FEMS Microbiology Ecology, 91, 2015, fiv004 doi: 10.1093/femsec/fiv004 Advance Access Publication Date: 12 January 2015 Research Article

RESEARCH ARTICLE

Long-term monitoring reveals stable and remarkably similar microbial communities in parallel full-scale biogas reactors digesting energy crops Rico Lucas1 , Anne Kuchenbuch1 , Ingo Fetzer2 , Hauke Harms1,3 and Sabine Kleinsteuber1,∗ 1

UFZ - Helmholtz Centre for Environmental Research GmbH, Department of Environmental Microbiology, ¨ Permoserstr. 15, 04318 Leipzig, Germany, 2 Stockholm Resilience Centre, Stockholm University, Kraftriket 2B, 10691 Stockholm, Sweden and 3 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, D-04103 Leipzig, Germany ∗ Corresponding author: UFZ—Helmholtz Centre for Environmental Research GmbH, Department of Environmental Microbiology, Permoserstr. 15, 04318 Leipzig, Germany, Tel: +49-341-235-1325; E-mail: [email protected] One sentence summary: Microbial assemblages in full-scale anaerobic digesters were analysed by community fingerprinting techniques, high-throughput sequencing and multivariate statistics, and revealed to be shaped by deterministic rather than stochastic effects. Editor: Alfons J. M. Stams

ABSTRACT Biogas is an important renewable energy carrier. It is a product of stepwise anaerobic degradation of organic materials by highly diverse microbial communities forming complex interlinking metabolic networks. Knowledge about the microbial background of long-term stable process performance in full-scale reactors is crucial for rationally improving the efficiency and reliability of biogas plants. To generate such knowledge, in the present study three parallel mesophilic full-scale reactors fed exclusively with energy crops were sampled weekly over one year. Physicochemical process parameters were determined and the microbial communities were analysed by terminal restriction fragment length polymorphism (T-RFLP) fingerprinting and 454-amplicon sequencing. For investigating the methanogenic community, a high-resolution T-RFLP approach based on the mcrA gene was developed by selecting restriction enzymes with improved taxonomic resolution compared to previous studies. Interestingly, no Methanosarcina-related generalists, but rather specialized hydrogenotrophic and acetoclastic methanogenic taxa were detected. In general, the microbial communities in the non-connected reactors were remarkably stable and highly similar indicating that identical environmental and process parameters resulted in identical microbial assemblages and dynamics. Practical implications such as flexible operation schemes comprising controlled variations of process parameters for an efficient microbial resource management under fluctuating process conditions are discussed. Key words: anaerobic digestion; community assemblage; maize silage; amplicon pyrosequencing; T-RFLP; mcrA

Received: 30 October 2014; Accepted: 24 December 2014  C FEMS 2015. All rights reserved. For Permissions, please e-mail: [email protected].

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INTRODUCTION Next to wind and solar power, biogas production is regarded as one of the cornerstones of the energy transition towards renewable energy production in Germany and worldwide (Martinot et al., 2002; Szarka et al., 2013). As a technically simple and lowcost technology, biogas production plays an important role in developing countries as well for comprehensive energy supply thereby mitigating greenhouse gas emissions (Surendra et al., 2014). Biogas can be flexibly utilized for heat and electricity generation in combined heat and power plants (CHP) or as vehicle fuel. In addition, it can be upgraded to biomethane, which can be easily stored and effectively distributed via the natural gas grid infrastructure. Thus, biogas can complement fluctuating renewable energy sources such as wind and solar power as it can provide base load as well as demand-oriented peak-load energy. Biogas is the final product of an anaerobic microbial degradation process in which carbonate serves as final electron acceptor. A vast number of mainly bacterial and archaeal species forms a complex metabolic network that stepwise degrades organic material. As feedstock for industrial scale biogas production, ensiled energy crops (in Germany mainly maize silage), grain grist, and various organic waste materials are utilized (Demirel 2014). The final metabolic step in anaerobic digestion, the formation of methane, strictly depends on the activity of methanogenic archaea which use either the hydrogenotrophic or the methylotrophic (including the acetoclastic) methanogenesis pathway (Costa and Leigh 2014). Compared to the highly diverse bacterial groups, methanogens are characterized by a low diversity and a lack of functional redundancy, making them more susceptible to adverse process conditions and thus crucial for the overall process stability (Demirel 2014). Solely seven phylogenetic orders of methanogenic archaea are known so far and six of them have been already detected in anaerobic digesters (Borrel et al., 2013). The composition and dynamics of the methanogenic groups in biogas systems have been found to provide valuable information for successful and efficient reactor operation in general (Demirel and Scherer 2008). However, even though it is of special importance for full-scale reactor operation regarding the prevention of and the recovery from process disturbances to facilitate long-term efficient biogas production, the microbiological background of anaerobic digestion and stability of biogas formation is not yet fully understood. For lab-scale reactors, a high functional stability but strong ´ microbial dynamics were reported (Fernandez et al., 1999), and recently the influences of deterministic or stochastic effects governing the community assemblages were controversially discussed for parallel lab-scale reactors (Zhou et al., 2013; Vanwonterghem et al., 2014). However, it appears logical that the resilience or resistance of the microbial communities facing fluctuating or even adverse process conditions are important for overall functional stability (Werner et al., 2011). Nevertheless, profound knowledge about the microbial ecology of full-scale anaerobic digesters is still scarce, especially regarding the utilization of energy crops as exclusive feedstock and the long-term microbial community dynamics (Demirel 2014). We therefore conducted a comprehensive long-term study of three technically identical full-scale continuous stirred tank reactors (CSTR) digesting maize silage and other energy crops exclusively. As these reactors had been operated separately for more than five years and had shown different kinds of process disturbances previous to this study, we aimed at comparing their microbial community assemblages with respect to their operational stability and performance, in order to identify microbial

