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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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CCA-based variation partitioning analysis showing the importance of abiotic (pH) and biotic (gases) deterministic factors in explaining the variations of microbial community functional structures. More than half of the variations in community structure could be not explained by all the deterministic factors measured and are most likely due to stochastic processes.
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fig4: CCA-based variation partitioning analysis showing the importance of abiotic (pH) and biotic (gases) deterministic factors in explaining the variations of microbial community functional structures. More than half of the variations in community structure could be not explained by all the deterministic factors measured and are most likely due to stochastic processes.

Mentions: To delineate the relative contributions of stochastic and deterministic factors to the assembly of these reactor communities, a canonical correspondence analysis (CCA)-based variation partitioning analysis (VPA) was performed (Fig. 4). CCA revealed 4 deterministic variables—effluent pH, H2, CH4, and CO2—that were significant predictors of community composition (Monte Carlo permutation: overall, F = 1.72 and P = 0.005; pH, F = 1.84 and P = 0.01; H2, F = 2.36 and P = 0.005, CH4, F = 1.48 and P = 0.088; CO2, F = 1.65 and P = 0.042) (see Fig. S6 in the supplemental material). Since these reactors were operated under identical conditions, the only measurable abiotic factor is the change in the effluent pH. Also, because the variations of gas production are most likely caused by the differences of community structure and their interactions (e.g., competition and mutualistic interactions), gas yields can, in turn, serve as a proxy for measuring the effects of biotic deterministic factors. Therefore, we can partition the variations in community composition into three main components: (i) biotic components (H2, CH4, and CO2), (ii) abiotic components (effluent pH), and (iii) an unexplained component (Fig. 4). VPA revealed that gases explained 30.1% (F = 1.59, P = 0.005) of variations in community structure, whereas pH explained 7.2% (F = 1.15, P = 0.35). Over half of the variations (56.6%) in community structure could be not explained by all the deterministic factors measured. Such unexplained components are most likely due to stochastic processes, because no other additional routine deterministic factors can be included. These results suggest that stochastic factors could have played major roles in determining the structure and functions of these MEC communities.


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)

CCA-based variation partitioning analysis showing the importance of abiotic (pH) and biotic (gases) deterministic factors in explaining the variations of microbial community functional structures. More than half of the variations in community structure could be not explained by all the deterministic factors measured and are most likely due to stochastic processes.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: CCA-based variation partitioning analysis showing the importance of abiotic (pH) and biotic (gases) deterministic factors in explaining the variations of microbial community functional structures. More than half of the variations in community structure could be not explained by all the deterministic factors measured and are most likely due to stochastic processes.
Mentions: To delineate the relative contributions of stochastic and deterministic factors to the assembly of these reactor communities, a canonical correspondence analysis (CCA)-based variation partitioning analysis (VPA) was performed (Fig. 4). CCA revealed 4 deterministic variables—effluent pH, H2, CH4, and CO2—that were significant predictors of community composition (Monte Carlo permutation: overall, F = 1.72 and P = 0.005; pH, F = 1.84 and P = 0.01; H2, F = 2.36 and P = 0.005, CH4, F = 1.48 and P = 0.088; CO2, F = 1.65 and P = 0.042) (see Fig. S6 in the supplemental material). Since these reactors were operated under identical conditions, the only measurable abiotic factor is the change in the effluent pH. Also, because the variations of gas production are most likely caused by the differences of community structure and their interactions (e.g., competition and mutualistic interactions), gas yields can, in turn, serve as a proxy for measuring the effects of biotic deterministic factors. Therefore, we can partition the variations in community composition into three main components: (i) biotic components (H2, CH4, and CO2), (ii) abiotic components (effluent pH), and (iii) an unexplained component (Fig. 4). VPA revealed that gases explained 30.1% (F = 1.59, P = 0.005) of variations in community structure, whereas pH explained 7.2% (F = 1.15, P = 0.35). Over half of the variations (56.6%) in community structure could be not explained by all the deterministic factors measured. Such unexplained components are most likely due to stochastic processes, because no other additional routine deterministic factors can be included. These results suggest that stochastic factors could have played major roles in determining the structure and functions of these MEC communities.

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