The role of hydrodynamics in shaping the composition and architecture of epilithic biofilms in fluvial ecosystems

Accepted Manuscript The role of hydrodynamics in shaping the composition and architecture of epilithic biofilms in fluvial ecosystems

Ute Risse-Buhl, Christine Anlanger, Katalin Kalla, Thomas R. Neu, Christian Noss, Andreas Lorke, Markus Weitere PII:

S0043-1354(17)30815-1

DOI:

10.1016/j.watres.2017.09.054

Reference:

WR 13248

To appear in:

Water Research

Received Date:

10 June 2017

Revised Date:

27 September 2017

Accepted Date:

28 September 2017

Please cite this article as: Ute Risse-Buhl, Christine Anlanger, Katalin Kalla, Thomas R. Neu, Christian Noss, Andreas Lorke, Markus Weitere, The role of hydrodynamics in shaping the composition and architecture of epilithic biofilms in fluvial ecosystems, Water Research (2017), doi: 10.1016/j.watres.2017.09.054

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Highlights

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

Demonstration of near bed hydrodynamics affecting biofilms in fluvial ecosystems.

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

Near bed turbulence significantly affected biofilm composition and architecture.

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

Effects were more pronounced under higher dissolved nutrient concentrations.

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

Elongated ripples and streamers known from experimental systems were not observed.

6



More compact biofilms spreading uniformly on mineral surfaces at high turbulence.

ACCEPTED MANUSCRIPT 1

The role of hydrodynamics in shaping the composition and architecture of epilithic

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biofilms in fluvial ecosystems

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Ute Risse-Buhl1, Christine Anlanger1,2, Katalin Kalla1, Thomas R. Neu1, Christian Noss2,

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Andreas Lorke2, Markus Weitere1

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1

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Brückstraße 3a, 39114 Magdeburg, Germany

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2

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Department River Ecology, Helmholtz Centre for Environmental Research – UFZ,

Institute for Environmental Sciences, University of Koblenz-Landau, Fortstrasse 7, 76829

Landau, Germany

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Correspondence: Ute Risse-Buhl, Department River Ecology, Helmholtz Centre for

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Environmental Research GmbH – UFZ Magdeburg, Brückstraße 3a, 39114 Magdeburg,

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Germany, E-mail: [email protected]

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ABSTRACT

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Previous laboratory and on-site experiments have highlighted the importance of

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hydrodynamics in shaping biofilm composition and architecture. In how far responses to

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hydrodynamics can be found in natural flows under the complex interplay of environmental

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factors is still unknown. In this study we investigated the effect of near streambed turbulence

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in terms of turbulent kinetic energy (TKE) on the composition and architecture of biofilms

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matured in two mountainous streams differing in dissolved nutrient concentrations. Over both

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streams, TKE significantly explained 7% and 8% of the variability in biofilm composition and

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architecture, respectively. However, effects were more pronounced in the nutrient richer

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stream, where TKE significantly explained 12% and 3% of the variability in biofilm

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composition and architecture, respectively. While at lower nutrient concentrations seasonally

26

varying factors such as stoichiometry of dissolved nutrients (N / P ratio) and light were more

27

important and explained 41% and 6% of the variability in biofilm composition and

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architecture, respectively. Specific biofilm features such as elongated ripples and streamers,

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which were observed in response to the uniform and unidirectional flow in experimental

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settings, were not observed. Microbial biovolume and surface area covered by the biofilm

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canopy increased with TKE, while biofilm thickness and porosity where not affected or

32

decreased. These findings indicate that under natural flows where near bed flow velocities and

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turbulence intensities fluctuate with time and space, biofilms became more compact. They

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spread uniformly on the mineral surface as a film of densely packed coccoid cells appearing

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like cobblestone pavement. The compact growth of biofilms seemed to be advantageous for

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resisting hydrodynamic shear forces in order to avoid displacement. Thus, near streambed

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turbulence can be considered as important factor shaping the composition and architecture of

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biofilms grown under natural flows.

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Keywords

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bacteria, cyanobacteria, algae, AAL-specific glycoconjugates, near streambed turbulence

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Abbreviations

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AALsGC

Aleuria Alantia Lectin-specific Glycoconjugates

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BT

Biofilm Thickness

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BT-FC

Thickness of Fluorescent Biofilm Components

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CLSM

Confocal Laser Scanning Microscopy

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DIN

Dissolved Inorganic Nitrogen

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DOC

Dissolved Organic Carbon

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EPS

Extracellular Polymeric Substances

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LCI

Leaf Coverage Index

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PAR.sum

5-day sum of Photosynthetic Active Radiation

54

POR

Porosity

55

Q

Discharge

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RDA

Redundancy Anlysis

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SC

Surface Coverage of the biofilm canopy

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SRP

Soluble Reactive Phosphorous

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TKE

Turbulent Kinetic Energy

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wdepth

Water Depth

3

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1. Introduction

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Epilithic biofilms growing on mineral surfaces are an integral part of lotic ecosystems

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constituting primary and secondary producers embedded within a matrix of extracellular

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polymeric substances (EPS) (Lock et al., 1984; Wetzel, 2001). They represent a major food

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source for the micro-, meio- and macrofauna (Augspurger et al., 2008; Norf et al., 2009). The

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functioning and performance of biofilms in the environment is strongly affected by both the

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biofilm composition in terms of microbial groups (i.e. bacteria, cyanobacteria and algae) and

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the complex three dimensional architecture (spatial arrangement of microbial cells and EPS)

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(e.g. Battin et al., 2016). The composition and architecture of stream biofilms are affected

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among others by hydrodynamic forces, such as drag force and skin friction as well as by the

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diffusional flux of nutrients (see, e.g. Larned, 2010; Riber and Wetzel, 1987).

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Bacterial biofilms grown under controlled conditions in flow cells, reactors, fermenters

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or heat exchangers have been found to have a lower biomass and thickness and to be more

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compact with a higher physical stability and density (microbial cells per volume) at higher

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mean flow velocities and/or at turbulent flow (e.g. Araújo et al., 2016; Beyenal and

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Lewandowsky, 2002; Melo and Vieira, 1999; Pereira et al., 2002). This compact growth form

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is considered to be more resistant to shear forces and detachment (Larned et al., 2011).

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Similarly in flumes that simulate stream conditions, autotrophic biofilms grown at faster flow

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and higher turbulence have lower bacterial and algal biomass and a higher EPS content than

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those grown at slower and less turbulent flows (Battin et al., 2003; Besemer et al., 2007;

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Coundoul et al., 2015; Graba et al., 2013). Microcolonies of bacterial and stream biofilms

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were found to be arranged along the streamlines and elongated along the downstream

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direction forming ridges and conspicuous streamers (Battin et al., 2003; Besemer et al., 2007;

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Neu and Lawrence, 1997; Stoodley et al., 1999). In strong turbulence, hydrodynamic forces

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are considered more important than mass transfer limitations for shaping the biofilm

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architecture (Biggs et al., 2005). While these findings are based on observations in controlled 4

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experimental systems, knowledge of the interaction between hydrodynamics and epilithic

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biofilms in natural flows of fluvial ecosystems is lacking so far.