indicators for stable or error-prone processes. A further aim of the study was to evaluate the potential influence of stochastic or deterministic effects governing the community assemblages and process stability to derive practical implications for efficient microbial resource management in full-scale biogas reactors. To address these issues, we continuously monitored the microbial communities and corresponding abiotic parameters of the three parallel reactors during one year. The microbial communities were analysed by PCR-based terminal restriction fragment length polymorphism (T-RFLP) fingerprinting and amplicon sequencing, targeting the bacterial 16S rRNA gene and the mcrA gene which encodes the alpha-subunit of the methyl coenzyme M reductase. The mcrA gene is ubiquitously and uniquely present in methanogenic archaea and can serve as a valuable functional and phylogenetic marker for this group (Luton et al., 2002; Steinberg and Regan 2008). The T-RFLP profiles in relation to potentially influencing external factors were analysed to draw comprehensive conclusions about the long-term microbial community dynamics and process performance between the reactors.

MATERIAL AND METHODS Operation and sampling of the full-scale biogas reactors Three full-scale CSTR biogas reactors, which were part of a large-scale biogas production facility in Mecklenburg-Western Pomerania (northeast of Germany) and of identical construction with volumes of 2570 m3 each, were sampled weekly between May 2011 and May 2012. The reactors, named R1, R2 and R3, respectively, were each connected to an individual CHP unit and had been operated in parallel as separate systems for more than 18 months previous to this study at a temperature of approximately 38◦ C. The reactors received the same feedstock quality and quantity which on average consisted of 97% maize silage and 3% of a mixture containing whole plant silage, grain grist, corn-cob-mixture and millet in varying percentages. Approximately, 26.3 t substrate fresh mass was loaded into the automatic dosage device of every reactor per day whereas the total solids (TS) and volatile solids (VS) contents of the substrate mixture were for all reactors on average 9660 kg d–1 (± 1034 kg d–1 ) and 9295 kg d–1 (± 1000 kg d–1 ), respectively (standard deviation in brackets). Further information on the feedstock characteristics is given in the supplemental information (SI reactor data.xlsx, Supporting Information). The commercial R (HeGo Biotech) for sulfide precipitairon additive FerroSorp tion was supplemented according to the supplier’s recommendation as well as a trace element mixture (ProEn) the qualitative composition of which is shown in the supplemental information as obtained from the ProEn webpage (SI reactor data.xlsx, Supporting Information). The reactors were automatically fed semicontinuously every 30 min and subsequently stirred for 5 min by submerged agitators. The organic loading rate (OLR) was constantly kept at 3.5 kgVS (m3 d)−1 , resulting in a hydraulic retention time (HRT) of approximately 80 days. For sampling, approximately 10 L of fermenter content was drawn after flushing the outlet mounted in the lower third of the reactor wall. The samples were aliquoted and stored at –20◦ C until further analyses. The reactors had been affected by different process disturbances within the last 18 months previous to this study. Whereas R1 had not shown any process disturbances, occasionally temporary process impairments leading to a reduced gas yield had been observed in R2. R3 had shown temporarily

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increased viscosity leading to foam formation. However, during this study none of the prior disturbances occurred.

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The electric energy yield from each CHP of the respective reactor as a reliable parameter for process efficiency and the process temperature were recorded on-site. Process parameters including TS, VS, pH value, concentrations of C2–C4 carboxylic acids, ratio of volatile organic acids to total inorganic carbon (VOA/TIC) as a measure of the buffering capacity, ammonium concentration (NH4 ), TIC, total organic carbon, total nitrogen, as well as the elemental composition were determined from the fermenter content according to Moeller et al., (2015).

As internal standards, MapMarker 1000 (BioVentures) and DMSR-100 (Nimagen) were applied for the 16S rRNA and mcrA gene analysis, respectively. The T-RFLP electropherograms were analysed by using the GeneMapper 5 software (Applied Biosystems) and processed according to Abdo et al., (2006) by using scripts implemented in R (R 2.13.0; R Development Core Team 2011). For noise removal, all signals with low-peak areas were removed according to five (for 16S rRNA profiles) and seven times (for mcrA profiles) the standard deviation of the data sets. For data illustration of the mcrA profiles, only terminal restriction fragments (T-RF) with a relative abundance of 1% at minimum in at least one sample were included. The reproducibility of the T-RFLP fingerprinting approach was validated by spot-checking samples as triplicates (Figure S1, Supporting Information).

