The use of antioxidant enzymes in freshwater biofilms: Temporal variability vs. toxicological responses

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Aquatic Toxicology 136– 137 (2013) 60– 71

Contents lists available at SciVerse ScienceDirect

Aquatic Toxicology

jou rn al hom epage: www.elsev ier .com/ locate /aquatox

he use of antioxidant enzymes in freshwater biofilms:emporal variability vs. toxicological responses

hloé Bonnineaua,∗, Ahmed Tlili b,c, Leslie Faggianoa,ernard Montuelleb,d, Helena Guascha

Institute of Aquatic Ecology, Campus Montilivi, 17071 Girona, SpainIrstea, UR MALY, 5 rue de la Doua, 69626 Villeurbanne Cedex, FranceInstitute of Freshwater Ecology and Inland Fisheries, Alte Fischerhütte 2, Neuglobsow, GermanyINRA – UMR Carrtel, 75 avenue de Corzent, BP 511, 74203 Thonon les Bains, France

a r t i c l e i n f o

rticle history:eceived 8 December 2011eceived in revised form 7 March 2013ccepted 12 March 2013

eywords:atalaseeriphytonxyfluorfenolerance acquisition

a b s t r a c t

This study aims to investigate the potential of antioxidant enzyme activities (AEA) as biomarkers ofoxidative stress in freshwater biofilms. Therefore, biofilms were grown in channels for 38 days and thenexposed to different concentrations (0–150 �g L−1) of the herbicide oxyfluorfen for 5 more weeks. Undercontrol conditions, the AEA of biofilms were found to change throughout time with a significant increasein ascorbate peroxidase (APX) activity during the exponential growth and a more important role of cata-lase (CAT) and glutathione reductase (GR) activities during the slow growth phase. Chronic exposureto oxyfluorfen led to slight variations in AEA, however, the ranges of variability of AEA in controls andexposed communities were similar, highlighting the difficulty of a direct interpretation of AEA values.After 5 weeks of exposure to oxyfluorfen, no clear effects were observed on chl-a concentration or on thecomposition of other pigments suggesting that algal group composition was not affected. Eukaryotic com-munities were structured clearly by toxicant concentration and both eukaryotic and bacterial richnesswere reduced in communities exposed to the highest concentration. In addition, during acute exposuretests performed at the end of the chronic exposure, biofilms chronically exposed to 75 and 150 �g L−1

oxyfluorfen showed a higher CAT activity than controls. Chronic exposure to oxyfluorfen provoked thenstructural changes but also functional changes in the capacity of biofilm CAT activity to respond to a

sudden increase in concentration, suggesting a selection of species with higher antioxidant capacity. Thisstudy highlighted the difficulty of interpretation of AEA values due to their temporal variation and to theabsence of absolute threshold value indicative of oxidative stress induced by contaminants. Nevertheless,the determination of AEA pattern throughout acute exposure test is of high interest to compare oxidativestress levels undergone by different biofilm communities and thus determine their antioxidant capacity.

. Introduction

In freshwater ecosystems, biofilm communities are now rec-gnized as pertinent indicators of perturbations (Sabater anddmiraal, 2005). These complex communities, composed of algae,

acteria, fungi, and protozoa, are embedded in a matrix consti-uted by extra-polymeric substances (EPS). They live attached toifferent types of substrates (cobbles, wood, sand, etc.) and are the

Abbreviations: EPS, extra-polymeric substances; AEA, antioxidant enzyme activ-ties; ROS, reactive oxygen species; CAT, catalase; APX, ascorbate peroxidase; GR,lutathione reductase.∗ Corresponding author at: Universitat de Girona, Facultat de Ciències, Institute ofquatic Ecology, Avinguda Montilivi s/n. 17071 Girona, Spain. Tel.: +34 479818036.

E-mail address: [email protected] (C. Bonnineau).

166-445X/$ – see front matter © 2013 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.aquatox.2013.03.009

© 2013 Elsevier B.V. All rights reserved.

main primary producers in open streams (Romaní, 2010; Stevensonet al., 1996). To assess biofilm status, different structural and func-tional variables are usually determined. They include communitycomposition (mostly of diatoms), photosynthesis, biomass andheterotrophic activity (Sabater et al., 2007; Weitzel, 1979). To com-plete the information given by these indicators, we propose the useof antioxidant enzyme activities (AEA) in biofilms as indicators ofoxidative stress induced by toxicants. In fact antioxidant enzymesparticipate in the regulation of reactive oxygen species (ROS) lev-els to avoid their accumulation and the resulting oxidative stress(Mittler, 2002). Previous studies highlighted the interest of AEA assensitive markers of stress induced by organic and inorganic tox-

icants. Dewez et al. showed that the catalase (CAT) activity was amore sensitive biomarker of fludioxionil toxicity than photosyn-thetic parameters in Scenedesmus obliquus (Dewez et al., 2005).In freshwater biofilms, AEA were found to be more sensitive to

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opper toxicity than photosynthetic parameters (Guasch et al.,010). The present study focused on three important antioxidantnzymes: CAT, ascorbate peroxidase (APX) and glutathione reduc-ase (GR). CAT (EC 1.11.1.6) and APX (EC 1.11.1.11) catalyse theransformation of hydrogen peroxide in water and oxygen mainlyn peroxisomes and chloroplast, respectively (Chelikani et al., 2004;esser, 2006). GR (1.8.1.7) participates in this reaction by regen-rating the cofactor needed by APX (ascorbate-glutathione cycle,ittler, 2002).Since temporal variations affect function and structure of

ommunities strongly, functional biomarkers chosen to reflecterturbations, such as AEA, may also change due to temporalariability. Indeed, biofilms are very dynamic communities inhich changes in biomass due to processes of attachment, col-

nization, exponential growth, senescence and sloughing (Biggs,996) are concomitant to species succession (Hudon and Bourget,981; Peterson and Stevenson, 1990). These processes are linkedo changes in community functioning. For instance, Sabater andomaní (1996) found a higher respiratory activity in youngerather than in mature biofilms from an undisturbed Mediterraneantream. Romaní et al. (2008) also observed that the release ofxtracellular bacterial enzymes allowing organic matter compoundegradation in the EPS matrix was higher at the beginning of theiofilm formation than at the end of colonization. Though, in eco-oxicology, the effect of these temporal variations is reduced bysing same age communities (Clements and Newman, 2002), theemporal variation represents an estimate of the “natural” rangef variation and thus may still be a pertinent scale to interprethe importance of further variations related to disturbances. Sincenzymes are sensitive to different factors (e.g. pH, temperature),hanges in biofilm environmental conditions due to growth arexpected to provoke variations in AEA. To our knowledge, pat-erns of AEA in freshwater biofilms throughout time are unknown.hus, the first aim of this study was to determine the “temporal”ange of variation of AEA of the biofilm. This background informa-ion is essential to interpret the importance of AEA’s variations inesponse to chemical exposure and thus to use AEAs as biomarkersf oxidative stress.