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Matured biofilms adapt their architecture to balance access to nutrients (Teodósio et al.,

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2010) resulting in an equilibrium between biofilm thickness and cell density (Liu and Tay,

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2002; Simões et al., 2007; Stewart, 2012). Less compact biofilms that are perforated by a

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network of channels facilitate better access to nutrients (Stoodley et al., 1994; Stoodley et al.,

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1997). Microbes, which are fully submerged within the viscous boundary layer, experience

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only laminar shear forces, however, also a reduced rate of mass transfer from the bulk flow,

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which is mainly governed by molecular diffusion (Larned et al., 2004). The distance over

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which nutrient transport is limited to molecular diffusion is controlled by the intensity of near

97

bed turbulence (Bryant et al., 2010; Lorke et al., 2003). We therefore assume that increasing

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turbulence reduces nutrient limitation, which is especially important under low nutrient

99

concentrations. Hence, dissolved nutrient concentrations need to be taken into account for

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evaluating hydrodynamic effects on stream matured biofilms.

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Previous studies on the effects of hydrodynamics on the composition and architecture of

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biofilms were mostly restricted to controlled experimental systems with simplified bed

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morphology and stationary and thus less complex flow fields. In fluvial ecosystems, however,

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the flow near the streambed is composed of chaotic motions, where water is flowing in

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longitudinal, transversal and horizontal direction at temporarily varying velocities (Nikora,

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2010).

107

In the present study, we analyzed the effect of near bed turbulence on epilithic biofilms

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matured under natural flow conditions. In order to test the generalizability of interactions, the

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effects of near bed turbulence on stream biofilms were observed at contrasting dissolved

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nutrient concentrations. Based on the results from controlled experiments, we hypothesize that

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near streambed turbulence is important in explaining the variability in composition

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(biovolume of bacteria, cyanobacteria, algae and EPS), architecture (surface coverage, 5

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thickness, porosity) and filamentous growth of the epilithic biofilms in spite of the multitude

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of interacting environmental factors acting on biofilms in fluvial ecosystems. We further

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hypothesize that biovolume of microbes and EPS are negatively correlated with the intensity

116

of near streambed turbulence. However, more EPS per microbial cell will be produced at

117

more energetic flows. In terms of biofilm architecture, we hypothesize that biofilms will be

118

thinner, more compact and without filamentous structures at higher turbulence intensities and

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that biofilm structures are orienting along mean streamlines.

120 121

2. Materials & Methods

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2.1. Field site characterization

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The study was conducted in two mountainous gravel bed streams which are comparable in

124

their streambed morphology with typical pool-riffle sequences. Both streams are located in

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the Bode catchment (Harz Mountains, Saxony-Anhalt, Germany). The Kalte Bode with a

126

length of 17 km originates in the upper Harz region draining a catchment of 50.8 km². The

127

590 m long study reach is located upstream of a freshwater reservoir at 460 m a.s.l. (N

128

51°44’33’’, E 10°42’09’’). The long term mean discharge is 0.7 m3 s-1 and the mean highest

129

discharge 15.7 m3 s-1 (data from 1951-2014). The Selke originates in the lower Harz region,

130

has a length of 64.4 km and drains a catchment of 468 km². The 500 m long study reach is

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located in the nature reserve ‘Selketal’ at 220 m a.s.l. (N 51°41’11.5’’, E 10°15’34’’) and has

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a long term mean discharge of 1.5 m3 s-1 and a mean highest discharge of 15.5 m3 s-1 (data

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from 1921-2015). Mean channel width of both streams was 7.3 m. Both stream reaches are

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slightly modified by bank stabilizations still exhibiting a natural flow and sediment transport

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regime.

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Samplings were performed within 5 days thrice per stream, in April/May 2014

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(hereafter April 2014), August 2014 and May/June 2015 (hereafter June 2015). Samples were

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collected in four consecutive riffle and pool sections at randomly chosen spots within both 6

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study reaches. A total of 34 and 33 sampling spots were characterized at the Kalte Bode and

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Selke, respectively. The sampling spots were located on medium sized gravel to cobble with a

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length between 1 and 12 cm. At each sampling spot, the near streambed turbulence was

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measured before removing the stones from the streambed to collect the epilithic biofilm.

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Discharge data were provided by the Flood Prediction Centre of the state Saxony-

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Anhalt (http://www.hochwasservorhersage.sachsen-anhalt.de), which were measured 1.5 km

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upstream and 2 km downstream of the study reaches of the Kalte Bode and Selke,

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respectively. Discharge at both streams was variable, ranging from 0.14 m3 s-1 to 6.61 m3 s-1

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at the Kalte Bode and from 0.09 m3 s-1 to 8.53 m3 s-1 at the Selke between March 2014 and

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June 2015 (Table 1). In both streams, discharge peaked after heavy rain fall and snow melt.

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However, discharges were always far below the mean highest discharge and bankfull

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discharge was never observed during April 2014 and June 2015 indicating that there was no

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bed forming discharge event. At the Kalte Bode, discharge was continuously decreasing from

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beginning of May reaching the mean lowest discharge of 0.24 m3 s-1 during the sampling in

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June 2015 (Table 1).

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Five light loggers (HOBO Pendant Temperature/Light Data Logger UA-002-64, Onset

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Computer Corporation, Bourne, MA, USA) were deployed along the stream reaches to

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characterize the sum of photosynthetic active radiation (PAR) during the 5-day samplings.

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Leaf coverage index (LCI) was calculated from skyward photographs as proportionate

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uncovered area above sampling locations. An EXO2 multiparameter probe (YSI Incorporated,

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Yellow Springs, OH, USA) was used to measure water temperature, oxygen concentration,

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pH and conductivity. Total nitrogen, bound and total phosphorus were determined

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photometrically (DIN EN ISO 11905-1- H36, 1998; DIN 38405-D11, 1983) from samples of

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unfiltered stream water. From filtered stream water samples (0.45 µm and 0.2 µm Sartorius),

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dissolved organic carbon (DOC) was measured using high temperature combustion

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(DIMATOC 2000, Dimatec Analysentechnik GmbH, Essen, Germany), nitrate (NO3-N), 7

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nitrite (NO2-N), ammonium (NH4-N) and soluble reactive phosphorous (SRP) were

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photometrically determined using the segmented flow analyzer (DIN EN ISO 13395-D28,

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1996; 11732- E23, 2005; 15681 Part 2-D46, 2005; respectively). Chl a trapped on glass fiber

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filters was extracted with ethanol and several freezing/thawing cycles and measured by high

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performance liquid chromatography (Dionex, Thermo Fisher Scientific Corporation,

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Waltham, MA, USA).

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Seasonally, light availability in terms of LCI and sum of PAR were low in August

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2014 in both streams (Table 1). Biofilms of the Selke sampled in August 2014 experienced

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the lowest light availability due to the dense canopy of the riparian vegetation. Water

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temperature was 0.6°C to 2.4°C lower and conductivity approximately 4 times lower in the

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Kalte Bode compared to the Selke (Table 2). Water of both streams was saturated with

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oxygen and had a mean pH between 7.1 and 8.1. DOC was highest in August 2014 and lower

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during spring samplings. Considering nutrients, SRP and dissolved inorganic nitrogen (DIN =

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sum of NO3-N, NO2-N and NH4-N) were always higher in stream water of the Selke

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compared to that of the Kalte Bode resulting in a lower DIN / SRP ratio in the latter (Table 2).

