Atmospheric deposition is a source of inorganic nutrients and organic matter to the ocean, and can favor the growth of some planktonic species over others according to their nutrient requirements. Atmospheric inputs from natural and anthropogenic sources are nowadays increasing due to desertification and industrialization, respectively. While the impact of mineral dust (mainly from the Saharan desert) on phytoplankton and bacterial community composition has been previously assessed, the effect of anthropogenic aerosols on marine bacterial assemblages remains poorly studied. Since marine bacteria play a range of roles in the biogeochemical cycles of inorganic nutrients and organic carbon, it is important to determine which taxa of marine bacteria may benefit from aerosol fertilization and which not. Here, we experimentally assessed the effect of Saharan dust and anthropogenic aerosols on marine bacterioplankton community composition across a spatial and temporal range of trophic conditions in the northwestern Mediterranean Sea. Results from 16S rDNA sequencing showed that bacterial diversity varied significantly with seasonality and geographical location. While atmospheric deposition did not yield significant changes in community composition when all the experiments where considered together, it did produce changes at certain places and during certain times of the year. These effects accounted for shifts in the bacterial community's relative abundance of up to 28%. The effect of aerosols was overall greatest in summer, both types of atmospheric particles stimulating the groups Alphaproteobacteria, Betaproteobacteria, and Cyanobacteria in the location with the highest anthropogenic footprint. Other bacterial groups benefited from one or the other aerosol depending on the season and location. Anthropogenic aerosols increased the relative abundance of groups belonging to the phylum Bacteriodetes (Cytophagia, Flavobacteriia, and Sphingobacteriia), while Saharan dust stimulated most the phytoplanktonic group of Cyanobacteria and, more specifically, Synechococcus.

Urban sewer systems consist of wastewater and stormwater sewers, of which only wastewater is processed before being discharged. Occasionally, misconnections or damages in the network occur, resulting in untreated wastewater entering natural water bodies via the stormwater system. Cultivation of faecal indicator bacteria (e.g. Escherichia coli; E. coli) is the current standard for tracing wastewater contamination. This method is cheap but has limited specificity and mobility. Here, we compared the E. coli culturing approach with two sequencing-based methodologies (Illumina MiSeq 16S rRNA gene amplicon sequencing and Oxford Nanopore MinION shotgun metagenomic sequencing), analysing 73 stormwater samples collected in Stockholm. High correlations were obtained between E. coli culturing counts and frequencies of human gut microbiome amplicon sequences, indicating E. coli is indeed a good indicator of faecal contamination. However, the amplicon data further holds information on contamination source or alternatively how much time has elapsed since the faecal matter has entered the system. Shotgun metagenomic sequencing on a subset of the samples using a portable real-time sequencer, MinION, correlated well with the amplicon sequencing data. This study demonstrates the use of DNA sequencing to detect human faecal contamination in stormwater systems and the potential of tracing faecal contamination directly in the field.

The diversity and composition of lake bacterial communities are driven by the interplay between local contemporary environmental conditions and dispersal of cells from the surroundings, i.e. the metacommunity. Still, a conceptual understanding of the relative importance of the two types of factors is lacking. For instance, it is unknown which sources of dispersal are most important and under which circumstances. Here, we investigated the seasonal variation in the importance of dispersal from different sources (mixing, precipitation, surface runoff and sediment resuspension) for lake bacterioplankton community and population dynamics. For that purpose, two small forest lakes and their dispersal sources were sampled over a period of 10 months. The influence of dispersal on communities and populations was determined by 454 sequencing of the 16S rRNA gene and SourceTracker analysis. On the community level direct effects of dispersal were questionable from all sources. Instead we found that the community of the preceding sampling occasion, representing growth of resident bacteria, was of great importance. On the population level, however, dispersal of individual taxa from the inlet could be occasionally important even under low water flow. The effect of sediment resuspension and precipitation appeared small.

The vastness of microbial diversity implies that an almost infinite number of individuals needs to be identified to accurately describe such communities. Practical and economical constraints may therefore prevent appropriate study designs. However, for many questions in ecology it is not essential to know the actual diversity but rather the trends among samples thereof. It is, hence, important to know to what depth microbial communities need to be sampled to accurately measure trends in diversity. We used three data sets of freshwater and sediment bacteria, where diversity was explored using 454 pyrosequencing. Each data set contained 6–15 communities from which 15 000–20 000 16S rRNA gene sequences each were obtained. These data sets were subsampled repeatedly to 10 different depths down to 200 sequences per community. Diversity estimates varied with sequencing depth, yet, trends in diversity among samples were less sensitive. We found that 1000 denoised sequences per sample explained to 90% the trends in β-diversity (Bray-Curtis index) among samples observed for 15 000–20 000 sequences. Similarly, 5000 denoised sequences were sufficient to describe trends in α-diversity (Shannon index) with the same accuracy. Further, 5000 denoised sequences captured to more than 80% the trends in Chao1 richness and Pielou's evenness.