Positive diversity‐functioning relationships in model communities of methanotrophic bacteria

Abstract

we tested effects of biodiversity on the functioning of methanotrophs, a specialized group of soil 27 bacteria that plays a key role in mediating greenhouse gas emissions from soils. Using pure 28 strains of methanotrophic bacteria, we assembled artificial communities of different diversity 29 levels, with which we inoculated sterile soil microcosms. To assess the functioning of these 30 communities, we measured methane oxidation by gas chromatography throughout the 31 experiment and determined changes in community composition and community size at several 32 time points by quantitative PCR and sequencing. We demonstrate that microbial diversity had a 33 positive overyielding-effect on methane oxidation, in particular at the beginning of the 34 experiment. This higher assimilation of CH 4 at high diversity translated into increased growth 35 and significantly larger communities towards the end of the study. The overyielding of mixtures 36 with respect to CH 4 consumption and community size was positively correlated. The temporal 37 CH 4 consumption profiles of strain monocultures differed, raising the possibility that temporal 38 complementarity of component strains drove the observed community-level strain richness 39 effects; however, the community niche metric we derived from the temporal activity profiles did 40 not explain the observed strain richness effect. The strain richness effect also was unrelated to 41 both the phylogenetic and functional trait diversity of mixed communities. Overall, our results 42 suggest that positive biodiversity-ecosystem function relationships show similar patterns across 43 different scales and may be widespread in nature. Additionally, biodiversity is probably also 44 important in natural methanotrophic communities for the ecosystem function methane oxidation. 45 3 Therefore, maintaining soil conditions that support a high diversity of methanotrophs may help 46 to reduce the emission of the greenhouse gas methane. 47 48

Experimental erosion of microbial diversity decreases soil CH4consumption rates

“…In contrast to diversity experiments in which artificial communities are assembled de novo from isolated strains [45], dilution treatments decrease the abundance of both target and non-target microorganisms, and our study was no exception. However, soil communities recovered well during the 4-week preincubation, with no remaining differences in total bacterial and only relatively small differences in methanotrophic community size at day zero except for the most diluted community which required more time to recover.…”

“…Headspace air was pumped through a heat exchanger that equilibrated the gas to room temperature. Then, the sampled air was dried in a tube filled with silica gel beads before CH 4 concentrations were determined in a CH 4 using a customized pipeline [45] that based on the algorithms implemented in USEARCH v9 [69]. All pmoA OTU sequences were blasted against the NCBI database and sequences that did not yield a match or were not pmoA were removed.…”

“…We therefore used the same sensor and To determine net CH 4 and CO 2 exchange rates, replicate microcosms of all 30 communities were removed from the incubators and placed in air-tight 3 L jars with elevated CH 4 concentrations. After 0, 31, 58, and 86 days of incubation, one of the replicates of each of the 15 dilution × dilution series combinations was removed from both20 incubators and DNA retrieved using the xanthogenate-based extraction described in[45]. Extracted DNA was purified twice using the PowerClean Pro DNA Clean-Up Kit (MoBio, Carlsbad, CA) to remove PCR inhibitors.…”

Community-level signatures of ecological succession in natural bacterial communities

“…[3], suggesting that there is a need for variables that increase the accuracy of biological processes [4]. 14 The development of a more mechanistic picture is hindered for several reasons, such as diculties in identi-15 fying the relative role of stochastic and deterministic processes in shaping microbial communities [5,6,7], the 16 pervasiveness of functional redundancy [8,9], and of priority eects [10]. In addition, it is often dicult to 17 identify which functions to assess.…”

Linking microbial communities to ecosystem functions: what we can learn from genotype-phenotype mapping in organisms

“…The abundance of these 75 markers is often hypothesized to be predictive of the rate of their associated processes (for example, the 76 abundance of mcrA is often hypothesized to be related to the rate of methanogenesis). There are 77 examples where this relationship is present (12)(13)(14). However, a review of such studies found that the 78 abundance of a functional gene or transcript is rarely correlated with the rate of the corresponding 79 process and depends on the function of interest, with most effects either negative or not significant (4).…”