Biogas‐producing microbial composition of an anaerobic digester and associated bovine residues

Abstract Influenced by feedstock type and microbial inoculum, different microbial groups must precisely interact for high‐quality biogas yields. As a first approach for optimization, this study aimed to identify through time the biogas‐producing microbial community in a 10‐ton dry anaerobic digester treating cattle manure by denaturing gradient gel electrophoresis (DGGE) and metagenomics. Moreover, the associated bovine residues or feedstocks (leachate, manure, oxidation lagoon water, rumen) were also characterized to determine their contribution. A diverse and dynamic community characterized by Bacteria (82%–88%) and a considerable amount of Archaea (8%–15%) presented profiles particular to each stage of biogas production. Eukaryotes (2.6%–3.6%), mainly fungi, were a minor but stable component. Proteobacteria represented 47% of the community at the start of the run but only 18% at the end, opposite to the Bacteroidetes/Chlorobi group (8% and 20%, respectively), while Firmicutes (12%–18%) and Actinobacteria (12%–32%) remained relatively constant. Methanogens of the order Methanomicrobiales represented by several species of Methanoculleus were abundant at the end of the run (77%) contrary to Methanosarcinales (11%) and Methanobacteriales (0.7%). Therefore, methanogenesis mainly occurred by the hydrogenotrophic pathway. Manure and oxidation lagoon water seemed to contribute key microorganisms, while rumen dominated by Methanobrevibacter (72%) did not proliferate in the digester. Manure particularly possessed Methanoculleus (24%) and uncultured methanogens identified by DGGE, whereas oxidation lagoon was exclusively abundant in Methanolinea (18%) and Methanosaeta (19%). Leachate, as the microbial inoculum from a previous run, adequately preserved the biogas‐producing community. These results could lead to higher biogas yields through bioaugmentation strategies by incorporating higher proportions or an enriched inoculum from the relevant feedstocks.