In the present study, temporal changes in AEA of non-exposediofilms were investigated on mature biofilm communities estab-

ished after several weeks of colonization, during their transitionrom an exponential to a slow growth phase. Mature communi-ies were used because ecotoxicological tests are better performedn those communities to test toxicant impact on a fully activeommunity (Clements and Newman, 2002). The study of theemporal variability of AEA in non-exposed communities was com-lemented with more traditional biofilm metrics, such as biomassariables, photosynthetic parameters (Sabater et al., 2007), relativebundance of the different algal groups (based on marker pig-ents; Jeffrey et al., 1997) and some antioxidant pigments (e.g.

arotenoids; Pinto et al., 2003).In the second part of this study, the temporal variability of AEA

bserved in a control situation was compared to the variations ofEA in response to contamination during acute and chronic eco-

oxicological tests. For these tests, the herbicide oxyfluorfen waselected since it is representative of compounds likely to be testedn ecotoxicological tests and it is expected to provoke oxidativetress. Indeed, this diphenyl-ether herbicide inhibits chlorophyll-aiosynthesis and provokes the accumulation in the cytoplasm ofrotoporphyrin IX, a potent photo-sensitizer that generates high

evels of singlet oxygen and so oxidative stress (Aizawa and Brown,999; Duke et al., 1991). Though its use has been recently re-

pproved by the UE, oxyfluorfen exposure has been shown torovoke oxidative stress in algae (Geoffroy et al., 2003; Kunert et al.,985; Sandmann and Böger, 1983) and cyanobacteria (Sheeba et al.,011). Consequently, the European Food Safety Authority (EFSA)

gy 136– 137 (2013) 60– 71 61

pointed out the high risk for algae by this compound as well as theneed for further studies on its potential impact on aquatic orga-nisms (EFSA, 2010). Based on these previous studies, oxyfluorfen isexpected to provoke oxidative stress and so changes in AEA also inbiofilms, but has never been tested. In the present study, changesin AEA after acute and chronic exposure to oxyfluorfen were com-pared to changes in more traditional metrics as described earlier.In addition, after five weeks of exposure, the structure (bacte-rial/eukaryotic diversity, algal composition) and the function (AEAresponse in short-term toxicity tests) of the exposed and controlcommunities were compared to assess whether AEA plays a rolein the selection of more resistant species expected to occur underchronic exposure of a community to a critical level of contaminant.

The objectives of the present study were then:

1. to characterize the pattern of temporal variation of AEA inmature biofilms.

2. to compare the temporal variation of AEA and the toxicologicalvariation provoked by the exposure to oxyfluorfen, a toxicantinducing oxidative stress in mature biofilms.

3. to determine the influence of chronic exposure on the capacityof biofilms to respond to a sudden increase in oxidative stress(induced by oxyfluorfen).

2. Materials and methods

2.1. Microcosm setup

Colonization and exposure were performed in an indoor micro-cosm system consisting of 7 recirculating channels previouslydescribed by Serra et al. (2009a). Biofilms were allowed to col-onize sandblasted glass substrata of 1.4 and 17 cm2 installed inthe bottom of each channel. In each channel, 10 L of dechlorinatedtap water was used as a culture medium and changed 3 times aweek; aquarium pumps allowed water recirculation. At each waterrenewal, phosphate was added to a final nominal concentrationof 30 �g L−1 to avoid nutrient depletion and P limitation. A cool-ing bath maintained the water temperature at 20 ◦C. Once a weekduring the first 5 weeks of colonization, an original inoculum ofbiofilm collected, during the Spring season, from the river Llémana(NE Spain, Serra et al., 2009a) was added to each channel. Light wasprovided by halogen lamps (80–120 �mol photons m−2 s−1) witha light regime of 12 h:12 h light:dark.

After 5 weeks of colonization, on day 38, biofilms were exposedto increasing concentrations of oxyfluorfen (CAS: 42874-03-3) fol-lowing an exponential design (Ricart et al., 2009). Three channelswere used as controls and the remaining 5 channels were exposedto 3, 7.5, 15, 75 or 150 �g L−1 of oxyfluorfen. Oxyfluorfen was addedin each channel from a stock solution at 15 mg L−1 in 2.5% acetoneto obtain 0.025% acetone in each channel, acetone was also addedin a similar amount in control channels. At each water renewal,toxicant and/or acetone (when appropriate) were added to com-pensate for potential degradation of the toxicant and to ensure amaximal exposure.

To characterize the temporal pattern of AEA and to link it withchanges in other biological variables, the 3 control channels weresampled on days 33, 36, 38, 39, 41, 59, 66 and 73. At each samp-ling and from each control channel, 3 samples (each consisting ofthree 1.4 cm2 glass substrata) were collected randomly for AEAmeasurements, 5 samples (1.4 cm2 glass substrata each) for themeasurement of photosynthetic efficiency and 3 samples (1.4 cm2

glass substrata each) for pigment analyses.To determine the variability of AEA due to contaminant expo-

sure, AEA but also photosynthetic efficiency, protein content andwet weight of biofilms from all channels were measured after

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packages were used to perform statistical analyses. The statisti-cal significance for all the analyses was set at p < 0.05. For each

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xposure to oxyfluorfen during 6 h (day 38), 24 h (day 39), 48 hday 41), 3 weeks (day 59), 4 weeks (day 66) and 5 weeks (day 73).

To determine the structural changes in biofilm communitiesfter 5 weeks of exposure to oxyfluorfen, samples were also col-ected for pigment (3 samples of 1.4 cm2 glass substrata each perhannel) and DGGE (1 sample of 17 cm2 glass substrata per chan-el) analyses, just before the start of the exposure (day 38) and 5eeks after the exposure (day 73).

.2. Short-term toxicity tests

To test the capacity of antioxidant response of biofilms acquiredfter 5 weeks of exposure to oxyfluorfen, short-term toxicity tests,ased on AEA, were performed. Biofilms from the different chan-els were exposed to increasing concentrations of oxyfluorfen (0,.5, 15, 75, 150 and 1000 �g L−1) in a microcosm set-up previ-usly described (Bonnineau et al., 2010). Briefly, 9 glass substrataf 1.4 cm2 were used for each concentration. Each glass substratumas incubated in a vial containing 10 ml of colonization medium

nd the corresponding toxicant concentration. Samples were incu-ated under the same conditions as the colonization, using aingle-speed orbital mixer (KS260 Basic, IKA®) to maintain con-tant agitation. After 6 h of exposure, for each concentration andach channel, 3 samples, each consisting of 3 glass substrata, wereollected for AEA measurements.

