The ecology of microbes frequently involves the mixing of entire communities (community coalescence), for example, flooding events, host excretion, and soil tillage [], yet the consequences of this process for community structure and function are poorly understood []. Recent theory suggests that a community, due to coevolution between constituent species, may act as a partially cohesive unit [], resulting in one community dominating after community coalescence. This dominant community is predicted to be the one that uses resources most efficiently when grown in isolation []. We experimentally tested these predictions using methanogenic communities, for which efficient resource use, quantified by methane production, requires coevolved cross-feeding interactions between species []. After propagation in laboratory-scale anaerobic digesters, community composition (determined from 16S rRNA sequencing) and methane production of mixtures of communities closely resembled that of the single most productive community grown in isolation. Analysis of each community’s contribution toward the final mixture suggests that certain combinations of taxa within a community might be co-selected as a result of coevolved interactions. As a corollary of these findings, we also show that methane production increased with the number of inoculated communities. These findings are relevant to the understanding of the ecological dynamics of natural microbial communities, as well as demonstrating a simple method of predictably enhancing microbial community function in biotechnology, health, and agriculture [].

Results and Discussion

2 , CO 2 , and short-chain fatty acids produced by hydrolysis and fermentation of more complex organic material, and it is often the only thermodynamically feasible way of actively removing inhibitory end metabolites [ 12 Schink B. Energetics of syntrophic cooperation in methanogenic degradation. 12 Schink B. Energetics of syntrophic cooperation in methanogenic degradation. 14 Hillesland K.L.

Stahl D.A. Rapid evolution of stability and productivity at the origin of a microbial mutualism. 15 Embree M.

Liu J.K.

Al-Bassam M.M.

Zengler K. Networks of energetic and metabolic interactions define dynamics in microbial communities. 9 Toquenaga Y. Historicity of a simple competition model. Table 1 List of Individual Communities Used in This Analysis and Their Source Sample Name Feed and/or Type Temperature Used in Experiment P01 silage and food waste AD 44°C –42.5°C 1, 2, 3 P02 silage and food waste AD 44°C –42.5°C 2, 3 P03 maize, cow slurry, and chicken manure AD 45°C 3 P04 maize, cow slurry, and chicken manure AD 45°C 2, 3 P05 sewage sludge AD 36°C 1, 2, 3 P06 raw sewage ambient 2, 3 P08 thickened sewage sludge ambient 2, 3 P09 sewage based AD 36°C 2, 3 P10 food waste AD 36°C 2, 3 P11 cow slurry ambient 3 P12 silage, slurry, and manure pre-digestate ambient 3 P13 silage, slurry, and manure AD 40°C 2, 3 P15 food waste AD 36°C 2 All anaerobic digester (AD) communities were derived from industrial ADs in the southwest of England. Specific locations cannot be provided because of commercial sensitivity. Note that experiment numbers correspond with figure numbers. Figure 1 Temporal Dynamics of Methane Production and Composition When Two Communities Are Mixed Show full caption (A) Cumulative methane production in milliliters (±SEM) over time of community P01 (white circles), community P05 (black circles), and their mixes (gray circles). Cumulative methane production differed between treatments (ANOVA: F 2,9 = 23.2, p < 0.001) but did not differ between the mixed community and P05 (Tukey-Kramer honest significant difference [HSD]: p = 0.5). P01 was lower than both other treatments (Tukey-Kramer HSD: p < 0.001 in both cases). (B) Non-metric multidimensional scaling (NMDS) plot of unweighted UniFrac of communities P01 (white), P05 (black), and their mixes (gray). Ancestral samples are represented by squares, and samples from the endpoint of the experiment are represented by circles. At the endpoint, P05 was compositionally more similar to the mixtures than P01, based on both unweighted (t tests of mean distance to each mixture for each replicate single community: t 6 = 8.3, p < 0.001) and weighted (t 6 = 2.3, p = 0.03) UniFrac distances. We wanted to determine whether coalesced methanogenic communities were dominated by the community that used resources most efficiently in isolation. We used methanogenic communities primarily because methane production is a useful proxy for the ability of an anaerobic community to fully exploit available resources: methanogenesis results from the conversion of H, CO, and short-chain fatty acids produced by hydrolysis and fermentation of more complex organic material, and it is often the only thermodynamically feasible way of actively removing inhibitory end metabolites []. Moreover, methanogenic communities are characterized by complex cross-feeding interactions []; hence, the role of community cohesion in shaping community performance is likely to be particularly important []. To provide insight into the temporal dynamics of compositional and functional change after community mixing, we first measured the methane production and composition of two methanogenic communities derived from industrial anaerobic digesters (ADs) ( Table 1 ) grown in isolation or as a mixture in laboratory-scale ADs. Both the individual communities and mixes were grown in four replicates. To remove any potentially confounding effects caused by differences in starting density of tested communities, we standardized microbial density based on qPCR-estimated counts of 16S rRNA gene copies. We found that the methane production of the mixed community was initially intermediate between the two individual communities, but after 5 weeks propagation, it started to produce gas at a rate indistinguishable from that of the more productive of the individual communities ( Figure 1 A). We examined both the starting-point and the endpoint composition of the single and mixed communities by Illumina sequencing 16 s rRNA gene amplicon libraries. Consistent with the phenotypic data, the composition of the mixture was much more similar to the better- than to the worse-performing community at the endpoint ( Figure 1 B). This was despite the single endpoint communities changing considerably from their ancestral composition over the 5 weeks ( Figure 1 B).