| INTRODUC TI ON
Biogas plants are an attractive technology for sustainable generation of renewable energy. During anaerobic digestion a complex microbial community transforms organic wastes into biogas. Therefore, this practice exemplifies a sustainable solution for waste management and energy generation. The quantity and quality of the biogas, a mixture of methane, carbon dioxide and other trace gases, appears to be controlled by the type of biomass being digested and the microbial inoculum fed into the plant (Abendroth, Vilanova, Günther, Luschnig, & Porcar, 2015;Nettmann et al., 2010;Sun, Pope, Eijsink, & Schnürer, 2015;Weiland, 2010).
Traditionally, animal manure and sludge from wastewater treatment plants have been used to generate biogas (Weiland, 2010).
Animal manure and slurries from cattle and swine production have been estimated as one of the largest waste streams for biogas generation (Holm-Nielsen, Al Seadi, & Oleskowicz-Popiel, 2009). In the European Union, it is estimated that more than 1,284 million ton/year of manure is produced by cattle, according to an average of 38.5 kg manure days −1 head −1 (Holm-Nielsen et al., 2009). Worldwide, in year 2016 livestock, represented 1,475 million heads of cattle, which would roughly account for 21 billion ton of animal manure (FAOStat, 2016). If left untreated or inadequately managed, animal manure becomes a major environmental problem because of nutrient leaching (N, P), ammonia evaporation and pathogen contamination. In addition, livestock production is estimated to be responsible of 18% of greenhouse gas (GHG) emissions and the anthropogenic source of 9% carbon dioxide, 37% methane and 65% nitrous oxide (Steinfeld et al., 2006). However, in most countries, only a small percentage of this waste is currently processed to generate biogas. As countries commit internationally to reduce GHG and incorporate renewable sources of energy, biogas generation and its optimization gains importance. Treated manure would also generate a residual solid and liquid fraction rich in bioavailable nutrients termed the digestate, considered a valuable end product and a mode to recycle nutrients from agriculture as it is commonly used as a biofertilizer (Weiland, 2010).
The stable operation of an anaerobic digester is dictated by a dynamic equilibrium among four bacterial groups involved in the sequential digestion of the biomass from complex polymers to simpler components that are used by methanogens to generate methane. Therefore, it is crucial to elucidate the microbial community structure and function during digestion to understand and potentially optimize the process, as it can easily explain biodigester malfunctions. First, hydrolytic bacteria transform complex polymers into sugars and amino acids, followed by the process of acidogenesis and acetogenesis to generate organic acids that are transformed into acetate, H 2 and CO 2 for methanogenesis. Recently, it has been suggested that fungi might play an important role in the hydrolytic stage assisting bacteria in gaining access to recalcitrant plant materials (Bengelsdorf, Gerischer, Langer, Zak, & Kazda, 2012). Therefore, it is of interest to study the whole microbial community and not only the prokaryotic component. Usually, culture-independent techniques that rely on the analysis of the 16S rRNA gene such as sequence analysis of clone libraries, fluorescence in situ hybridization, denaturing gradient gel electrophoresis (DGGE), restriction fragment length polymorphism, and 16S amplicon sequencing are used to study these complex microbial interactions (Ariesyady, Ito, & Okabe, 2007;Bengelsdorf et al., 2012;Goberna, Insam, & Franke-Whittle, 2009;Sun et al., 2015;Tuan, Chang, Yu, & Huang, 2014;Whitford, Teather, & Forster, 2001;Zhou, Hernandez-Sanabria, & Guan, 2010).
However, these methods might present certain bias toward specific microbial groups and, since they are based on known sequences, do not cover all microbial diversity. Today, next generation sequencing allows a real-time assessment of the whole microbial community involved in the process with applications such as metagenomics that do not rely on PCR-targeted amplification. These data can be used to establish taxonomy, genome composition, and metabolic potential of the microorganisms in a sample. Nevertheless, as many microorganisms are still uncultured, not all microbial components of the community are identified, and bioinformatics methods and analysis platforms not always facilitate data interpretation (Menzel, Ng, & Krogh, 2016). Hence, it is still of relevance to compare results using different approaches.
In an interest to achieve higher biogas yields and improve the fertilizer value of the digestate, addition of other agricultural substrates or residues is being considered to increase the organic content of the treated biomass (Fantozzi & Buratti, 2009;Nettmann et al., 2010).
Considering bovine residues, there has always been an interest in the methanogenic composition of the rumen not only to increase meat production but also as a microbial inoculum to digest plant-material during anaerobic digestion (Fantozzi & Buratti, 2009;Zhou et al., 2010). Mexico is in the top ten producers and exporters of bovine meat in the world (SAGARPA, 2017). One of the largest meat producers in Mexico, in an effort to adopt sustainable practices, is implementing a biodigester to treat most of its wastes. Water from the oxidation lagoon of the company as well as fresh rumen and leachate from previous digester runs is incorporated into a dry mesophilic digester in order to efficiently treat animal manure. The objective of this study was to conduct a time-lapse composition analysis of the biogas-producing microbial community in a 10-ton anaerobic digester and its associated bovine residues by an integrated approach that contemplates DGGE and shotgun metagenomics. By analyzing the microbial component of the different feedstocks, we pretend to establish the best substrates for biogas generation by taking into account particular microorganisms already adapted to this type of waste.

| Sample collection
Residual biomass from the bovine industry associated with cattle raising and meat processing was evaluated as potential feedstocks and microbial inocula for biogas generation. Samples were taken from an oxidation lagoon (OL) treating wastewater from the meat production plant, leachate (L) from a previous biodigester run, 6month-old dried cattle manure (M) and fresh rumen (R) fluid. In addition, a pilot-scale dry anaerobic digester fed with these feedstocks and other supporting substrates (64.2% manure, 18.2% oxidation lagoon water, 3% rumen, 9.6% wood chips, 4.0% corn stover, 0.9% dust mill) was evaluated by sampling through time the recirculating leachate between two 10-ton serial reactors. The digester was operated under batch mesophilic dry condition (60% total solids and 32% All samples were kept at 4°C until DNA extraction. Feedstock samples were processed during the following days (4-7 days) after arrival at the laboratory and samples from the biodigester run were processed all together after the run concluded (22 days). A preliminary evaluation of sample processing time was conducted by comparing DGGE profiles (data not shown), the same community profiles were obtained from samples stored for 4-7 days compared to around one month.