.3. Biofilm parameters

.3.1. AEASampling, protein extraction (by homogenisation followed by

lass beads disruption) and AEA measurements were performed asescribed previously (Bonnineau et al., 2011). The protein concen-ration was measured in triplicates for each sample by the methodf Bradford (1976) using dye reagent concentrate from Bio-RadBio-Rad Laboratories GmbH, Germany) and bovine serum albumins a standard. The final concentration of protein was then expressedn �g mg−1 of biofilm wet weight.

AEA measurements were performed as previously describedBonnineau et al., 2012) in microtiter plates (UV-Star 96 well plate,reiner®), changes in absorbance were followed using a microtiterlate reader Synergy4 (BioTek®). For all assays, the optimal proteinoncentration was determined using protein amounts between 0.5nd 6.5 �g.

CAT activity was measured spectrophotometrically by followinghe decomposition of H2O2 at 240 nm and 25 ◦C during 2 min (Aebi,984). After determination of the optimal substrate concentra-ion, the 250 �L reaction mixture contained in final concentration0 mM of potassium phosphate buffer (pH 7.0) and 2 �g of proteins.he reaction was started by adding 35 mM of H2O2. CAT activity wasalculated as �mol H2O2 mg prot.−1 min−1 (extinction coefficient,: 0.039 cm2 �mol−1).

Oxidation of sodium ascorbate by APX was measured at 290 nmnd 25 ◦C for 2 min according to Nakano and Asada (1981). Afteretermination of the optimal substrate concentration, the 250 �Leaction mixture contained in final concentration: 80 mM of potas-ium phosphate buffer (pH 7.0), 150 �M of sodium ascorbate and

�g of proteins. The reaction was started by adding 4 mM of H2O2.PX activity was calculated as �mol ascorbate mg prot−1 min−1 (ε:.8 cm2 �mol−1).

The oxidation of NADPH by GR was determined by measuringhe decrease in absorbance at 340 nm and 25 ◦C for 2 min (Schaedle

nd Bassham, 1977). After determination of the optimal cofactorNADPH) concentration, the 200 �L reaction mixture contained innal concentration: 100 mM Tris–HCl (pH 7.5), 1 mM EDTA, 1 mMxidized glutathione and 4 �g of proteins. The reaction was started

gy 136– 137 (2013) 60– 71

by adding 0.25 mM NADPH. GR activity was calculated as �molNADPH mg prot−1 min−1 (ε: 6.22 cm2 �mol−1).

2.3.2. Photosynthetic parametersFor each sample, in vivo photosynthetic efficiency determina-

tion was performed using a PAM (Pulse Amplitude Modulated)fluorometer. For technical reasons, measurements from day 33to 41 were performed using a PhytoPAM (Heinz Walz, Effeltrich,Germany) while a MiniPAM (Heinz Walz, Effeltrich, Germany)was used for measurements from day 59 to day 73. The distancebetween the fibre optics and the sample surface was set at 2 mm.The fluorescence signal was determined by the emitter-detectorunit (PHYTO-EDF). After light acclimation, 5 strong saturatingpulses of light (8000 �mol photons m−2 s−1) were applied to thesamples to obtain the fluorescence signal at the steady-state (F),the maximal fluorescence yield (Fm′) of an actinic-adapted sampleand the minimal fluorescence yield (Fo′). These parameters wereused to calculate the photosynthetic efficiency (Ph. eff. = Fv′/Fm′

with Fv′ = Fm′ − F) following Genty et al. (1989). All calculationswere done using the fluorescence signal recorded at 665 nm andare given as relative units of fluorescence.

2.3.3. Pigment analysis by high pressure liquid chromatography(HPLC)

Samples were stored in 15 mL tubes at −80 ◦C until furtheranalysis. Pigment extraction was performed by ultrasonicationas described by Dorigo et al. (2007). Determination of lipophilicpigment composition of biofilm was performed by HPLC asdescribed by Tlili et al. (2008). The injection volume was 100 �Lof purified biofilm extract and pigments were separated on a4.6 mm × 250 mm column (Waters Spherisorb ODS5 25 �m). Pig-ment identification was done based on their retention time andabsorption spectrum according to the Scientific Committee forOceanic Research (SCOR, Jeffrey et al., 1997). For each sample, therelative abundance (expressed as the percentage of the sum of theareas for all the pigments in the sample) of each pigment was cal-culated. In addition, standard o chlorophyll-a was used to quantifyits concentration in each sample, final concentrations are given in�g cm−2.

2.3.4. DNA extraction – amplification and denaturing gradient gelelectrophoresis (DGGE) analysis

For each sample, biofilm was removed from the glass sub-strata with a cell scraper (Nunc, Wiesbaden, Germany) and putinto a 15 mL tube. Samples were then centrifuged for 30 min at10,000 × g and 4 ◦C to remove the excess of water and stored at−80 ◦C. Nucleic extraction, PCR amplification of eukaryotic 18SrRNA gene fragments and bacterial 16S rRNA gene fragments andtheir DGGE analysis were performed as described by Tlili et al.(2008). Samples collected on day 38 and after 5 weeks of expo-sure were loaded on a same gel to allow comparison betweensamples.

2.4. Statistical analyses

The R software (R Development Core Team, 2008; Ihaka andGentleman, 1996), the ‘ade4’ (Dray and Dufour, 2007), the ‘proxy’(Meyer and Butcha, 2010) and the ‘vegan’ (Oksanen et al., 2010)

sampling time and each biological variable, a mean was calculatedfrom the different samples collected from one channel. These meanvalues (one per channel and per time) were then used in furtheranalyses as independent replicates.

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.4.1. Temporal variationThe temporal variation of biofilms was studied using biofilms

rom the 3 unexposed channels sampled at day 33, 36, 38 (t = 0 hnly), 39, 41, 59, 66 and 73. To determine the different growthhases of biofilms, chlorophyll-a concentration was adjusted tohe sigmoidal model previously proposed by Romaní (2010): chl-

= K/(1 + exp(−r × (day − d0)) where K is the carrying capacitymaximal chlorophyll-a concentration reached), r the growth ratend d0 the time when maximal growth rate is achieved. Differ-nces between colonization time in terms of AEA were estimatedy analysis of variance (ANOVA) and post hoc analysed by a Tukeyest.

