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Stochastic assembly leads to alternative communities with distinct functions in a bioreactor microbial community.

Zhou J, Liu W, Deng Y, Jiang YH, Xue K, He Z, Van Nostrand JD, Wu L, Yang Y, Wang A - MBio (2013)

Bottom Line: Neutral community modeling analysis revealed that deterministic factors also played significant roles in shaping microbial community structure in these reactors.Moreover, while microorganisms mediate many ecosystem processes, the relationship between microbial diversity and ecosystem functioning remains largely elusive.The results presented in this study represent important contributions to the understanding of the mechanisms, especially stochastic processes, involved in shaping microbial biodiversity.

View Article: PubMed Central - PubMed

Affiliation: State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China. jzhou@ou.edu

ABSTRACT
ABSTRACT The processes and mechanisms of community assembly and its relationships to community functioning are central issues in ecology. Both deterministic and stochastic factors play important roles in shaping community composition and structure, but the connection between community assembly and ecosystem functioning remains elusive, especially in microbial communities. Here, we used microbial electrolysis cell reactors as a model system to examine the roles of stochastic assembly in determining microbial community structure and functions. Under identical environmental conditions with the same source community, ecological drift (i.e., initial stochastic colonization) and subsequent biotic interactions created dramatically different communities with little overlap among 14 identical reactors, indicating that stochastic assembly played dominant roles in determining microbial community structure. Neutral community modeling analysis revealed that deterministic factors also played significant roles in shaping microbial community structure in these reactors. Most importantly, the newly formed communities differed substantially in community functions (e.g., H2 production), which showed strong linkages to community structure. This study is the first to demonstrate that stochastic assembly plays a dominant role in determining not only community structure but also ecosystem functions. Elucidating the links among community assembly, biodiversity, and ecosystem functioning is critical to understanding ecosystem functioning, biodiversity preservation, and ecosystem management. IMPORTANCE Microorganisms are the most diverse group of life known on earth. Although it is well documented that microbial natural biodiversity is extremely high, it is not clear why such high diversity is generated and maintained. Numerous studies have established the roles of niche-based deterministic factors (e.g., pH, temperature, and salt) in shaping microbial biodiversity, the importance of stochastic processes in generating microbial biodiversity is rarely appreciated. Moreover, while microorganisms mediate many ecosystem processes, the relationship between microbial diversity and ecosystem functioning remains largely elusive. Using a well-controlled laboratory system, this study provides empirical support for the dominant role of stochastic assembly in creating variations of microbial diversity and the first explicit evidence for the critical role of community assembly in influencing ecosystem functioning. The results presented in this study represent important contributions to the understanding of the mechanisms, especially stochastic processes, involved in shaping microbial biodiversity.

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Detrended correspondence analysis (DCA) of GeoChip hybridization data showing the relationships of microbial community functional gene structures among different reactors. Four distinct groups can be defined based on the DCA ordination, which could represent different alternative community states. The overall community composition and structure among these groups were also all significantly different, as shown in Table 1.
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fig2: Detrended correspondence analysis (DCA) of GeoChip hybridization data showing the relationships of microbial community functional gene structures among different reactors. Four distinct groups can be defined based on the DCA ordination, which could represent different alternative community states. The overall community composition and structure among these groups were also all significantly different, as shown in Table 1.

Mentions: In this study, the β-diversity was represented by Sorensen’s incidence-based (DS) and Bray-Curtis’s abundance-based (DBC) indexes, which both range from 0 to 1. Substantial variations in community structure were observed among these reactor communities, as indicated by both Sorensen and Bray-Curtis dissimilarities (DS = 0.47 ± 0.11; DBC = 0.54 ± 0.11). Similarly, high variations were also observed at the level of individual functional gene groups based on DS and DBc (see Table S1 in the supplemental material). Also, four distinct groups can be visualized in the detrended correspondence analysis (DCA) ordination space (Fig. 2): (i) group A, which consists of only one reactor (number 9); (ii) group B, which is comprised of six reactors (numbers 2 to 7); (iii) group C, which contains five reactors (number 1, 8, 10, 11, 13, and 14); and (iv) group D, which encompasses one reactor (number 12). Interestingly, SIMPER (similarity percentage) analysis indicated that the functional populations involved in metal resistance and antibiotic resistance rather than typical electricigens (e.g., metal-reducing bacteria) were most important in driving the separation of the communities among different reactors, as observed in Fig. 2 (see Fig. S4 in the supplemental material), which implies that selection could be an important process after initial colonization of these reactors, because the functioning of the resistance genes is critical for successful competition and survival. In addition, as revealed by three nonparametric tests, multiple response permutation procedure (MRPP), permutational multivariate analysis of variance (Adonis), and analysis of similarity (ANOSIM), the overall community structures among these four groups were all significantly different (P < 0.05) (Table 1). Similar observations were obtained at the level of individual functional gene groups (see Table S2 in the supplemental material). Finally, as shown above, two groups (B and C) contained more than three samples, so that further statistical tests could be performed. Similarly, the community structures between these two groups were also significantly different with all three methods (Table 1). These results suggest that these MEC reactor communities were substantially different and most likely formed at least two distinct community states. Though the functions of these MEC communities were relatively stable (see Fig. S3A, B, and C) during the experimental period after initial 10 days, it is less clear whether the structures of these community states were stable over time. Further experiments with specific environmental perturbations and/or species invasions need to be designed to unequivocally test the stability of these multiple community states.