| DNA extraction
Different volumes of the liquid samples (25 and 50 ml) were first evaluated to optimize extraction. Samples were filtered through a series of pore sizes to remove large particles (coffee filter, 20-25 μm and 2.5 μm) until a cell pellet was obtained at the last filtration step (0.45 or 0.22 μm). By comparing DGGE profiles (data not shown), it was established that 25 ml filtered to 0.45 μm was ideal to reduce sample manipulation in a timely matter. Filters from liquid samples and 0.5 g from solid samples were used for DNA extraction using the FastDNA Spin Kit for Soil (MP Biomedicals, USA). DNA quality was evaluated by agarose gel electrophoresis and spectrophotometry (Nanodrop 1000, Thermo Scientific, USA), and quantified by fluorometry (Qubit 2.0, Invitrogen, USA).
Denaturing gradient gel electrophoresis of PCR products was performed using a DCode Universal Mutation System (Bio-Rad, USA) in 1X TAE buffer with a 1.0 mm-thick vertical gel containing 6% (w/v) polyacrylamide (37.5:1 acrylamide:bisacrylamide) and a 35-55% (w/v) linear gradient of denaturants (100% denaturation solution contained 7 M urea and 40% (w/v) formamide). Gel wells were loaded with 35-45 µl of the nested PCR product according to agarose gel band intensity and 1 ⁄4-volume of loading buffer. Running conditions were 3.5 hr at 150 V. After, the gel was stained with ethidium bromide according to the manufacture's protocol, visualized on a UV transilluminator at 312 nm using a Molecular Imager ChemiDoc XRS System (Bio-Rad, USA). The most intense bands were excised in the middle with RNase/DNase clean scalpels and DNA was eluted according to Chory and Pollard (2001). An aliquot (2 µl) was used for PCR re-amplification using conditions described above and a second DGGE was run to confirm band purity. PCR products were cleaned with a PCR Clean-Up System (Promega, USA) and sequenced with the same primer pair without the GC clamp at Eton Bioscience, Inc.
(San Diego, USA). To determine the closest known relative species, sequences were blasted against the NCBI GenBank and MiDAS 2.1 database (Mcllroy et al., 2017). Sequences were deposited in NCBI under accession numbers MH393448-MH393458.
Similarities among DGGE community profiles were defined by analyzing gel images using ImageJ 1.48 (Rasband, 1997(Rasband, -2016. Bands of each lane were detected automatically and their relative intensity measured by the peak area. Bands with <1% intensity with respect to the total intensity of the lane were removed from the analysis. A Manhattan distance matrix was generated for pairwise comparisons between lanes using MeV_4_8 v. 10.2 (Saeed et al., 2003). This matrix was used for hierarchical clustering using the unweighted pair group method with arithmetic mean.

| Shotgun metagenomic analysis
Genomic DNA was used for library preparation using Nextera XT Archive under BioProject no. PRJNA378243. Relative read abundance as the proportion of raw reads of each taxon from the total amount of reads was used to assess the distribution of taxa across the different samples. Reads that were not assigned to any taxa at the phylum level were removed from the analysis.
For multivariate statistical analysis of metagenomic data, raw reads were normalized using DESeq2 (Love, Huber, & Anders, 2014) with the counts function and the parameter normalized=TRUE. A normalized data matrix of phyla from each microbial domain was used for hierarchical clustering using the package Pvclust (Suzuki & Shimodaira, 2006) with Ward's method and a bootstrap number of 10,000. This data matrix was also analyzed by principal component analysis (PCA) with Euclidean distances and the total amount of inertia equal to number of species, using the vegan package (Oksanen et al., 2017). All statistical analyses were conducted in R v.3.3.2 (R Core Team, 2016).

