Wastewater constituents impact biofilm microbial community in receiving streams

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Wastewater constituents impact biofilm microbial community in receiving streams © 2021 The Authors. Published by Elsevier B.V. Published version Tamminen, Manu; Spaak, Jenny; Tlili, Ahmed; Eggen, Rik; Stamm, Christian; Räsänen, Katja Tamminen, M., Spaak, J., Tlili, A., Eggen, R., Stamm, C., & Räsänen, K. (2022). Wastewater constituents impact biofilm microbial community in receiving streams. Science of the Total Environment, 807, Article 151080. https://doi.org/10.1016/j.scitotenv.2021.151080 2022 Wastewater constituents impact biofilm microbial community in receiving streams Manu Tamminen a, ⁎,Jenny Spaak b ,Ahmed Tlili c ,Rik Eggen c,d , Christian Stamm e ,Katja Räsänen f,g a Department of Biology, University of Turku, Finland b Department of Environmental Microbiology, Eawag, Switzerland c Department of Environmental Toxicology, Eawag, Switzerland d Department of Environmental Systems Science, ETH Zürich, Switzerland e Department of Environmental Chemistry, Eawag, Switzerland f Department of Aquatic Ecology, Eawag, Switzerland g Dept. of Biology and Environmental Science, University of Jyväskylä, Finland HIGHLIGHTS •Stream biofilms are exposed to anthropogenic impacts from agriculture and WWTPs. •Biofilms were sequenced from 4 streams upstream and downstream of WWTPs. •Bacterial communities had a strong regional signal while eukaryotes had not. •Bacterial and eukaryotic communities shifted significantly in correlation with MPs. •Shifted groups include diatoms and Alphaproteobacteria, potential marker organisms. GRAPHICAL ABSTRACT abstractarticle info Article history: Received 26 August 2021 Received in revised form 1 October 2021 Accepted 15 October 2021 Available online xxxx Editor: Paola Verlicchi Microbial life in natural biofilms is dominated by prokaryotes and microscopic eukaryotes living in dense association. In stream ecosystems, microbial biofilms influence primary production, elemental cycles, food web interactions as well as water quality. Understanding how biofilm communities respond to anthropogenic impacts, such as wastewater treatment plant (WWTP) effluent, is important given the key role of biofilms in stream ecosystem function. Here, we implemented 16S and 18S rRNA gene sequencing of stream biofilms upstream (US) and downstream (DS) of WWTP effluents in four Swiss streams to test how bacterial and eukaryotic communities respond to wastewater constituents. Stream biofilm composition was strongly affected by geographic location –particularly for bacteria. However, the abundance of certain microbial community members was related to micropollutants in the wastewater –among bacteria, micropollutant-associated members were found e.g. in Alphaproteobacteria, and among eukaryotes e.g. in Bacillariophyta (algal diatoms). This study corroborates several previously characterized responses (e.g. as seen in diatoms), but also reveals previously unknown community responses –such as seen in Alphaproteobacteria. This study advances our understanding of the ecological impact of the current wastewater treatment practices and provides information about potential new marker organisms to assess ecological change in stream biofilms. © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Keywords: Biofilm Wastewater treatment Bacterial community Diatom Micropollutant Science of the Total Environment xxx (xxxx) xxx ⁎Corresponding author at: Department of Biology, University of Turku, Finland. E-mail address: mavatam@utu.fi(M. Tamminen). STOTEN-151080; No of Pages 8 https://doi.org/10.1016/j.scitotenv.2021.151080 0048-9697/© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Contents lists available at ScienceDirect Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv Please cite this article as: M.Tamminen, J. Spaak, A. Tlili, et al., Wastewater constituents impact biofilm microbial community in receiving streams, Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2021.151080 1. Introduction Microbial communities perform fundamentally important biological functions within organisms (i.e. host-associated microbiomes), and across ecosystems (Bardgett and van der Putten, 2014;Donaldson et al., 2015;Fuhrman et al., 2015;Grice and Segre, 2011;Guttman et al., 2014;Kwong and Moran, 2016). In stream ecosystems, microbial life is dominated by dense biofilms consisting of bacteria, archaea and microscopic eukaryotes. These biofilms form the basis of the stream food webs by performing crucial functions, such as elemental cycles, ecosystem respiration, primary production and maintenance of good water quality (Aristi et al., 2015;Battin et al., 2016). By being in contact with flowing water, stream biofilms are continuously in contact with various microorganisms and exposed to suspended nutrients and substrates. Stream biofilms are capable of resisting invasions by microorganisms to some degree and therefore have a central role in attenuating microbiological changes in stream water (Battin et al., 2007, 2003;Carles et al., 2021). Stream biofilms also take up substances that are present in the stream water, including nutrients and chemical pollutants (Flemming and Wingender, 2010;Singer et al., 2010), which can be toxic and/or be metabolized. Moreover, stream biofilm composition can be influenced by wastewater-born microorganisms (Carles et al., 2021). As a consequence, stream biofilms can be strongly affected by human activities such as agriculture, industry or wastewater discharge –resulting in potential alterations of the ecological functions they provide (Besemer et al., 2009). One of the main anthropogenic influences on stream ecosystems is chemical pollution from excess nutrients and toxicants due to agricultural activities and discharged wastewater (Dudgeon et al., 2006; Schwarzenbach et al., 2006). Although wastewater treatment is aimed to assure clean water, by chemical and microbiological removal of toxic compounds and harmful microbes, wastewater discharge still contains hundreds of compounds (e.g., pharmaceuticals and pesticides), collectively known as micropollutants (Schwarzenbach et al., 2006; Stamm et al., 2016), and a large variety of microbes. These components