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The pattern of DIN was mainly driven by NO3-N dynamics, while NH4-N that made up 1 to

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15% of the DIN pool was always higher at the Kalte Bode than at the Selke. Chl a

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concentration was 3 to 9 times higher in stream water of the Selke compared to the Kalte

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Bode. Accordingly, we refer to the Kalte Bode as nutrient poorer and to the Selke as nutrient

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richer stream.

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2.2. Hydrodynamic measurements and data analyses

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The three-dimensional flow velocity was measured using a multi-static acoustic Doppler

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velocity profiler (Vectrino II, Nortek AS, Norway) at each sampling spot for a period of 20

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min at a sampling frequency of 64 Hz. The minimum interval ping algorithm was used, which

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is recommended by the manufacturer for highly turbulent environments. The profiling range 8

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was set that the lowermost velocity measurement coincided with the streambed. Since most

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trustworthy turbulence measurements can be obtained at the so-called sweet spot (Brand et al.,

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2016; Koca et al., 2017), only velocity measurements at 2.3 cm above the streambed were

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used for further analysis. Since near streambed turbulence has been observed to provide better

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correlation with biofilm composition and architecture than the cross-sectional mean flow

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velocities (Biggs et al., 1998a), we focused on the turbulent kinetic energy (TKE) of the flow.

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TKE accounts for the intensity of turbulent velocity fluctuations in all three spatial

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dimensions around a time-averaged mean. These fluctuations are even stronger in the vicinity

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of the streambed and are thus of major importance when considering ecological processes in

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biofilms (Hart and Finelly, 1999; Statzner et al., 1988).

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The measured velocity time series were loaded in MATLAB (R2013b, The

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MathWorks, Inc., Natick, MA, USA) and filtered according to Koca et al. (2017). Finally we

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performed a coordinate rotation, forcing mean values of the vertical and transversal velocity

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component to zero to account for any misalignments of the probe head. Turbulent kinetic

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energy was estimated from the velocity variance in all three velocity components (u, v, w) as:

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1 𝑇𝐾𝐸 = (𝑢'2 + 𝑣'2 + 𝑤'2) 2

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where the overbar denotes temporal averaging and the prime denotes deviations of

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instantaneous velocities from their mean value. The velocity variances were estimated by

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integration of velocity power spectra after excluding noise dominated frequencies and

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extrapolating the spectral part where the slope of the power spectral density was constant.

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2.3. Handling and microscopy of epilithic biofilms

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A macro-photograph of each stone was taken where a laser point highlighted the location of

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the Vectrino sampling volume on the stone’s surface. The measured spot on the stone’s

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surface was edged with the scalpel, to recover it for examination by confocal laser scanning 9

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microscopy (CLSM). For fixation, gravel and small cobbles were placed in formaldehyde

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(3.7% final concentration) for 2 h. Until microscopic observation, samples were stored in

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sterile filtered stream water at 4°C in the dark.

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Epilithic biofilms were observed using a TCS SP5X CLSM (Leica, Heidelberg,

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Germany). The upright system was equipped with a white laser and controlled by the software

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LAS-AF 2.7.3. Due to the three dimensional topography of the stone surfaces, a long working

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distance water-immersible objective lens (25x NA 0.95) was used. For visualization of matrix

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glycoconjugates, biofilms were incubated with Aleuria aurantia lectin (AAL, Vector

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Laboratories, Burlingame, CA, USA; called hereafter AAL-specific glycoconjugates) labelled

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with ALEXA Fluor 568® (Molecular Probes, Eugene, OR USA) at a final concentration of

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0.1 mg mL-1 for 30 min in the dark (Neu et al., 2001). Excessive stain was removed by

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washing the sample carefully with tap water. Then biofilms were counterstained with SYBR

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Green I nucleic acid stain (final concentration 1 µl mL-1, Molecular Probes, Eugene, OR,

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USA) for 1 min to visualize bacteria. In order to prevent crosstalk between channels, the

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emission signals of fluorescent dyes and autofluorescence of phototrophs were recorded

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sequentially. SYBR Green I (excitation 490 nm, emission 505-560 nm) and Chlorophyll a and

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b (excitation 633 nm, emission 650-720 nm) were recorded in a first scan. The

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autofluorescence of Phycoerythrin / Phycocyanin (excitation 578 nm, emission 590-650 nm)

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and AAL-Alexa 568 (excitation 578 nm, emission 590-650 nm) were recorded in the second

235

scan. A z-stack had a resolution of 1024 pixels × 1024 pixels in xy direction and a step size in

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z direction that varied according to the thickness of the scanned volume from one to four µm,

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realizing a voxel (volume covered by a pixel between consecutive images of the z-stack) size

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of 0.6 µm × 0.6 µm × 1 - 4 µm. Five z-stacks covering an xy area of 625 µm × 625 µm each

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within the spot of interest covering a total area of 1.9 mm2 were taken to cover biofilm

240

heterogeneity.

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2.4. Digital image analyses

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Biovolumes of bacteria, algae, cyanobacteria and AAL-specific glycoconjugates were

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quantified to characterize biofilm composition and to characterize biofilm architecture we

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quantified surface coverage of the biofilm canopy, biofilm thickness, thickness of fluorescent

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biofilm components and porosity. Quantification was done using the freeware Fiji (Fiji

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plugins ‘voxel_counter.java v.366989’ and ‘Analyze Particles’; Schindelin et al., 2012 and

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MATLAB R2015b). To improve the signal to noise ratio, a smoothing filter and manual

249

thresholding for pixel intensity were applied. The manual threshold of a randomly chosen

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subset of 20 z-stacks of one particular season and stream (total of 6 sets) was determined and

251

used to calculate an average threshold that was then applied for all z-stacks in each set.

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Images were binarized before quantification. The co-localization plugin of Fiji was used to

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separate the emission signals of cyanobacteria, algae and AAL-specific glycoconjugates

254

(AALs-GC).

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Besides the quantification of biovolume of each compartment, biovolume data were

256

used to calculate two different ratios namely the ratio of cyanobacteria and algae to bacteria

257

(autotrophs / bacteria) and the ratio of AAL-specific glycoconjugates to all microbes (AALs-

258

GC / microbes) as measures of biofilm functioning.

259

To determine the surface covered by the biofilm canopy of all signals with a

260

fluorescence intensity exceeding the chosen thresholds (hereafter surface coverage, SC), z-

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stacks were merged and were projected in 2D. As for z-stacks, a smoothing filter and manual

262

threshold were applied to 2D images prior to quantification.