| Performance of the 10-ton anaerobic digester
During sampling, the biodigester showed a lag-phase of 6 days followed by biogas formation and 8 days of peak production until activity came to a halt ( Figure 1). Residual oxygen in the biodigester decreased from day 1 to 8 from 3.8% to 2.1% v/v and remained around 2% during biogas production. Volatile fatty acids (VFAs) in the digester leachate increased from 2.4 ± 0.2 g acetic acid L −1 , at the start of the run, to 14.4 ± 0.7 g/L at peak gas production and significantly decreased to a range of 4.6-7.6 g/L by the end of the run, indicating biomass breakdown and conversion of VFAs. Other performance parameters during the run showed a pH of 6.8-7.9, total inorganic carbon (TIC) of 5.6-9.3 g CaCO 3 L −1 and VFAs/TIC ratios of 0.4-1.34. At peak production, 9.7 ± 0.7 m 3 days −1 of biogas were produced with 49.6 ± 3.3% methane content. After 22 days of digestion, biogas and methane yield (46.1 L biogas kg VS −1 and 25.5 L CH 4 kg VS −1 , 55.3% CH 4 ) were in the low range of reported values for mesophilic treatment of cattle manure due to mechanical problems with the run (i.e. pump failure during leachate recirculation at day 4 to day 14, when it was re-establish). Although, biogas production peak during this period with an accumulation of VFAs that were later consumed. Fantozzi and Buratti (2009) reported productivities of 40 L CH 4 kg VS −1 for bovine fresh manure in a laboratory reactor (17 L working volume) and cited literature values of 170 to 220 L CH 4 kg VS −1 . However, these reports contemplate thermophilic operations and not necessarily dry fermentation conditions.

| Methanogen DGGE analysis
Our first approach contemplated DGGE analysis of archaeal PCR products from the V3 region of the 16S rRNA gene. In this technique, community profiles from different samples were evaluated by studying the most abundant members, which corresponded to the most intense bands in the DGGE profile ( Figure 2a). A total of 38 bands were cut from the gel and 68% of bands were successfully purified and sequenced. These bands were annotated based on their closest similarity to cultured or uncultured species in two gene databases.
Nine methanogen species were identified, and five bands were classified as uncultured archaeons (Table S1). Most sequences belonged to the Methanobrevibacter genus, which seemed to produce a mul- (OL) and rumen (R) shared similar methanogen compositions and differed from the biodigester run and other feedstocks.

| Whole-microbial community at high taxonomic level
To analyze all microbial components of the samples a metagen- As community dynamics of eukaryotes and bacteria influence the ability of methanogens to generate biogas, classified reads were further analyzed at lower taxonomic levels ( Figure 3). All samples en-  Percentage of classified reads out of the total amount of paired-end reads passing quality filters. reestablished by day B27, when biogas was steadily produced (B27 and B29, Figure 1). Therefore, peak biogas production was charac-

| Methanogen community at genus and species level
Overall, methanogen orders were represented by 12 genera and 33 species, which possessed ≥1% read relative abundance in at least one sample ( Figure 5 and Table S3). Hydrogenotrophic and acetotrophic methanogens were found in high abundances, contrary to methylo-  Relevant methanogen species in the biodigester run were consistent with the pattern described for genera. Methanoculleus, as one of the most abundant genera, was represented by eight species (Table S3), where M. marisnigri was the most abundant at the end of the run (7%) followed by M. horonobensis and strain MH98A (6%). On the contrary, Methanosaeta, F I G U R E 5 Distribution of methanogens at the genus level (≥1% relative abundance) in the biodigester run (a) and associated feedstocks (b-e). Proportions of the genus relative abundance are shown in the represented by Methanosaeta concilii and Methanosaeta harundinacea, were abundant at the start of the run (11%-13%). At peak biogas production the most abundant species in decreasing order were M. concilii (15%-17%), Methanolinea sp. strain SDB (8%-14%), Methanolinea tarda (4%-5%) and M. marisnigri (5%-4%). However, as mentioned above, Methanoculleus was represented by several species that add up to 20%-25% read relative abundance followed by Methanosaeta (18%-19%) and Methanolinea (12%-19%) species. Other species that appeared at the end of the run (B04) but in low abundance (around 2%) were  (Table S3).