can substantially influence the chemistry, community structure and ecosystem function of the receiving water bodies (Burdon et al., 2020, 2016;Munz et al., 2017;Pascual-Benito et al., 2020;Schäfer et al., 2016;Stamm et al., 2016;Thompson et al., 2016). The mixture of micropollutants and microorganisms in wastewater can impact stream biofilm communities in complex ways, reflected for example as increased community tolerance and altered functions of algal and bacterial communities (e.g., photosynthesis and metabolic rates (Aristi et al., 2015;Corcoll et al., 2014;Tlili et al., 2017)). Here, we apply next-generation DNA sequencing (NGS) of 16S (bacterial) and 18S (eukaryotic) rRNA genes to test how wastewater effluent affects biofilm community diversity in four Swiss streams. We also extend a recent study (Tlili et al., 2017) that found consistent changes in biofilm diversity, as well as increased tolerance to micropollutants of algal and bacterial constituents of biofilm communities, downstream of wastewater treatment plants (WWTPs). We here study the same sites and explicitly i) profile the taxonomic composition of the stream biofilm communities upstream (US) and downstream (DS) of WWTPs and ii) test whether any specific community-level or specific OTUlevel changes can be attributed to MPs or nutrients. This approach allows us to identify specific taxa responding to wastewater exposure – thereby providing detailed insight into community level variation that underlie ecotoxicological and ecological responses to pollution. 2. Results 2.1. Overall biofilm diversity Sequencing of the 16S and 18S genes provided, after quality control, 2.8 million paired end 16S and 0.7 million paired end 18S reads. Phylogenetic classification of the OTUs (Greengenes for 16S, SILVA for 18S) revealed that these stream biofilm communities are most commonly dominated by the bacterial phyla Bacteroidetes and Proteobacteria, and the eukaryotic phyla Chlorophyta,Ochrophyta and Metazoa (Fig. 1). For both bacteria and eukaryotes, alpha diversity measures Chao1 and Shannon were significantly different among streams (stream main effect: all p< 0.001;factorial ANOVA, Fig. S1). There was no significant overall difference between US/DS locations (US/DS location main effect: all p> 0.5; factorial ANOVA). However, significant stream × US/DS location interactions (for both bacterial and eukaryotic communities) indicated that the effect of wastewater input on alpha diversity was stream specific(allp< 0.001; factorial ANOVA, Fig. S1). 2.2. Biofilm community responses To test the community differences across the streams and sampling locations, we performed principal coordinate analysis (PCoA) of the UniFrac distances of the proportional OTU counts across the samples. For bacteria, the UniFrac-PCoA-Axis 1 16S explained 77% and the UniFrac-PCoA-Axis 2 16S 7.2% of variation in multivariate community diversity (Fig. 2A). For UniFrac-PCoA-Axis 1 16S there was a highly significant stream main effect, but no significant US/DS location or stream × US/DS location interaction (Fig. S2). Pairwise Tukey tests on UniFrac-PCoA-Axis 1 16S further showed that Steinach and Herisau differed strongly from the other two streams (Steinach-Buttisholz and Steinach-Hochdorf: both p< 0.001, Herisau-Hochdorf: p< 0.005 and Herisau-Buttisholz: p< 0.05; factorial ANOVA). The significance of the stream effect was further confirmed by permutational ANOVA (p < 0.001; 999 permutations). There were no significant effects of stream, US/DS location or their interaction on UniFrac-PCoA-Axis 2 16S . These results indicate that the multivariate diversity of bacterial communities in these stream biofilms is determined primarily by the source stream and are not detectably influenced by wastewater input. For eukaryotes, UniFrac-PCoA-Axis 1 18S explained 50.7% and UniFrac-PCoA-Axis 2 18S 22.7% of variation. For both axes, there was a significant stream main effect (both axes p< 0.001; factorial ANOVA, Fig. 2B). The US/DS location main effect was not significant for either axis, but a significant stream × US/DS location interaction (both axes p < 0.001; factorial ANOVA) suggests that community shifts of eukaryotes (in response to wastewater input) are specifictoeach stream (i.e. indicating context dependency; Fig. S2). Visual inspection of the UniFrac-PCoA-Axes indicates no consistent patterns in Axis 1 18S , whereas inspection of Axis 2 18S indicates that DS locations become relatively similar but US locations remain distinct across streams (Fig. 2B). 2.3. Biofilm composition responses To test whether micropollutants (MPs) in the wastewater effluent affected the biofilm composition, we performed redundancy analyses (RDA) using the proportional bacterial or eukaryotic OTU counts as response variables and nutrients and selected MPs (measured in all samples, including upstream and downstream of the WWTPS) as explanatory variables, providing 21 independent observations. Community changes in the stream biofilms were significantly associated with MPs for both bacteria and eukaryotes (RDA and permutational ANOVAs; p< 0.001), with the first two RDA axes explaining 56.5% of the total bacterial and 53.3% of thetotal eukaryotic variation (Fig. 3). In contrast, biofilm community changes were not significantly related to nutrient levels in the effluent - either for bacteria or eukaryotes (RDA and permutational ANOVAs p> 0.2; Fig. S03). For bacteria, the 10 most strongly affected OTUs, identified by RDA, included various unidentified species belonging to Rhodobacters as well as a Gemmata sp. and a Rhodococcus fascians. For eukaryotes, the 10 most strongly affected OTUs, identified by RDA, included various Diatom species, as well as a golden alga M. Tamminen, J. Spaak, A. Tlili et al. Science of the Total Environment xxx (xxxx) xxx 2 Chrysochaete britannica, an ulvophyte Pseudendoclonium sp. and a spirotrich Aspidisca sp. 3. Discussion The extent of microbial community diversity has long been poorly understood because of the difficulties in characterizing large numbers of taxa. With the advent of next generation DNA sequencing, high microbial diversity has been revealed in various natural systems (Bardgett and van der Putten, 2014;Donaldson et al., 2015;Fuhrman et al., 2015), including stream biofilms (Battin et al., 2016;Nega et al., 2019;Peng et al., 2018). The challenge remains to establish thedeterminants of microbial diversity patterns in nature and how microbial communities may respond to environmental perturbations (Lee et al., 2021;Mansfeldt et al., 2020). Here we studied microbial diversity patterns of stream biofilms in four Swiss lowland streams and tested how bacterial and eukaryotic communities respond to wastewater, which contains microorganisms, micropollutants (MPs) and nutrients. We did find clear community shifts in bacteria and eukaryotes in response to MPs in the wastewater but observed no such responses to increased nutrient levels. 