In ecology, biodiversity-ecosystem functioning (BEF) research has seen a shift in perspective from taxonomy to function in the last two decades, with successful application of trait-based approaches. This shift offers opportunities for a deeper mechanistic understanding of the role of biodiversity in maintaining multiple ecosystem processes and services. In this paper, we highlight studies that have focused on BEF of microbial communities with an emphasis on integrating trait-based approaches to microbial ecology. In doing so, we explore some of the inherent challenges and opportunities of understanding BEF using microbial systems. For example, microbial biologists characterize communities using gene phylogenies that are often unable to resolve functional traits. Additionally, experimental designs of existing microbial BEF studies are often inadequate to unravel BEF relationships. We argue that combining eco-physiological studies with contemporary molecular tools in a trait-based framework can reinforce our ability to link microbial diversity to ecosystem processes. We conclude that such trait-based approaches are a promising framework to increase the understanding of microbial BEF relationships and thus generating systematic principles in microbial ecology and more generally ecology.

Summary Methane‐oxidizing bacteria (MOB) possess the ability to use methane for energy generation and growth, thereby, providing a key ecosystem service that is highly relevant to the regulation of the global climate. MOB subgroups have different responses to key environmental controls, reflecting on their functional traits. Their unique features (C1‐metabolism, unique lipids and congruence between the 16S rRNA and pmoA gene phylogeny) have facilitated numerous environmental studies, which in combination with the availability of cultured representatives, yield the most comprehensive ecological picture of any known microbial functional guild. Here, we focus on the broad MOB subgroups (type I and type II MOB), and aim to conceptualize MOB functional traits and observational characteristics derived primarily from these environmental studies to be interpreted as microbial life strategies. We focus on the functional traits, and the conditions under which these traits will render different MOB subgroups a selective advantage. We hypothesize that type I and type II MOB generally have distinct life strategies, enabling them to predominate under different conditions and maintain functionality. The ecological characteristics implicated in their adopted life strategies are discussed, and incorporated into the Competitor‐Stress tolerator‐Ruderal functional classification framework as put forward for plant communities. In this context, type I MOB can broadly be classified as competitor‐ruderal while type II MOB fit more within the stress tolerator categories. Finally, we provide an outlook on MOB applications by exemplifying two approaches where their inferred life strategies could be exploited thereby, putting MOB into the context of microbial resource management.

The utilization of methane, a potent greenhouse gas, is an important component of local and global carbon cycles that is characterized by tight linkages between methane-utilizing (methanotrophic) and nonmethanotrophic bacteria. It has been suggested that the methanotroph sustains these nonmethanotrophs by cross-feeding, because subsequent products of the methane oxidation pathway, such as methanol, represent alternative carbon sources. We established cocultures in a microcosm model system to determine the mechanism and substrate that underlay the observed cross-feeding in the environment. Lanthanum, a rare earth element, was applied because of its increasing importance in methylotrophy. We used co-occurring strains isolated from Lake Washington sediment that are involved in methane utilization: a methanotroph and two nonmethanotrophic methylotrophs. Gene-expression profiles and mutant analyses suggest that methanol is the dominant carbon and energy source the methanotroph provides to support growth of the nonmethanotrophs. However, in the presence of the nonmethanotroph, gene expression of the dominant methanol dehydrogenase (MDH) shifts from the lanthanide-dependent MDH (XoxF)-type, to the calcium-dependent MDH (MxaF)-type. Correspondingly, methanol is released into the medium only when the methanotroph expresses the MxaF-type MDH. These results suggest a cross-feeding mechanism in which the nonmethanotrophic partner induces a change in expression of methanotroph MDHs, resulting in release of methanol for its growth. This partner-induced change in gene expression that benefits the partner is a paradigm for microbial interactions that cannot be observed in studies of pure cultures, underscoring the importance of synthetic microbial community approaches to understand environmental microbiomes. M icrobial communities and their members are part of every ecosystem and drive important biogeochemical processes on Earth (1). They typically comprise a range of phylogenetically and functionally diverse microbes (2) that are structured by biotic and abiotic factors (3) and are entangled through specific interactions in complex networks (4).The significance of microbial communities in diverse ecosystems including the human body is now widely accepted in science and has led to a range of initiatives focused on the world's microbiomes (5, 6). One important goal within these efforts is to understand how microbes interact with each other and the consequences of such interactions at the level of their transcriptomes, proteomes, and metabolomes. For instance, it has been demonstrated that the metabolism of yeast can be transformed by bacteria-induced prions to decrease the release of inhibiting ethanol (7). In another study, an oral biofilm of the genus Streptococcus displayed different transcriptional responses toward the presence of other species in mixed-species cultures (8).Measuring interactions in complex environmental communities is still a difficult task. Laboratory cocultures represent a simplified approach to assessing...

