Article | Open | Published:

Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range

Scientific Reports volume 5, Article number: 14567 (2015) | Download Citation

  • An Erratum to this article was published on 20 January 2016

This article has been updated


Ruminant livestock are important sources of human food and global greenhouse gas emissions. Feed degradation and methane formation by ruminants rely on metabolic interactions between rumen microbes and affect ruminant productivity. Rumen and camelid foregut microbial community composition was determined in 742 samples from 32 animal species and 35 countries, to estimate if this was influenced by diet, host species, or geography. Similar bacteria and archaea dominated in nearly all samples, while protozoal communities were more variable. The dominant bacteria are poorly characterised, but the methanogenic archaea are better known and highly conserved across the world. This universality and limited diversity could make it possible to mitigate methane emissions by developing strategies that target the few dominant methanogens. Differences in microbial community compositions were predominantly attributable to diet, with the host being less influential. There were few strong co-occurrence patterns between microbes, suggesting that major metabolic interactions are non-selective rather than specific.


Ruminants are one of the most successful groups of herbivorous mammals on the planet, with around 200 species represented by approximately 75 million wild and 3.5 billion domesticated individuals worldwide1. Ruminants are defined by their mode of plant digestion, and have evolved a forestomach, the rumen, that allows partial microbial digestion of feed before it enters the true stomach. Ruminants themselves do not produce the enzymes needed to degrade most complex plant polysaccharides, and the rumen provides an environment for a rich and dense consortium of anaerobic microbes that fulfil this metabolic role. These rumen microbes ferment feed to form volatile fatty acids that are major nutrient sources for the host animal and contribute significantly to ruminant productivity. The host also uses microbial biomass and some unfermented feed components once these exit the rumen to the remainder of the digestive tract. Ruminants have evolved various rumen anatomies and behaviours to thrive on a range of plant species, and this flexibility has enabled them to occupy many different habitats spanning a wide range of climates2. These were also important factors in their domestication, allowing conversion of human-indigestible plant material into readily-accessible animal goods, especially dairy products, meat, and useful fibres. Ruminants have thus played a vital role in sustaining and developing many human cultures, as well as being used as draft animals and having religious and status values.

Rumen microbes can be assigned to different functional groups, such as cellulolytics, amylolytics, proteolytics, etc., which degrade the wide variety of feed components or further metabolize some of the products formed by other microbes3. For example, methanogens, the methane-forming archaea, are among those that metabolize hydrogen formed by some fermentative microbes to form methane. The methane generated during this fermentation contributes to global anthropogenic greenhouse gas emissions4 and represents a 2–12% loss of feed energy for the animal5. Differences in rumen microbial communities underlie variations in methane formation6 and the conversion of feed to animal products7,8. Therefore, understanding these communities is key to understanding ruminal transformations of plant material to both undesirable and useful ruminant products.

The aim of this study was to determine the composition of the microbiota in rumen and foregut samples from 742 individual animals from around the world. The resulting dataset allowed us to determine that dietary factors dominate over host species in determining microbial community composition, identify the dominant microbes and their potential associations, and describe the degree of similarity of rumen microbial communities worldwide.

Results and Discussion

This is the largest single study to examine microbial communities across a range of ruminant and camelid species, diets, and geographical regions. A standardised pipeline was used to process samples in order to minimise variation introduced by processing steps such as DNA extraction or PCR amplification. This is important for detecting authentic patterns rather than ones introduced by methodological differences between different studies9. The primers chosen amplify, to the best of our current knowledge, the target gene regions from nearly all known bacteria, archaea, and rumen ciliates.

Dominant rumen microbes

Despite the range of ruminants with different feeding strategies and diets, similar rumen bacteria were abundant around the world (Fig. 1). There was some variation in bacterial community compositions in animals from different regions, likely to be caused by differences in diet, climate, and farming practices. The 30 most abundant bacterial groups (Greengenes10 taxonomy summarised at the genus-level) were all found in over 90% of samples, and together comprised 89.4% of all sequence data (Supplementary Table 1) and were similar to those described in an earlier meta-analysis of rumen microbial communities11. All 30 are known rumen-inhabiting bacteria. Because the samples came from a wide range of ruminant species, diets, and geographical locations, these data suggest that new dominant bacteria are not likely to be found in future studies. The seven most abundant bacterial groups comprised 67.1% of all bacterial sequence data, were detected in all samples (Supplementary Fig. 1), and can be considered the “dominant” rumen bacteria. They were Prevotella, Butyrivibrio, and Ruminococcus, as well as unclassified Lachnospiraceae, Ruminococcaceae, Bacteroidales, and Clostridiales. These might be considered a “core bacterial microbiome” at the genus level or higher, because they are present in a large selection of ruminants, so confirming the suggestion that there is a core rumen microbiome9. However, these bacterial groups were not equally abundant in all animal species (P ≤ 0.005; Supplementary Table 2). With the exception of Butyrivibrio12, these groups are not adequately represented by characterised cultures13, and their functions are not well understood.

Figure 1: Origins of samples and their bacterial and archaeal community compositions in different regions.
Figure 1

Numbers below pie charts represent the number of samples for which data were obtained. The most abundant bacteria and archaea are named in clockwise order starting at the top of the pie chart. Further details of samples and community composition are given in Supplementary Tables 1, 2, 3, and 4 and Supplementary Data 1. Mmc. Methanomassiliicoccales. The map was sourced from Wikimedia Commons (, original uploader Roke, accessed May 2013). Pie charts were produced in Microsoft Excel and the composite image generated with Microsoft PowerPoint and Adobe Illustrator.

Inspection of the most abundant and prevalent bacterial operational taxonomic units (OTUs) in the dataset showed that only 14% fell within a named species, and 70% were not even within a formally recognised genus (Fig. 2a). When cultured isolates from as-yet unnamed species were included in the analysis, the dominant OTUs were better (35%) but still poorly represented by cultures that belonged to potentially the same species (Fig. 2b). This study shows that, while we appear to recognize the dominant rumen bacteria, considerable microbiological effort is still required to understand them. Some efforts have been made to isolate more cultures and gather more information about these bacteria13,14. For example, the genomes of Prevotella aff. ruminicola Tc2-24, rumen bacterium R-7, and other isolates whose 16S rRNA gene sequences are similar to those of dominant rumen bacterial OTUs (Fig. 2b), have been sequenced as part of the Hungate1000 project15.

Figure 2: Dominant bacterial and archaeal operational taxonomic units (OTUs).
Figure 2

Similarities (Supplementary Tables 8 and 9) of the 50 most abundant and 50 most prevalent bacterial (77 unique OTUs, (a,b) and archaeal (64 unique OTUs, c,d) OTUs to the most closely related type (a,c) and cultured (b,d) strains are plotted together with prevalence and abundance data. Background shading indicates nominal within-species (dark grey), within-genus (mid grey) and below genus (light grey) similarities. Prevalence indicates the percentage of samples that an OTU occurs in. The size of each circle indicates the mean abundance of each OTU (Supplementary Tables 8 and 9). Bacterial OTU abundances were multiplied by a factor of 15 relative to archaeal OTUs. Mbb. Methanobrevibacter.