To understand the temporal variation of all the biological vari-bles and to determine the importance of differences betweenhe replicated channels, a multivariate approach was used. Two

atrices were constructed (with samples as rows and biologicalariables as columns). The pigment matrix contained the rela-ive abundance of each pigment (normalized using an arcsinequare root transformation) and the function-biomass matrix con-ained different log-transformed variables: AEA, photosyntheticfficiency and biomass variables (chlorophyll-a in �g cm−2, pro-ein in �g mg−1, wet weight in mg). Then, for each matrix, twoetween-PCAs (Principal Component Analysis) were carried out

n which, for each sample, an information factor was added (i.e.he sampling day for the factor time or the channel number forhe factor channel). The percentage of variance explained by oneactor was calculated as the ratio between the sum of the eigen-alues of the between-PCA and the sum of the eigenvalues of theCA (Dolédec and Chessel, 1987; Dray and Dufour, 2007). To mea-ure the concordance between the two matrices (pigment andunction-biomass), a co-inertia analysis was performed. This multi-ariate technique analyses co-structure by maximizing covarianceetween two matrices (Dray et al., 2003a; Dolédec and Chessel,994) and the calculation of the RV-coefficient allowed estimat-

ng the degree of concordance between the matrices (Robert andscoufier, 1976). A Monte-Carlo permutation test on the sum of theigenvalues of the co-inertia analysis was also performed to assesshe significance of the RV-coefficient (Heo and Gabriel, 1998).

.4.2. Oxyfluorfen exposure

.4.2.1. Long-term exposure. AEA, photosynthetic efficiency, pro-ein content and wet weight measured in all channels at days 38t = 6 h), 39, 41, 59, 66 and 73 were used to study the effects ofxyfluorfen exposure on biofilms. To focus on the chemical’s effects,he effect of time was removed by carrying out a within-PCA inhich the mean of the samples in a same group (i.e. collected at

he same time) is substracted to each sample of a group, for eachariable. Thus, the patterns of variation obtained at each samplingime can be compared among them (Dray and Dufour, 2007).

To compare the variability of AEA between control and exposed

iofilms, variances of AEA from all biofilms collected at days 38t = 6 h), 39, 41, 59, 66 and 73 were compared between them.

distance matrix was built based on Euclidean distance and aermutation-based test of multivariate homogeneity of group

able 1hysico-chemical conditions in all channels during colonization (day 0–38) and exposundicated.

Flow (L min−1) T (◦C) Dissolvedoxygen (mg L−1) pH

Colonization 1.45 ± 0.01n = 117

20.5 ± 0.1n = 93

8.88 ± 0.03n = 93

8.58 ± 0n = 93

Exposure 1.42 ± 0.02n = 84

20.2 ± 0.1n = 102

9.26 ± 0.02n = 102

8.64 ± 0n = 102

gy 136– 137 (2013) 60– 71 63

dispersions (variance) was performed, each group correspondingto a concentration of oxyfluorfen (Anderson et al., 2006).

For samples collected on day 38 (just before exposure) and 73 (5weeks after exposure), the relative abundance of pigments betweenthe different treatments was analysed by a PCA. In addition, DGGEprofiles of biofilms from the different channels were compared forpresence or absence of bands by calculating the dissimilarity indexof Jaccard; matrices were then used to perform the average methodof hierarchical cluster analysis (HCA).

2.4.2.2. Short-term toxicity tests. To test the influence of previouschronic exposure to oxyfluorfen on AEA of biofilms exposed to acuteexposure to higher oxyfluorfen concentrations, a two-way ANOVAwas performed. The AEA for which the interaction term of the two-way ANOVA was significant were selected for further analyses. Foreach channel, a one-way ANOVA followed by a Tukey test, as a posthoc analysis, were performed on these AEA to reveal the differencesbetween samples after acute exposure to different concentrationsof oxyfluorfen.

3. Results

3.1. Biofilm colonization

Physical and chemical conditions were stable during all theexperiment although small differences were observed between thetwo periods (Table 1). Since the water used during this experi-ment has previously been characterized by Serra et al. (2009b)for NO3 (1.68 ± 0.14 mg L−1), NO2 (0.07 ± 0.01 mg L−1) and NH4(<0.1 mg L−1) among others (n = 20 for all), only phosphorus con-centration was measured (Table 1). Total phosphorus depletionwas not observed neither during colonization nor during exposureperiods.

3.2. Changes in unexposed biofilms throughout time

3.2.1. Algal growthChlorophyll-a concentration in unexposed biofilms increased

during the 10 weeks of the experiment and could be successfullyadjusted to a sigmoidal growth curve (Fig. 1) with K = 17.2 ± 4.2 �gchlorophyll-a cm−2 (p < 0.05), d0 = 43.6 ± 7.8 days (p < 0.05) andr = 0.10 ± 0.07 day−1 (p < 0.2), all parameters are presented withtheir corresponding standard errors and their p-value for the t-statistic. Since the transition from the exponential growth phase tothe slow growth phase is observed at the inflexion point, i.e. at dayd0, biofilms collected between day 33 and 41 of colonization werein exponential growth phase while samples collected between 59and 73 days were in slow growth phase. The loss phase was notreached during this experiment as indicated by the d0 values andthe continuous increase of biomass along the experiment (Fig. 1).

3.2.2. Temporal variations in AEATemporal variations in AEA were observed in unexposed

biofilms (Fig. 2). Although CAT and GR activities seemed to increase

re period (day 39–73). For each parameter mean values and standards errors are

Cond. (�S cm−1) P concentration (�g L−1)

Before water changes After water changes

.03 418 ± 4n = 93

22.5 ± 2.1n = 42

5.6 ± 0.9n = 42

.04 404 ± 5n = 102

7.5 ± 0.6n = 54

8.9 ± 1.3n = 54

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Fig. 3. Temporal changes in unexposed biofilms. Ordination of the samples fromthe three control channels by the co-inertia analysis. Each sample is represented byone arrow on which the sampling day and the channel number (c1, c2 or c3) areindicated in a box. The black dots (beginning of the arrows) represent the samples

ig. 1. Chlorophyll-a concentration (�g cm−2) in unexposed biofilms throughoutime. Triangles correspond to the means (and standard errors) of three channels.he plain line shows the sigmoidal growth model fitted to the data.

hroughout time, these differences were only slightly significant0.05 < p < 0.1). The maximum CAT and GR activities were observedt day 59 and correspond to an increase by 142 ± 54 and 163 ± 16%f activities at day 33, respectively. APX activities from biofilms col-ected at days 39 and 41 were significantly higher (by 142 ± 20 and38 ± 47%, respectively) than APX activity of biofilms from day 33F = 4.7, p < 0.05).

.2.3. Temporal variations in the whole biofilmResults of the two between-PCAs showed that the factor time

xplained 48.5% of the total variance in the pigment matrix (rel-tive abundance of pigments) and 56.7% of the total variance inhe function-biomass matrix (AEA, photosynthetic and biomassariables) highlighting a high temporal variability for all biolog-cal variables. The inter-channel variability was also estimatednd results of the between-PCAs showed that the factor channelxplained 12.0% and 11.9% of the total variance in the pigment andunction-biomass matrices, respectively.