DNA extraction and PCR amplification

Statistical analysis of the T-RFLP data sets

For the molecular analysis of the bacterial and methanogenic communities, genomic DNA was extracted from frozen aliquots of approximately 200 mg using the ‘NucleoSpin Soil’ kit (Macherey Nagel) according to the manual. The buffers SL2 and SX were applied. The DNA was finally eluted with 50 µL elution buffer. The purity, quantity and integrity of the eluted genomic DNA were determined by spectrophotometric measurements (NanoDrop1000, ThermoScientific) and gel electrophoresis in 1% agarose gels. The aliquoted DNA samples were kept at −20◦ C until further analysis. All PCR amplifications for T-RFLP fingerprinting and cloning were carried out as 12.5 µL reactions using the MyTaq HS RedMix kit (Bioline) including 0.7 µL of each primer (5 µM) and 1 µL undiluted template DNA (50–100 ng) using a ThermalCycler (Bio-Rad) according to the PCR conditions listed below. The bacterial community was analysed based on the 16S rRNA genes, applying the primers 27F (5 -GAGTTTGATCMTGGYTCAG-3 ) and 1492R (modified according to Lane 1991). The cycling protocol comprised an initial denaturation at 95◦ C for 5 min, followed by 30 cycles of 45 s at 95◦ C, 60 s at 54◦ C, 120 s at 72◦ C and a final elongation step at 72◦ C for 20 min. The methanogenic community was analysed based on the mcrA genes using the primer set mlas/mcrA rev by applying the following PCR protocol according to Steinberg and Regan (2008): initial denaturation at 95◦ C for 3 min, followed by 5 cycles of 20 s at 95◦ C, 20 s at 48◦ C, a temperature ramp from 48 to 72◦ C at 0.1 Ks−1 and 72◦ C for 15 s, followed by 25 cycles of 20 s at 95◦ C, 20 s at 55◦ C, 15 s at 72◦ C and a final elongation step at 72◦ C for 10 min.

The variability of the microbial communities based on the TRFLP profiles was assessed by the Bray–Curtis dissimilarity index which takes occurrence and relative abundance of T-RF into account, respectively (Bray and Curtis 1957). The ordination of the dissimilarity matrices was achieved by non-metric multidimensional scaling (NMDS), where greater distances between plotted samples indicate less similar community compositions. Multivariate data analysis was based on the ‘vegan’ R package (Oksanen et al., 2011). Besides the T-RFLP data obtained in this study, unpublished data either from two parallel thermophilic full-scale reactors fed with maize silage and sugar beets (S1 and S2) or from a mesophilic lab-scale two-stage system (first stage Z1 fed with maize silage; second stage Z2 charged with intermediate metabolites in the liquid phase from Z1) were used for reference plots, respectively. Correlations of abiotic parameters and single T-RF corresponding to the T-RFLP profiles were analysed using the ‘envfit’ function. The significance of correlations was tested by a Monte Carlo test with 1000 permutations and the significance threshold was set to 0.05 at maximum. The R-script for the NMDS analysis including example data is provided in the supplemental information (SI NMDS analysisscript, Supporting Information). Correlations between particular abiotic parameters and single T-RF were further examined based on the Spearman’s rank correlation coefficient.

Analytical methods

T-RFLP analysis The T-RFLP analysis of the bacterial and methanogenic communities using purified PCR products was conducted as de¨ ¨ scribed previously (Strauber, Schroder and Kleinsteuber 2012; Lv et al., 2014). All restriction enzymes were purchased from New England Biolabs and applied according to the supplier’s recommendations. The bacterial 16S rRNA amplicons were analysed by the enzymes HaeIII and RsaI. The Restriction Endonuclease Picker v.1.3 (Collins and Rocap 2007) was used to identify suitable restriction enzymes based on a representative data set of partial mcrA sequences obtained from this study, public databases and unpublished data. Only mcrA sequences originating from mesophilic environments were included. The identified enzymes were experimentally tested for T-RFLP analysis of the mcrA amplicons by digesting varying amounts of DNA (1–10 ng) with different enzyme concentrations (0.05–1 unit) and incubation times (5–120 min), respectively.

Cloning and sequencing of mcrA amplicons Cloning and sequencing of the mcrA amplicons were conducted as described by Lv et al., (2014). Briefly, the cloned fragments were reamplified and screened by T-RFLP analysis using the FAM-lableled mcrA primer set and methods as described above. For sequencing, selected clones were instead reamplified using the vector specific M13uni(-21) /M13rev(-29) primer set as described previously (Kleinsteuber et al., 2006). The obtained partial mcrA gene sequences were accordingly assigned to specific T-RF values. The closest cultured relatives of the sequenced clones were identified by using the BlastX algorithm and the NCBI database excluding uncultured sample sequences (http://blast.st-va.ncbi.nlm.nih.gov/Blast.cgi). The obtained partial mcrA sequences have been deposited under the EMBL-EBI accession numbers LN614730 to LN614755 in the European Nucleotide Archive.

Amplicon pyrosequencing The bacterial communities of the three reactors at sampling week 44 were analysed by amplicon pyrosequencing of the

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bacterial 16S rRNA genes using the 454-pyrosequencing platform GS Junior (Roche) as described previously (Ziganshin et al., 2013). Raw sequence data were processed with the QIIME 1.8.0 Virtual Box release (Caporaso et al., 2010) according to Kuczynski et al., (2011) and QIIME documentation (http://qiime.org/). Briefly, the raw reads were de-multiplexed and quality filtered using default settings, except that reads with lengths between 150 and 590 bp were kept and read end sections of 50 bp dropping below the quality score threshold of 25 were trimmed. Further, quality filtering and the clustering into operational taxonomic units (OTU) based on the 97% identity threshold were achieved by the implemented USEARCH pipeline (Edgar 2010). Only OTU identified as non-chimeric (de novo and reference-based check) and being represented by at least five reads were kept. For the taxonomic classification, the latest Greengenes reference OTU builds (release ‘gg 13 8’) (McDonald et al., 2012) and the RDP Classifier 2.2 were used (Wang et al., 2007). The taxonomic alignment was based on the Infernal algorithm using default settings (Nawrocki, Kolbe and Eddy 2009). The filtered OTU abundance tables were finally summarized based on their taxonomy and visualized via spreadsheet programme. De-multiplexed sequences from each reactor were deposited under the EMBL-EBI accession number PRJEB7413 (http://www.ebi.ac.uk/ena/data/view/PRJEB7413).