Because there is a flux of both liquids and solids through the rumen16, microbes must actively metabolize to gain energy and multiply to counteract washout and so maintain populations in the rumen17. The dominant bacteria found in this study are therefore likely to be responsible for the majority of the transformation of ingested feed in the rumen and camelid foregut, especially of cellulose, hemicellulose, pectin, starch, fructan, organic acids, and protein, as these are the major energy-yielding substrates used for microbial growth17. There is also a convergence of bacterial community structure in the rumen and in the crop of the hoatzin, a bird that relies on a foregut fermentation of ingested leaves18. Thus microbial community structure seems to be driven by the similarity of organ function extending across the rumen, the camelid foregut, and the crop of this unusual bird. More efforts should go into characterizing the metabolism and roles of these bacteria that are the responsible for the majority of feed fermentation, with the aim of enhancing animal productivity and reducing methane emissions.

Nearly all the archaea were identified as methanogens known to be residents of the rumen (Supplementary Table 3, Supplementary Text 1), and their relative abundances were comparable to previous studies19. The dominant archaeal groups were remarkably similar in all regions of the world (Fig. 1). This universality and limited diversity was also recently noted in survey of archaea in New Zealand ruminants20 and could make it possible to successfully mitigate methane emissions by developing strategies, such as vaccines or small-molecule inhibitors, that target the few dominant methanogens.

Members of the Methanobrevibacter gottschalkii and Methanobrevibacter ruminantium clades were found in almost all samples, and were the two largest groups, accounting for 74% of all archaea. Together with a Methanosphaera sp. and two Methanomassiliicoccaceae-affiliated groups, the five dominant methanogen groups comprised 89.2% of the archaeal communities (Supplementary Fig. 1), showing that rumen archaea are much less diverse than rumen bacteria. This likely reflects the narrow range of substrates they use. Methanomicrobium has previously been reported as abundant in ruminants in Asia19. In our study, they were found to comprise >5% of the archaeal community of some Australian, Brazilian, Chinese, North American, and South African cattle, as well as South African sheep, showing them to be widely distributed, but not universally prevalent. The five dominant methanogen groups were not equally abundant in all animal species groups (P ≤ 0.005; Supplemental Table 4). In contrast to bacteria, the rumen archaea are better represented by cultures, with 58% of the most abundant and prevalent OTUs falling within a named species, and all but 22% within named genera (Fig. 2c). All of the latter were members of Methanomassiliicoccales, which is an order of relatively poorly-characterised methanogens21 for which representative cultures of as-yet unnamed species and genera are available (Fig. 2d)22. The 50 most abundant OTUs accounted for 74.5% of the archaeal sequence data, again indicating a much lower diversity than in the bacteria, where the 50 most abundant OTUs made up only 11.0%.

By assigning physiologies (Supplementary Table 5) to the sequence abundance information (Supplementary Table 3), it can be concluded that 77.7% of archaea were hydrogenotrophic methanogens, while 22.1% had the ability to grow with hydrogen plus methyl groups derived from methanol or methylamines. Methanogens able to form methane from acetate (Methanosarcina spp. and Methanosaeta spp.) were extremely rare (<0.015%; Supplemental Data 1), as expected based on their general slow growth rates that would not allow them to be maintained in the rumen under normal conditions.

Almost all protozoal sequence data (>99.9%) were assigned to 12 genus-equivalent protozoal groups (Supplementary Table 6). It was apparent that the variability of protozoa between and within cohorts of co-located animals was much greater than that of bacteria and archaea (Supplementary Fig. 2). It has been reported that there is strong host individuality of rumen protozoal community structure9, and this is evident in our study. The genera Entodinium and Epidinium dominated, occurring in more than 90% of samples and representing 54.7% of protozoal sequence data (Supplementary Fig. 1). Many of the protozoal genera were present in greater than 70% of the samples, indicating a wide prevalence. Genera such as Enoploplastron and Ophryoscolex had a wider than expected host distribution. They are considered to be mainly present in sheep and cattle, respectively23, but we also found Enoploplastron in cattle, deer, and reindeer samples from twelve countries, and Ophryoscolex in buffalo, goats, deer, sheep, and giraffe samples from 18 countries. Although different rumen protozoa are reported to have limited host and geographical distributions, host specificity has been questioned24. It seems likely that further investigation will demonstrate greater ubiquity of the rumen protozoa.

Effects of diet and host on microbial community composition

Because the abundance of microbial groups varied between animal species groups and cohorts (Supplementary Tables 2 and 4; Supplementary Fig. 2), we looked for factors that might underlie this. Rumen and camelid foregut microbial community structure could be expected to be shaped by morphological, physiological, and even behavioural characteristics that evolved along with the varied feeding strategies in the various ruminant lineages2. Indeed, adaptation has resulted in a diversity of rumen sizes and passage rates of rumen contents, allowing ruminant species to exploit a range of feed types. In addition to feed composition effects25, these host adaptations might also play a role in regulating rumen microbial community structure. Because our dataset was from ruminants and camelids from different lineages consuming a range of diets, host and diet effects on rumen microbial community structure could be separated.

To look at diet and host effects, we classified the diets based on forage and browse or concentrate content (Supplementary Table 7) and grouped the animals according to their lineage (Supplementary Data 1). Microbial communities could clearly be discriminated by both host and diet (Fig. 3a), with bacteria being the main drivers behind the observed differences (Fig. 3b). This probably reflects their more diverse metabolic capabilities compared with the less versatile archaea and protozoa. We investigated the patterns of microbial abundances across hosts and diets (Fig. 3c, Supplementary Fig. 3–6). Ruminococcus, one of the dominant bacteria, was relatively evenly distributed, but this was an exception. For many bacteria, diet was the major factor determining relative abundance. Bacterial communities from forage-fed animals were similar to each other, those from concentrate-fed animals were similar to each other, but distinct from those in forage-fed animals, and those from animals fed mixed diets were intermediate between these. Unclassified Bacteroidales and Ruminococcaceae were more abundant in all animals fed forages. Some as-yet poorly characterised Bacteroidales are postulated to be able to degrade cellulose, and their genomes encode a broad range of plant polysaccharide degrading capabilities26,27, which could explain their pattern of distribution. In contrast, members of Prevotella and unclassified Succinivibrionaceae were more abundant in animals fed diets containing concentrate. Based on the physiologies of cultured relatives28,29, these are probably major producers of propionate and the propionate-precursor succinate, and so are responsible for the greater levels of propionate formed from concentrate-rich diets25. The abundance of only a few other major bacterial groups was associated with host lineage (Fig. 3c). For example, unclassified Veillonellaceae were proportionally more abundant in sheep, deer, and camelids (Fig. 3d). This may be related to differences in rumen and camelid foregut sizes, anatomy, and feeding frequencies compared to bovines2.

Figure 3: Effect of host species and dietary forage to concentrate ratios on microbial communities.
Figure 3

Diets were grouped (Supplementary Table 7) as forage-dominated (F), mixed forage-concentrate (50–70% forage, FC), mixed concentrate-forage (50–70% concentrate, CF), or concentrate-dominated (C). (a) Discriminant analysis of microbial communities in samples (represented by points coloured by animal and diet) revealed that both host and diet determined community composition. (b) Bi-plot that shows microbial groups (identified by colours) underlying the separation of samples in panel (a). Several bacterial groups strongly discriminate the samples by host and diet, indicated by their presence towards the outside of the bi-plot. Archaeal and protozoal groups are less discriminatory, and so are clustered nearer the centre. (c) The heatmap shows that bacterial abundances are differentially associated with diet and host (colour key shows the association score; see Supplementary Figs 3–5 for additional data). (d) Unclassified Veillonellaceae, and (e) Fibrobacter are examples of bacteria that caused bovines and caprids to cluster separately from other species in the heat map. The number of samples in each category is given in parentheses in panels (c–e). *indicates unclassified bacteria within an order or family.