As a result of the co-inertia analyses on the pigment andiomass-function matrices, a significant RV coefficient of 39.0% wasbtained (permutation test, p < 0.05). A high coefficient indicatesimultaneous variations (either positive or negative) of the two setsf variables while a low coefficient indicates independent varia-ions (Dray et al., 2003b). Thus, this result indicated a good degreef concordance between the two matrices. The separation of theamples was strongly driven by the axis 1, which explained 64.6%

f the variance while the axis 2 explained 24.6%. Samples from day3 to 41 were separated from those from day 59 to 73 along therst axis, whereas the second axis accounted for variability withinach group of samples (Fig. 3).

ig. 2. Temporal changes in AEA of unexposed biofilms. Symbols correspond to theeans (and standard errors) of CAT (�, black plain line), GR (�, black dashed line) andPX (�, grey line) activities in biofilms from the three control channels throughout

ime.

which have been ordinated by the PCA performed on the pigment matrix while thetops of the arrows represent the samples which have been ordinated by the PCAperformed on the function-biomass matrix.

Biofilms from day 33 to 41 were characterized by a higherprotein concentration and a higher APX activity (Fig. 4); these bio-logical variables were associated with a higher relative abundanceof the pigments diadinochrome II, chlorophyll c and violaxanthin(Fig. 4). Various pigments (lutein, �,�-carotene, chlorophyll b, dia-toxanthin) were characterized by low scores (<|0.06|) on the axis1 of the co-inertia indicating a low variability of these pigments inbiofilms along this axis (Fig. 4). Biofilms from day 59 to 73 werethen characterized by a higher biomass (chl-a and wet weight)and higher CAT and GR activities (Fig. 4); these biological vari-ables were also associated with a higher relative abundance ofchlorophyll-a, carotenoid P468, zeaxanthin, and antheraxanthin(Fig. 4). Variability in biofilms from day 33 to 41 was higher alongthe second axis than in older biofilms. In addition, samples from day33 to 41 were distributed chronologically along the second axis, thetime being negatively correlated with this axis (Fig. 3). Inside thisgroup of samples, the oldest ones (day 39–41) were characterized

by higher AEA (Fig. 4), which were associated with higher rela-tive abundance of chlorophyll b, lutein, �,�-carotene, neoxanthin(Fig. 4). The youngest biofilm samples (day 33) were characterized

Table 2List of the 18 identified pigments included in multivariate analyses of unexposedand exposed biofilms. Code and corresponding pigment name.

Code Pigment name Code Pigment name

ANT Antheraxanthin DIAT DiatoxanthinbbCAR �,�-Carotene FUC FucoxanthinCAR Carotenoid P468 LUT LuteinCHLa Chlorophyll-a NEO NeoxanthinCHLb Chlorophyll b PHEPa Pheophytin aCHLc Chlorophyll c PHERa Pheophorbide aDIAD Diadinoxanthin tNEO Trans-neoxanthinDIADcI Diadinochrome I VIO ViolaxanthinDIADcII Diadinochrome II ZEA Zeaxanthin

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Fig. 4. Temporal changes in unexposed biofilms. Normed coefficients of the different variables on the axes of the co-inertia analyses shown in Fig. 3. As a result of thec matrixo efficiep is give

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o-inertia analysis, the samples were ordinated by the PCA on the function-biomass

f the variables of the function-biomass matrix (Ph. eff. stands for photosynthetic

igment matrix, the correspondence between pigment codes and complete names

y higher wet weight and protein concentration (Fig. 4) and alsoy a higher relative abundance of diadinochrome I and II, fucoxan-hin and chlorophyll c (Fig. 4). The scores of each variable on theo-inertia axes are available in the supporting information (Tables1 and A2).

Supplementary data associated with this article can beound, in the online version, at http://dx.doi.org/10.1016/j.aquatox.013.03.009.

.3. Changes in biofilms during oxyfluorfen exposure

Throughout the 5 weeks of exposure, variations were observedainly in AEA and photosynthetic efficiency (Fig. 5). While time

xplained about half of the variance (intra-group variance: 49.8%),nly 7.4% of the total variance could be attributed to oxyfluor-en (result of between-PCA with factor oxyfluorfen). The first twoxes of the within-PCA explained 26.2% of the variance (wPCA1:3.1%, wPCA2: 11.8%). The pattern of biological response changedhroughout exposure and was never concentration-dependent, butome general trends could be observed (Fig. 5). Samples exposed to5 and 150 �g L−1 were mainly distributed along axis 1 which wasegatively correlated with APX activity and positively correlatedith photosynthetic efficiency and CAT activity mainly (Fig. 5).

amples exposed to 3 to 15 �g L−1 were mainly distributed alonghe axis 2 which was negatively correlated with GR and APX activ-ties mainly (Fig. 5).

The difficulty to observe clear concentration-response pat-erns can be due to the high variability of AEA throughout timebserved in all treatments. Indeed, during the 5 weeks of expo-ure, CAT activities fluctuated in the range of 102.2–197.1 (control)nd 87.5–274.3 (exposed) �mol H2O2 mg prot.−1 min−1, APXctivities in the range of 0.369–1.120 (control) and 0.272–1.136

exposed) �mol ascorbate mg prot−1 min−1 and GR activities inhe range of 124.4–265.9 (control) and 95.7–330.2 (exposed) �MADPH mg prot−1 min−1. Though lower and higher activities werebserved in exposed biofilms than in control ones, the variances

or by the one on the pigment matrix. The left graph shows the normed coefficientsncy). The graph on the right shows the normed coefficients of the variables of then in Table 2.

of AEA of biofilms control and exposed to different oxyfluorfenconcentrations were not significantly different (F = 1.68, p > 0.05,n = 999 permutations), indicating that AEA’s ranges of variation incontrol and exposed communities throughout time were similar.Despite of these similar ranges of variation, differences betweencontrol and exposed were observed in AEA after 5 weeks of expo-sure, the two-way ANOVA reveals a significant effect of chronicexposure to oxyfluorfen on CAT, APX and GR activities (Table 3).

3.4. Changes in community structure and function after 5 weeksof chronic exposure

3.4.1. Pigment compositionThe first two axes of the PCA explained 68.6% of the variance

(PCA1: 39.7%, PCA2: 28.8%). Most of the differences between sam-ples were due to the effect of time as observed previously. Indeed,the axis 2 clearly separated samples from day 38 (just before expo-sure) from those sampled on day 73, after 5 weeks of exposure. Thesamples exposed to oxyfluorfen were not separated from the con-trols, indicating that pigment composition was not influenced byoxyfluorfen exposure (Fig. 6).