| D ISCUSS I ON
Methanogens play a key role in biogas production and, therefore, have been the focus of many microbial community studies (Guo et al., 2015;Nettmann et al., 2010;Traversi, Villa, Lorenzi, Degan, & Gilli, 2012;Whitford et al., 2001;Zhou et al., 2010). Current high-throughput sequencing technologies allow a deeper insight into the whole microbial community structure and functioning, shifting the attention on understanding complex interactions to optimize biogas yield (Guo et al., 2015;Stolze et al., 2015;Sun et al., 2015;Wirth et al., 2012;Yang et al., 2014). In this study, two approaches were used to unravel the microbial community structure of a 10-ton anaerobic digester designed to treat bovine residues under mesophilic dry fermentation conditions. DGGE was used to target methanogens and a metagenomic approach was used to study the whole microbial community. As expected, DGGE failed to cover and resolve many species that were assessed by the metagenomic study. Multiple DGGE bands were assigned to the same methanogen species and some incongruities between techniques were detected probably due to DGGE identity assignment by band positioning (Figure 2). For example, the genus Methanobrevibacter,   -7, 10, 11, 17). Conversely, the metagenomic analysis identified ten Methanobrevibacter species and strains suggesting that some of these bands might correspond to different genotypes, underestimating this genus diversity (Table S3). Zhou et al. (2010) found similar DGGE patterns for several methanogen genera. For example, five bands appeared to correspond to different strain sequence types of Methanobrevibacter gottschalkii. Other unrepresented member in the DGGE analysis was the genus Methanoculleus with only one species identified (Figure 2, Band 24), while the metagenomic study recovered eight abundant species and strains (Table S3). Nonetheless, we were able to elucidate with both methods the most abundant methanogen genera that characterized each microbial community. Also, both approaches were consistent in the role that uncultured or unclassified methanogens might play during anaerobic digestion. DGGE bands classified as uncultured methanogens were among the most intense bands during the biodigester run ( Figure 2, Band 4, 15, 18). Accordingly, the metagenome study showed a high proportion of the reads (23%-48%) that corresponded to unclassified methanogens at the species level (Table S3). Thus, the DGGE analysis was relevant to qualitatively identify changes in the microbial community related to the uncultured component of the community.
Understanding how to maintain a balance among the four microbial metabolic stages of biogas production (hydrolysis, acidogenesis, acetogenesis, and methanogenesis) is key to improve productivity (Weiland, 2010). However, the microbial process of generating biogas cannot be generalized as it has been shown that microbial diversity and shifts in the community strongly depend on the type of substrate being treated and the reactor system (Abendroth et al., 2015;Bengelsdorf et al., 2012;Nettmann et al., 2010;Weiland, 2010). Nevertheless, extremely stable bacterial and methanogenic community profiles have also been reported (Goberna et al., 2009;Kampmann et al., 2012;Stolze et al., 2015), generally associated with higher taxonomic levels and attributed to functional redundancy among phylogenetic groups or being defined by a crucial process parameter such as high salt content (Goberna et al., 2009).
In this study, fungi were dominant players among the eukaryote microbial community (Figure 3a). Surprisingly, their abundance was almost the same in every sample but during biogas production represented a higher percentage of the community, which might suggest a key role during biogas generation (Table 1). Stable fungal presence in biogas plants has been reported (Bengelsdorf et al., 2012), but knowledge of their role remains unclear. It has been suggested that fungi assist in lignocellulose decomposition, penetrating the lignified material first for cellulolytic bacteria to gain access. Contrary to what was observed with Eukaryota, Bacteria composition considerably varied during biogas generation and among feedstocks (Figure 3b). It has been shown that for treatment of solid feedstocks, the community is usually dominated by Firmicutes (Abendroth et al., 2015;Stolze et al., 2015;Tuan et al., 2014;Wirth et al., 2012). Our results showed that Firmicutes only dominated the rumen (R) feedstock and, during biogas production, Firmicutes was third in frequency and characterized by a stable presence throughout time. Kampmann et al. (2012) also reported Firmicutes as a stable phylum during liquid manure treatment. Other studies associated with the digestion of liquid feedstocks as sludge have reported Proteobacteria, Firmicutes and Bacteroidetes as dominant bacterial phyla, followed by Actinobacteria and Chloroflexi (Guo et al., 2015;Yang et al., 2014).
Spirochaetes has also been observed as an abundant phylum along with Bacteroidetes when treating sludge (Abendroth et al., 2015). These six phyla collectively characterized the biodigester community that changed in abundance during the run. Each feedstock seemed to contribute a different bacterial group to the biodigester as a particular phylum dominated each residue: oxidation lagoon (OL) by Proteobacteria, manure (M) by Actinobacteria and rumen (R) by Firmicutes. During peak biogas production, Proteobacteria decreased in abundance opposite to Bacteroidetes/Chlorobi, while Firmicutes and Actinobacteria remained stable and Spirochaetes, a minor component, also increased (Figure 3b).
It seems that hydrolytic bacteria were present and active from the start of the run, first represented by members of the phyla Firmicutes and Actinobacteria, and then assisted by an increase of the Bacteroidetes/ Chlorobi group. Firmicutes and Bacteroidetes members possess cellulose and hydrogenase activity (Wirth et al., 2012), while Actinobacteria produce lignin-degrading enzymes that break down complex organic materials (Wirth et al., 2012;Yang et al., 2014). In addition, Firmicutes and Bacteroidetes participate in the fermentation of the generated products into organic acids, CO 2 and H 2 (Traversi et al., 2012;Wirth et al., 2012).
Also, Firmicutes can proceed with the consumption of butyrate and various VFAs (Ariesyady et al., 2007). These hydrolytic bacteria seemed to be assisted in glucose degradation by Spirochaetes and Proteobacteria known to consume glucose, propionate, butyrate, and acetate. The observed decrease of Proteobacteria in the biodigester, probably incorporated by the use of water from the oxidation lagoon, might be related to the capacity of other feedstocks to contribute microbial species already adapted to the treated substrates in the digester as rumen and manure, which were abundant in Firmicutes and Actinobacteria, respectively.
Abundance of Chloroflexi and Proteobacteria has been correlated to low biogas yield while Firmicutes and Bacteroidetes characterized high biogas production (Abendroth et al., 2015). In this study, the latter phyla were observed to remain stable or even increase in abundance during biogas peak production while the former decreased.

