Summary Shallow thermokarst ponds are a conspicuous landscape element of the Arctic Siberian tundra with high biogeochemical variability. Little is known about how microbes from the regional species pool assemble into local pond communities and how the resulting patterns affect functional properties such as dissolved organic carbon (DOC) remineralization and greenhouse gas (GHG) turnover. We analysed the pelagic microbiomes of 20 ponds in north‐eastern Siberia in the context of their physico‐chemical properties. Ponds were categorized as polygonal or trough according to their geomorphological origin. The diversity of bacteria and eukaryotic microbes was assessed by ribosomal gene tag sequencing. Null model analysis revealed an important role of stochastic assembly processes within ponds of identical origin, in particular for genotypes only occurring in few systems. Nevertheless, the two pond types clearly represented distinct niches for both the bacterial and eukaryotic microbial communities. Carbon dioxide concentration, indicative of heterotrophic microbial processes, varied greatly, especially in the trough ponds. Methane concentrations were lower in polygonal ponds and were correlated with the estimated abundance of methanotrophs. Thus, the overall functional variability of Arctic ponds reflects the stochastic assembly of their microbial communities. Distinct functional subcommunities can, nevertheless, be related to GHG concentrations.

Stochasticity is a major cause of compositional β‐diversity in communities that develop under similar environmental conditions. Such communities may exhibit functional similarity due to sympatric taxa with equivalent metabolic capacities in the source assemblage. However, the redundancy of individual physiological traits may differ in the original source community, which in turn might lead to more or less pronounced variability of single functions among newly formed communities. We analyzed the degree of stochasticity during the primary assembly of bacterial communities originating from the same source and growing under identical conditions. We tested the links between community composition and functioning in parallel microcosms containing glucose and its dimer cellobiose. Bacteria from prefiltered lake water were diluted in artificial lake water and grown to the stationary phase. The resulting assemblages exhibited high compositional variability of taxa that were rare in the source communities. Simulations showed that the observed richness and incidence‐based β‐diversity could be reproduced by dispersal limitation, or by low dispersal rates associated with the ecological drift of the colonizers. Further null model analysis supported an important influence of stochasticity, as well as a synergy between dispersal limitation and both, heterogeneous and homogeneous selection. The communities functionally differed and the magnitude of functional variability depended on the substrate: more communities consumed glucose than cellobiose. However, there was no relationship between community structure and growth kinetics or substrate consumption. Thus, both structural and functional variability may be a consequence of stochastic processes during initial colonization in closed microbial communities.

Planktonic ciliate species form multiple trophic guilds and are central components of freshwater food webs. Progress in molecular analytical tools has opened new insight into ciliate assemblages. However, high and variable 18S rDNA copy numbers, typical for ciliates, make reliable quantification by amplicon sequencing extremely difficult. For an exact determination of abundances, the classical morphology-based quantitative protargol staining is still the method of choice. Morphotype analyses, however, are time consuming and need specific taxonomic expertise. Catalyzed reporter deposition fluorescence in situ hybridization (CARD-FISH) may represent a promising tool for the analysis of planktonic ciliates by combining molecular identification with microscopic quantification. We tested the applicability of CARD-FISH using nine cultured ciliate species. Eight species- and three genus-specific oligonucleotide probes were designed based on their 18S rRNA genes. The CARD-FISH protocol was adapted and the specificity of probes was established. We subsequently examined the precision of quantitation by CARD-FISH on single cultures and mock assemblages. Successful tests on lake water samples proved that planktonic ciliates could be identified and quantified in field samples by CARD-FISH. Double hybridizations allowed studying interspecific predator prey interactions between two ciliate species. In summary, we demonstrate that CARD-FISH with species-specific probes can facilitate studies on the population dynamics of closely related, small sized or cryptic species at high sampling frequencies.

Bacterial community composition among individual, experimentally generated ‘lake snow’ particles may be highly variable. Since such aggregates are seasonally abundant in the mixed upper layer of lakes, we hypothesized that particle‐attached (PA) bacteria disproportionally contribute to the small‐scale spatial beta diversity of pelagic communities. Community composition was analysed in sets of small (10 mL) samples collected from a pre‐alpine lake in May, July and October 2018. Bacteria were classified as free‐living (FL) or PA depending on their presence in large, 5‐μm pre‐filtered reference samples. FL exhibited clear seasonal differences in community composition and assembly. They were spatially uniform in May and July, and only a few FL taxa exhibited significant spatial variability. Spatial heterogeneity of FL in October was caused by high alpha and beta diversity of rare taxa, many with a presumably ‘tychoplanktic’ (alternating attached and free‐living) lifestyle. The spatial beta diversity of PA was always high, and only about 10% of their seasonal richness was present in any single sample. Thus, most compositional variability of pelagic bacteria at spatial scales of cm to m either directly or indirectly originated from PA. On a functional level, this genotypic heterogeneity might affect the spatial distribution of rare metabolic traits.