3.4.2. Eukaryotic community structureConcerning the eukaryotic diversity, 36 bands were detected

in the biofilm sample as a whole. In samples from the beginningof the exposure (t = 0 h) the number of bands detected was 16 or17, whereas after 5 weeks of exposure (t = 73 days), this numberranged from 23 to 27 (average: 25). Biofilm exposed to 150 �g L−1

of oxyfluorfen had the lowest number of bands detected (23) within73 days old samples. The cluster analysis, performed using the pres-ence/absence of bands within each sample, allowed six groups to beseparated (Fig. 7A). First, samples collected just before the exposure

(t = 0 h) formed a group with very similar eukaryotic communityand were clearly different from the samples collected after fiveweeks of exposure (t = 73 days). Within the chronically-exposedsamples, five groups with similar eukaryotic community were

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Fig. 5. Effects of oxyfluorfen on biofilms throughout time: results of the within-PCA performed on control and exposed biofilms collected at days 38 (t = 6 h), 39, 41, 59, 66,73. On the left graph, the factorial maps represent the ordination of the samples along the first two axes of the within-PCA. Each exposed sample is represented by a label onwhich the oxyfluorfen concentration is indicated while the three control samples are represented by an ellipse the centre of which is indicated by the control label (0). Eachfactorial map correspond to a different sampling day and so to a different time of exposure (both are indicated on the bottom left angle of each map, wks stands for weeks).On the right graph the normed coefficients of the variables on the first two axes of the within-PCA are represented, Ph. eff. stands for photosynthetic efficiency.

Table 3F and p-value from the two-way ANOVA analysis of biofilms’ AEA as influenced by oxyfluorfen concentration during chronic exposure (i.e. exposure to 0, 3, 7.5, 15, 75 or150 �g L−1 during 5 weeks) and acute exposure (i.e. exposure to 0, 1.5, 15, 75, 150, 1000 �g L−1 during 6 h).

Source CAT APX GR

F p-value F p-value F p-value

Chronic exposure to oxyfluorfen 7.37 <0.05 5.44 <0.05 7.58 <0.05Acute exposure to oxyfluorfen 3.05 <0.05 1.32 >0.05 0.67 >0.05Chronic × acute 4.39 <0.05 2.04 <0.05 0.86 >0.05

Values in bold indicate significant result (p < 0.05).

Fig. 6. Results of the PCA performed on the pigment composition of biofilms collected on day 38 (before exposure) and 73 (after 5 weeks of exposure). The left graph showsthe ordination of the samples, the black plain dots correspond to biofilms collected on day 38, just before exposure, while the black circle corresponds to biofilms collectedon day 73, after 5 weeks of exposure. Next to each dot the oxyfluorfen concentration to which the sample will be (for the black dots) or has been (for the circle) exposed isindicated. The right graph shows the normed coefficient of the variables on the two first axes of the PCA, the correspondence between pigment codes and complete namesis given on Table 2.

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C. Bonnineau et al. / Aquatic Toxicology 136– 137 (2013) 60– 71 67

Fig. 7. Cluster analysis of the biofilms eukaryotic (A) and bacterial (B) community structure before exposure (day 38) and after 5 weeks of exposure (day = 73) to differentconcentrations of oxyfluorfen. For each clustering, the bar plots of the node heights used to determine the number of groups are shown. t = 0 h indicates biofilms sampled ond r five

0

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ay 38 just before exposure and t = 5wks indicates biofilms sampled on day 73 aftec correspond to control.

bserved. The first one contained samples exposed to 150 �g L−1

f oxyfluorfen, the second one the samples exposed to 75 �g L−1, third group was formed by the controls, a fourth one by samplesxposed to 3 �g L−1 and a last group by samples exposed to 7.5 and5 �g L−1 (Fig. 7A).

.4.3. Bacterial community structureConcerning the bacterial diversity, 54 bands were detected in

he biofilm sample as a whole. In samples from the beginning of thexposure (t = 0 h) the number of bands detected ranged from 25 to7 (average: 26), whereas after 5 weeks of exposure (t = 73 days)his number ranged from 30 to 35 (average: 32). Biofilms exposed to50 �g L−1 of oxyfluorfen had the lowest number of bands detected30) within 73 days old samples. The cluster analysis allowed threeroups to be separated (Fig. 7B). First, samples from t = 0 h werelearly separated from chronically-exposed samples; then, in thisormer group, biofilms exposed to 15 and 75 �g L−1 of oxyfluorfenere separated from the other ones (Fig. 7B).

.4.4. Antioxidant enzymes activitiesAfter 5 weeks of chronic exposure, biofilms from all channels

ere exposed during 6 h to a wider range of oxyfluorfen concentra-ions during an acute exposure test. The two-way ANOVA showed

main effect of the chronic exposure on all the AEA tested and aain effect of the following acute exposure on CAT activity but not

n APX and GR. In addition, the interaction term between acutend chronic exposure was significant for CAT and APX activitiesTable 3). This last result suggests an influence of chronic exposure

n the CAT and APX response of biofilms to further acute exposureo oxyfluorfen.

CAT activities of biofilms from the different channels (chron-cally exposed and controls) not exposed to oxyfluorfen during

weeks of exposure. For each sample oxyfluorfen concentration is indicated; 0a, 0b,

the short-term ecotoxicological tests were not significantly differ-ent (F = 1.33, p > 0.05) whereas the response patterns throughoutthe acute oxyfluorfen gradient differed between biofilms chron-ically exposed to different concentrations of oxyfluorfen (Fig. 8).After acute exposure, CAT activity of control biofilms (not exposedto chronic contamination by oxyfluorfen) showed an unimodalresponse throughout oxyfluorfen gradient with a maximum ofactivity reached after exposure to 15 �g L−1. Indeed, CAT activ-ity increased by 61.0 ± 5.8, 33.2 ± 4.5 and 21.5 ± 4.7% in controlbiofilms exposed at 15 �g L−1, compared to non-exposed com-munities (Fig. 8). In communities chronically exposed to 3, 7.5and 15 �g L−1, no differences were found between communitiesexposed 6 h to oxyfluorfen and non-exposed ones (Fig. 8). Inbiofilms chronically exposed to 75 �g L−1, CAT activity increasedsignificantly by 27.1 ± 6.2% after acute exposure to 1000 �g L−1.In communities chronically exposed to 150 �g L−1, CAT activityincreased significantly by 75.6 ± 4.2, 91.0 ± 17.2, 88.3 ± 8.5 and129.6 ± 10.5% after acute exposure to 15, 75, 150 and 1000 �g L−1

of oxyfluorfen, respectively (Fig. 8). Consequently, after acute expo-sure to 1000 �g L−1 of oxyfluorfen, biofilms chronically exposed to150 �g L−1 presented a CAT activity 2.5 times significantly higherthan biofilms not chronically exposed (F = 55.47, p < 0.05).