The relative abundances of several major bacterial groups were affected by both host and diet (Fig. 3c). Unclassified Clostridiales were most abundant in bovines fed forage and least abundant in bovines fed high concentrate diets, while in caprids, cervids, and camelids these diet differences were far less pronounced. Butyrivibrio was most abundant in rumen samples from bovines fed mixes of forage and concentrates. Fibrobacter was most abundant in bovines fed forage. When concentrate was included in bovine diets, the relative abundance of Fibrobacter was decreased, but it was still more abundant than in other animals. To examine its distribution in more detail, we compared Fibrobacter abundances across different ruminant species and found significantly higher levels in bovines compared to deer, sheep, or camelids (Fig. 3e). These data suggest that Fibrobacter is favoured in the bovine rumen and, given that it is cellulose degrader30, may play an essential role in the degradation of plant fibre in cattle.

Overall, diet was a major determinant of bacterial community structure. This may be because physical and chemical characteristics of the feed determine the different microbial niches available. In contrast to the post-gastric mammalian digestive tract31, and due to the sheer volume of digesta and feed input, there is probably less shaping of the rumen microbial community by local host biological factors such as the immune system, secreted antimicrobial peptides, host-cell glycosylation, and host-derived nutrients.

Associations between rumen microbes

The abundance patterns within bacterial, archaeal, and protozoal communities in different hosts fed different diets showed that certain microbes exhibited parallel patterns of relative abundance (Fig. 3c, Supplementary Figs 2–6). We therefore looked for correlations within and between bacteria, archaea, and protozoa (Fig. 4 and Supplementary Fig. 7), reasoning that specific associations should be seen across diets, hosts, and geography. Negative correlations of abundances of groups were observed within the bacteria, archaea, and protozoa, including replacement effects between dominant groups within each of these (Supplementary Text 1 and Supplementary Fig. 7). Few strong positive correlations were found within bacteria, archaea, and protozoa. For example, there was a strong correlation between Veillonellaceae and the TG5 group, driven by their co-occurrence within cervids and caprids. These microbes may cooperate in the rumen, or they may share similar requirements and so certain hosts and diets would offer better opportunities for their growth. This explanation could also underlie the strong positive correlations observed between different groups of methylotrophic methanogens (Supplementary Fig. 7). They may be responding to diets rich in methyl groups, such as feeds with high levels of pectins or osmolytes such as betaine. The strongest correlation within protozoa was a positive one between Dasytricha and Isotricha. These two genera of holotrichous protozoa display very similar spectra of substrate use, including use of plant soluble sugars and storage carbohydrates24, again suggesting that co-occurrence may be due to exploitation of similar opportunities.

Figure 4: Associations between bacteria and archaea.
Figure 4

The network is based on association scores computed via regularised canonical correlation analysis with an absolute association score greater than 0.15. The colour of the lines indicates the strength of the association. The sizes of the diamonds and circles indicate the mean average abundance and microbial groups are identified by numbers (Supplementary Tables 1 and 3). Mbb. Methanobrevibacter, Mmc. Methanomassiliicoccales, *indicates unclassified bacteria within a family.

We also investigated associations between bacteria, archaea, and protozoa. Strikingly, no strong correlations were detected between archaea and protozoa (Supplementary Fig. 7). Methanogens are known to colonize protozoa, and this mutualistic relationship is believed to enhance methane formation in the rumen32. The occurrence of specific symbioses between methanogens and rumen protozoa has been speculated on, but not convincingly demonstrated33. The lack of strong co-occurrence patterns within this study indicates that these undoubtedly important associations are probably non-specific, or occur at a strain level. Further investigation is required to corroborate this interesting finding, as mechanisms that mediate the colonization of protozoa by archaea remain to be elucidated. These could have interesting evolutionary aspects if they allow non-specific interactions to form or are mediated by strain-specific mechanisms that confer different partner specificities within archaeal or protozoal species. In contrast, there were some positive associations between bacterial and protozoal groups. Most noticeable were the associations of Isotricha and Dasytricha with Fibrobacter. Fibrobacter were reported to decrease in abundance in animals where protozoa were eliminated34, indicating that there may be a mutually beneficial relationship between these protozoa and Fibrobacter, which are surface colonizers of plant material23.

No strong associations were found between the most abundant bacteria and archaea (Fig. 4). This was surprising, since rumen bacteria degrade feed and produce the substrates that methanogens use for growth, mainly hydrogen and methyl groups. In contrast, there were distinct positive associations between some less abundant bacteria and archaea. The strongest association was between bacteria such as the succinate-producing Succinivibrionaceae, the succinate-using Dialister, and the amino-acid-fermenting Acidaminococcus, and methanogens belonging to the Methanomassiliicoccaceae, Methanosphaera sp. A4, and Methanobrevibacter boviskoreani. Succinivibrio spp. degrade pectin28, and methanol is required for growth of Methanomassiliicoccaceae35 and Methanosphaera36, explaining part of this pattern. Other associations were between the methylotrophic methanogen Methanosphaera sp. ISO3-F5 and different bacteria, including members of Lachnospiraceae. These associations may be based on the ability of Lachnospiraceae to degrade pectin and so provide methanol as a substrate for the methylotrophs37. The associations between other Methanomassiliicoccaceae groups and various unclassified members of Bacteroidales suggest the possibility of yet further methanol-dependent metabolic interactions. In contrast to archaeal-protozoal interactions, these findings suggest that some archaeal-bacterial interactions are specific, inferring specialised mechanisms for partner recognition or very similar requirements for growth. The basis for these associations remains to be determined. However, the general lack of strong association patterns between protozoa and the major bacteria on the one hand, and the major methanogen groups on the other, suggests that conserved mechanisms may mediate the interactions between hydrogen producing and hydrogen consuming microbes, allowing flexible interactions. This may aid methane mitigation research, since interfering with these potentially universal mechanisms could slow the rate of hydrogen transfer and so slow methane formation38. It may also be that the interactions mainly occur via pools of common metabolites, especially where the end products of one group form the substrates of another.

The results of this survey showed that the rumen microbial ecosystem is dominated by a core community composed of poorly-characterised microbes, especially amongst the bacteria. Diet had more influence than animal species on rumen or camelid foregut microbial community composition. Rumen ecosystems are typified by strong metabolic interactions between microbes that facilitate the fermentation of plant material to products useful for both the host and other rumen microbes3,17,25,32. The relatively few co-occurrence patterns seen in this study suggest that these microbial interactions do not rely on exclusive associations, and could indicate considerable promiscuity between members of interacting functional groups. Analysis at metagenomic and metatranscriptomic levels could in future uncover whether common functional elements that facilitate interactions are shared among multiple species. It seems plausible that functional redundancy among the microbes9 means that multiple microbial species can fulfil the same function, with different combinations of microbes being co-selected depending on the diet. This flexibility of rumen microbial community structure would confer on the ruminant host the ability to exploit a variety of different plant feeds.