263

To further characterize biofilm architecture (biofilm thickness and porosity), z-stacks

264

were imported in the software MATLAB. Biofilm thickness (BT) is determined by the axial

265

extent (i.e., in z direction) between the lowermost and the uppermost detection of any

266

fluorescent signal. Thickness of fluorescent biofilm components (BT-FC) is defined as the

267

axial extent of exclusively fluorescent voxels of microbes and AAL-specific glycoconjugates. 11

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Finally, the ratio between the axial extent of non-fluorescent voxels and the biofilm thickness

269

determines the porosity (POR) of the biofilm. Both thickness measures and porosity are first

270

estimated pixel-wise (i.e., for each pixel in the image) and then averaged for the scanned

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sampling area covered by the biofilm.

272

As measure of filamentousness of the epilithic biofilms, the area covered by

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filamentous morphotypes of bacteria and autotrophs was estimated visually and classified in 5

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categories, namely 1: 0 – 20%, 2: >20 – 40%, 3: >40 – 60 %, 4: >60 – 80 %, 5: >80 – 100%.

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2.5. Statistical analyses

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All statistical analyses were performed in R (3.3.2/2016; R Core Team, Vienna, Austria).

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Data of TKE, biofilm composition and architecture were log-transformed. In order to select

279

environmental factors that contribute to the variability in composition and architecture of the

280

biofilms we used redundancy analyses (RDA, R package ‘vegan’ 2.2-1) in combination with

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stepwise selection. For all RDA, the same number of environmental factors was included in

282

the constrained ordination namely TKE, the ratio of DIN / SRP (DIN.SRP), the five day sum

283

of photosynthetic active radiation of the stream reach (PAR.sum) and water depth (wdepth).

284

These environmental factors were completely independent as observed by the inflation factors

285

(< 20). Only those environmental factors were included in the reduced models that

286

significantly contributed to explain the variability in biofilm composition and architecture.

287

A 2-factorial and 1-factorial analysis of co-variance (ANCOVA) with TKE as

288

covariate and either stream and season or season as environmental factors, respectively, were

289

used to test if TKE significantly affected changes in biofilm parameters and if TKE driven

290

effects differed between streams and seasons. Before applying ANCOVA, outliers, i.e. data

291

points beyond the 1.5× inter quartile range minus the 25th percentile and the 1.5 times inter

292

quartile range plus the 75th percentile, were excluded. The effect of TKE on covered area by

293

filaments of bacteria, cyanobacteria and algae was tested using the Spearman rank correlation. 12

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3. Results

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3.1. Near streambed turbulence

297

In both streams, measurements of near streambed turbulence (TKE) varied over two orders of

298

magnitude ranging from 1.58 × 10-4 to 3.26 × 10-2 m2 s-2 (Table 1). Near streambed flow

299

velocity ranged from 0.035 to 0.685 m s-1 over both streams. Near streambed flow velocity

300

and TKE correlated positively (R2 = 0.63, p < 0.001). In the nutrient poorer Kalte Bode, mean

301

TKE and near streambed flow velocity were lowest in June 2015, which corresponded to the

302

lowest discharge period sampled. In the nutrient richer Selke, mean TKE and near streambed

303

flow velocity were highest in June 2015. Stones were sampled at a depth of 13 to 45 cm in the

304

Kalte Bode and 19 to 47 cm in the Selke (Table 1).

305 306

3.2. Effects of TKE on composition and architecture of stream matured biofilms

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As revealed by redundancy analyses over both streams and stepwise selection of

308

environmental factors, TKE significantly explained the variation in biofilm composition and

309

architecture (Fig. 1a, b). The reduced models significantly (permutation test for RDA for

310

reduced model P < 0.05) explained 11.7% and 8.1% of the variability of composition and

311

architecture of stream matured biofilms, respectively. In both models, TKE correlated more to

312

axis RDA 1 explaining a significant (P < 0.05) fraction of the variability in the datasets of

313

7.0% and 7.8%, respectively.

314

To test whether TKE was of similar importance in the two streams differing in

315

dissolved nutrients, constrained ordinations were independently performed for each stream. In

316

the nutrient poorer Kalte Bode, the DIN / SRP ratio, sum of PAR and water depth were more

317

important in explaining the variability in composition and architecture of stream matured

318

biofilms (Fig. 1c, d). DIN / SRP ratio and sum of PAR indicated seasonal trends. The reduced

319

models significantly (P < 0.01) explained 28.1% and 21.3% of the variability in composition 13

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and architecture of stream matured biofilms. A different pattern was observed for the nutrient

321

richer Selke. Here, TKE significantly (P < 0.05) explained 11.7% and 3.2% of the variability

322

in composition and architecture of stream matured biofilms (Fig. 1e, f). Besides TKE, the

323

ratio of DIN / SRP contributed significantly to explain 8.4% and 11.1% of the variability in

324

the datasets.

325

Fig. 2 shows examples of epilithic stream matured biofilms. Biofilms were built up by

326

coccoid and filamentous bacteria, cyanobacteria and algae and adnate and prostrate diatoms.

327

Bacteria were found to colonize algal filaments and diatoms. Stalked peritrich ciliates were

328

also detected in some images. AAL-specific glycoconjugates were observed surrounding

329

cyanobacterial and algal cells and colonies of cells or as net-like structures. In general,

330

biofilms were autotrophic, where algae and cyanobacteria made up 40% of the visualized

331

biofilm biovolume and 10% was made up by bacteria (means of all samples). Approximately

332

50% of the visualized biofilm biovolume was made up of AAL-specific glycoconjugates.

333

Biofilm thickness of stream matured biofilms did not exceed 250 µm. At low TKE, adnate

334

diatoms contributed to the architecture of stream matured biofilms, while those growth forms

335

were not observed at high TKE. Typical diatoms in stream matured biofilms were Cocconeis

336

spp., which were observed at all turbulence intensities. A typical feature of stream biofilms

337

matured at high TKE were cells laying one by the other covering the stone surface like

338

cobblestone pavement (Fig. 2 b, d).

339 340

3.3. Effects of TKE on individual biofilm parameters

341

Visualized biovolumes ranged (min - max) between 4.9 × 10-3 and 2.5 µm3 bacteria µm-2,

342

1.0 × 10-4 and 2.3 µm3 cyanobacteria µm-2, 8.3 × 10-4 and 8.0 µm3 algae µm-2 and 3.5 × 10-2

343

and 6.4 µm3 AAL-specific glycoconjugates µm-2. Biovolumes of bacteria, cyanobacteria and

344

algae but not that of AAL-specific glycoconjugates were significantly (P < 0.05) affected by

345

TKE as revealed by 2-factorial ANCOVA (Table 3). However, the significant interaction term 14

ACCEPTED MANUSCRIPT 346

indicates different responses of biofilms in the two streams and three seasons. Separating the

347

datasets for each stream indicates that TKE effects were more pronounced in the nutrient

348

richer Selke stream (Table 3). Biovolumes of bacteria, cyanobacteria and AAL-specific

349

glycoconjugates correlated positively with TKE (Fig. 3). Algal biovolume showed seasonally

350

different effects. In spring, algal biovolume was not affected or correlated positively with

351

TKE. However, algal biovolume correlated negatively with TKE in August 2014. Indicated

352

by the significant interaction of season and TKE, AAL-specific glycoconjugates showed

353

seasonally different trends with a positive correlation with TKE in April 2014 and no

354

correlation at the other two seasons. In the nutrient poorer Kalte Bode, biovolumes of

355

bacteria, cyanobacteria, algae and AAL-specific glycoconjugates were not affected by TKE.

