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

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.

FIG 1

Stochastic community assembly processes in the MECs. The diagram shows a schematic of neutral dynamics in the assembly of the MEC reactor biofilm community. It is assumed that the regional pool has 20 individuals of 5 species (A, B, C, D, and E), with each species having a different abundance (A). During the initial inoculation, different species colonize the anodes of the MECs to produce current. Due to the stochastic process of colonization, the established biofilm composition varies considerably among different MEC reactors to form 4 different community structure states (B). Following that, the reactor solutions were replaced with new sterile medium every 24 h. Due to competition for resources and space, some species could detach from the biofilm and subsequently be lost during medium exchange, whereas some species could recolonize the anodes, which further creates variation of the communities among different bioreactors (C). Thus, even though these reactors were operated under identical conditions with the same source community, the community structures were quite different due to ecological drift in colonization.

FIG 2

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.

FIG 3

Average yields of H₂, CH₄, and CO₂ for the four reactor groups as shown in Fig. 2. The average yields and standard deviations of each gas were obtained based on individual measurements across experimental time among reactors. H₂ yields were dramatically different among these reactors, whereas lesser variations were observed for CH₄ and CO₂.

FIG 4

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.