Geographical distribution and diversity of gastrointestinal tract content samples

A total of 742 samples from 32 species or sub-species of ruminants and other foregut fermenters in 35 countries and seven global regions were selected for sequencing of microbial marker genes (Fig. 1, Supplementary Data 1). The samples were from cattle, bison, and buffalo (bovines), sheep and goats (caprids), deer (cervids), and alpacas, llamas, and guanacos (camelids), including diverse breeds of domestic cattle, sheep, and goats, and were largely made up of small cohorts of four or more co-located individuals consuming the same diet. We included foregut samples of camelids in this study, recognizing that these organs have a common function but evolved separately39. The use of animals, including welfare, husbandry, experimental procedures, and the collection of samples used for this study, was, where applicable, approved by named institutional and/or licensing committees and performed in accordance with approved institutional and regulatory guidelines (please refer to Supplementary Data 1 for details of these).

Sample collection, DNA extraction, amplification and processing of samples for high-throughput sequencing

To minimise variation introduced by differing methodologies, such as choice of sampling or DNA extraction method40 and primer-driven gene amplification biases41, we used a standardised pipeline to process samples (unless indicated otherwise in Supplementary Data 1). Briefly, approximately 20 g of whole (i.e., solid and liquid) mid-rumen or camelid foregut contents were collected via stomach tube, cannula, or post mortem as previously described35. Samples were immediately frozen, freeze-dried, and then couriered to AgResearch. Freeze-dried samples were homogenised in a coffee blender and DNA was extracted from a representative 30 mg subsample using the PCQI method40,42. We assessed the structure of microbial communities by sequencing regions of bacterial and archaeal 16S rRNA genes and ciliate protozoal 18S rRNA genes in triplicate as described previously35,37 using primers comprised of (5′ to 3′) a sequencing adapter (A or B), a sample-unique 12-base error-correcting Golay barcode on one of each primer pair, a two-base linker, and a group-specific sequence targeting the marker gene. For bacteria, the primers were Ba515Rmod1 (adapter A-barcode-GT-CCGCGGCKGCTGGCAC) and Ba9F (adapter B-AC-GAGTTTGATCMTGGCTCAG). For archaea, the primers were Ar915aF (adapter A-barcode-GT-AGGAATTGGCGGGGGAGCAC) and Ar1386R (adapter B-CA- GCGGTGTGTGCAAGGAGC). For protozoa, the primers were Reg1320R (adapter A-barcode-TC-AATTGCAAAGATCTATCCC) and RP841F (adapter B-AA-GACTAGGGATTGGARTGG). Linker A was CCATCTCATCCCTGCGTGTCTCCGACTCAG and linker B was CCTATCCCCTGTGTGCCTTGGCAGTCTCAG. Amplicons were sequenced using 454 GS FLX Titanium chemistry at Eurofins MWG Operon (Ebersberg, Germany). Sample processing and pipeline reproducibility controls were performed to identify variation introduced during sample processing (Supplementary Text 1). Sequence data are available from GenBank [accession numbers PRJNA272135, PRJNA272136, and PRJNA273417].

Phylogenetic analysis of sequencing data

Pyrosequence data were processed and analysed using the QIIME software package version 1.843. Sequences over 400 bp in length with an average quality score over 25 were assigned to a specific sample via the barcodes. The number of bacterial, archaeal, and ciliate protozoal sequencing reads available for analysis are summarised in Supplementary Data 1. Sequence data were grouped into operational taxonomic units (OTUs) sharing over 97% (bacteria – UCLUST44), 99% (archaea - UCLUST) or 100% (ciliate protozoa – prefix_suffix option in QIIME) sequence similarity. Sequences were assigned to phylogenetic groups by BLAST45. Bacterial 16S rRNA genes were assigned using the Greengenes database version 13_510, archaeal 16S rRNA genes using RIM-DB version 13_11_1322 and ciliate protozoal 18S rRNA genes against an in-house database46. Bacterial and ciliate protozoal data were summarised at the genus level. Archaea were summarised at the species level. Samples for which low read numbers were obtained or that contained high proportions of sequences from “exogenous” bacteria (i.e., likely environmental contaminants such as Stenotrophomonas) were excluded from further analyses (Supplementary Text 1).

The identity of the most abundant and prevalent OTUs was determined using BLAST45 against sequences from type material and against all sequences (excluding sequences from model organisms or environmental samples) in the nt database47. Bellerophon (version 3, 200 bp window, Huber-Hugenholtz correction48) was used to identify chimeric OTU sequences. Sequence similarities greater than 97% and 93% were used as cut-offs to classify OTUs at species- and genus level, respectively. The rationale for these cut-offs was discussed by Kenters et al.49.

Simplified classification of dietary information and other factors

The range of diets consumed by the animals from which the samples came was highly diverse and complex. For this reason, and where the information was available, diets were categorised in terms of forage type, forage plant, and forage to concentrate ratio (Supplementary Table 7). Diets likely to contain >5% starch (e.g., whole or grain crops of maize, barley, wheat, rice, as well as pea, potato, sorghum, etc.) or >5% pectin (e.g., beets or legumes such as alfalfa and clover) were also identified. Animals that had been fed their respective diets for less than a two-week period were noted in Supplementary Data 1. Factors such as gender, age, modifications (e.g., cannulation), treatments (e.g., antibiotics, drench, surgery), farming conditions, season, contact with other animals, and sample processing steps that may affect apparent microbial community compositions (e.g., DNA extraction method, sample fraction used, sample storage, etc.) were also recorded (Supplementary Data 1). Where details were not provided, latitude, longitude, and elevation were estimated using Climate zones were designated according to the Köppen-Geiger climate classification scheme50.

Statistical analyses

The resulting dataset allowed us to establish whether animal or dietary factors relate to rumen and camelid foregut microbial community composition, identify the dominant microbes and their potential associations, and describe the degree of similarity of rumen and camelid foregut microbial communities worldwide. Statistical analyses of microbial data were performed using GenStat for Windows51, R software52, and QIIME43. Principal coordinate analysis of Bray-Curtis dissimilarity matrices, analysis of variance, sparse partial least squares discriminant analysis (sPLS-DA, using a sPLS regression approach), and canonical discriminant analyses (CDA) of microbial community composition data in context of the metadata (Supplementary Data 1) were used to identify impacts of factors such as host lineage, diet, etc. on rumen and camelid foregut microbial communities and to identify the groups associated with these factors. Pearson, Spearman, SparCC53, and regularised canonical correlation analyses (CCA) were used to identify associations within and between archaeal, bacterial, and protozoal groups. Association scores were visualised as relevance networks and clustered image maps (CIM, heatmaps) representing the first two dimensions. González et al. provides a comprehensive overview of sPLS-DA, CCA and the corresponding ‘pairwise associations’, network and CIM techniques and their application54.

Additional Information

How to cite this article: Henderson, G. et al. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci. Rep. 5, 14567; doi: 10.1038/srep14567 (2015).

Change history

  • 20 January 2016

    A correction has been published and is appended to both the HTML and PDF versions of this paper. The error has been fixed in the paper.



  1. 1.