356

Similar to the Selke, algal biovolume correlated positively with TKE in both spring seasons

357

(Fig. 3), while this correlation was not significant.

358

The autotrophic / bacteria ratio was not affected by TKE (Table 3, Fig. 4). However,

359

the increased autotrophic / bacteria ratio with TKE in August 2014 was in line with decreased

360

algal biovolume at increased TKE during that season (Fig. 4). The AAL-specific

361

glycoconjugates / microbes ratio, which indicates how much AAL-specific glycoconjugates

362

are produced by microbial biovolume, was significantly affected by TKE (Table 3, Fig. 4).

363

Significantly less AAL-specific glycoconjugates per microbial biovolume with increasing

364

TKE was produced in biofilms at the nutrient poorer Kalte Bode. The same but not significant

365

correlation is obvious in biofilms of the nutrient richer Selke in both spring seasons but not in

366

August 2014.

367

Over both streams, the stone surface area covered by the biofilm canopy varied

368

between 8% and 98%. Biofilm thickness, thickness of fluorescent biofilm components and

369

biofilm porosity ranged between 1.7 - 83.2 µm, 2.0 - 9.7 µm and 0.49 - 0.97, respectively.

370

Among the parameters of biofilm architecture, surface coverage was significantly affected by

371

TKE (Table 3). Significantly more surface area was covered at increased TKE in the nutrient 15

ACCEPTED MANUSCRIPT 372

poorer Kalte Bode, except in June 2015 (Fig. 5). The same pattern was found at the nutrient

373

richer Selke in April 2014. In June 2015, surface coverage was high in all samples at the

374

Selke. Contrastingly, surface coverage was highly variable at low light conditions in August

375

2014. Surprisingly, biofilm thickness, thickness of fluorescent biofilm components and

376

biofilm porosity were not significantly affected by TKE in both streams (Table 3). However,

377

there was a significant effect of season on the three architectural parameters. The seasonal

378

effect is displayed in contrasting trends of biofilm porosity at the nutrient poorer Kalte Bode,

379

which decreased for increasing TKE in both spring seasons but showed no correlation in

380

August 2014. At the nutrient richer Selke, biofilm thickness was positively correlated with

381

TKE in both spring seasons and negatively correlated at low light conditions in August 2014.

382

TKE and area covered by filamentous morphotypes were not correlated (nutrient

383

poorer Kalte Bode: Spearman rho -0.20, P = 0.27, nutrient richer Selke: Spearman rho 0.14, P

384

= 0.44). At the nutrient poorer stream, filaments were found at TKE < 0.01 m2 s-2 (Fig. 6). At

385

the nutrient richer stream, biofilms covered by filamentous morphotypes were sampled also at

386

higher TKE.

387 388

4. Discussion

389

In fluvial ecosystems, improved understanding of physical - biological coupling at interfaces

390

will yield deeper insights into the ecological organization, performance and functioning of

391

microbial biofilms. This study, for the first time, investigated the interaction between near bed

392

turbulence and the composition and architecture of epilithic biofilms matured on cobbles and

393

gravels under natural flow in streams. Besides the complex spatial and temporal dynamics of

394

all environmental factors in the fluvial ecosystems, our results demonstrate the importance of

395

near streambed turbulence in shaping the composition and architecture of epilithic biofilms.

396

Near streambed turbulence (i.e., TKE) has been observed to better correlate with biofilm

397

composition and architecture than mean flow velocity (Biggs et al., 1998a). While the mean 16

ACCEPTED MANUSCRIPT 398

flow velocity behind a rock or other obstacle may be strongly reduced or close to zero, shear

399

forces acting on the biofilm in this zone can be dominated by turbulent eddies advected from

400

the main flow. Therefore, we considered TKE as a more robust measure of the local

401

hydrodynamic forcing that act on epilithic stream biofilms. Our in-stream measurements

402

complement results for heterotrophic and autotrophic, single- and multi-species biofilms

403

grown in reactors, fermenters, heat exchangers, flow cells or flumes at contrasting flow

404

regimes. Biovolume of microbes, the ratio of AAL-specific glycoconjugates / microbial

405

biovolume, area covered by filamentous morphotypes and surface coverage of the biofilm

406

canopy were affected by near streambed turbulence quantified in terms of turbulent kinetic

407

energy. Besides hydrodynamics, the stoichiometry of dissolved nutrients (DIN / SRP ratio)

408

and light availability that varied seasonally and among streams were identified as additional

409

significant predictors of biofilm composition and architecture.

410 411

4.1. Effect of TKE on composition and architecture of stream matured biofilms.

412

In contradiction to our hypothesis, the biovolume of microbes was positively correlated to

413

TKE. Increasing TKE was associated with increasing surface coverage of biofilms but did not

414

affect the thickness of the biofilm and biofilm objects. This discrepancy between our results

415

and those reported in literature may be related to the spatial variability of hydrodynamic

416

conditions typical for mountainous streams exhibiting a heterogeneous streambed roughness.

417

In a broad range of turbulence intensities, algal biomass changed in an unimodal manner,

418

peaking at an intermediate turbulence level, while decreasing towards higher and lower

419

turbulence intensities (Hondzo and Wang, 2002). Changes in biofilm composition and

420

architecture within a smaller range of hydrodynamic conditions may result in contrasting

421

trends. However, in our study TKE varied over two orders of magnitude and biofilm

422

responses should reflect typical conditions present in fluvial ecosystems. Moreover, the

423

biovolume of biofilms at the streambed can be affected by the microtopography of the mineral 17

ACCEPTED MANUSCRIPT 424

surface, i.e. rougher surfaces support larger biofilm biomass (Murdock and Dodds, 2007;

425

Schönborn, 1998). Biofilms grown on ceramic tiles or sandblasted polyurethane resins are

426

thinner and have a lower biomass and density when grown at high flow velocities (Battin et

427

al., 2003; Graba et al., 2013). However, micro-roughness elements such as pits and crevices

428

provide additional surfaces for microbial growth protected from shear forces. In addition,

429

small structures such as shells of the snail Ancylus create microniches with reduced near bed

430

turbulence and influence the distribution of biofilm microbes (Willkomm et al., 2007). While

431

microbial cells need to cope with limited access to nutrients, it is presumed that the mosaic of

432

light intensities created by crevices promote species with different light requirements to

433

coexist, resulting in an overall larger biofilm biomass (Hart and Finelly, 1999; Jørgensen,

434

2001; Steinman, 1992). Taken together, the interaction of surface micro-roughness and near

435

streambed turbulence possibly diminishes effects of hydrodynamic forces at larger spatial

436

scales.

437

Contrasting to previous findings (Battin et al., 2003), our results showed that less

438

AAL-specific glycoconjugates were produced per microbial biovolume with increasing TKE.