Methanogenesis as the last crucial step in anaerobic digestion
is where the stability of the process is more susceptible (Traversi et al., 2012). Our results showed a high representation of Archaea in the microbial community associated to the biodigester and leachate (8%-17%, Table 1). Other metagenomic studies of biogas plants have reported 6%-10% archaeal abundance (Guo et al., 2015;Stolze et al., 2015;Wirth et al., 2012). Methanogens were represented by the or- . The presence of this methanogen has also been correlated to critical process parameters such as high concentrations of ammonia and salt (Goberna et al., 2009;Nettmann et al., 2010). Methanoculleus is capable of forming biofilms increasing its capability to attach to solids and tolerate inhibitor substances and reactor disturbances (Abendroth et al., 2015;Goberna et al., 2009). This characteristic might explain the increase in abundance of this genus with time and entire dominance by the end of the biodigester run ( Figure 5). As methanogenesis pathways are well known for methanogen genera, it can be suggested that the main pathway in the biodigester was the hydrogenotrophic, as Methanoculleus is known to use H 2 and CO 2 to generate methane (Anderson et al., 2009 Methanosaeta is favored under low acetate conditions commonly found in sludge digesters (Abendroth et al., 2015;Guo et al., 2015;Yang et al., 2014). In the studied biodigester, it seems as the importance of the acetoclastic pathway shifts with time towards the hydrogenotrophic.
Presence of Methanoculleus has been correlated with high biogas yield, whereas Methanosaeta has been linked to low biogas output (Abendroth et al., 2015). Our results are congruent with the characteristics of the biogas plant under study and previous literature reports of similar biodigesters (Stolze et al., 2015;Wirth et al., 2012). The plant operates under dry fermentation conditions with a high content of total solids and treats bovine residues that are rich in ammonia and alkaline (Goberna et al., 2009;Nettmann et al., 2010;Weiland, 2010). High ammonia concentrations might inhibit susceptible methanogens, while alkaline substances help stabilize the reactor pH. All these conditions appeared to contribute to the predominance of Methanoculleus, which potentially forms biofilms over the treated substrates in close proximity to acetate-oxidizing bacteria allowing an adequate syntrophic relationship between H 2 producers and consumers (Abendroth et al., 2015;Weiland, 2010;Wirth et al., 2012;Zhao et al., 2013). This would ensure an optimum H 2 balance and an efficient operation of the biogas-producing microbial community.
Concerning the associate bovine residues, each feedstock possessed a characteristic methanogenic community. As expected, leachate (L) composition was similar to the biodigester but main- probably associated to the diet of the animals. Overall, feedstocks that significantly contribute to the biogas-producing microbial community in the biodigester were leachate (L), as expected, and oxidation lagoon (OL) and manure (M) more than rumen (R).
Finally, during anaerobic digestion of the bovine residues three distinct microbial community profiles of the bacterial and archaeal component were observed, with the exception of Eukarya mainly represented by a stable presence of fungi. These changes appeared to correlate to each stage of biogas production. At the beginning of the run (samples B17 and B22), during the first 6 days, an adaptation phase was observed where no biogas was produced.
This period was followed by biogas production, in the middle of the run (B27, B29), and, as a final phase, the last days of the run (B04) when biogas production reached zero.