    & Invited review: ruminant ecology and evolution: perspectives useful to ruminant livestock research and production. J. Dairy Sci. 93, 1320–1334 (2010).

  2. 2.

    Evolutionary steps of ecophysiological adaptation and diversification of ruminants: a comparative view of their digestive system. Oecologia 78, 443–457 (1989).

  3. 3.

    The Rumen and its Microbes. (Academic Press, 1966).

  4. 4.

    et al. Ruminants, climate change and climate policy. Nat. Clim. Change 4, 2–5 (2014).

  5. 5.

    & Estimates of animal methane emissions. Environ. Monit. Assess. 42, 113–141 (1996).

  6. 6.

    et al. Two different bacterial community types are linked with the low-methane emission trait in sheep. PLoS ONE 9, e103171 (2014).

  7. 7.

    , & Potential role of the bovine rumen microbiome in modulating milk composition and feed efficiency. PLoS ONE 9, e85423 (2014).

  8. 8.

    , , , & Effect of phenotypic residual feed intake and dietary forage content on the rumen microbial community of beef cattle. Appl. Environ. Microbiol. 78, 4949–4958 (2012).

  9. 9.

    Redundancy, resilience, and host specificity of the ruminal microbiota: implications for engineering improved ruminal fermentations. Front. Microbiol. 6, 296 (2015).

  10. 10.

    et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).

  11. 11.

    , & Status of the phylogenetic diversity census of ruminal microbiomes. FEMS Microbiol. Ecol. 76, 49–63 (2011).

  12. 12.

    et al. Relation between phylogenetic position, lipid metabolism and butyrate production by different Butyrivibrio-like bacteria from the rumen. Antonie van Leeuwenhoek 91, 417–422 (2007).

  13. 13.

    , , & Determining the culturable accessibility of the rumen bacterial microbiome. Microb. Biotechnol. 7, 467–479 (2014).

  14. 14.

    , , & Effect of media composition, including gelling agents, on isolation of previously uncultured rumen bacteria. Lett. Appl. Microbiol. 56, 63–70 (2013).

  15. 15.

    et al. Ruminant Nutrition Symposium: Use of genomics and transcriptomics to identify strategies to lower ruminal methanogenesis. J. Anim. Sci. 93, 1431–1449 (2015).

  16. 16.

    , , & The measurement of liquid and solid digesta retention in ruminants, equines and rabbits given timothy (Phleum pratense) hay. Br. J. Nutr. 48, 329–339 (1982).

  17. 17.

    Nutritional Ecology of the Ruminant. 2 edn, (Cornell University Press, 1994).

  18. 18.

    et al. Comparative analyses of foregut and hindgut bacterial communities in hoatzins and cows. ISME J. 6, 531–541 (2012).

  19. 19.

    & Structure of the archaeal community of the rumen. Appl. Environ. Microbiol. 74, 3619–3625 (2008).

  20. 20.

    , & Few highly abundant operational taxonomic units dominate within rumen methanogenic archaeal species in New Zealand sheep and cattle. Appl. Environ. Microbiol. 81, 986–995 (2015).

  21. 21.

    et al. Comparative genomics highlights the unique biology of Methanomassiliicoccales, a Thermoplasmatales-related seventh order of methanogenic archaea that encodes pyrrolysine. BMC Genomics 15, 679 (2014).

  22. 22.

    , , & RIM-DB: a taxonomic framework for community structure analysis of methanogenic archaea from the rumen and other intestinal environments. PeerJ 2, e494 (2014).

  23. 23.

    & The Rumen Protozoa. (Springer-Verlag New York Inc., 1992).

  24. 24.

    in Parasitic Protozoa Vol. 3 (eds & ) Ch. 1, 1–42 (Academic Press, Inc., 1993).

  25. 25.

    & Factors that alter rumen microbial ecology. Science 292, 1119–1122 (2001).

  26. 26.

    et al. Metagenomics of the Svalbard reindeer rumen microbiome reveals abundance of polysaccharide utilization loci. PLoS ONE 7, e38571 (2012).

  27. 27.

    et al. Do rumen Bacteroidetes utilize an alternative mechanism for cellulose degradation? mBio 5, e01401–e01414 (2014).

  28. 28.

    & Characteristics of two new genera of anaerobic curved rods isolated from the rumen of cattle. J. Bacteriol. 72, 22–26 (1956).

  29. 29.

    Vitamin B12-dependent propionate production by the ruminal bacterium Prevotella ruminicola 23. Appl. Environ. Microbiol. 58, 2331–2333 (1992).

  30. 30.

    et al. The complete genome sequence of Fibrobacter succinogenes S85 reveals a cellulolytic and metabolic specialist. PLoS ONE 6, e18814 (2011).

  31. 31.

    , & Interactions between the microbiota and the immune system. Science 336, 1268–1273 (2012).

  32. 32.

    , & The importance of methanogens associated with ciliate protozoa in ruminal methane production in vitro. Lett. Appl. Microbiol. 21, 230–234 (1995).

  33. 33.

    in (Endo)symbiotic Methanogenic Archaea Microbiology Monographs 19 (ed. ) 13–23 (Springer-Verlag Berlin Heidelberg, 2010).

  34. 34.

    , , & Real-time PCR detection of the effects of protozoa on rumen bacteria in cattle. Curr. Microbiol. 52, 158–162 (2006).

  35. 35.

    , , & Methanoplasmatales”, Thermoplasmatales-related archaea in termite guts and other environments, are the seventh order of methanogens. Appl. Environ. Microbiol. 78, 8245–8253 (2012).

  36. 36.

    & Methanosphaera stadtmaniae gen. nov., sp. nov.: a species that forms methane by reducing methanol with hydrogen. Arch. Microbiol. 141, 116–122 (1985).

  37. 37.

    Pectin-fermenting bacteria isolated from the bovine rumen. J. Bacteriol. 99, 189–196 (1969).

  38. 38.

    & Electron transfer in syntrophic communities of anaerobic bacteria and archaea. Nat. Rev. Microbiol. 7, 568–577 (2009).

  39. 39.

    Remarks on the stomach and the systematic position of the Tylopoda. J. Zool. 134, 207–215 (2009).

  40. 40.

    et al. Effect of DNA extraction methods and sampling techniques on the apparent structure of cow and sheep rumen microbial communities. PLoS ONE 8, e74787 (2013).

  41. 41.

    & Community structure analysis of methanogens associated with rumen protozoa reveals bias in universal archaeal primers. Appl. Environ. Microbiol. 78, 4051–4056 (2012).

  42. 42.

    et al. Nitrogen metabolism and rumen microbial enumeration in lactating cows with divergent residual feed intake fed high-digestibility pasture. J.Dairy Sci. 95, 5024–5034 (2012).

  43. 43.

    et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

  44. 44.

    Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

  45. 45.

    , , , & Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

  46. 46.

    & Characterization of rumen ciliate community composition in domestic sheep, deer, and cattle, feeding on varying diets, by means of PCR-DGGE and clone libraries. FEMS Microbiol. Ecol. 75, 468–481 (2011).

  47. 47.

    Type material in the NCBI Taxonomy Database. Nucleic Acids Res. 43, D1086–D1098 (2014).

  48. 48.

    , & Bellerophon: a program to detect chimeric sequences in multiple sequence alignments. Bioinformatics 20, 2317–2319 (2004).