439

This discrepancy is based on the different lectins used for staining glycoconjugates in the

440

biofilm matrix. From numerous studies with all commercially available lectins it was found

441

that the AAL lectin is superior as a probe for glycoconjugates in pure culture as well as in

442

environmental studies (e.g., Neu & Kuhlicke, 2017; Staudt et al., 2003). Since the EPS matrix

443

of biofilms produced by diverse microbial communities is polymeric and of complex identity,

444

it still remains somehow intractable in its entirety with existing methods (Neu and Lawrence,

445

2016). Here we used an lectin that specifically binds at N-Acetyllactosamine preferentially of

446

linked Fucose (α-1,6), N-Acetylglucosamine and Fucose (α-1,3) units (Neu et al., 2001).

447

Glycoconjugates are produced mainly by cyanobacteria and algae (Neu and Lawrence, 2016),

448

which made up a major proportion of the stream matured biofilms in the present study.

18

ACCEPTED MANUSCRIPT 449

Microbial growth forms, which shape the architecture of biofilms, developed in

450

response to environmental factors including near streambed turbulence, dissolved nutrients

451

and light conditions. Ripples or ridges that orient their axis along the stream lines of the

452

moving fluid as observed in experimental systems (Battin et al., 2003; Besemer et al., 2007;

453

Neu and Lawrence, 1997), were not observed in the stream matured biofilms. The near

454

streambed turbulence in our studied fluvial systems was turbulent, varying temporally in all

455

spatial dimensions, which probably hampered the establishment of these architectural

456

characteristics. At low TKE, adnate diatoms and filamentous bacteria, cyanobacteria and

457

algae contributed to the architecture of stream matured biofilms. In the nutrient poorer stream,

458

filaments were exclusively present at TKE < 0.01 m2 s-2, while this pattern was not found in

459

the nutrient richer stream. Optimum light and nutrient conditions promote the growth of

460

filamentous algae with specific holdfast organelles also at higher TKE (Steinman & McIntire

461

1987). Hence, hydrodynamic forces modulating filament occurrence seemed to be off-set by

462

light availability and dissolved nutrients.

463

Filamentous morphotypes never exceeded to cover more than 60% of the

464

microscopically observed biofilms. As observed in flume experiments, the effects of

465

hydrodynamics on nascent biofilms attenuate as biofilms mature and the growth of

466

filamentous algal structures modify the flow field by creating an architecture that reduces the

467

hydrodynamic forces acting on biofilms (Battin et al., 2003; Besemer et al., 2007; Borchardt,

468

1996; Stevenson, 1996). However, autotrophic filamentous growth forms can dominate later

469

successional stages of biofilms only in systems of low disturbance frequency (Biggs et al.,

470

1998b). The high and stochastic discharge variability in the studied streams seemed to create

471

habitats unfavorable for the establishment of filamentous structures. While filamentous

472

morphotypes were less relevant, the R strategist Cocconeis spp., a diatom dominating the

473

stream matured biofilms in our study, occurred along the entire range of TKE. Due to their

474

small cell size, high resistance to removal, high immigration rates, high growth rates and low 19

ACCEPTED MANUSCRIPT 475

half saturation coefficients for nutrient uptake, they are expected to dominate biofilms in

476

highly dynamic habitats (Biggs et al., 1998b). Besides Cocconeis sp., biofilms in both streams

477

were typically composed of coccoid cells, cyanobacteria and algae, laying one by the other

478

covering the stone surface like cobblestone pavement at high TKE.

479 480

4.2. Site-specific effects in nutrient poorer versus nutrient richer systems

481

Although dissolved nutrients and ions differed among the two streams studied, the

482

biovolumes of microbes and AAL-specific glycoconjugates as well as architectural features of

483

the biofilms were comparable. However, the importance of the near streambed turbulence on

484

the composition and architecture of the stream matured biofilms differed among both streams.

485

In contrast to our hypothesis, the effects of the near streambed turbulence were overlapping

486

and partially off-set by other interacting environmental factors, such as seasonally varying

487

dissolved nutrients and light availability at the nutrient poorer Kalte Bode. As proposed in

488

previous studies, some algal taxa have strong physiological plasticity to better cope with

489

extreme turbulence, not necessarily resulting in variability in overall biofilm Chlorophyll a

490

(e.g. Biggs and Hickey, 1994). Furthermore, the AAL-specific glycoconjugates surrounding

491

biofilm microbes might help ameliorating environmental stress and, thus, buffering

492

hydrodynamic forces affecting biofilm composition and architecture (Biggs and Hickey,

493

1994; Freeman and Lock, 1995). Proia et al. (2012) emphasized that light and nutrient

494

availability are key environmental factors in structuring biofilm characteristics. Taken

495

together, seasonally varying nutrient and light availability were more important at nutrient

496

poorer conditions in shaping seasonally different biofilms than the near streambed turbulence.

497

An obvious trend at the nutrient poorer Kalte Bode was an increasing surface area

498

covered by the biofilm canopy for increasing TKE, which possibly resulted from increased

499

algal biovolume, a comparable or decreasing biofilm thickness and a lower porosity of stream

500

matured biofilms. This trend indicates that the biofilms became more compact, spreading 20

ACCEPTED MANUSCRIPT 501

uniformly over the mineral surface with a film of more densely packed cells at high TKE.

502

This pattern is consistent with results from controlled experiments where biofilm microbes

503

adjusted their growth to the local streambed turbulence in order to sustain increased drag

504

(Battin et al., 2003; Battin et al., 2007; Beyenal et al., 2004; Biggs and Thomsen, 1995). The

505

larger colonized surface area suggests that microbial cells optimize their access to dissolved

506

nutrients. Hence, both drag forces and mass transfer processes are important under nutrient

507

poorer conditions to shape the architecture of stream matured biofilms.

508

At nutrient richer conditions, the near streambed turbulence was important in

509

modulating the composition and architecture of stream matured biofilms. Besides TKE, also

510

dissolved nutrients and light availability played a role in explaining patterns of the observed

511

biofilms that differed between the three seasons studied. As reported recently, the relative

512

importance of environmental factors structuring biofilms can vary seasonally (Rosemond et

513

al., 2000; Tornés and Sabater, 2010). In the nutrient richer Selke, biovolumes of microbes and

514

AAL-specific glycoconjugates, surface coverage, biofilm thickness and porosity increased

515

with increasing TKE at conditions of higher light availability in spring. This pattern indicates

516

that mass transport controlled biofilm biovolume and was enhanced at high TKE. In contrast

517

to these trends, algal biovolume was negatively correlated. The decrease of the ratio

518

autotrophs / bacteria showed that biofilms shifted towards heterotrophy with increased TKE,

519

indicating light limitation in summer.

520 521 522

5. Conclusions 

This is the first study representing the interaction between near streambed turbulence

523

(TKE) and biofilm composition and architecture measured in situ in two mountainous

524

streams at a comparable scale.

525 526



Despite the complex interplay of environmental factors, such as the stochastic nature of hydrological and hydraulic conditions and seasonal changes in light availability and 21

ACCEPTED MANUSCRIPT 527

water chemistry, near streambed turbulence was important in shaping the composition

528

and architecture of epilithic biofilms matured in mountainous streams.