Figure 2 Methane Production and Community Composition When Multiple Communities Are Mixed Show full caption (A) Total methane production of mixed (gray) and individual (white) communities, with mean values shown as horizontal lines. Mean total methane production was greater for mixtures than for individual communities (t test: p < 0.001 in nine cases), except when measured against community P13 (the best performer). (B) NMDS plot of unweighted UniFrac of ten mixtures (gray) and nine individual communities (white). Numbers in circles refer to individual community identifiers ( Table 1 ). Community P13 was significantly closer in composition to the ten mixed communities than any other community (weighted and unweighted UniFrac distances; paired t tests: p < 0.001 in all cases). There was also a significant link between the community composition and the difference in gas production between the communities (see Figure S1 ). Note that DNA yield from community P06, which had the lowest gas production of all communities, was insufficient for sequencing; therefore, it is excluded from this and the following graphs. 1,26 = 5.4, p = 0.03). For community composition of the mixes, see (C) Individual communities (white circles) and their average methane production (white line), as well as mixes of communities (gray circles) and their averages (gray line). There was a monotonic increase in methane production with number of communities used (regression: F= 5.4, p = 0.03). For community composition of the mixes, see Tables 1 and S1 We next determined whether a single community dominated when multiple communities were mixed. To this end, we propagated ten single communities (from either industrial ADs or sewage or agricultural waste AD feedstocks, with each replicated three times), and ten replicates of a mixture of all ten communities ( Table 1 ). The results were consistent with those from the two-community mixture. First, methane production in mixtures of ten communities was higher than the average of the individual communities. However, methane production of the mixtures did not differ from the best-performing single community, P13, ( Figure 2 A), which, like each of the single communities used, was a constituent of all of the mixtures. Second, the community composition of mixtures (which varied very little between replicates, presumably because they all had the same ten community-starting inocula) most closely resembled the best-performing community, P13 ( Figure 2 B). More generally, the more compositionally similar an individual community was to the replicated ten-community mixtures, the greater the gas production of the community when grown in isolation ( Figure S1 ). Other community characteristics that positively correlated with methane production were bacterial cell densities and within-community (alpha) diversity, but not methanogen density ( Figure S2 ). In summary, the results demonstrate that the community most efficient at using resources (which in these experiments was also the most diverse) dominates when multiple communities are mixed together, thus enhancing mixed-community productivity beyond the average of the component communities.