RESULTS Abiotic reactor parameters A broad range of abiotic parameters was determined during long-term reactor operation. In all three reactors, the TS was on average 8.3% (±0.6%), whereas the VS was on average 78.2% (±1.8%) (standard deviation in brackets). The average ammonium concentrations during the first 4 weeks were 1706 mg L−1 (±136), 1652 mg L−1 (±121) and 1661 mg L−1 (±159) in R1, R2 and R3, respectively. During the sampling period, the ammonium concentrations steadily decreased and reached average concentrations of 1070 mg L−1 (±141), 1162 mg L−1 (±29) and 892 mg L−1 (±80) in R1, R2 and R3, respectively, during the last 4 weeks (standard deviation in brackets). Exemplarily, the abiotic parameters pH value, VOA/TIC ratio, acetate and propionate concentrations, fermenter temperature and energy yield of the CHP from the three investigated reactors are shown in Fig. 1. Additional abiotic parameters are shown in the supplemental information (SI reactor data.xlsx, Supporting Information). Fig. 1 illustrates that, in general, the three parallel reactors showed a comparable, relatively constant performance. The energy yield mirrored the mostly efficient reactor operation during this study (personal communication of the operator). Average pH values were around 8 (standard deviation ±0.2) with 7.6 at minimum and 8.6 at maximum. The samples of weeks 21, 35, 45 and 53 showed clearly increased VOA/TIC, acetate and propionate levels, which were not reflected by decreased pH values or lowered energy yields of the respective CHP. The fermenter temperature was relatively stable around 37◦ C (standard deviation ±1.2) in all reactors and was slightly correlated with the respective outdoor temperatures (data not shown). As an exception, in February 2012 corresponding to week 40 a temperature drop was recorded in the three reactors. Only R3 showed a prolonged phase of lowered fermenter temperature from week 30 to 45. However, across all reactors the temporal temperature shift was neither reflected by community shifts (see results below) nor by a decreased methane production and energy yield. On average, more than 80 MW per week were produced by each reac-

tor CHP, but the mean relative deviations were as high as 10, 12 and 14% for R1, R2 and R3, respectively. The fluctuations neither correlated with other biotic or abiotic parameters nor with the obtained energy yield. In particular, significant correlations between the T-RFLP profiles, major single T-RF and the abiotic parameters were neither detected by NMDS analysis applying the ‘envfit’ function nor by analysing the Spearman’s rank correlation coefficients (results not shown).

Long-term stability of the microbial communities The methanogenic community compositions and dynamics were analysed by mcrA-based T-RFLP fingerprinting revealing valuable information on the phylogenetic composition of the methanogenic communities on the genus level. In silico, the restriction enzymes BstNI and MwoI were able to resolve the methanogenic communities completely. However, MwoI revealed strong non-specific activity and incomplete digestion under all tested experimental conditions (results not shown). In contrast, BstNI showed specific activity and complete digestion in any case. Consequently, this restriction enzyme was exclusively used for the mcrA-based T-RFLP analysis in this study. In Fig. 2, the T-RFLP profiles of the methanogenic community from R3 are exemplarily shown including sequence information obtained by cloning and sequencing (SI-Table 1, Supporting Information). Similar results were obtained from R1 and R2 as shown in Fig. S1 and Fig. S2 (Supporting Information). The methanogenic communities of R1, R2 and R3 were clearly dominated by three major T-RF assigned to the genera Methanobacterium (T-RF 122), Methanosaeta (T-RF 128) and Methanoculleus (TRF 93) occurring at average relative abundances of 43, 31 and 17%, respectively, across all reactors. The presence of these TRF in all samples at relatively similar percentages is indicative of the long-term stability of the methanogenic community composition. Furthermore, a T-RF assigned to the family Methanobacteriaceae (T-RF 470) with relative abundances between 2 and 14% was present in all samples but just distantly related to a Methanobrevibacter strain as its closest cultured relative (Table S1, Supporting Information). Additionally, less abundant TRF were detected in most samples and assigned to the taxa Methanobacterium (T-RF 464) and Methanomicrobiales (T-RF 295, T-RF 417) (Fig. 2). All detected methanogenic taxa use the hydrogenotrophic pathway for methane production, except the obligate acetoclastic genus Methanosaeta. Interestingly, no T-RF affiliated to the genus Methanosarcina were detected in any of the three reactors at any time. Some T-RF present in eight samples at relative abundances around 2% were assigned to the genus Methanomassiliicoccus within the Methanomassiliicoccus luminyensis Cluster and to the archaeal candidate strain DCM1 within the Methanomethylophilus alvus Cluster, respectively. Fig. 3 shows the NMDS analysis of the mcrA-based T-RFLP data. Even though being operated as separate systems for years, the reactors harboured highly similar methanogenic communities with little temporal dynamics. This is illustrated by the NMDS analysis, where all sample points from the three reactors, denoted with the respective sampling time, formed one group. Therein, neither any temporal tendency regarding the TRFLP patterns of the individual reactors nor significant differences between the reactors were observed. To illustrate the degree of similarity, the same T-RFLP data set from R1, R2 and R3 was analysed in combination with T-RFLP data obtained from a two-stage lab-scale system (Z1, Z2) as shown in the NMDS plot in the upper-right insert of Fig. 3. The bacterial communities reflected by the T-RFLP patterns from R1, R2 and R3 now