Concerning APX activity, no significant differences wereobserved throughout the gradient of acute exposure in controlbiofilms and in communities chronically exposed to 3, 7.5, 75 and150 �g L−1 of oxyfluorfen. Only in biofilms chronically exposed to15 �g L−1 of oxyfluorfen, a significant decrease (by 43.6 ± 7.0%) inAPX activity was observed after acute exposure to 1.5 �g L−1 of

oxyfluorfen (supporting information, Fig. A1).

Supplementary data associated with this article can befound, in the online version, at http://dx.doi.org/10.1016/j.aquatox.2013.03.009.

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Fig. 8. Acute toxicity test: CAT activity of biofilms after 6 h of exposure to oxyfluorfen. Each line corresponds to the response of biofilms collected from a same channel.The black square (�) indicated the response of the 3 channels non-exposed to oxyfluorfen during the 5 weeks while the other symbols indicates the response of biofilmsc fen. Fos ng the

4

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hronically exposed to 3 (�), 7.5 (�), 15 (�), 75 (�) or 150 (�) �g L−1 of oxyfluorignificantly different from the one of this biofilm non-exposed to oxyfluorfen duri

. Discussion

.1. Temporal variations in unexposed biofilms

The use of microcosms to study ecosystems under controlledonditions is common in aquatic ecology (Taub, 1997). The sys-em presented here (recirculating channels) allowed active andomplex biofilm communities to be maintained during 10 weeksith a good reproducibility since the factor channel accounted for

ess than 12% of the variance in unexposed biofilms (Giddings andddlemon, 1979).

Temporal variations of biological variables of unexposediofilms revealed both structural and functional differencesetween the exponential (day 33 to 41) and slow-growth phasesday 59–73). Moreover, a higher variability was found within expo-entially growing biofilms than within slow-growing biofilms.

ndeed, algal richness in biofilms has been observed to increaseuickly during the first days of the colonization before reaching

stable value (Hillebrand and Sommer, 2000; Szabó et al., 2008).esemer et al. (2007) also observed a decrease in Operational Taxo-omic Units turnover (i.e. the appearance of new bacterial species)hroughout time during biofilm colonization in a mesocosm exper-ment. Sabater and Romaní (1996) reported a sharp increase inacterial densities and ectoenzymes activities in the first 5 daysf a 43 days colonization sequence in a shaded stream. There-ore, our results and those reported in the literature suggest thathe changes occurring during biofilm development may be fastern exponentially growing biofilms due to the higher growth rate

hile communities in the slow-growth phase may have reached ateady-state and thus may be more stable.

As expected, older biofilms were characterized by a higheriomass (chlorophyll-a and wet weight) than exponentially grow-

ng biofilms (Romaní and Sabater, 1999). However, the proportionf proteins in total biofilm biomass decreased throughout time,ndicating temporal changes in biofilm composition. As biofilmges, it becomes thicker and the proportion of molecules differentrom proteins (e.g. polysaccharides from EPS matrix) may increaseFernandes da Silva et al., 2008).

The shift from the exponential to the slow growth phase was alsoharacterized by changes in biofilm composition. The complexity

f the communities increased with time as shown by the higheracterial and eukaryotic richness at the end of the experiment, inccordance with previous studies (Hillebrand and Sommer, 2000;ear et al., 2008; Sabater and Romaní, 1996). Temporal variations

r each type of biofilm (control or chronically exposed), * indicates a CAT activity acute test.

in pigment composition during exponential growth suggested adecrease in the proportion of diatoms (fucoxanthin, diadinochromeI, chlorophyll c) and an increase in the proportion of green algae(chlorophyll b, lutein, �,�-carotene, neoxanthin) throughout time.Moreover, slow-growing biofilms were characterized by a higherproportion of cyanobacteria (zeaxanthin). These results indicate aclassic succession in biofilms in agreement with previous observa-tions (Biggs, 1996).

A temporal shift in the pool of antioxidant enzymes used foroxidative stress management was also observed in biofilms. Onthe one hand, APX seemed to play a more important role in expo-nentially than in slow growing biofilms with a significant peak ofactivity observed at the end of the exponential growth phase. Onthe other hand, though CAT and GR activities seemed to be inde-pendent from time, these enzymes may have a more important roleduring the slow-growth phase due to the reduction in APX activity.Temporal variations of AEA may be explained by both inter-specificvariations and changes in the biofilm micro-environment. In fact,these two aspects are likely to be linked as changes in micro-environment may favour the species with the most appropriateAEA pattern. For instance, communities dominated by diatoms arelikely to have a low CAT activity as the occurrence of CAT in diatomshas rarely been observed (Branco et al., 2010; Wilhelm et al., 2006;Winkler and Stabenau, 1995). Besides, as biofilm ages it becomesthicker and marked gradients of light and oxygen can be observed.Therefore, the upper layers of biofilm are characterized by oxy-gen supersaturation resulting from a high photosynthetic activitywhile in the bottom layers, where light can be strongly attenuated,photosynthesis and oxygen concentrations are reduced (Carltonand Wetzel, 1987; Dodds, 1989). These changes may create micro-zones with different levels of oxidative stress. The balance betweenthese zones may then determine the oxidative stress level of thewhole community. Since on the first sampling of this study, colo-nization was already on the late exponential phase, a high oxygenconcentration and pH may be expected within biofilms. Thereforethe balance between the different zones may point towards a highlevel of oxidative stress. In this case, the stability of AEA (especiallyof GR and CAT) may reflect a saturation of the antioxidant defencesystem.

4.2. Variations due to oxyfluorfen exposure

On contrary to previous observations on green algae (Geoffroyet al., 2003; Kunert and Böger, 1981; Sandmann and Böger, 1983),

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xyfluorfen did not provoke any clear decrease in chlorophyll-aoncentration nor in photosynthetic efficiency in exposed biofilmst the concentrations tested (3–150 �g L−1). These lower effectsbserved on biofilms may be due to the lower diffusion of oxyflu-rfen through the protective layer of EPS as observed for otherolecules (Franz et al., 2008; Guasch et al., 2003). Further investi-

ation including exposure to higher oxyfluorfen concentrations andetermination of oxyfluorfen concentration within the EPS matrixay help elucidating this point.Chronic exposure to 3–150 �g L−1 of oxyfluorfen provoked

light variations in biofilms’ AEA. The activation of one ornother antioxidant enzyme to cope with oxidative stress inducedy oxyfluorfen seemed concentration dependent but not timeependent. Indeed, exposure to low oxyfluorfen concentration3–15 �g L−1) led to an increase in APX, whereas exposure to higheroncentration (75 and 150 �g L−1) stimulated CAT activity. Thisesult may be related to the higher affinity of APX for hydrogeneroxide. Hence, APX is expected to be activated at lower levels ofOS than CAT (Mittler, 2002). This result may be specific to biofilmommunities as Geoffroy et al. (2002, 2003) observed an activa-ion of CAT at lower oxyfluoren concentrations (7.5–22.5 �g L−1)han APX (15–22.5 �g L−1) in Scenedesmus obliquus exposed to thisoxicant.