  49. 49.

    , , , & Isolation of previously uncultured rumen bacteria by dilution to extinction using a new liquid culture medium. J. Microbiol. Methods 84, 52–60 (2011).

  50. 50.

    & Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorol. Z. 19, 134–141 (2010).

  51. 51.

    GenStat for Windows 14th Edition (Hemel Hempstead, UK, 2011).

  52. 52.

    R: A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria, 2014).

  53. 53.

    & Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).

  54. 54.

    , , & Visualising associations between paired ‘omics’ data sets. BioData Min. 5, 19 (2012).

Download references


We thank Ron Ronimus, Paul Newton, and Christina Moon for reading and commenting on the manuscript. We thank all who provided assistance that allowed Global Rumen Census collaborators to supply samples and metadata (Supplemental Text 1). AgResearch was funded by the New Zealand Government as part of its support for the Global Research Alliance on Agricultural Greenhouse Gases. The following funding sources allowed Global Rumen Census collaborators to supply samples and metadata, listed with the primary contact(s) for each funding source: Agencia Nacional de Investigación e Innovación, Martín Fraga; Alberta Livestock and Meat Agency, Canada, Tim A. McAllister; Area de Ciencia y Técnica, Universidad Juan A Maza (Resolución Proy. N° 508/2012), Diego Javier Grilli; Canada British Columbia Ranching Task Force Funding Initiative, John Church; CNPq, Hilário Cuquetto Mantovani, Luiz Gustavo Ribeiro Pereira; FAPEMIG, Hilário Cuquetto Mantovani; FAPEMIG, PECUS RumenGases, Luiz Gustavo Ribeiro Pereira; Cooperative Research Program for Agriculture Science & Technology Development (project number PJ010906), Rural Development Administration, Republic of Korea, Sang-Suk Lee; Dutch Dairy Board & Product Board Animal Feed, André Bannink, Kasper Dieho, Jan Dijkstra; Ferdowsi University of Mashhad, Vahideh Heidarian Miri; Finnish Ministry of Agriculture and Forestry, Ilma Tapio; Instituto Nacional de Tecnología Agropecuaria, Argentina (Project PNBIO1431044), Silvio Cravero, María Cerón Cucchi; Irish Department of Agriculture, Fisheries and Food, Alexandre B. De Menezes; Meat & Livestock Australia; and Department of Agriculture, Fisheries & Forestry (Australian Government), Chris McSweeney; Ministerio de Agricultura y desarrollo sostenible (Colombia), Olga Lucía Mayorga; Montana Agricultural Experiment Station project (MONB00113), Carl Yeoman; Multistate project W-3177 Enhancing the competitiveness of US beef (MONB00195), Carl Yeoman; NSW Stud Merino Breeders’ Association, Alexandre Vieira Chaves; Queensland Enteric Methane Hub, Diane Ouwerkerk; RuminOmics, Jan Kopecny, Ilma Tapio; Rural and Environment Science and Analytical Services Division (RESAS) of the Scottish Government and the Technology Strategy Board, UK, R. John Wallace; Science Foundation Ireland (09/RFP/GEN2447), Sinead Waters; Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación, Mario A. Cobos-Peralta; Slovenian Research Agency (project number J1-6732 and P4-0097), Blaz Stres; Strategic Priority Research Program, Climate Change: Carbon Budget and Relevant Issues (Grant No.XDA05020700), ZhiLiang Tan; The European Research Commission Starting Grant Fellowship (336355—MicroDE), Phil B. Pope; The Independent Danish Research Council (project number 4002-00036), Torsten Nygaard Kristensen; and The Independent Danish Research Council (Technology and Production, project number 11-105913), Jan Lassen. These funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Author notes

    • Omar Cristobal Carballo

    Present addresses: AgResearch Ltd. Grasslands Research Centre, Tennent Drive, Palmerston North 4442, New Zealand.

    • Alexandre B. De Menezes

    Present addresses: CSIRO Plant Industry, Canberra, ACT 2601, Australia.

    • Diego Javier Grilli

    Present addresses: ¶Instituto de Histología y Embriología de Mendoza, Universidad Nacional de Cuyo, Centro Universitario, Parque General San Martín S/N, 5500 Mendoza, Argentina.

    • Torsten Nygaard Kristensen

    Present addresses: ΦDepartment of Chemistry and Bioscience, Aalborg University, DK-9220 Aalborg East, Denmark.

    • Florian Leiber

    Present addresses: ¥Research Institute of Organic Agriculture (FiBL), CH-5070 Frick, Switzerland.

    • Cesar Pinares-Patiño

    Present addresses: #CSIRO, Canberra, ACT 2601, Australia.

    • Kevin Shingfield

    Present addresses: §Institute of Biological, Rural and Environmental Sciences, Aberystwyth University, Gogerddan, SY23 3EE, UK.

    • Andre-Denis G. Wright

    Present addresses: ǁSchool of Animal and Comparative Biomedical Sciences, University of Arizona, Tucson, Arizona, USA.

    • Johanna Zeitz

    Present addresses: ǂInstitute of Animal Nutrition and Nutritional Physiology, Justus Liebig University Giessen, 35392 Giessen, Germany.


  1. AgResearch Limited, Grasslands Research Centre, Palmerston North 4442, New Zealand

    • Gemma Henderson
    • , Faith Cox
    • , Siva Ganesh
    • , Arjan Jonker
    • , Wayne Young
    • , Graeme T. Attwood
    • , Mariano Batistotti
    • , Adrian L. Cookson
    • , Dragana Gagić
    • , William J. Kelly
    • , Suzanne Lambie
    • , Sinead C. Leahy
    • , Cesar Pinares-Patiño
    • , Xuezhao Sun
    •  & Peter H. Janssen
  2. Estacion Experimental del Zaidin, Consejo Superior de Investigaciones Cientificas, 18100 Armilla, Granada, Spain

    • Leticia Abecia
    • , Gonzalo Martinez
    •  & David R. Yáñez-Ruiz
  3. Animal Nutrition, Biotechnology and Bioindustry Center, Corporación Colombiana de Investigaciones Agropecuarias, Bogota, Colombia

    • Erika Angarita
    • , Claudia Ariza
    • , Edgar Macipe
    • , Olga Lucía Mayorga
    • , Lorena Mestre
    • , Sonia Ospina
    • , Tatiana Rodriguez
    •  & Ricardo Zambrano
  4. Facultad de Ciencias Veterinarias, Universidad de Concepción, Chillan, Chile

    • Paula Aravena
    •  & Jorge Avila-Stagno
  5. Área Microbiología, Departamento de Patología, Facultad de Ciencias Médicas, Universidad Nacional de Cuyo, Centro Universitario, Parque General San Martín S/N, 5500 Mendoza, Argentina

    • Graciela Nora Arenas
    •  & Diego Javier Grilli
  6. University of Cundinamarca, Cundinamarca, Colombia

    • Jose Mauricio Avila
    •  & Diana Parra
  7. Animal Nutrition, Wageningen UR Livestock Research, 6700 AH Wageningen, The Netherlands

    • André Bannink
  8. Departamento de Producción Animal, National University of Colombia, AA 1779, Medellín, Colombia

    • Rolando Barahona
    •  & Isabel Molina
  9. Copenhagen Zoo, DK 2000, Frederiksberg, Denmark