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Figure 1. Selected operational parameters from the sampled reactors, comprising pH value (A), VOA/TIC value (B), acetate concentration (C), propionate concentration (D) and fermenter temperature (E) are represented along with the energy yield (F) from each CHP per week. Data from the different reactors were colour-coded as follows: R1, black; R2, red; R3, green.

appear as a separated cluster relatively to the reference systems Z1 and Z2. Fig. 4 shows the NMDS analysis of the T-RFLP data set obtained from bacterial 16S rRNA amplicons by RsaI digestion. Similar as observed for the methanogenic communities (Fig. 3), all three reactors harboured highly similar bacterial communities with little temporal dynamics. As with the methanogenic communities, bacterial community patterns showed random scattering rather than temporal or seasonal compositional shifts. This is further illustrated by the reference plot in the upper-left insert of Fig. 4 comparing the bacterial communities of the three reactors to two parallel thermophilic full-scale reactors S1 and S2. This plot shows stronger similarity of the bacterial communities of the full-scale reactors R1, R2 and R3 in relation to the reference reactors. In general, similar results were gained by the bacterial community analysis based on HaeIII digestion (Fig. S4, Supporting Information). However, here the T-RFLP patterns obtained by HaeIII indicated a slightly increased variation in R2.

Phylogenetic composition of the bacterial communities By amplicon pyrosequencing of the bacterial 16S rRNA genes, approximately 3000 high-quality sequence reads were obtained per reactor. The phylogenetic affiliation of the reads from the three reactors is shown on phylum level in Fig. 5. The respective rarefaction curves and a complete list of all OTU obtained in this study are illustrated in the supplemental information (SI-Fig. 5; SI OTU table bacteria.xlsx, Supporting Information). From the total 172 OTU obtained in this study, 112 OTU (65%) were shared by all reactors, including the most abundant OTU accounting for nearly 89.7% of all sequence reads. Besides, 35 OTU (20%) comprising 3.8% of all reads were shared by two particular reactors. Only 25 OTU (5%), accounting for 6.5% of all reads, were unique to one of the reactors. Thus, the bacterial community composition was highly similar in all three reactors as already illustrated by the T-RFLP results. The two phyla Firmicutes and Bacteroidetes as well as the candidate phylum WWE1 clearly dominated the bacterial communities. Together, these three phyla comprised

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Figure 2. T-RFLP profiles of the methanogenic community from R3 represented by the mcrA gene analysis obtained with BstNI are shown exemplarily. The data points were continuously numbered according to the sampling time [week]. T-RF dropping below a minimum abundance of 1% relative abundance in all samples were excluded. The major T-RF were assigned to methanogenic taxa, whereas their taxonomic notations were abbreviated as follows: Methanoculleus, Mc; Methanobacterium, Mb, Methanosaeta, Ms; and Methanobacteriaceae, Mbc. Besides, two T-RF were affiliated to Methanomassiliicoccaceae (Mm). For the missing time points, no sample was obtained. The respective sequences obtained in this study were deposited under the EMBL-EBI accession numbers from LN614730 to LN614755.

83, 85 and 82% of all sequence reads in R1, R2 and R3, respectively. The Firmicutes were the most abundant phylum in R1 and R2 as well as the second most abundant phylum in R3. Additionally, the Firmicutes were by far the most diverse taxonomic group with in total 70 OTU, mainly represented by the order Clostridiales in which the families Clostridiaceae and Ruminococcaceae were predominant. OTU affiliated with Sedimentibacter were equally present in the three reactors at a relative abundance of 10% among the Firmicutes-affiliated reads. Besides, further OTU affiliated to the order Clostridiales were detected at lower abundances (SI OTU table bacteria.xlsx, Supporting Information). The order Lactobacillales with the genus Streptococcus was detected in all three reactors whereupon its relative abundance was highest in R1 (13%) and R2 (6%). In contrast, the genus Lactobacillus was only detected in R3 at an abundance of 3% whereas the Streptococcusaffiliated OTU were less abundant (2%) than in R1 and R2. The second most diverse phylum Bacteroidetes was highly abundant in all three reactors and comprised in total 29 OTU which were all affiliated to the order Bacteroidales. The two most abundant OTU could only be assigned to the order level. Besides, two OTU were assigned to the families Bacteroidaceae and Marinilabiaceae (genus Ruminofilibacter). Furthermore, OTU affiliated to the families Porphyromonadaceae, Prevotellaceae, as well as

the candidate families SB-1 and Paraprevotellaceae were detected (SI OTU table bacteria.xlsx, Supporting Information). Additionally to the aforementionend phyla, the candidate phylum WWE1 was the most abundant phylum in R3 and still highly abundant in R1 and R2. Interestingly, the candidate phylum WWE1 was represented by only four OTU, and thus by far less diverse than the Firmicutes and Bacteroidetes. Only two of the WWE1-affiliated OTU, related to the candidate family Cloacamonaceae and its candidatus genera Cloacamonas and BHB21, comprised the preponderant majority of the according sequence reads (98%) in the three reactors (SI OTU table bacteria.xlsx, Supporting Information). Furthermore, OTU affiliated to the phylum Spirochaetes were detected in all three reactors with their highest relative abundance in R2 (5.4%). Therein, the most abundant OTU was assigned to the genus Sphaerochaeta (Sphaerochaetaceae). The phylum Chloroflexi was also detected in all three reactors with the highest relative abundance in R1 (6.7%). Most of these sequence reads were affiliated to the candidate genus T78 (Anaerolineacae). In addition, Planctomycetes, Actinobacteria and the candidate phylum OD1 as well as further low-abundant phyla were detected in this study and are listed in the supplemental information (SI OTU table bacteria.xlsx, Supporting Information).