Although the set of functional and structural biofilm biomarkerseasured throughout exposure was slightly affected by oxyflu-

rfen, the communities were strongly structured after 5 weeksf exposure, as shown by the clustering of the DGGE profiles.xyfluorfen was expected to affect mainly the eukaryotic commu-ity due to its direct effect on chlorophyll-a biosynthesis. Indeed,he eukaryotic community was structured in a concentration-ependent manner by oxyfluorfen. These changes could not bettributed to changes in the relative proportion of algal groups,s indicated by the lack of effects on pigments. It suggests thatpecies resistant to oxyfluorfen were likely to be found in all algalroups. In addition, the highest concentration of oxyfluorfen had aegative impact on algae and bacteria, as indicated by the low-st bacterial and eukaryotic richness observed. This result alsoointed out the potential indirect effect of oxyfluorfen on the bac-erial community in biofilms. In agreement with this idea, Ricartt al. (2009) described how toxic exposure of biofilms to a pho-osynthesis inhibitor (the herbicide diuron) indirectly affected theacterial community. In this study, the interaction of bacteria withhe potential primary target organisms was the basis of the chain offfects that diuron caused on biofilms. Another study highlightedhe importance of the bacterial-algal link in fluvial biofilms byssessing the ecotoxicological effects of the bactericide triclosann these communities. Though triclosan directly damaged the het-rotrophic compartment, the algal component was also affected,nd it was attributed to the common use of space and resourcesithin the biofilm (Ricart et al., 2010). These examples and our

esults emphasize the importance of indirect effects of pollutantsn the aquatic system. Indeed, the toxicity of chemicals is not sim-ly a consequence of their direct toxic effect, but it might extendo other trophic levels.

The structural changes observed in biofilm communities afterhronic exposure to oxyfluorfen were linked to an enhance-ent of the CAT capacity to answer to higher levels of oxidative

tress caused by this same toxicant. Therefore chronically-exposediofilms may better tolerate oxidative stress at intermediatexyfluorfen concentrations and may be able to cope with higherevels of oxidative stress than control biofilms. Differences in thebility to reduce oxidative damage may, for instance, be due to

arious mechanisms, such as an increase in ROS excretion (Choot al., 2004). These patterns of response of CAT activity showed thatxyfluorfen chronic exposure induced the selection of species withapacity to cope with higher oxidative stress levels, being probably

gy 136– 137 (2013) 60– 71 69

one of the mechanisms of adaptation to oxyfluorfen exposure. Thisresult highlights then the importance of toxicant chronic exposureon the development of biofilm structure and function and thus onthe adaptation of the community as demonstrated on several occa-sions (e.g. Berard et al., 2002; Dorigo et al., 2007; Schmitt-Jansenand Altenburger, 2005; Tlili et al., 2010). Nevertheless further stud-ies are needed, in particular, to demonstrate the link between anincrease in CAT capacity to answer to oxidative stress and a bettertolerance to peroxidizing compounds.

Previous studies reported concentrations of oxyfluorfenbetween 0.1 and 1 �g L−1 (estimated concentrations in San JoaquinRiver based on sediment data, US EPA/OPP, 2001) with a maxi-mum of 541 �g L−1 being observed after an accidental spillage (USEPA/OPP, 2001). Then, based on our result, oxyfluorfen may rep-resent a risk in the environment. In particular chronic exposureto environmental concentrations may provoke changes in com-munity structure as in the present study eukaryotic diversity wasaffected after chronic exposure to 3 �g L−1 of oxyfluorfen. Chronicexposure may thus affect the community by selecting resistantspecies and provoking a decrease in biofilm biodiversity. Scenariosof chronic contamination by oxyfluorfen may be especially prob-lematic in multiple stress situations. Indeed, species tolerant toother types of stresses (other herbicides, temperature increase, etc.)might disappear due to oxyfluorfen exposure and, therefore, theresistance of the whole community might be lowered (Vinebrookeet al., 2004).

4.3. AEA: temporal variation vs. ecotoxicological effects

Knowing the magnitude and typology of changes occurring innon-exposed biofilms throughout the duration of the experimentfacilitates the evaluation of the effects caused by toxicant expo-sure. In the present study, while some extreme values of AEA weredetected in exposed biofilms, it has not been possible to determineactivity thresholds indicative of adverse effects. This limitationwas due to the high variability observed in both non-exposed andexposed communities but also to the unimodal pattern of varia-tion of AEA. Indeed, differences between exposed and non-exposedcommunities were better highlighted by comparing the pattern ofAEA response throughout oxyfluorfen gradient after acute expo-sure. Therefore, the estimation of the concentration range leadingto the maximal short-term response may be especially useful todiscriminate between controls and chronically exposed communi-ties.

5. Conclusion

Since the pool of AEA dedicated to oxidative stress manage-ment changed as biofilm aged, the AEA of biofilms depend stronglyon biofilm age. This natural variability prevents direct interpre-tation of biofilm AEA, especially in the field where biofilm age israrely known. Nevertheless, AEA brought valuable information onthe oxidant effects of oxyfluorfen when the classical biomarker:chlorophyll-a was not found sensitive to this toxicant at theconcentrations tested. In particular, the use of AEA in acute eco-toxicological tests revealed that the structural changes observedwere linked with functional changes provoked by chronic expo-sure to oxyfluorfen, that is a higher CAT capacity to respond tohigher levels of oxidative stress (induced by oxyfluorfen) in biofilmschronically exposed to the same compound (oxyfluorfen). Then,

we proposed the use of AEA in biofilms in short-term toxicitytests to compare the oxidative stress levels undergone by differentcommunities and determine their capacity to cope with oxidativestress.

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cknowledgements

We thank Berta Bonet for her technical help during samplingnd Wim Admiraal for his helpful and constructive commentsn the manuscript. Financial support was provided by thepanish Ministry of Science and Education (FLUVIALMULTISTRESS-TM2009-1411-CO2-01) and the EC Sixth Framework ProgramKEYBIOEFFECTS MRTN-CT-2006-035695).

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