3.1. Bacterial diversity For bacteria, we found a strong geographic community shift as the two westernmost streams (Hochdorf and Buttisholz) differed strongly from the two easternmost streams (Steinach and Herisau) (Fig. 2). This geographic pattern is broadly similar to the denaturating gradient gel electrophoresis (DGGE) -based community profiling on the same biofilms (Tlili et al., 2017). Our current study allowed in addition to identify OTUs within these community shifts. The bacterial OTUs found at our study sites belong to groups previously reported in association with stream water and stream biofilms, including Actinobacteria,Proteobacteria, Bacteroidetes and Planctomycetes (Besemer et al., 2012;Levi et al., 2017; Mansfeldt et al., 2020;Zeglin, 2015). For instance, we found certain Burkholderia (belonging to Betaproteobacteria) which have the potential to degrade xenobiotics (O'Sullivan and Mahenthiralingam, 2005), and Rhizobiales (belonging to Alphaproteobacteria) which are known for Fig. 1. Stream biofilm was collected in four streams in Switzerland (Buttisholz, Hochdorf, Herisau, Steinach), upstream (US) and downstream (DS) of wastewater treatment plants. The relative presence of the most common phylogenetic classes is presented as pie charts for each study location. The gray section indicates OTUs that fall below the relative abundance of 0.005% (Bacteria) or 0.01% (Eukaryota). M. Tamminen, J. Spaak, A. Tlili et al. Science of the Total Environment xxx (xxxx) xxx 3 Fig. 2. Principal coordinate analysisof UniFrac distances across the four study streams and two locations (Upstream; US and Downstream; DS) for bacterial (16S, left) and eukaryotic(18S, right) taxa. For bacteria, there is a highly significant community difference among streams (p< 0.001, factorial ANOVA). This reflects the strong separation of Herisau and Steinach from Buttisholz and Hochdorf along PCoA Axis 1 (p< 0.05; TukeyHSD post-hoc test). For eukaryotes, the communities differ significantly along both PCoA axes among streams (stream main effect: both p< 0.001, factorial ANOVA), but a significant stream US/DS location interaction along axes indicates context dependent variation in communities (p < 0.001; factorial ANOVA). Fig. 3. Two main axes from redundancy analyses (RDA) of bacterial (left) and eukaryotic (right) community responses to micropollutants. Micropollutant exposure causes highly significant changes in both bacteria and eukaryotes in the stream biofilm communities (RDA with permutational ANOVA; p < 0.001). The first two axes explain 56.5% of the bacterial and 53.3% of the eukaryotic variation. M. Tamminen, J. Spaak, A. Tlili et al. Science of the Total Environment xxx (xxxx) xxx 4 their role in nitrogen fixation (Newton et al., 2011) (Fig. S3). None of the observed bacterial groups, however, differed consistently between US and DS sites –indicating no obvious responses to wastewater. 3.2. Eukaryote diversity We found a high abundance of Ciliophora (protozoans) and some fungi - particularly Cryptomycota and Bacillariophyceae, corresponding to previous reports of stream biofilms (Dopheide et al., 2008;Heino et al., 2010;Levi et al., 2017). Ciliophora (ciliates) are grazers in stream biofilms and highly important in transferring nutrients to higher trophic levels (Dopheide et al., 2009). In general, ciliates occupy a wide range of ecological niches, respond rapidly to environmental changes and are therefore used as indicators of ecosystem health (Lear et al., 2011). The most abundant Fungi in our biofilms were Cryptomycota (Baschien et al., 2008;Jones et al., 2011), which are common in aquatic systems (Livermore and Mattes, 2013). Whereas fungi are generally known as degraders of organic matter (Miura and Urabe, 2015), the scarcity of information about the ecology and lifestyles of the microscopic Cryptomycetes complicates the interpretation of their ecological role in stream biofilms and calls for further studies to identify their role in stream biofilms. We also found certain members of Chytridiomycota, a group which is known for their parasitic lifestyles, pathogenity for aquatic taxa as well as ability to degrade organic matter (Letcher et al., 2006). Finally, we found a high abundance and diversity of Bacillariophyta (diatoms), which are common inhabitants of river and river biofilm communities (Battin et al., 2016). Diatoms are known for their photosynthetic lifestyle, and contribute to ecosystem functioning by exuding carbohydrates and amino acids (Battin et al., 2016). Like protists (above), diatoms are sensitive indicators of pollutants and classically used in ecosystem biomonitoring as well as ecotoxicological studies, often using their morphological characteristics for identification (Keck et al., 2016). We note that NGS-based characterization of diatoms has recently been shown tobe comparable with the accuracyof morphological characterization (Apothéloz-Perret-Gentil et al., 2017). In contrast to Besemer et al. (2012), who reported abundant taxonomic groups in biofilms to be omnipresent, we found clear geographic differences in the distribution of both bacterial and eukaryotic OTUs in stream biofilms. This is seen both as substantial differences among streams (particularly for bacteria) and US/DS locations (particularly for eukaryotes). Our results support, overall, the view that stream biofilm communities vary strongly locally (Battin et al., 2016). It is important to note that these differences only became obvious at the OTU level (Fig. 3)–broader phylogenetic level distribution would be in agreement with (Besemer et al., 2012)(Fig. 1), which provides clear evidence for the benefitsof NGS based estimationsof biofilm diversity. The specific determinants of biofilm diversity across streams are unclear, but can reflect differences in catchment characteristics (e.g. catchment land-use; (Burdon et al., 2016)), local climatic conditions (Fierer et al., 2007;Lear et al., 2014) or historical colonization patterns (Martiny et al., 2006). 