    • Mads F. Bertelsen
  10. Instituto de Investigaciones Agropecuarias, INIA Remehue, Osorno, Región de Los Lagos, Chile

    • Andres M. Carvajal
    • , Rodrigo de la Barra
    • , Maria Eugenia Martinez
    •  & Camila Muñoz
  11. Department of Animal Science, University of Vermont, Burlington, Vermont 05405, USA

    • Laura Cersosimo
    • , Suzanne Ishaq
    • , Hannah LaChance
    • , Woulter Van Hoven
    •  & Andre-Denis G. Wright
  12. Faculty of Veterinary Science, The University of Sydney, New South Wales 2006, Australia

    • Alexandre Vieira Chaves
  13. Department of Biological Sciences, Thompson Rivers University, Kamloops, B.C., V2C 0C8, Canada

    • John Church
    •  & Jonathan Van Hamme
  14. Microbiology, School of Biology and Environmental Science, Science Centre West, University College Dublin, Belfield, Dublin 4, Ireland

    • Nicholas Clipson
    • , Alexandre B. De Menezes
    • , Eva Lewis
    • , Michael O’Donovan
    •  & Brendan O’Neill
  15. Colegio de Postgraduados, Institución de Ensenanza e Investigación en Ciensias Agrícolas, CP 56230, Montecillo, Mexico

    • Mario A. Cobos-Peralta
  16. Instituto de Biotecnología, Instituto Nacional de Tecnología Agropecuaria, Los Reseros y Repetto, 1686 Hurlingham, Argentina

    • Silvio Cravero
  17. Campo Experimental La Posta, Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias, C.P. 94277, Col. Paso del Toro Municipio de Medellín de Bravo, Veracruz, Ver., México

    • Omar Cristobal Carballo
    •  & Alma Ximena Ibarra Gomez
  18. Rowett Institute of Nutrition and Health, University of Aberdeen, Bucksburn, Aberdeen, AB21 9SB, Scotland

    • Katie Crosley
    •  & R. John Wallace
  19. Cargill Animal Nutrition Innovation Center, Elk River, Minnesota 55330, USA

    • Gustavo Cruz
  20. Instituto de Patobiología, Instituto Nacional de Tecnología Agropecuaria, Los Reseros y Repetto, 1686 Hurlingham, Argentina

    • María Cerón Cucchi
    •  & Gisela Ariana Marcoppido
  21. Departamento de Microbiologia, Universidade Federal de Viçosa, Campus UFV, 36570-000 Viçosa, Minas Gerais, Brazil

    • Edenio Detmann
    • , William L. S. dos Reis
    •  & Hilário Cuquetto Mantovani
  22. Animal Nutrition Group, Wageningen University, 6700 AH Wageningen, The Netherlands

    • Kasper Dieho
    •  & Jan Dijkstra
  23. Agriculture & Agri-Food Canada, Lacombe, Alberta T4L 1W1, Canada

    • Mike E. R. Dugan
  24. Department of Animal Sciences, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

    • Seyed Hadi Ebrahimi
    •  & Reza Valizadeh
  25. Faculty of Land and Animal Resources, Agricultural University of Iceland, Keldnaholt, IS-112 Reykjavik, Iceland

    • Emma Eythórsdóttir
  26. Department of Agriculture, University of Zululand, KwaDlangezwa, Empangeni, 3886, South Africa

    • Fabian Nde Fon
  27. Departamento de Microbiología, Instituto de Investigaciones Biológicas Clemente Estable, Av. Italia 3318, CP 11600, Montevideo, Uruguay

    • Martín Fraga
    •  & Pablo Zunino
  28. IVITA Marangani, Universidad Nacional Mayor de San Marcos, Lima, Peru

    • Francisco Franco
  29. Ministry for Primary Industries Verification Services Hawkes Bay, Silver Fern Farms—Pacific, Whakatu, Hastings, New Zealand

    • Chris Friedeman
  30. Laboratory of Animal Nutrition, Research Faculty of Agriculture, Hokkaido University, N9W9, Kita-ku, Sapporo 060–8589, Japan

    • Naoki Fukuma
    • , Yasuo Kobayashi
    • , Satoshi Koike
    •  & Hidehisa Yamano
  31. Institute of Agricultural Sciences, Animal Nutrition, ETH Zürich, CH-8092 Zürich, Switzerland

    • Isabelle Gangnat
    • , Florian Leiber
    •  & Johanna Zeitz
  32. Agricultural, Food & Nutritional Science, University of Alberta, Edmonton, Alberta, T6G 2P5, Canada

    • Le Luo Guan
    • , Emma Hernandez-Sanabria
    •  & Mi Zhou
  33. Quality Control Department, Mashhad Feed Mill, Mashhad, Iran

    • Vahideh Heidarian Miri
  34. Department of Animal Nutrition, Federal University of Agriculture, Abeokuta (FUNAAB), Nigeria

    • Olubukola A. Isah
  35. Department of Ruminant Sciences, Agricultural Research Center (ARO), Volcani Institute, Bet Dagan 50250, Israel

    • Aya Brown-Kav
    • , Elie Jami
    •  & Itzhak Mizrahi
  36. Investigación en Ciencias Animales, Instituto de Investigaciones Agropecuarias Kampenaike, Angamos 1056, Punta Arenas, Región de Magallanes, Chile

    • Juan Jelincic
    • , Raúl Lira
    • , Cristian Márquez
    •  & Francisco Sales
  37. Natural Resources Institute Finland, FI-31600 Jokioinen, Finland

    • Juha Kantanen
  38. Department of Biology, University of Eastern Finland, FI-70211 Kuopio, Finland

    • Juha Kantanen
  39. Department of Animal Science & Technology, Sunchon National University, Suncheon, Jeonnam 540–742, Korea

    • Seon-Ho Kim
    • , Sang-Suk Lee
    •  & Lovelia L. Mamuad
  40. Animal Studies Building, Gatton Campus and Ecosciences Precinct, The University of Queensland, Dutton Park, Queensland, Australia

    • Athol Klieve
  41. Institute of Animal Physiology and Genetics, Laboratory of Anaerobic Microbiology, Academy of Sciences of the Czech Republic, Videnska 1083, 142 20, Prague 4, Czech Republic

    • Jan Kopecny
    •  & Jiri Simunek
  42. Nordic Genetic Resource Center, NO-1431 As, Norway

    • Torsten Nygaard Kristensen
  43. Swedish University of Agricultural Sciences, Department of Agricultural Research for Northern Sweden, SE-901 83 Umea, Sweden

    • Sophie Julie Krizsan
  44. Department of Animal and Range Sciences, Montana State University, Bozeman, Montana, 59717, USA

    • Medora Lachman
    • , Jeffery Swartz
    •  & Carl J. Yeoman
  45. Animal Science Genetics, University of Missouri-Columbia, Columbia, Missouri 65211, USA

    • William R. Lamberson
    •  & Tasia M. Taxis
  46. Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark

    • Jan Lassen
  47. Buffalo Research Institute, The Chinese Academy of Agricultural Science, Nanning, Guangxi, China

    • Bo Lin
    •  & Cai Xia Zou
  48. Department of Animal Science, Aarhus University, DK-8830 Tjele, Denmark