529



The effects of near streambed turbulence on biofilm composition and architecture

530

were more pronounced in the nutrient richer stream, while in the nutrient poorer

531

stream they were overlapping and partially off-set by other interacting environmental

532

factors, such as seasonally varying dissolved nutrients and light availability.

533



In line with results from controlled experiments, our results demonstrate that stream

534

matured biofilms became more compact in highly turbulent flow. At increasing TKE,

535

biofilms uniformly covered the mineral surface and appeared like cobblestone

536

pavement. In contradiction to controlled experiments in stream side flumes, the

537

temporally and spatially varying near streambed turbulence did not result in the

538

formation of elongated biofilm ripples and streamers.

539



Hydrodynamically driven changes in composition and architecture of stream matured

540

biofilms eventually affect retention and transformation of carbon and nutrients in

541

fluvial ecosystems.

542 543

Acknowledgements

544

We are grateful to P. Portius and his team for technical support, C. Mendoza-Lera, N.

545

Oberhoffner, M. Diener and S. Bauth for field assistance, U. Kuhlicke for carrying out the

546

confocal laser scanning microscopy and C. Seiler and M. Frassl for statistical discussions. The

547

research profited from the TERENO (Terrestrial Environmental Observatories) infrastructure.

548

The project was financed by research grants from the German Research Foundation (WE

549

3545/6-1 and LO 1150/8-1).

550

22

ACCEPTED MANUSCRIPT 551

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ACCEPTED MANUSCRIPT

1 2 3

Fig. 1. Redundancy analyses of biofilm composition and architecture of epilithic biofilms

4

matured in fluvial systems. The composition (a, c, e) and architecture (b, d, f) of stream

5

matured biofilms of (a, b) both streams, (c, d) the nutrient poorer Kalte Bode and (e, f) the

ACCEPTED MANUSCRIPT 6

nutrient richer Selke sampled at three seasons. Biofilm composition includes biovolumes of

7

bacteria, cyanobacteria (cyanos), algae and AAL-specific glycoconjugates of the EPS

8

(AALsGC) and biofilm architecture surface coverage of the biofilm canopy (SC), biofilm

9

thickness (BT), thickness of fluorescent biofilm components (BT-FC) and porosity (POR)

10

Stepwise selection of the constrained model was used to select most important environmental

11

factors that are displayed in the plot. Included environmental factors: TKE, DIN / SRP ratio

12

(DIN.SRP), 5-day sum of photosynthetic active radiation (PAR.sum) and water depth

13

(wdepth). Inserts display relative importance of selected environmental factors. Each data

14

point represents the mean of 5 spots observed per sample.

AC C

EP

TE

D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

1 2

ACCEPTED MANUSCRIPT Fig. 2. CLSM xyz-projections of biofilms matured at contrasting TKE in two different

4

streams. The square image displays a xy-projection in axial direction. The crosshair indicates

5

the plane of side projections in xz (below) and yz directions (right side). TKE measured at the

6

sampling spot is indicated at the lower left side of the images. (a, b) Plane biofilms dominated

7

by Cocconeis of the nutrient poorer Kalte Bode at TKE (a) < 0.01 m2 s-2 and (b) > 0.01 m2 s-2,

8

(c) heterogeneous biofilm composed of Cocconeis, coccoid bacteria and cyanobacteria,

9

filamentous cyanobacteria and diatoms of the nutrient poorer Kalte Bode at TKE < 0.01 m 2 s-

RI PT

3

10

2

11

at TKE > 0.01 m2 s-2, (e) biofilms with bushes of adnate diatoms of the nutrient richer Selke at

12

TKE < 0.01 m2 s-2, (f) plane biofilm of the nutrient richer Selke at TKE > 0.01 m2 s-2. Colour

13

allocations: green – nucleic acid stain, cyan – autofluorescence of cyanobacteria, blue –

14

autofluorescence of Chl a (algae), and red – AAL-specific glycoconjugates of the matrix EPS.

AC C

EP

TE

D

M AN U

SC

, (d) plane biofilms of coccoid bacteria and cyanobacteria of the nutrient poorer Kalte Bode

AC C -2

1

2

Algae 3 -2 log 10 biovolume (µm µm )

EP

AALsGC 3 -2 log10 biovolume (µm µm ) -1

TE

0

D

Cyanobacteria 3 -2 log10 biovolume (µm µm ) -1

SC

-2

-3

-4

1

M AN U

Bacteria 3 -2 log10 biovolume (µm µm ) -2

-3

c)

0

-0.5

-4

-3

-2

RI PT

ACCEPTED MANUSCRIPT

1

a) b)

0

-1

d)

e)

f)

g) h)

0.5

0.0

Apr 2014 Aug 2014 Jun 2015

April 2014 Aug 2014 Jun 2015

-1.0

-4

log10 TKE (m2 s-2)

-3

-2

-1

ACCEPTED MANUSCRIPT Fig. 3. Log10-transformed mean values of biovolumes of bacteria (a, b), cyanobacteria (c, d),

4

algae (e, f) and AAL-specific glyconjugates of the EPS (AALsGC) (g, h) of stream matured

5

biofilms as function of log10-transformed TKE of the nutrient poorer Kalte Bode (a, c, e, g)

6

and the nutrient richer Selke (b, d, f, h) for each season. Lines display linear correlations.

7

Each data point represents the mean of 5 spots observed per sample.

AC C

EP

TE

D

M AN U

SC

RI PT

3

ACCEPTED MANUSCRIPT

Autotrophic / Bacteria log10 biovolume

1.0 0.5

a)

b)

c)

d)

Apr 2014 Aug 2014 Jun 2015

Apr 2014 Aug 2014 Jun 2015

0.0 -0.5 -1.0 -1.5 -2.0

AALsGC / microbes log10 biovolume

2

1

0

-1

-2 -4

-3

-2

-4

-3 2

1

-2

-1

-2

log10 TKE (m s )

2 3

Fig. 4. Log10-transformed ratios of biovolumes of (a, b) autotrophic / bacteria and (c, d) AAL-

4

specific glyconjugates of the EPS / microbes (AAL-specific glycoconjugates / microbes) of

5

stream matured biofilms of the (a, d) nutrient poorer Kalte Bode and (c, d) nutrient richer

6

Selke as function of log10-transformed TKE for each season. Lines display linear correlations.

7

Each data point represents the mean of 5 spots observed per sample.

ACCEPTED MANUSCRIPT 2.4

b)

2.1 1.8 1.5

RI PT

Surface coverage log10 (%)

a)

1.2 0.9

c)

1.8 1.7 1.6 1.5 1.4 1.0

e)

f)

g)

h)

TE

D

0.8 0.6 0.4

EP

Thickness of fluorescent biofilm components log10 (µm)

d)

SC

1.9

M AN U

Biofilm thickness log10 (µm)

2.0

0.2

Biofilm porosity log10

AC C

0.00 -0.03 -0.06 -0.09 -0.12

April 2014 Aug 2014 Jun 2015

Apr 2014 Aug 2014 Jun 2015

-0.15 -0.18 -4

-3

-2

-4

-3 2

1 2

-2

log10 TKE (m s )

-2

-1

ACCEPTED MANUSCRIPT Fig. 5. Log10-transformed mean values of surface coverage by the biofilm canopy (a, b),

4

biofilm thickness (c, d), thickness of biofilm objects (e, f) and biofilm porosity (g, h) as

5

function of log10-transformed TKE of the nutrient poorer Kalte Bode (a, c, e, g) and the

6

nutrient richer Selke (b, d, f, h) for each season. Lines display linear correlations. Each data

7

point represents the mean of 5 spots observed per sample.