3.3. Micropollutants are affecting bacterial and eukaryotic communities We found no significant relationship between community shifts and dissolved phosphorus, nitrogen or organic carbon within streams or in response to effluent exposure. However, we found a significant correlation between micropollutants and a shift in the stream biofilm bacterial and eukaryotic communities. Among the 44 MPs (22) which were measured in the river water of our study sites, the strongest associations of bacterial communities, revealed by constrained ordination analysis, were with atrazin, chlortoluron, penconazole, pirimicarb, tebuconazole and terbuthylazine. The strongest associations of eukaryotic communities, revealed by constrained ordination analysis, were with azoxystrobin, chlortoluron, diuron, fexofenadine, simazine and terbuthylazine. Interestingly, the herbicides terbuthylazine and chlortoluron correlated with both bacterial and eukaryotic community shifts in our analyses, which could indicate potential direct effects of herbizides on algae and perhaps indirect effects on bacteria (i.e. via altered trophic interactions). 3.4. Affected bacterial and eukaryotic taxa We observed a significant bacterial community shift in response to wastewater exposure. In previous studies, several MPs have been shown to cause changes in biofilm communities. For instance, (Chonova et al., 2019;Kim Tiam et al., 2014) reported changes in the diatom community within stream biofilms in response to wastewater and to a mixture of fungicides, pesticides and herbicides, respectively, and molecular fingerprinting experiments (e.g. (Proia et al., 2013; Vercraene-Eairmal et al., 2010)) have demonstrated biofilm community changes in response to specific pollutants. UsingNGSanalyses,wewereabletoidentifyspecificmembersofthe biofilm communities that were negatively related to the presence of chemical pollution. For example, there was a negative response of three distinct Rhodobacter species to herbicides chlortoluron, atrazine and terbutylazine as well as insecticide pirimicarb and fungicide tebuconazole. Since chlortoluron, atrazine and terbutylazine are known inhibitors of photosynthesis (Zhu et al., 2009), this effect may be mediated through the photosynthetic lifestyle of certain Rhodobacterial groups (Ravi et al., 2019;Strnad et al., 2010). While literature regarding the responses of Rhodobacters to MPs in stream biofilms is scarce, certain studies also indicate their usefulness in bioremediation of pesticidecontaminated wastewater (e.g. (Wu et al., 2019)). The communitylevel correlations with exposure to insecticide pirimicarb, and fungicides tebuconazole and penconazole, may reflect indirect effects on grazers or fungal communities (Stamm et al., 2016)–orunknowndirectactionon bacterial communities, but this finding currently remains unexplained due to lack of information. We further observed that several diatoms had complex relationships to the MPs in the wastewater. For instance, the presence of diatom Achnanthidium minutissimum coincided with a high concentration of the herbicide diuron, whereas another diatom Cocconeis sp. exhibited an opposite response. This variability in the sensitivity of diatoms is consistent with the morphology-based assays of MP sensitivity (e.g. (Tiam et al., 2014)). Since most diatoms are photosynthetic, the observed correlations may be mediated by photosynthesis inhibition by diuron, simazine, terbutylazine and chlortoluron. Other taxa apparently correlated with MP exposure in our data were an unidentified Pseudendoclonium species (a green algae in the class Ulvophyceae), an unidentified Aspidisca species (a bacterivorous ciliate in the class Spirotrichea) and Chrysochaete britannica (an algae in the class Chrysophyceae-Synurophyceae). The community-level responses observedin correlation with exposure to antihistaminefexofenadine, antidiabetic sitagliptin and fungicide azoxystrobin remain unexplained due to our lack of understanding of the mechanisms by which they might impact microscopic eukaryotes. 4. In conclusion Our NGS data of four Swiss lowland streams showed that eukaryotic community members respond to wastewater input (particularly algal community members), whereas bacterial community composition reflects more strongly the geographic location. While these patterns reflect a combination of local and regional ecological processes typical for biofilms (e.g. (Besemer, 2015)), and likely a high turnover of bacterial communities, we also show that the biofilm community composition is related to observed patterns of chemical pollution. Most importantly, the NGS approach allowed us to identify specifictaxathatseemto respond to wastewater in both bacteria and eukaryotes, suggesting the potential for new bioindicator taxa. Future work should explicitly M. Tamminen, J. Spaak, A. Tlili et al. Science of the Total Environment xxx (xxxx) xxx 5 test how functional endpoints of community level assays (e.g. enzymatic activity-based assays) correlate with taxa identity. 