12 Schink B. Energetics of syntrophic cooperation in methanogenic degradation. 15 Embree M.

Liu J.K.

Al-Bassam M.M.

Zengler K. Networks of energetic and metabolic interactions define dynamics in microbial communities. 16 Großkopf T.

Soyer O.S. Microbial diversity arising from thermodynamic constraints. 17 Schluter D. The Ecology of Adaptive Radiation. 18 Roughgarden J. Resource partitioning among competing species--a coevolutionary approach. 19 MacArthur R. Species packing and competitive equilibrium for many species. 11 Tikhonov M. Community-level cohesion without cooperation. 20 Gardner A.

Grafen A. Capturing the superorganism: a formal theory of group adaptation. 21 Baker-Austin C.

Wright M.S.

Stepanauskas R.

McArthur J.V. Co-selection of antibiotic and metal resistance. We next explored the ecological mechanism(s) underpinning the observed dominance by the community that produced the most methane. One explanation is that multiple taxa from the same community act as semi-cohesive units and are selected together. This might arise as a result of coevolved mutualistic (or unidirectional) cross-feeding interactions, notably between methanogenic Archaea and hydrogen and/or acetate producers, where each organism both provides essential resources and removes damaging waste products for the other []. Moreover, coevolved resource partitioning can result in taxa being selected together, because species are expected to coevolve to minimize competition with co-occurring taxa []. Note that the selection of multiple taxa together in these contexts does not require any form of group selection [], but simply selection of particular individuals from a key taxon whose presence provides an advantage for individuals from taxa they have coevolved with. This process can be described as ecological co-selection, equivalent to genetic co-selection, where a gene can hitchhike to high frequency purely as consequence of being linked to genes under positive selection [].

Figure 3 The Role of Co-selection in Explaining Dominance by a Single Community Show full caption (A–C) The top panels illustrate three hypothetical scenarios describing how communities contribute to a mixture of communities, and the bottom panels show the expected relationships between a community’s contribution and its methane production. The letters within the top panels indicate taxa that drive two biochemical processes (abcd and ef); capitalized letters are the best representatives of a taxon among all of the communities. (A) No co-selection. (B) Co-selection of all taxa within a community. (C) Co-selection of taxa within two independent modules. (D) Mean estimated relative contribution of each individual community (numbered) toward the ten coalesced communities calculated using the NNLS method, plotted against mean cumulative methane production for each community; there is no significant relationship (regression: F 1,7 = 1.7, p > 0.2). 1,7 = 1.7, p > 0.5). Note that the relative contribution is not a fractional contribution because some operational taxonomic units (OTUs) present in the mixture were not detected in the constituent communities. This is presumably because they only reached detected frequencies in the mixture, but we can’t rule out that the community that we failed to get sufficient reads from contributed to the composition of the mixtures. Mind that the cell densities of Archaea and bacteria do not significantly correlate with the gas production (see (E) As in (D), but with relative contribution plotted against number of bacterial and archaeal cells calculated based on the 16S rRNA gene copy number (regression: F= 1.7, p > 0.5). Note that the relative contribution is not a fractional contribution because some operational taxonomic units (OTUs) present in the mixture were not detected in the constituent communities. This is presumably because they only reached detected frequencies in the mixture, but we can’t rule out that the community that we failed to get sufficient reads from contributed to the composition of the mixtures. Mind that the cell densities of Archaea and bacteria do not significantly correlate with the gas production (see Figure S2 ). An alternative explanation is that coevolved interactions within individual communities are relatively unimportant, and the dominant community simply contains more competitive taxa (for any functional task and/or interaction) than other communities. This does not imply that coevolved cross-feeding interactions are unimportant for methanogenic communities, but that these co-evolved interactions are no more specific for taxa isolated from within a community than for taxa isolated from different communities. In other words, functionally equivalent taxa are interchangeable between communities. These different scenarios, selection for the best individual taxa and co-selection, are two extremes of a continuum. The distinction is important because dominance by a single community is necessarily a more likely consequence of community coalescence when co-selection operates. Figures 3 A–3C provide an illustration of the two extreme scenarios, no co-selection and co-selection of the entire community, and an intermediate case in which there are two groups of interacting taxa, or modules, and co-selection occurs within each.