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DISCUSSION Methanogenic communities

Figure 3. NMDS analysis of the methanogenic T-RFLP profiles (mcrA genes) obtained by BstNI from the reactors R1 (black), R2 (red) and R3 (green) based on Bray–Curtis dissimilarity. The data points were continuously numbered according to the sampling time [week]. The upper-right reference plot depicts the same NMDS analysis including additional reference samples from the first stage Z1 (blue) and the second stage Z2 (orange) of a two-stage lab-scale system (all data points were depicted as symbols). The reference plot stresses the high similarity of the methanogenic community composition in R1, R2 and R3.

Figure 4. NMDS analysis of the bacterial T-RFLP profiles (16S rRNA genes) obtained by RsaI from the reactors R1 (black), R2 (red) and R3 (green) based on Bray–Curtis dissimilarity. The weekly drawn, parallel samples from each reactor are depicted in continuous numbering. The upper-left reference plot depicts the same NMDS analysis including additional samples from the two reference full-scale reactors S1 (blue) and S2 (orange) (data points were depicted as symbols). The reference plot stressed the high similarity of the bacterial community composition in R1, R2 and R3.

All major members of the methanogenic communities were unambiguously resolved by T-RFLP analysis of the mcrA genes using the restriction enzyme BstNI. This was validated in silico with additional mcrA sequences from mesophilic biogas reactors digesting different substrates. In contrast, other restriction enzymes such as HaeIII and MspI, which were used in earlier studies for mcrA-based T-RFLP analysis (Nikolausz et al., 2013; Lv et al., 2014) were not suitable to distinguish all representative sequences in this study. Therefore, we suggest applying generally the restriction enzyme BstNI for mcrA-based high-resolution TRFLP analysis of methanogenic communities in mesophilic biogas reactors. Previous studies proposed that roughly 70% of the methane formation from anaerobic sludge would originate from the acetoclastic pathway as discussed in Demirel (2014). In contrast, recent studies reported that hydrogenotrophic methanogenesis strongly dominates the biogas formation from energy crop (co-)digestion since high abundances of Methanomicrobiales, in particular Methanoculleus were detected (Schluter et al., 2008; ¨ Nettmann et al., 2010). Our study revealed that hydrogenotrophic and acetoclastic methanogens co-occurred in all reactors at any time. T-RF assigned to the hydrogenotrophic genera Methanobacterium (Methanobacteriales) and Methanoculleus (Methanomicrobiales) were together twice as abundant as T-RF affiliated to the obligate acetoclastic genus Methanosaeta (Methanosarcinales), confirming the dominance of the hydrogenotrophic pathway with energy crops as major feedstock (Demirel 2014). However, in contrast to the aforementioned studies, in our reactors Methanomicrobiales including Methanoculleus were less abundant than the Methanobacteriales. The reactors in the present study were characterized by relatively long HRT, low VFA levels but comparably high pH values, low ammonium concentrations and moderate OLR, respectively. This is in line with the detection of specialized Methanosaetaceae at relatively high abundances. Interestingly, generalists belonging to the Methanosarcinaceae appear to be absent in all samples at any time, which was reported previously from other full-scale reactor systems as well (Nettmann et al., 2010; Sundberg et al., 2013). The Methanosarcinaceae have usually been considered as highly important for an efficient biogas process, as comprehensively reviewed by De Vrieze et al., (2012). In fact, members of the Methanosarcinaceae are metabolically more versatile than any other methanogenic group (Costa and Leigh 2014). In particular, Methanosarcinaceae seem to occur predominantly in biogas reactors digesting concentrated waste materials and different kinds of manure while applying higher OLR. They are characterized by a relatively high resistance to adverse process conditions such as high ammonia or salt concentrations, or fluctuating operational conditions in general such as temperature shifts (De Vrieze et al., 2012). Due to their higher growth rates, Methanosarcinaceae may dominate over Methanosaetaceae at high VFA levels accompanied by low pH whereas Methanosaetaceae are characterized by a higher substrate affinity instead (Demirel and Scherer 2008), thus outcompeting Methanosarcinaceae under low acetate levels as it was apparently the case under the given moderate OLR applied to the investigated reactors. Furthermore, the reactors were supplemented with a mixture of trace elements. In particular, nickel, cobalt and molybdenum were previously shown to improve the biogas production from maize silage (Evranos and Demirel 2015). Thus, the absence of Methanosarcinaceae in the investigated reactors cannot be explained by a scarcity of these trace elements.

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Figure 5. Phylogenetic composition of the bacterial communities in parallel samples taken at week 44 from R1, R2 and R3. Relative abundances of the detected phyla are based on pyrosequencing of bacterial 16S rRNA gene amplicons. De-multiplexed sequence data from each reactor were deposited under the EMBL-EBI accession number PRJEB7413.