5. Material and methods 5.1. Study locations We profiled the stream biofilm communities upstream (US) and downstream (DS) of WWTPs in four streams in central Switzerland (Buttisholz, Herisau, Hochdorf and Steinach, Fig. 1). The streams were initially chosen so that no wastewater was present upstream of the WWTPs and that the wastewater effluent contributed at least 20% to the total stream flow at the DS locations during low flow conditions (Stamm et al., 2016). All streams were small to moderately sized. In each stream, one US and one DS location was selected as the reference and the impacted location, respectively. The US/DS locations were chosen to be as similar as possible with regard to stream morphology, riparian land use and vegetation (Burdon et al., 2016;Munz et al., 2017). Furthermore, the DS locations were selected so that water from the effluent was completely mixed with the stream water during low flow conditions. Our samples originate from the same sampling campaign that recently documented Pollution induced tolerance (PICT) in these streams (Tlili et al., 2017). The details of the biofilm sampling can be found in Tlili et al. (2017) and we here provide only a summary. At each sampling location, glass slides (35.5 × 13.0cm) were fixed vertically in perforated plastic boxes (three boxes per location and three glass slides per box) and immersed at the center of each stream to allow colonization by local biofilm. After a colonization period of 6 weeks (from 15th of March to the 30th of April 2014), the glass slides were retrieved, placed individually in plastic bags containing stream water from the corresponding sampling location and transported to the laboratory at Eawag, Dübendorf, Switzerland. The samples were transported in cooling boxes within 5 h of sampling. Immediately upon arrival at the laboratory, the stream biofilm from each glass slide was carefully scraped using a polypropylene spatula and suspended in 250 mL of Evian mineral water prior to functional and structural analyses of the stream biofilm –see (Tlili et al., 2017). The samples from each of the three glass slides within a box were pooled, resulting in three independent replicates/location. For genomic DNA extraction, 2 mL of the biofilm suspension within each biological replicate were centrifuged at 14000 ×gfor 30 min at 4 °C. Afterward, the supernatants were removed and the pellets stored at −80 °C. 5.2. Micropollutant and nutrient data We used micropollutant (MP) and nutrient data for each of the streams from Tlili et al. (2017) to characterize potential chemical exposure. This data included measurements of 44 MPs as well as dissolved organic carbon, dissolved nitrogen and dissolved phosphorus across our four study streams. 5.3. Molecular analyses DNA extraction from the pellets was conducted using the PowerBiofilm DNA Isolation Kit (MO BIO Laboratories, CA) following the manufacturer's instructions. The isolated DNA was stored at −20 °C until further processing. For the molecular genetic analyses, each biological replicate was further divided into three technical replicated to account for stochastic effects originating from PCR. However, some variation in replication resulted because of DNA recovery and technical issues. Therefore, only one biological replicate was available for the 16S for Buttisholz and Hochdorf at the DS locations and for the 18S for Buttisholz at the DS location. After having assessed the optimal cycle number on an ABI 7500 cycler, the 16S and 18S regions were amplified resulting in 463 bp and 558 bp fragments for the 16S and 18S, respectively. The primer sequences are listed in Table S01. The correct cycle number for each sample was determined using qPCR with reaction conditions for 16S and 18S as follows: 1× Phusion HF buffer (catalogue no. M0530S), 0.2 mM dNTPs (Promega catalogue no. U1515), 0.4 μM of each primer, 1× EvaGreen (catalogue no 31000-T,31000) and 0.5 U Phusion polymerase (catalogue no. M0530S). Thermocycling conditions consisted of an initial denaturation of 98 °C for 30 s; 25 cycles of 98 °C for 5 s, 60 °C for 30 s, 72 °C for 30 s.The reactions were performed in three technicalreplicates for 16S amplicons as follows: 1× GoTaq G2 (catalogue no. M7832), 0.2 mM dNTPs (Promega catalogue no. U1515) and 0.4 μMof each primer. Thermocycling conditions consisted of an initial denaturation of 95 °C for 120 s; 20 cycles of 95 °C for 30 s, 55 °C for 45 s, 72 °C for 50 s. The reactions were performed in three technical replicates for 18S amplicons as follows: 1× Phusion HF buffer (catalogue no. M0530S), 0.2 mM dNTPs (Promega catalogue no. U1515), 0.4 μMof each primer and 0.5 U Phusion polymerase (catalogue no. M0530S). Thermocycling conditions consisted of an initial denaturation of 98 °C for 30 s; 23 cycles of 98 °C for 5 s, 60 °C for 30 s, 72 °C for 30 s, with the exception of Steinach US for which, because of low DNA yield, a cycle number of 28 was used. The three technical replicates were re-combined and subsequently run on an agarose gel to confirm expected amplification or nonamplification (for negative controls in which PCR templates were omitted). PCR products were cleaned using AMPure XP beads in a ratio of 0.8× per sample. The samples were indexed using the standard Illumina TruSeq indices and sequenced on an Illumina® MiSeq machine in a single flow cell in paired-end mode with 300 bp-read-lengths at the Genetic Diversity Center (GDC), ETHZürich.The sequences were deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive database under BioProject ID PRJNA414710. 5.4. Sequence processing The raw data was processed through the UPARSE pipeline (http:// drive5.com/usearch/manual/uparse_pipeline.html;(Edgar, 2013)). For the 16S sequences, the Illumina paired reads pipeline was used. For 18S sequences, the Illumina forward reads pipeline was used because only approximately 10% of the reads remained after merging. The reads were filtered by discarding reads with a total expected error > 1 for all bases. Afterwards the sequences were dereplicated using fulllength matching. Finally, the operational taxonomic units (OTU's, (Edgar, 2013)) were clustered with a separation limit of 0.97 sequence similarity, and the chimeras filtered. The 97% sequence similarity cut off was used to reflect approximate species level divergence for microorganisms (Huse et al., 2010). The taxonomic affiliations of the OTUs were assigned using the assign_taxonomy.py script from QIIME (www.qiime.org;(Caporaso et al., 2010)) with the Greengenes database (release 13_5; (DeSantis et al., 2006)) for the 16S sequences, and the SILVA database (release 123; (Quast et al., 2013)) for the 18S sequences. The sequences were aligned using the align_seqs.py script from QIIME with the PyNAST method (Caporaso et al., 2010) using the Greengenes, Silva and alignment databases for 16S and 18S, respectively. Phylogenetic relationships between the OTUs were determined by inferring a phylogenetic tree using FastTree (Price et al., 2010). For the 16S data, the phylum Parvarchaeota (from the kingdom of the Archaea) and for the 18S data, Homo sapiens, were used to root the tree, respectively. 