11 Tikhonov M. Community-level cohesion without cooperation. The most direct way to demonstrate a role of co-selection would be to show that the outcome of competition between single taxa from different communities does not predict the outcome of competition at the community level []. Unfortunately, this is not feasible for such complex communities, in which many taxa are very difficult to grow in isolation. However, there are other testable predictions associated with the operation of co-selection or otherwise. If the success of an individual taxon is independent of whether they are in the presence of taxa from the same community (i.e., co-selection does not occur), communities that use resources most efficiently and hence achieve the highest biomass per unit of time (productivity) will contain the highest number of the best-performing taxa. It then follows that there will be a positive relationship between the productivity of a community and the proportion of taxa it contributes to the mixture ( Figure 3 A). If instead taxa are co-selected as modules, the correlation between individual community contribution and productivity is likely to break down. This is best illustrated by the extreme scenario whereby all taxa within a mixed community are co-selected from a single community: the mixture will be entirely dominated by a single constituent community, and hence the contribution of all other communities will be independent of their individual productivity (i.e., they will contribute null to the mixture’s composition, even though they have non-zero productivity individually; Figure 3 B). The intermediate scenario, where co-selection occurs within two independent modules, can also break down this correlation if one module contributes much more to community productivity than the other ( Figure 3 C).

To determine whether co-selection contributed to our findings, we first estimated the contribution of each community to the ten-community mixtures using a non-negative least-squares (NNLS) approach. The community that had the most similar composition to the mixtures (and produced the most methane) contributed an estimated 40% of its taxa to the mixtures, with only two other communities contributing more than 10% of their taxa to the mixtures ( Figure 3 D). We then correlated the contribution each community made to the mixtures with two measures of community productivity: methane production and cell densities (based on 16 s rRNA gene copy number), which themselves were positively correlated ( Figure S2 A). We found no suggestion of a positive correlation between either measure of productivity and contribution to the community ( Figures 3 D and 3E). These results suggest that co-selection of taxa played an important role in dominance by the community that produced the most methane.

22 Hodgson D.J.D.

Rainey P.B.

Buckling A. Mechanisms linking diversity, productivity and invasibility in experimental bacterial communities. 23 Tilman D.

Lehman C.L.

Thomson K.T. Plant diversity and ecosystem productivity: theoretical considerations. 24 Tilman D. The ecological consequences of changes in biodiversity: a search for general principles. 25 Harper D. The Population Biology of Plants. That community coalescence results in the most productive individual community dominating the mixed community has direct implications for biotechnological uses of microbial communities. Given that the best-performing community in isolation largely determined both the composition and performance of mixtures of communities, methane production should increase with increasing number of communities in a mixture. We therefore inoculated laboratory-scale anaerobic digesters with 1, 2, 3, 4, 6, or 12 communities, ensuring that each of the 12 starting communities was used an equal amount of times at each diversity level [] (see Table S1 ). Cumulative methane production over a 5-week period increased with increasing number of communities used as an inoculum ( Figure 2 C). The positive correlation between community function and the number of inoculating communities is analogous to the commonly observed finding that community productivity increases with increasing species diversity []. In this case, the mechanism underlying this positive relationship between the number of communities and productivity is a “sampling effect”: inoculating more communities increases the chance that the best-performing community will be present in the mix []. However, given that domination of mixtures by one community was not complete ( Figures 3 D and 3E), it is possible that mixing communities could increase performance beyond that of the maximum of single communities in some circumstances (transgressive over-yielding []).