A methodical bias was excluded, as BstNI T-RF of Methanosarcina-affiliated sequences in the representative mcrA sequence data set would have been distinct from the other taxa. Additionally, the applied mcrA primer set was shown to cover all methanogenic groups relevant in biogas reactors including the genus Methanosarcina (Steinberg and Regan 2008; Nikolausz et al., 2013; Lv et al., 2014). In general, the relatively high and stable proportion of Methanosaeta among the methanogenic communities was in line with a previous study, which proposed using the presence of Methanosaeta as an indicator for process stability (Blume et al., 2010). It should be noted that the taxonomic notation Methanosaeta (and Methanosaetaceae) was rejected recently in favour of the valid genus Methanothrix as discussed by Tindall (2014). However, to prevent confusion we use the established notation here as it is commonly used in very recent studies (Demirel 2014; Vanwonterghem et al., 2014; Werner et al., 2014). Interestingly, some low-abundant T-RF present in a few samples were affiliated to the recently established seventh order of methanogenic Euryarchaeota, which represents a deepbranching lineage related to the wall-less Thermoplasmatales (Paul et al., 2012). Members of this order have been detected in various habitats, including marine and freshwater sediments, soil, arthropod and mammalian digestive tracts as well as in anaerobic reactors (Borrel et al., 2013), but their ecophysiological role in anaerobic digesters remains elusive.

Bacterial communities Amplicon sequencing of bacterial 16S rRNA genes revealed that besides the phyla Firmicutes and Bacteroidetes, the candidate phylum WWE1 was highly abundant in the studied reactors. The candidate phylum WWE1 was first reported from a mesophilic anaerobic reactor treating municipal wastewater sludge and is regarded as a phylogenetic sister group of the Spirochaetes and Verrucomicrobia (Chouari et al., 2005). Recent studies detected this candidate phylum in reactors treating sludges from wastewater treatment plants (Pelletier et al., 2008), but also in municipal solid waste reactors (Cardinali-Rezende et al., 2012; Limam et al., 2014), biogas plants digesting dairy manure or swine slurry (Merlino et al., 2012; Li, Chen and Yu 2014), and a full-scale reactor charged with concentrated cassava processing wastewater (Gao et al., 2014). These studies reported the occurrence of WWE1affiliated taxa at relative abundances of below 5% up to 27%. This study illustrates for the first time that the candidate phylum WWE1 occurs at significant proportions with even higher

relative abundances in mesophilic full-scale reactors digesting energy crops. Interestingly, the WWE1-affiliated sequences clustered in an extremely low number of OTU compared to the sequences assigned to the Firmicutes and Bacteroidetes, respectively. Also Li, Chen and Yu (2014) detected a relatively large OTU affiliated to WWE1 which comprised a significant amount of sequence reads. These findings imply that the candidate phylum WWE1 occurs at significant proportions in anaerobic reactors digesting energy crops on the one hand but is characterized by a remarkably low OTU richness on the other hand. In the study ` of Riviere et al., (2009), WWE1 was detected at abundances of up to 12% but did not belong to the ‘core community’ as it was not present in all of their studied reactors digesting sludge from wastewater treatment plants. This finding points at a potential adaptation of WWE1 to specific reactor conditions. As Limam et al., (2014) demonstrated its contribution to the degradation of cellulose in batch experiments, this candidate phylum may play an important role in full-scale CSTR digesting cellulose-rich substrates such as maize silage and other energy crops. However, the ecophysiological role of WWE1 still remains largely unknown and its potential contribution to syntrophic amino acid and propionate degradation was also discussed previously (Pelletier et al., 2008). In contrast to the detection of WWE1, the presence of Firmicutes and Bacteroidetes at high abundances has been commonly reported for anaerobic reactor systems digesting various substrates, e.g. (Schluter et al., 2008; Sundberg et al., 2013; Zigan¨ shin et al., 2013). In particular, taxa affiliated to the Clostridiaceae and Ruminococcaceae are well known to be involved in the degra¨ dation of fibre-rich feedstocks such as maize silage (Strauber, ¨ Schroder and Kleinsteuber 2012). The ecophysiological traits of other phyla detected in this study and potential syntrophic interactions between bacterial and archaeal taxa have been discussed elsewhere (Werner et al., 2011; Sundberg et al., 2013; Ziganshin et al., 2013; Demirel 2014; Li, Chen and Yu 2014).

Deterministic selection rather than stochastic effects governed the microbial community assemblages The bacterial and methanogenic communities of the investigated reactors were characterized by a remarkable long-term stability reflecting the relatively constant performance of the reactors. Although the same initial inoculum had been used for reactor start-up, the three systems have been separately operated without any exchange of fermenter content for more than 18 months previous to and during this study. Significantly, the