5.5. Statistical analyses All computational analyses were conducted separately for 16S and 18S data in Rversion 3.2.4 (R language core team 2008). We used multiple approaches to, on onehand, characterize taxonomic diversity overall and, on the other, to test the extent and context dependency of differentiation between US and DS locations. First, alpha diversity at M. Tamminen, J. Spaak, A. Tlili et al. Science of the Total Environment xxx (xxxx) xxx 6 each sampling site was estimated using Chao1 and Shannon indices, which account for rare taxa and overall number of taxa, respectively (Chao and Shen, 2003;Shannon, 1948). To gain multivariate descriptors of community change across sampling sites, we calculated weighted UniFrac distances based on OTU phylogeny and Bray-Curtis dissimilarities of the OTU tables (containing OTU countsin each sample), and conducted principal coordinate analyses (PCoA). This was performed for all eukaryotic and bacterial OTUs as well as for individual eukaryotic and bacterial phyla and classes. These analyses were performed using functions embedded in the phyloseq framework (McMurdie et al., 2013)on R. The MPs causing the strongest effects on bacterial and eukaryotic communities were identified using ordistep function embedded in the vegan package (Oksanen et al., 2019) in R. The RDA analyses for MP and nutrient effects were calculated using capscale function embedded in the vegan package on R using the bacterial or eukaryotic OTU counts as response variables and nutrients and selected MPs, measured in all samples, as explanatory variables. CRediT authorship contribution statement Manu Tamminen: Methodology, Software, Formal analysis, Visualization, Writing –original draft. Spaak Jenny: Methodology, Software. Ahmed Tlili: Resources, Data curation. Rik Eggen: Conceptualization, Supervision, Funding acquisition. Christian Stamm: Conceptualization, Supervision, Funding acquisition. Katja Räsänen: Conceptualization, Supervision, Formal analysis, Writing –original draft. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work wassupported bythe project EcoImpact funded by Eawag. The authors acknowledge Silvia Kobel and Aria Minder at the Genetic Diversity Centre (GDC) of ETH Zürich for technical support and Francesco Pomati, Cresten Mansfeldt and Louis Carles for commenting on earlier drafts of the manuscript. The authors declare no conflict of interest. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2021.151080. References Apothéloz-Perret-Gentil, L., Cordonier, A., Straub, F., Iseli, J., Esling, P., Pawlowski, J., 2017. Taxonomy-free molecular diatom index for high-throughput eDNA biomonitoring. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.12668. Aristi, I., von Schiller, D., Arroita, M., Barceló, D., Ponsatí, L., García-Galán, M.J., Sabater, S., Elosegi, A., Acuña, V., 2015. Mixed effects of effluents from a wastewater treatment plant on river ecosystem metabolism: subsidy or stress? Freshw. Biol. 60, 1398–1410. https://doi.org/10.1111/fwb.12576. Bardgett, R.D., van der Putten, W.H., 2014. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511. https://doi.org/10.1038/nature13855. Baschien, C., Manz, W., Neu, T.R., Marvanová, L., Szewzyk, U., 2008. In situ detection of freshwater fungi in an alpine stream by new taxon-specificfluorescence in situ hybridization probes. Appl. Environ. Microbiol. 74, 6427–6436. https://doi.org/10. 1128/AEM.00815-08. Battin, T.J., Kaplan, L.A., Newbold, J.D., Cheng, X., Hansen, C., 2003. Effects of current velocity on the nascent architecture of stream microbial biofilms. Appl. Environ. Microbiol. 69, 5443–5452. https://doi.org/10.1128/AEM.69.9.5443-5452.2003. Battin, T.J., Sloan, W.T., Kjelleberg, S., Daims, H., Head, I.M., Curtis, T.P., Eberl, L., 2007. Microbial landscapes: new paths to biofilm research. Nat. Rev. Microbiol. 5, 76–81. https://doi.org/10.1038/nrmicro1556. Battin, T.J., Besemer, K., Bengtsson, M.M., Romani, A.M., Packmann, A.I., 2016. The Ecology and Biogeochemistry of Stream Biofilms. https://doi.org/10.1038/nrmicro.2016.15. Besemer, K., 2015. Biodiversity, community structure and function of biofilms in stream ecosystems. Res. Microbiol. 166, 774–781. https://doi.org/10.1016/j.resmic.2015.05. 006. Besemer, K., Singer, G., Hödl, I., Battin, T.J., 2009. Bacterial community composition of stream biofilms in spatially variable-flow environments. Appl. Environ. Microbiol. 75, 7189–7195. https://doi.org/10.1128/AEM.01284-09. Besemer, K., Peter, H., Rg, J., Logue, B., Langenheder, S., Lindströ, M., E.S, Tranvik, L.J., Battin, T.J., 2012. Unraveling assembly of stream biofilm communities. ISME J. 6, 1459–1468. https://doi.org/10.1038/ismej.2011.205. Burdon, F.J., Reyes, M., Alder, A.C., Joss, A., Ort, C., Räsänen, K., Jokela, J., Eggen, R.I.L., Stamm, C., 2016. Environmental context and magnitude of disturbance influence trait-mediated community responses to wastewater in streams. Ecol. Evol. 6, 3923–3939. https://doi.org/10.1002/ece3.2165. Burdon, F.J., Bai, Y., Reyes, M., Tamminen, M., Staudacher, P., Mangold, S., Singer, H., Räsänen, K., Joss, A., Tiegs, S.D., Jokela, J., Eggen, R.I.L., Stamm, C., 2020. Stream microbial communities and ecosystem functioning show complex responses to multiple stressors in wastewater. Glob. Chang. Biol. 26, 6363–6382. https://doi.org/10.1111/ gcb.15302. Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., Fierer, N., Peña, A.G., Goodrich, J.K., Gordon, J.I., Huttley, G.A., Kelley, S.T., Knights, D., Koenig, J.E., Ley, R.E., Lozupone, C.A., McDonald, D., Muegge, B.D., Pirrung, M., Reeder, J., Sevinsky, J.R., Turnbaugh, P.J., Walters, W.A., Widmann, J., Yatsunenko, T., Zaneveld, J., Knight, R., 2010. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336. https://doi.org/10.1038/nmeth.f.303. Carles, L., Wullschleger, S., Joss, A., Eggen, R.I.L., Schirmer, K., Schuwirth, N., Stamm, C., Tlili, A., 2021. Impact of wastewater on the microbial diversity of periphyton and its tolerance to micropollutants in an engineered flow-through channel system. Water Res. 117486. https://doi.org/10.1016/J.WATRES.2021.117486. Chao, A., Shen, T.