Soil microbial communities play a crucial role in ecosystem functioning, but it is unknown how co-occurrence networks within these communities respond to disturbances such as climate extremes. This represents an important knowledge gap because changes in microbial networks could have implications for their functioning and vulnerability to future disturbances. Here, we show in grassland mesocosms that drought promotes destabilising properties in soil bacterial, but not fungal, co-occurrence networks, and that changes in bacterial communities link more strongly to soil functioning during recovery than do changes in fungal communities. Moreover, we reveal that drought has a prolonged effect on bacterial communities and their co-occurrence networks via changes in vegetation composition and resultant reductions in soil moisture. Our results provide new insight in the mechanisms through which drought alters soil microbial communities with potential long-term consequences, including future plant community composition and the ability of aboveground and belowground communities to withstand future disturbances.

Soil microorganisms act as gatekeepers for soil–atmosphere carbon exchange by balancing the accumulation and release of soil organic matter. However, poor understanding of the mechanisms responsible hinders the development of effective land management strategies to enhance soil carbon storage. Here we empirically test the link between microbial ecophysiological traits and topsoil carbon content across geographically distributed soils and land use contrasts. We discovered distinct pH controls on microbial mechanisms of carbon accumulation. Land use intensification in low-pH soils that increased the pH above a threshold (~6.2) leads to carbon loss through increased decomposition, following alleviation of acid retardation of microbial growth. However, loss of carbon with intensification in near-neutral pH soils was linked to decreased microbial biomass and reduced growth efficiency that was, in turn, related to trade-offs with stress alleviation and resource acquisition. Thus, less-intensive management practices in near-neutral pH soils have more potential for carbon storage through increased microbial growth efficiency, whereas in acidic soils, microbial growth is a bigger constraint on decomposition rates.

Summary: Studying fungal biodiversity using data generated from Illumina MiSeq sequencing platforms poses a number of bioinformatic challenges with the analysis typically involving a large number of tools for each analytical step from quality filtering to generating identified operational taxonomic unit (OTU) abundance tables. Here, we introduce PIPITS, an open‐source stand‐alone suite of software for automated processing of Illumina MiSeq sequences for fungal community analysis. PIPITS exploits a number of state of the art applications to process paired‐end reads from quality filtering to producing OTU abundance tables. We provide detailed descriptions of the pipeline and show its utility in the analysis of 9 396 092 sequences generated on the MiSeq platform from Illumina MiSeq. PIPITS is the first automated bioinformatics pipeline dedicated for fungal ITS sequences which incorporates ITSx to extract subregions of ITS and exploits the latest RDP Classifier to classify sequences against the curated UNITE fungal data set.

Lotic ecosystems such as rivers and streams are unique in that they represent a continuum of both space and time during the transition from headwaters to the river mouth. As microbes have very different controls over their ecology, distribution and dispersion compared with macrobiota, we wished to explore biogeographical patterns within a river catchment and uncover the major drivers structuring bacterioplankton communities. Water samples collected across the River Thames Basin, UK, covering the transition from headwater tributaries to the lower reaches of the main river channel were characterised using 16S rRNA gene pyrosequencing. This approach revealed an ecological succession in the bacterial community composition along the river continuum, moving from a community dominated by Bacteroidetes in the headwaters to Actinobacteria-dominated downstream. Location of the sampling point in the river network (measured as the cumulative water channel distance upstream) was found to be the most predictive spatial feature; inferring that ecological processes pertaining to temporal community succession are of prime importance in driving the assemblages of riverine bacterioplankton communities. A decrease in bacterial activity rates and an increase in the abundance of low nucleic acid bacteria relative to high nucleic acid bacteria were found to correspond with these downstream changes in community structure, suggesting corresponding functional changes. Our findings show that bacterial communities across the Thames basin exhibit an ecological succession along the river continuum, and that this is primarily driven by water residence time rather than the physico-chemical status of the river.

Summary: The heterogeneous nature of lotic habitats plays an important role in the complex ecological and evolutionary processes that structure the microbial communities within them. Due to such complexity, our understanding of lotic microbial ecology still lacks conceptual frameworks for the ecological processes that shape these communities. We explored how bacterial community composition and underlying ecological assembly processes differ between lotic habitats by examining community composition and inferring community assembly processes across four major habitat types (free‐living, particle‐associated, biofilm on benthic stones and rocks, and sediment). This was conducted at 12 river sites from headwater streams to the main river in the River Thames, UK. Our results indicate that there are distinct differences in the bacterial communities between four major habitat types, with contrasting ecological processes shaping their community assembly processes. While the mobile free‐living and particle‐associated communities were consistently less diverse than the fixed sediment and biofilm communities, the latter two communities displayed higher homogeneity across the sampling sites. This indicates that the relative influence of deterministic environmental filtering is elevated in sediment and biofilm communities compared with free‐living and particle‐associated communities, where stochastic processes play a larger role.

Background: Shotgun metagenomics is increasingly used to characterise microbial communities, particularly for the investigation of antimicrobial resistance (AMR) in different animal and environmental contexts. There are many different approaches for inferring the taxonomic composition and AMR gene content of complex community samples from shotgun metagenomic data, but there has been little work establishing the optimum sequencing depth, data processing and analysis methods for these samples. In this study we used shotgun metagenomics and sequencing of cultured isolates from the same samples to address these issues. We sampled three potential environmental AMR gene reservoirs (pig caeca, river sediment, effluent) and sequenced samples with shotgun metagenomics at high depth (~ 200 million reads per sample). Alongside this, we cultured single-colony isolates of Enterobacteriaceae from the same samples and used hybrid sequencing (short- and long-reads) to create high-quality assemblies for comparison to the metagenomic data. To automate data processing, we developed an open-source software pipeline, ‘ResPipe’. Results: Taxonomic profiling was much more stable to sequencing depth than AMR gene content. 1 million reads per sample was sufficient to achieve < 1% dissimilarity to the full taxonomic composition. However, at least 80 million reads per sample were required to recover the full richness of different AMR gene families present in the sample, and additional allelic diversity of AMR genes was still being discovered in effluent at 200 million reads per sample. Normalising the number of reads mapping to AMR genes using gene length and an exogenous spike of Thermus thermophilus DNA substantially changed the estimated gene abundance distributions. While the majority of genomic content from cultured isolates from effluent was recoverable using shotgun metagenomics, this was not the case for pig caeca or river sediment. Conclusions: Sequencing depth and profiling method can critically affect the profiling of polymicrobial animal and environmental samples with shotgun metagenomics. Both sequencing of cultured isolates and shotgun metagenomics can recover substantial diversity that is not identified using the other methods. Particular consideration is required when inferring AMR gene content or presence by mapping metagenomic reads to a database. ResPipe, the open-source software pipeline we have developed, is freely available (https://gitlab.com/hsgweon/ResPipe).