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microbial communities within all reactors remained highly similar in the long term. To our knowledge, for full-scale CSTR in parallel operation digesting energy crops exclusively without manure addition this is reported here for the first time. The highly reproducible routine operation scheme applied to the identically constructed reactors strongly determined their microbial assemblages rather than stochastic effects as the latter would presumably have led to more different community assemblages in the long term, as described for lab-scale reactors (Zhou et al., 2013; Lv et al., 2014). This is further supported by the results of previous studies of lab- and full-scale reactors digesting various substrates. Vanwonterghem et al., (2014) showed that the reproducible succession of the microbial communities in parallel lab-scale reactors digesting alpha-cellulose as a model substrate was governed strongly by deterministic effects. Likewise, Werner et al., (2014) found deterministically shaped and relatively stable microbial communities in parallel lab-scale reactors digesting swine waste during a three-year operation period. First, the microbial communities developed highly similar in all four reactors during the identical startup phase and the first year of operation. Subsequently, parallel operation was continued for two years with modified OLR and process temperatures in all reactors but in addition with an increased ammonia load in just two of the reactors whereas the remaining ones served as a control. During this two-year phase, in each reactor pair under the given parallel operational conditions highly similar community dynamics were observed (Werner et al., 2014). Furthermore, the influence of operational conditions like OLR, process temperature and feedstock composition on the community dynamics was reported from several full-scale anaerobic reactors of different construction and operation (Sundberg et al., 2013). In addition, in their long-term study Werner et al., (2011) reported stable and resilient microbial communities in nine anaerobic reactors from different wastewater treatment facilities digesting brewery wastewater, respectively. In general, the results of our and the above-mentioned studies imply that deterministic effects significantly shape community assemblages in parallel full-scale reactors in the long term. Furthermore, it should be noted that the reactor scale itself may significantly influence long-term microbial community dynamics according to the theory of island biogeography as discussed previously (van der Gast et al., 2006). Hence, large-scale CSTR may eventually harbour more stable microbial communities than small-scale systems as the particular and specific ecological niche spaces are enhanced (van der Gast et al., 2006). In general, process-relevant abiotic factors appear to fluctuate less drastically in full-scale reactors. Thus, respective stochastic effects on the community assemblage seem to be less important ´ in full-scale compared to lab-scale systems (Fernandez et al., 1999; Zhou et al., 2013; Lv et al., 2014). These aspects further imply that results from lab-scale community studies may not be directly transferred to full-scale without critical verification of relevant aspects such as fluctuations of abiotic parameters and scale-dependent factors (i.e. surface/volume ratio, particle size of substrates, feeding intervals, stirring regime, shearing forces, etc.). In addition, the results of this study illustrate that the longterm community assemblage was not significantly influenced by the different operational history of the reactors. This illustrates that obvious process disturbances such as foam formation or reduced gas production may not be directly linked to severe and irreversible alterations of the community assemblage. In a few samples, increased acetate and propionate concentrations accompanied by a higher VOA/TIC ratio were de-

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tected but not representative for the particular time point as they were not reflected by the molecular fingerprinting results. Instead, these fluctuations can be explained by sporadic inconsistencies during sampling which was conducted by the personnel of the biogas plant during routine operation (i.e. delayed freezing). The fluctuations of the electric energy yield mirroring the overall process efficiency originated mainly from scheduled and unscheduled downtimes of the respective CHP. However, the stable methanogenic and bacterial community profiles obtained in this study illustrated that the agitator power and applied stirring intervals were sufficient for complete mixing of the reactor content. Due to harsh winter conditions with low outdoor temperatures and incomplete compensation by the internal heating and temperature control system, the fermenter temperature dropped below the average for more than two weeks across all reactors at the same time and additionally in one reactor for a prolonged period. However, the microbial communities did not reflect this temperature drop or further fluctuations of operational parameters. This shows the high resistance of the microbial communities in the investigated reactors against moderate shifts of abiotic parameters in general.

Implications for microbial resource management in anaerobic digestion As our results show, the biogas production from energy crops as exclusive feedstock on moderate OLR is efficient and was characterized by long-term stable and deterministically shaped microbial communities. This knowledge may be required to develop appropriate measures to meet upcoming challenges in the field of commercial large-scale biogas production. According to the latest revision of the German Renewable Energy Act Bundesgesetzblatt (2014), a more demand-oriented biogas production and the sustainable utilization of rather inhomogeneous substrates (i.e. various agricultural or industrial organic wastes) instead of primary energy crops will be of increasing importance in the mid-term (Szarka et al., 2013). Eventually, this will result in new strategies for a flexible but throughout scheduled routine reactor management in future to facilitate an efficient and reliable biogas production under more fluctuating operational conditions. The microbial communities will be continuously faced with infrequent feeding intervals and changing feedstock qualities which result in temporary varying OLR and intermediate levels (Mauky et al., 2015). This would theoretically increase the number of ecological niches within the reactors. Thus, most likely, the microbial community assemblages may be characterized by higher community dynamics, a higher functional redundancy and a possibly higher diversity in contrast to in these days stably operated full-scale reactors. Interestingly, the process efficiency was not negatively influenced by intermittent feeding regimes as reported recently from lab-scale studies (Lv et al., 2014; Mauky et al., 2015). Furthermore, a higher phylogenetic variability was shown to correlate with higher process efficiency (Werner et al., 2011). However, such flexible operation schemes would be in strong contrast to the common practice to keep the operational conditions as stable as possible at any time in commercial full-scale biogas systems.

SUPPLEMENTARY DATA Supplementary data is available at FEMSEC online.

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ACKNOWLEDGEMENTS R (NAWARO

We gratefully thank Lars Wolf BioEnergie AG) for providing the sample material, process data and the fruitful cooperation. We also want to thankfully acknowledge Daniel Beyer (Department Bioprocess Technology) for supporting the sampling campaign and conducting the extensive physicochemical measurements. Ute Lohse (Department Environmental Microbiology) is acknowledged for her skilled technical assistance during the molecular analyses.

FUNDING This work was part of the Helmholtz Research Programme Renewable Energies, Topic Bioenergy. RL was supported by the Graduate School HIGRADE and the Helmholtz Initiative and Networking fund. Conflict of interest statement. None declared.

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