AC C

EP

TE

D

M AN U

SC

RI PT

3

ACCEPTED MANUSCRIPT

Area covered by autotrophic filaments (%)

>80 - 100

a)

b)

Apr 2014 Aug 2014 Jun 2015

>60 - 80 >40 - 60 >20 - 40 >0 - 20 0 -4

-3

-2

-4

-3 2

-2

-1

-2

log10 TKE (m s )

1 2 3

Fig. 6. Area covered by filamentous autotrophic microbes as function of log10-transformed

4

TKE of the nutrient poorer Kalte Bode (a) and nutrient richer Selke (b). Each data point

5

represents the median of 5 spots per sample.

1

Table 1. Turbulent kinetic energy (TKE), near streambed flow velocity, discharge, water depth, leaf coverage index (LCI) and sum of

2

photosynthetic active radiation (PAR.sum) at the three seasons and the two streams studied. Discharge data were provided by the Flood Prediction

3

Centre of the state Saxony-Anhalt (http://www.hochwasservorhersage.sachsen-anhalt.de/) measured upstream and downstream the study reaches at

4

Elenda and Meisdorfb, respectively. Stream

Sampling date

Kalte Bode

Selke

5

No. of samples

TKE (m2 s-2) min – max

Near bed flow (m s-1) min – max

28th Apr – 2nd May 2014

11

6.87 × 10-4 – 3.11 × 10-2

0.060 – 0.666

4th – 7th Aug 2014

13

4.29 × 10-4 – 1.68 × 10-2

0.041 – 0.394

8th – 11th June 2015

10

1.58 × 10-4 – 0.82 × 10-2

0.035 – 0.258

8th – 13th Apr 2014

10

7.69 × 10-4 – 1.69 × 10-2

0.060 – 0.384

17th – 22nd Aug 2014

10

7.65 × 10-4 – 0.95 × 10-2

0.074 – 0.550

11th – 14th May 2015

14

11.92 × 10-4 – 3.26 × 10-2

0.108 – 0.685

Qdaily (m3 s-1) mean (min – max) 0.495 a (0.320 - 0.692) 0.486 a (0.395 - 0.649) 0.236 a (0.235 – 0.236) 0.396 b (0.360 - 0.436) 0.664 b (0.487 - 0.954) 0.599 b (0.482 - 0.688)

Water depth (cm) min – max

LCI (%) mean of reach

15 – 43

64

PAR.sum (µmol m-2 s-1) mean of reach of 5-day sum 97,500

13 – 45

46

45,070

16 – 43

65

66,465

19 – 37

66

52,909

20 – 47

22

25,498

20 – 46

40

99,659

1

Table 2. Abiotic parameters and dissolved carbon, nutrient and Chl a concentrations in stream water of the two systems during the one week

2

sampling period. Data are given as mean (SD). T, O2, pH and conductivity (cond) were logged at 15 min interval. Chl a, dissolved carbon and

3

nutrient concentrations were sampled thrice. Concentrations of NO2-N were always below the detection limit of 0.006 mg L-1. Stream

Sampling date

Kalte Bode

28th Apr – 2nd May 2014 4th – 7th Aug 2014 8th – 11th June 2015 8th – 13th Apr 2014 17th – 22nd Aug 2014 11th – 14th May 2015

Selke

4

T (°C) 8.7 (1.0) 12.4 (0.4) 10.0 (0.7 10.1 (1.5) 13.1 (0.9) 12.4 (1.3)

O2 (mg L-1) 11.0 (0.3) 10.0 (0.1) 10.9 (0.2) 10.8 (0.7) 9.7 (0.3) 10.1 (0.7)

pH 7.2 (0.1) 7.1 (0.1) 7.6 (0.1) 8.1 (0.2) 8.1 (0.1) 7.9 (0.2)

Cond (µS cm-1) 101.4 (9.5) 87.3 (2.6) 94.9 (1.2) 431.7(8.9) 402.5 (14.4) 399.4 (13.9)

DOC SRP NO3-N (mg L-1) (µg L-1) (µg L-1) 3.66 (0.05) 3.0 (0.0) 722.0 (35.4) 11.23 (0.06) 3.0 (0.0) 416.7 (7.6) 2.31 (0.55) 3.0 (0.0) 641.3 (12.7) 2.24 (0.10) 31.3 (0.6) 806.3 (8.5) 4.07 (0.07) 42.3 (0.6) 1040.0 (10.0) 2.82 (0.22) 10.0 (0.0) 878.0 (16.5)

NH4-N DIN:SRP (µg L-1) 36.3 (1.5) 295 13.0 (3.5) 167 111.7 (4.5) 293 18.0 (6.9) 31 10.0 (0.0) 29 82.3 (0.6) 112

Chl a (µg L-1) 0.47 (0.06) 0.52 (0.03) 0.55 (0.15) 1.29 (0.21) 2.65 (0.17) 3.74 (0.41)

ACCEPTED MANUSCRIPT 1

Table 3. Results of 2-factorial and 1-factorial ANCOVA with TKE as covariate with stream

2

and season or season as respective environmental factors on biofilm parameters. All data were

3

log10 transformed before the analyses. Significant interactions are shown, while interactions of

4

stream : TKE, season : TKE and stream : season : TKE of the 2-factorial ANCOVA were not

5

significant (P > 0.05). AALsGC – AAL-specific glycoconjucates of the EPS, df – degree of

6

freedom. Significance levels: P < 0.001 ***, P < 0.01 **, P < 0.05 *, P > 0.05 -. 1 factorial ANCOVA

*** *** *** * * -

Ratio autotrophs / bacteria - ** Ratio AALsGC / microbes - *** Surface coverage of biofilm canopy *** Biofilm thickness Thickness of fluorescent biofilm components Porosity

7

2

1

2

-

-

* ** -

60 59 56 54

*** *** *** -

** **

* -

59 60 47

*** * * ***

Season : TKE

-

TKE

Bacteria Cyanobacteria Algae AALsGC

Season

2

Selke

2

1

2

23 21 24 26

*** -

* * **

**

26 25 23 21

*

25 24 22

*** ** ***

-

**

24 23 18

Residuals

1

Season : TKE

Stream : Season

2

TKE

TKE

1

Season

Season

df

Residuals

Biofilm parameter

Stream

Kalte Bode

Residuals

2 factorial ANCOVA

-

***

-

-

58

-

-

-

26

***

-

-

20

-

***

-

-

60 57

* ***

-

-

26 27

***

-

-

27 23