Aerobic methane-oxidizing bacteria (MOB) use a restricted substrate range, yet >30 species-equivalent operational taxonomical units (OTUs) are found in one paddy soil. How these OTUs physically share their microhabitat is unknown. Here we highly resolved the vertical distribution of MOB and their activity. Using microcosms and cryosectioning, we sub-sampled the top 3-mm of a water-saturated soil at near in situ conditions in 100-μm steps. We assessed the community structure and activity using the particulate methane monooxygenase gene pmoA as a functional and phylogenetic marker by terminal restriction fragment length polymorphism (t-RFLP), a pmoA-specific diagnostic microarray, and cloning and sequencing. pmoA genes and transcripts were quantified using competitive reverse transcriptase PCR combined with t-RFLP. Only a subset of the methanotroph community was active. Oxygen microprofiles showed that 89% of total respiration was confined to a 0.67-mm-thick zone immediately above the oxic–anoxic interface, most probably driven by methane oxidation. In this zone, a Methylobacter-affiliated OTU was highly active with up to 18 pmoA transcripts per cell and seemed to be adapted to oxygen and methane concentrations in the micromolar range. Analysis of transcripts with a pmoA-specific microarray found a Methylosarcina-affiliated OTU associated with the surface zone. High oxygen but only nanomolar methane concentrations at the surface suggested an adaptation of this OTU to oligotrophic conditions. No transcripts of type II methanotrophs (Methylosinus, Methylocystis) were found, which indicated that this group was represented by resting stages only. Hence, different OTUs within a single guild shared the same microenvironment and exploited different niches.

We focused on the functional guild of methane oxidizing bacteria (MOB) as model organisms to get deeper insights into microbial biogeography. The pmoA gene was used as a functional and phylogenetic marker for MOB in two approaches: (i) a pmoA database (> 4000 sequences) was evaluated to obtain insights into MOB diversity in Italian rice paddies, and paddy fields worldwide. The results show a wide geographical distribution of pmoA genotypes that seem to be specifically adapted to paddy fields (e.g. Rice Paddy Cluster 1 and Rice Paddy Cluster 2). (ii) On the smaller geographical scale, we designed a factorial experiment including three different locations, two rice varieties and two habitats (soil and roots) within each of three rice fields. Multivariate analysis of terminal restriction fragment analysis profiles revealed different community patterns at the three field sites, located 10-20 km apart. Root samples were characterized by high abundance of type I MOB whereas the rice variety had no effect. With the agronomical practice being nearly identical, historical contingencies might be responsible for the field site differences. Considering a large reservoir of viable yet inactive MOB cells acting as a microbial seed bank, environmental conditions might have selected and activated a different subset at a time thereby shaping the community.

In lake ecosystems a major proportion of methane (CH(4) ) emissions originate from the littoral zone, which can have a great spatial variability in hydrology, soil quality and vegetation. Hitherto, spatial heterogeneity and the effects it has on functioning and diversity of methanotrophs in littoral wetlands have been poorly understood. A diagnostic microarray based on the particulate methane monooxygenase gene coupled with geostatistics was used to analyse spatial patterns of methanotrophs in the littoral wetland of a eutrophic boreal lake (Lake Kevätön, Eastern Finland). The wetland had a hydrology gradient with a mean water table varying from -8 to -25 cm. The wettest area, comprising the highest CH(4) oxidation, had the highest abundance and species richness of methanotrophs. A high water table favoured the occurrence of type Ib methanotrophs, whereas types Ia and II were found under all moisture conditions. Thus the spatial heterogeneity in functioning and diversity of methanotrophs in littoral wetlands is highly dependent on the water table, which in turn varies spatially in relation to the geomorphology of the wetland. We suggest that changes in water levels resulting from regulation of lakes and/or global change will affect the abundance, activity and diversity of methanotrophs, and consequently CH(4) emissions from such systems.

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In this perspective article, we question how well model organisms, the ones that are easy to cultivate in the laboratory and that show robust growth and biomass accumulation, reflect the dynamics and interactions of microbial communities observed in nature. Today’s -omics toolbox allows assessing the genomic potential of microbes in natural environments in a high-throughput fashion and at a strain-level resolution. However, understanding of the details of microbial activities and of the mechanistic bases of community function still requires experimental validation in simplified and fully controlled systems such as synthetic communities. We have studied methane utilization in Lake Washington sediment for a few decades and have identified a number of species genetically equipped for this activity. We have also identified co-occurring satellite species that appear to form functional communities together with the methanotrophs. Here, we compare experimental findings from manipulation of natural communities involved in metabolism of methane in this niche with findings from manipulation of synthetic communities assembled in the laboratory of species originating from the same study site, from very simple (two-species) to rather complex (50-species) synthetic communities. We observe some common trends in community dynamics between the two types of communities, toward representation of specific functional guilds. However, we also identify strong discrepancies between the dominant methane oxidizers in synthetic communities compared to natural communities, under similar incubation conditions. These findings highlight the challenges that exist in using the synthetic community approach to modeling dynamics and species interactions in natural communities.