-J., 2003. Nonparametric estimation of Shannon’s index of diversity when there are unseen species in sample. Environ. Ecol. Stat. 10, 429–443. https://doi.org/ 10.1023/A:1026096204727. Chonova, T., Kurmayer, R., Rimet, F., Labanowski, J., Vasselon, V., Keck, F., Illmer, P., Bouchez, A., 2019. Benthic diatom communities in an alpine river impacted by waste water treatment effluents as revealed using DNA metabarcoding. Front. Microbiol. 10. https://doi.org/10.3389/fmicb.2019.00653. Corcoll, N., Acuña, V., Barceló, D., Casellas, M., Guasch, H., Huerta, B., Petrovic, M., Ponsatí, L., Rodríguez-Mozaz, S., Sabater, S., 2014. Pollution-induced community tolerance to non-steroidal anti-inflammatory drugs (NSAIDs) in fluvial biofilm communities affected by WWTP effluents. Chemosphere 112, 185–193. https://doi.org/10.1016/j. chemosphere.2014.03.128. DeSantis, T.Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E.L., Keller, K., Huber, T., Dalevi, D., Hu, P., Andersen, G.L., 2006. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072. https://doi.org/10.1128/AEM.03006-05. Donaldson, G.P., Lee, S.M., Mazmanian, S.K., 2015. Gut biogeography of the bacterial microbiota. Nat. Rev. Microbiol. 14, 20–32. https://doi.org/10.1038/nrmicro3552. Dopheide, A., Lear, G., Stott, R., Lewis, G., 2008. Molecular characterization of ciliate diversity in stream biofilms. Appl. Environ. Microbiol. 74, 1740–1747. https://doi.org/10. 1128/AEM.01438-07. Dopheide, A., Lear, G., Stott, R., Lewis, G., 2009. Relative diversity and community structure of ciliates in stream biofilms according to molecular and microscopy methods. Appl. Environ. Microbiol. 75, 5261–5272. https://doi.org/10.1128/AEM.00412-09. Dudgeon, D., Arthington, A.H., Gessner, M.O., Kawabata, Z.-I., Knowler, D.J., Lévêque, C., Naiman, R.J., Prieur-Richard, A.-H., Soto, D., Stiassny, M.L.J., Sullivan, C.A., 2006. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol. Rev. 81, 163. https://doi.org/10.1017/S1464793105006950. Edgar, R.C., 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998. https://doi.org/10.1038/nmeth.2604. Fierer, N., Morse, J.L., Berthrong, S.T., Bernhardt, E.S., Jackson, R.B., 2007. Environmental controls on the landscape-scale biogeography of stream bacterial communities. Ecology 88, 2162–2173. Flemming, H.-C., Wingender, J., 2010. The biofilm matrix. Nat. Publ. Group 8. https://doi. org/10.1038/nrmicro2415. Fuhrman, J.A., Cram, J.A., Needham, D.M., 2015. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146. https://doi. org/10.1038/nrmicro3417. Grice, E.A., Segre, J.A., 2011. The skin microbiome. Nat. Rev. Microbiol. https://doi.org/10. 1038/nrmicro2537. Guttman, D.S., McHardy, A.C., Schulze-Lefert, P., 2014. Microbial genome-enabled insights into plant–microorganism interactions. Nat. Rev. Genet. 15, 797–813. https://doi.org/ 10.1038/nrg3748. Heino, J., Bini, L.M., Karjalainen, S.M., Soininen, J., Vieira, L.C.G., Mykrä, H., Diniz-Filho, J.A.F., 2010. Geographical patterns of micro-organismal community structure: are diatoms ubiquitously distributed across boreal streams? Oikos 119, 129–137. https:// doi.org/10.1111/j.1600-0706.2009.17778.x. Huse, S.M., Welch, D.M., Morrison, H.G., Sogin, M.L., 2010. Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environ. Microbiol. 12, 1889–1898. https://doi.org/10.1111/j.1462-2920.2010.02193.x. Jones, M.D.M., Forn, I., Gadelha, C., Egan, M.J., Bass, D., Massana, R., Richards, T.A., 2011. Discovery of novel intermediate forms redefines the fungal tree of life. Nature 474. https://doi.org/10.1038/nature09984. Keck, F., Rimet, F., Franc, A., Bouchez, A., 2016. Phylogenetic signal in diatom ecology: perspectives for aquatic ecosystems biomonitoring. Ecol. Appl. 26, 861–872. https://doi. org/10.1890/14-1966. Kim Tiam, S., Morin, S., Pesce, S., Feurtet-Mazel, A., Moreira, A., Gonzalez, P., Mazzella, N., 2014. Environmental effects of realistic pesticide mixtures on natural biofilm M. Tamminen, J. Spaak, A. Tlili et al. Science of the Total Environment xxx (xxxx) xxx 7 communities with different exposure histories. Sci. Total Environ. 473–474, 496–506. https://doi.org/10.1016/J.SCITOTENV.2013.12.060. Kwong, W.K., Moran, N.A., 2016. Gut microbial communities of social bees. Nat. Rev. Microbiol. 14, 374–384. https://doi.org/10.1038/nrmicro.2016.43. Lear, G., Dopheide, A., Ancion, P., Lewis, G.D., 2011. A comparison of bacterial, ciliate and macroinvertebrate indicators of stream ecological health. Aquat. Ecol. 45, 517–527. https://doi.org/10.1007/s10452-011-9372-x. Lear, G., Bellamy, J., Case, B.S., Lee, J.E., Buckley, H.L., 2014. Fine-scale spatial patterns in bacterial community composition and function within freshwater ponds. ISME J. 8, 1715–1726. https://doi.org/10.1038/ismej.2014.21. Lee, J., Ju, F., Maile-Moskowitz, A., Beck, K., Maccagnan, A., McArdell, C.S., Dal Molin, M., Fenicia, F., Vikesland, P.J., Pruden, A., Stamm, C., Bürgmann, H., 2021. Unraveling the riverine antibiotic resistome: the downstream fate of anthropogenic inputs. Water Res. 197, 117050. https://doi.org/10.1016/j.watres.2021.117050. Letcher, P.M., Powell, M.J., Churchill, P.F., Chambers, J.G., 2006. Ultrastructural and molecular phylogenetic delineation of a new order, the rhizophydiales (Chytridiomycota). Mycol. Res. 110, 898–915. https://doi.org/10.1016/j.mycres.2006.06.011. Levi, P.S., Starnawski, P., Poulsen, B., Baattrup-Pedersen, A., Schramm, A., Riis, T., 2017. Microbial community diversity and composition varies with habitat characteristics and biofilm function in macrophyte-rich streams. Oikos 126, 398–409. https://doi.org/10. 1111/oik.03400. Livermore, J.A., Mattes, T.E., 2013. Phylogenetic detection of novel cryptomycota in an Iowa (United States) aquifer and from previously collected marine and freshwater targeted high-throughput sequencing sets. Environ. Microbiol. 15, 2333–2341. https://doi.org/10.1111/1462-2920.12106. Mansfeldt, C., Deiner, K., Mächler, E., Fenner, K., Eggen, R.I.L., Stamm, C., Schönenberger, U., Walser, J.C., Altermatt, F., 2020. Microbial community shifts in streams receiving treated wastewater effluent. Sci. Total Environ. 709, 135727. https://doi.org/10. 1016/j.scitotenv.2019.135727. Martiny, J.B.H., Bohannan, B.J.M., Brown, J.H., Colwell, R.K., Fuhrman, J.A., Green, J.L., Horner-Devine, M.C., Kane, M., Krumins, J.A., Kuske, C.R., Morin, P.J., Naeem, S., Øvreås, L., Reysenbach, A.