    • Peter Lund
    • , Morten Poulsen
    •  & Catherine Sauer
  49. Institut National de la Recherche Agronomique, UMR1213 Herbivores, F-63122 Saint-Genes-Champanelle, France

    • Cécile Martin
  50. Clermont Université, VetAgro Sup, UMR1213 Herbivores, F-63000 Clermont-Ferrand, France

    • Cécile Martin
  51. Université de Lyon, VetAgro Sup, UMR1213 Herbivores, F-69280 Marcy l’Etoile, France

    • Cécile Martin
  52. Lethbridge Research Centre, Agriculture & Agri-Food Canada, Lethbridge, Alberta, Canada T1J 4B1

    • Tim A. McAllister
  53. CSIRO Agriculture, Queensland Bioscience Precinct, St. Lucia, Queensland 4067, Australia

    • Chris McSweeney
  54. DairyNZ, Cnr Ruakura and Morrinsville Roads, Newstead, Hamilton 3240, New Zealand

    • Elena Minnee
    •  & Garry Waghorn
  55. NARO Institute of Livestock and Grassland Science, 2 Ikenodai, Tsukuba, Ibaraki, 305-0901 Japan

    • Makoto Mitsumori
  56. Agroscope, Institute for Livestock Sciences ILS, CH-1725 Posieux, Switzerland

    • Andreas Muenger
  57. University of Ljubljana, Biotechnical Faculty, Department of Animal Science, SI-1230 Domzale, Slovenia

    • Bostjan Murovec
    •  & Blaz Stres
  58. Cargill Animal Nutrition, Cargill Innovation Center Velddriel, 5334 LD Velddriel, The Netherlands

    • John Newbold
    •  & Peter van Adrichem
  59. Research and Innovation Center, Diamond V, Cedar Rapids, Iowa 52404-5260, USA

    • Victor Nsereko
  60. Forestry Research Institute of Nigeria, Jericho, Ibadan, Oyo State, Nigeria

    • Sunday Okunade
  61. Agri-Science Queensland, Department of Agriculture and Fisheries (DAF), Ecosciences Precinct Dutton Park, Queensland 4012, Australia

    • Diane Ouwerkerk
  62. Embrapa Gado de Leite, Rua Eugenio do Nascimento, EP 36038-330 Juiz de Fora (MG), Brazil

    • Luiz Gustavo Ribeiro Pereira
  63. Department of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Aas, Norway

    • Phil B. Pope
  64. University of Hohenheim, Institute of Animal Science, Animal Nutrition Group, 70599 Stuttgart, Germany

    • Markus Rodehutscord
    •  & Maren Witzig
  65. Department of Technology, National Livestock Breeding Center 1, Shibahara, Mafune, Nishigou, Nishishirakawa, Fukushima 961-8511, Japan

    • Kunihiko Saito
  66. Natural Resources Institute Finland, FI-31600 Jokioinen, Finland

    • Kevin Shingfield
    •  & Ilma Tapio
  67. Yamagata Prefectural Animal Industrial Institute, Agricultural Research Center, 1076 Torigoe, Shinjo, Yamagata 996-0041, Japan

    • Noriaki Shoji
  68. Department of Biology and Ecology, Faculty of Science and Mathematics, 18000 Niš, Serbia

    • Zorica Stojanović-Radić
  69. Institute of Subtropical Agriculture, The Chinese Academy of Sciences, Changsha, China

    • Zhi Liang Tan
    • , Min Wang
    •  & Hua Wei Zhou
  70. CSIRO Animal Food and Health Sciences, James Cook University, Townsville, Queensland, Australia

    • Nigel Tomkins
  71. Instituto de Investigaciones Agropecuarias INIA Carillanca, Temuco, Chile

    • Emilio Ungerfeld
  72. Animal and Bioscience Research Department, Animal & Grassland Research and Innovation Centre, Teagasc, Grange, Dunsany, Co. Meath, Ireland

    • Sinéad M. Waters
    •  & Kate Keogh
  73. Agri-Food and Biosciences Institute, Hillsborough, County Down BT26 6DR, Northern Ireland

    • Tianhai Yan


  1. Global Rumen Census Collaborators


  1. Search for Gemma Henderson in:

  2. Search for Faith Cox in:

  3. Search for Siva Ganesh in:

  4. Search for Arjan Jonker in:

  5. Search for Wayne Young in:

  6. Search for Peter H. Janssen in:


The study was designed by GH and PHJ. Sample processing was carried out by FC and GH. Data analysis was done by GH, PHJ, SG, AJ, and WY. The manuscript was written by GH and PHJ, assisted by all co-authors. Global Rumen Census Collaborators performed animal trials, collected and provided samples and associated metadata used in this study, critically revised the manuscript, contributed intellectual content, and approved the manuscript for publication. The following Global Rumen Census Collaborators acted as primary liaisons: Jorge Avila-Stagno, André Bannink, Alexandre Vieira Chaves, John Church, Mario A. Cobos-Peralta, Silvio Cravero, Omar Cristobal Carballo, Gustavo Cruz, Alexandre B. De Menezes, Kasper Dieho, Jan Dijkstra, Fabian Nde Fon, Martín Fraga, Chris Friedeman, Diego Javier Grilli, Le Luo Guan, Vahideh Heidarian Miri, Olubukola A. Isah, Elie Jami, Satoshi Koike, Jan Kopecny, Torsten Nygaard Kristensen, Sophie Julie Krizsan, Suzanne Lambie, Jan Lassen, Sang-Suk Lee, Bo Lin, Lovelia L. Mamuad, Hilário Cuquetto Mantovani, Cécile Martin, Olga Lucía Mayorga, Tim A. McAllister, Chris McSweeney, Makoto Mitsumori, Itzhak Mizrahi, Andreas Muenger, Camila Muñoz, John Newbold, Victor Nsereko, Diane Ouwerkerk, Luiz Gustavo Ribeiro Pereira, Cesar Pinares-Patiño, Morten Poulsen, Francisco Sales, Zorica Stojanović-Radić, Blaz Stres, Zhi Liang Tan, Ilma Tapio, Tasia M. Taxis, Emilio Ungerfeld, Jonathan Van Hamme, Garry Waghorn, R. John Wallace, Sinéad M. Waters, Maren Witzig, Andre-Denis G. Wright, Tianhai Yan, David R. Yáñez-Ruiz, Carl J. Yeoman, and Johanna Zeitz.

Competing interests

The AgResearch component of this study was funded by the New Zealand Government via the Ministry for Primary Industries (MPI) as part of MPI’s support for the Global Research Alliance on Agricultural Greenhouse Gases. The publication of the data reported here is at the discretion of MPI. MPI did not control which data were presented or how these data were interpreted within this paper. This does not alter the authors’ adherence to all the Scientific Reports policies on sharing data and materials. Material transfer agreements, limiting the use of samples to this study, are in place between AgResearch and Global Rumen Census Collaborators from The University of Alberta (Canada), The Department of Agriculture, Fisheries and Forestry (Queensland, Australia), The University of Aberdeen (Scotland), and The National Institute of Livestock and Grassland Science (Japan). There are no patents, products in development or marketed products to declare. No competing interests were declared by Global Rumen Census Collaborators.

Corresponding authors

Correspondence to Gemma Henderson or Peter H. Janssen.

Supplementary information

About this article

Publication history





Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.