive Kritik an Manuskripten.

Robert Radke danke ich besonders für die sprachliche Überarbeitung der Arbeit.

Herrn H. Baumert möchte ich danken für die Erstellung des statistischen Modells.

Margret Kuschel weihte mich in die Molekularbiologie ein.

Bei folgenden Personen möchte ich mich bedanken, die in verschiedenster Weise geholfen haben: Michael Böhme, Frau R. Eidner, Norbert Große, Kathleen Haase, Astrid Käppler, Johannes Kranich, Sandra Kroewer, Herr H.-P. Lange, Katrin Nagel, Jürgen Neumann, Klaus Ockenfeld, Herr M. Pusch, Sabine Wilczek, Sandro Wolf, Heike Zimmermann-Timm.

Der DREWAG, insbesondere Herrn Bunnemann möchte ich danken für die freundliche Bereitstellung des Standortes für die Versuchsanlage im Wasserwerk Dresden-Saloppe. Besonders Herr Richter hat bei der Einrichtung und während der Versuche sehr geholfen.

Weiterhin möchte ich mich bei den Mitarbeitern des Institutes für Mikrobiologie bedanken, die zum Gelingen der Arbeit beigetragen haben. Besonders bei Frau R. Schöne, die mit mir das Zimmer teilte.

Carmen Wolf danke ich besonders für private Unterstützung sowie für großes Verständnis bei der Anfertigung dieser Arbeit.

126

Diese Arbeit ist am Institut für Mikrobiologie der TU Dresden unter der wissenschaftlichen Betreuung von Frau Prof. Dr. Isolde Röske im Rahmen eines vom Bundesministerium für Bildung und Forschung (BMBF-Projektträger BEO FKZ 0339604) angefertigt worden.