-L., Smith, V.H., Staley, J.T., 2006. Microbial biogeography: putting microorganisms on the map. Nat. Rev. Microbiol. 4, 102–112. https://doi. org/10.1038/nrmicro1341. McMurdie, P.J., Holmes, S., Kindt, R., Legendre, P., O’Hara, R., 2013. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217. https://doi.org/10.1371/journal.pone.0061217. Miura, A., Urabe, J., 2015. Spatial and seasonal changes in species diversity of epilithic fungi along environmental gradients of a river. Freshw. Biol. 60, 673–685. https:// doi.org/10.1111/fwb.12514. Munz, N.A., Burdon, F.J., de Zwart, D., Junghans, M., Melo, L., Reyes, M., Schönenberger, U., Singer, H.P., Spycher, B., Hollender, J., Stamm, C., 2017. Pesticides drive risk of micropollutants in wastewater-impacted streams during low flow conditions. Water Res. 110, 366–377. https://doi.org/10.1016/j.watres.2016.11.001. Nega, M., Braun, B., Künzel, S., Szewzyk, U., 2019. Evaluating the impact of wastewater effluent on microbial communities in the panke, an Urban River. Water 11, 888. https:// doi.org/10.3390/w11050888. Newton, R.J., Jones, S.E., Eiler, A., McMahon, K.D., Bertilsson, S., 2011. A guide to the natural history of freshwater lake bacteria. Microbiol. Mol. Biol. Rev. 75, 14–49. https://doi. org/10.1128/MMBR.00028-10. O’Sullivan, L.A., Mahenthiralingam, E., 2005. Biotechnological potential within the genus burkholderia. Lett. Appl. Microbiol. 41, 8–11. https://doi.org/10.1111/j.1472-765X. 2005.01758.x. Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R., O’Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H., Szoecs, E., Wagner, H., 2019. vegan: Community Ecology Package. Pascual-Benito, M., Ballesté, E., Monleón-Getino, T., Urmeneta, J., Blanch, A.R., GarcíaAljaro, C., Lucena, F., 2020. Impact of treated sewage effluent on the bacterial community composition in an intermittent mediterranean stream. Environ. Pollut. 266, 115254. https://doi.org/10.1016/j.envpol.2020.115254. Peng, Y., Li, J., Lu, J., Xiao, L., Yang, L., 2018. Characteristics of microbial community involved in early biofilms formation under the influence of wastewater treatment plant effluent. J. Environ. Sci. (China) 66, 113–124. https://doi.org/10.1016/j.jes. 2017.05.015. Price, M.N., Dehal, P.S., Arkin, A.P., Rojas, M., Brodie, E., 2010. FastTree 2 –approximately maximum-likelihood trees for large alignments. PLoS One 5, e9490. https://doi.org/ 10.1371/journal.pone.0009490. Proia, L., Osorio, V., Soley, S., Köck-Schulmeyer, M., Pérez, S., Barceló, D., Romaní, A.M., Sabater, S., 2013. Effects of pesticides and pharmaceuticals on biofilms in a highly impacted river. Environ. Pollut. 178, 220–228. https://doi.org/10.1016/J.ENVPOL.2013. 02.022. Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., Glockner, F.O., 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596. https://doi.org/10.1093/nar/ gks1219. Ravi, S.K., Rawding, P., Elshahawy, A.M., Huang, K., Sun, W., Zhao, F., Wang, J., Jones, M.R., Tan, S.C., 2019. Photosynthetic apparatus of Rhodobacter sphaeroides exhibits prolonged charge storage. Nat. Commun. 101 (10), 1–10. https://doi.org/10.1038/ s41467-019-08817-7 2019. Schäfer, R.B., Kühn, B., Malaj, E., König, A., Gergs, R., 2016. Contribution of organic toxicants to multiple stress in river ecosystems. Freshw. Biol. 61, 2116–2128. https:// doi.org/10.1111/fwb.12811. Schwarzenbach, R.P., Escher, B.I., Fenner, K., Hofstetter, T.B., Johnson, C.A., von Gunten, U., Wehrli, B., 2006. The challenge of micropollutants in aquatic systems. Science (80-.) 313, 1072–1077. https://doi.org/10.1126/science.1127291. Shannon, C.E., 1948. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x. Singer, G., Besemer, K., Schmitt-Kopplin, P., Hödl, I., Battin, T.J., 2010. Physical heterogeneity increases biofilm resource use and its molecular diversity in stream mesocosms. PLoS One 5, e9988. https://doi.org/10.1371/journal.pone.0009988. Stamm, C., R?s?nen, K., Burdon, F.J., Altermatt, F., Jokela, J., Joss, A., Ackermann, M., Eggen, R.I.L., 2016. Unravelling the Impacts of Micropollutants in Aquatic Ecosystems, pp. 183–223 https://doi.org/10.1016/bs.aecr.2016.07.002. Strnad, H., Lapidus, A., Paces, J., Ulbrich, P., Vlcek, C., Paces, V., Haselkorn, R., 2010. Complete genome sequence of the photosynthetic purple nonsulfur bacterium rhodobacter capsulatus SB 1003. J. Bacteriol. 192, 3545. https://doi.org/10.1128/JB. 00366-10. Thompson, M.S.A., Bankier, C., Bell, T., Dumbrell, A.J., Gray, C., Ledger, M.E., Lehmann, K., McKew, B.A., Sayer, C.D., Shelley, F., Trimmer, M., Warren, S.L., Woodward, G., 2016. Gene-to-ecosystem impacts of a catastrophic pesticide spill: testing a multilevel bioassessment approach in a river ecosystem. Freshw. Biol. 61, 2037–2050. https://doi. org/10.1111/fwb.12676. Tiam, S.K., Libert, X., Morin, S., Gonzalez, P., Feurtet-Mazel, A., Mazzella, N., 2014. Single and mixture effects of pesticides and a degradation product on fluvial biofilms. Environ. Monit. Assess. 186, 3931–3939. https://doi.org/10.1007/s10661-014-3669-x. Tlili, A., Hollender, J., Kienle, C., Behra, R., 2017. Micropollutant-induced tolerance of in situ periphyton: establishing causality in wastewater-impacted streams. Water Res. 111, 185–194. https://doi.org/10.1016/j.watres.2017.01.016. Vercraene-Eairmal, M., Lauga, B., Saint Laurent, S., Mazzella, N., Boutry, S., Simon, M., Karama, S., Delmas, F., Duran, R., 2010. Diuron biotransformation and its effects on biofilm bacterial community structure. Chemosphere 81, 837–843. https://doi.org/ 10.1016/J.CHEMOSPHERE.2010.08.014. Wu, P., Zhang, Y., Chen, Z., Wang, Y., Zhu, F., Cao, B., Wu, Y., Li, N., 2019. The organophosphorus pesticides in soil was degradated by rhodobacter sphaeroides after wastewater treatment. Biochem. Eng. J. 141, 247–251. https://doi.org/10.1016/J.BEJ.2018.07. 019. Zeglin, L.H., 2015. Stream microbial diversity in response to environmental changes: review and synthesis of existing research. Front. Microbiol. 6, 454. https://doi.org/ 10.3389/fmicb.2015.00454. Zhu, J., Patzoldt, W.L., Radwan, O., Tranel, P.J., Clough, S.J., 2009. Effects of photosystem-IIinterfering herbicides atrazine and bentazon on the soybean transcriptome. Plant Genome 2, 191–205. https://doi.org/10.3835/PLANTGENOME2009.02.0010. M. Tamminen, J. Spaak, A. Tlili et al. Science of the Total Environment xxx (xxxx) xxx 8