| CON CLUS ION
At high taxonomic level, the two sets of information from DGGE and metagenomics correlated to some extent. Relevant methanogen genera as Methanoculleus and Methanobrevibacter were underestimated in the DGGE analysis. However, this technique was indispensable to discern the role that uncultured or unidentified methanogens played during biogas generation. The 10-ton dry digester presented a diverse and dynamic community of bacteria and methanogens, which correlated to a particular stage during biogas production. These community profiles appeared to be supported by specific members that characterized each feedstock or residue. Water from the oxidation lagoon and manure were the most relevant substrates, while rumen methanogenic members did not proliferate in the reactor. It was confirmed that leachate, as the biodigester microbial inoculum, adequately preserved the biogasproducing microbial community. Therefore, we were able to correlate presence of certain microorganisms in the biodigester to type of feedstock, which could lead to bioaugmentation strategies by incorporating a higher proportion or an enriched microbial inoculum from the most relevant feedstocks. Process adjustments would help reduce the adaptation phase in the digester and, consequently, decrease retention time and increase biogas yield if augmented microorganisms could further breakdown the organic waste.

CO N FLI C T O F I NTE R E S T S
The authors declare no conflict of interest.

AUTH O R CO NTR I B UTI O N S
CSG designed, analyzed and wrote metagenomic analysis, FACC performed DGGE experiment, JFRL, BTP and JHSC provided the biological material, designed the study and revised the manuscript, and AP designed the study, analyzed data and wrote the manuscript.
All authors read and approved the final manuscript.

E TH I C S S TATEM ENT
None required.

DATA ACCE SS I B I LIT Y
All data are included in the manuscript. DGGE sequences were de-