Stochastic assembly leads to alternative communities with distinct functions in a bioreactor microbial community.

Zhou J, Liu W, Deng Y, Jiang YH, Xue K, He Z, Van Nostrand JD, Wu L, Yang Y, Wang A - MBio (2013)

Detrended correspondence analysis (DCA) of GeoChip hybridization data showing the relationships of microbial community functional gene structures among different reactors. Four distinct groups can be defined based on the DCA ordination, which could represent different alternative community states. The overall community composition and structure among these groups were also all significantly different, as shown in Table 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3585448&req=5

fig2: Detrended correspondence analysis (DCA) of GeoChip hybridization data showing the relationships of microbial community functional gene structures among different reactors. Four distinct groups can be defined based on the DCA ordination, which could represent different alternative community states. The overall community composition and structure among these groups were also all significantly different, as shown in Table 1.
Mentions: In this study, the β-diversity was represented by Sorensen’s incidence-based (DS) and Bray-Curtis’s abundance-based (DBC) indexes, which both range from 0 to 1. Substantial variations in community structure were observed among these reactor communities, as indicated by both Sorensen and Bray-Curtis dissimilarities (DS = 0.47 ± 0.11; DBC = 0.54 ± 0.11). Similarly, high variations were also observed at the level of individual functional gene groups based on DS and DBc (see Table S1 in the supplemental material). Also, four distinct groups can be visualized in the detrended correspondence analysis (DCA) ordination space (Fig. 2): (i) group A, which consists of only one reactor (number 9); (ii) group B, which is comprised of six reactors (numbers 2 to 7); (iii) group C, which contains five reactors (number 1, 8, 10, 11, 13, and 14); and (iv) group D, which encompasses one reactor (number 12). Interestingly, SIMPER (similarity percentage) analysis indicated that the functional populations involved in metal resistance and antibiotic resistance rather than typical electricigens (e.g., metal-reducing bacteria) were most important in driving the separation of the communities among different reactors, as observed in Fig. 2 (see Fig. S4 in the supplemental material), which implies that selection could be an important process after initial colonization of these reactors, because the functioning of the resistance genes is critical for successful competition and survival. In addition, as revealed by three nonparametric tests, multiple response permutation procedure (MRPP), permutational multivariate analysis of variance (Adonis), and analysis of similarity (ANOSIM), the overall community structures among these four groups were all significantly different (P < 0.05) (Table 1). Similar observations were obtained at the level of individual functional gene groups (see Table S2 in the supplemental material). Finally, as shown above, two groups (B and C) contained more than three samples, so that further statistical tests could be performed. Similarly, the community structures between these two groups were also significantly different with all three methods (Table 1). These results suggest that these MEC reactor communities were substantially different and most likely formed at least two distinct community states. Though the functions of these MEC communities were relatively stable (see Fig. S3A, B, and C) during the experimental period after initial 10 days, it is less clear whether the structures of these community states were stable over time. Further experiments with specific environmental perturbations and/or species invasions need to be designed to unequivocally test the stability of these multiple community states.

Bottom Line: Neutral community modeling analysis revealed that deterministic factors also played significant roles in shaping microbial community structure in these reactors.Moreover, while microorganisms mediate many ecosystem processes, the relationship between microbial diversity and ecosystem functioning remains largely elusive.The results presented in this study represent important contributions to the understanding of the mechanisms, especially stochastic processes, involved in shaping microbial biodiversity.

View Article: PubMed Central - PubMed

Affiliation: State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China. jzhou@ou.edu

ABSTRACT
ABSTRACT The processes and mechanisms of community assembly and its relationships to community functioning are central issues in ecology. Both deterministic and stochastic factors play important roles in shaping community composition and structure, but the connection between community assembly and ecosystem functioning remains elusive, especially in microbial communities. Here, we used microbial electrolysis cell reactors as a model system to examine the roles of stochastic assembly in determining microbial community structure and functions. Under identical environmental conditions with the same source community, ecological drift (i.e., initial stochastic colonization) and subsequent biotic interactions created dramatically different communities with little overlap among 14 identical reactors, indicating that stochastic assembly played dominant roles in determining microbial community structure. Neutral community modeling analysis revealed that deterministic factors also played significant roles in shaping microbial community structure in these reactors. Most importantly, the newly formed communities differed substantially in community functions (e.g., H2 production), which showed strong linkages to community structure. This study is the first to demonstrate that stochastic assembly plays a dominant role in determining not only community structure but also ecosystem functions. Elucidating the links among community assembly, biodiversity, and ecosystem functioning is critical to understanding ecosystem functioning, biodiversity preservation, and ecosystem management. IMPORTANCE Microorganisms are the most diverse group of life known on earth. Although it is well documented that microbial natural biodiversity is extremely high, it is not clear why such high diversity is generated and maintained. Numerous studies have established the roles of niche-based deterministic factors (e.g., pH, temperature, and salt) in shaping microbial biodiversity, the importance of stochastic processes in generating microbial biodiversity is rarely appreciated. Moreover, while microorganisms mediate many ecosystem processes, the relationship between microbial diversity and ecosystem functioning remains largely elusive. Using a well-controlled laboratory system, this study provides empirical support for the dominant role of stochastic assembly in creating variations of microbial diversity and the first explicit evidence for the critical role of community assembly in influencing ecosystem functioning. The results presented in this study represent important contributions to the understanding of the mechanisms, especially stochastic processes, involved in shaping microbial biodiversity.

Show MeSH
Related in: MedlinePlus