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Soil bacteria are largely missing from future biodiversity assessments hindering comprehensive forecasts of ecosystem changes. Soil bacterial communities are expected to be more strongly driven by pH and less by other edaphic and climatic factors. Thus, alkalinisation or acidification along with climate change may influence soil bacteria, with subsequent influences for example on nutrient cycling and vegetation. Future forecasts of soil bacteria are therefore needed. We applied species distribution modelling (SDM) to quantify the roles of environmental factors in governing spatial abundance distribution of soil bacterial OTUs and to predict how future changes in these factors may change bacterial communities in a temperate mountain area. Models indicated that factors related to soil (especially pH), climate and/or topography explain and predict part of the abundance distribution of most OTUs. This supports the expectations that microorganisms have specific environmental requirements (i.e., niches/envelopes) and that they should accordingly respond to environmental changes. Our predictions indicate a stronger role of pH over other predictors (e.g. climate) in governing distributions of bacteria, yet the predicted future changes in bacteria communities are smaller than their current variation across space. The extent of bacterial community change predictions varies as a function of elevation, but in general, deviations from neutral soil pH are expected to decrease abundances and diversity of bacteria. Our findings highlight the need to account for edaphic changes, along with climate changes, in future forecasts of soil bacteria.

High-throughput 16S rRNA gene amplicon sequencing is an essential method for studying the diversity and dynamics of microbial communities. However, this method is presently hampered by the lack of high-identity reference sequences for many environmental microbes in the public 16S rRNA gene reference databases and by the absence of a systematic and comprehensive taxonomy for the uncultured majority. Here, we demonstrate how high-throughput synthetic long-read sequencing can be applied to create ecosystem-specific full-length 16S rRNA gene amplicon sequence variant (FL-ASV) resolved reference databases that include high-identity references (>98. High-throughput 16S rRNA gene amplicon sequencing is an essential method for studying the diversity and dynamics of microbial communities. However, this method is presently hampered by the lack of high-identity reference sequences for many environmental microbes in the public 16S rRNA gene reference databases and by the absence of a systematic and comprehensive taxonomy for the uncultured majority. Here, we demonstrate how high-throughput synthetic long-read sequencing can be applied to create ecosystem-specific full-length 16S rRNA gene amplicon sequence variant (FL-ASV) resolved reference databases that include high-identity references (>98.7% identity) for nearly all abundant bacteria (>0.01% relative abundance) using Danish wastewater treatment systems and anaerobic digesters as an example. In addition, we introduce a novel sequence identity-based approach for automated taxonomy assignment (AutoTax) that provides a complete seven-rank taxonomy for all reference sequences, using the SILVA taxonomy as a backbone, with stable placeholder names for unclassified taxa. The FL-ASVs are perfectly suited for the evaluation of taxonomic resolution and bias associated with primers commonly used for amplicon sequencing, allowing researchers to choose those that are ideal for their ecosystem. Reference databases processed with AutoTax greatly improves the classification of short-read 16S rRNA ASVs at the genus- and species-level, compared with the commonly used universal reference databases. Importantly, the placeholder names provide a way to explore the unclassified environmental taxa at different taxonomic ranks, which in combination with in situ analyses can be used to uncover their ecological roles.

It is becoming increasingly clear that microbial symbionts influence key aspects of their host’s fitness, and vice versa . This may fundamentally change our thinking about how microbes and hosts interact in influencing fitness and adaptation to changing environments. Here we explore how reductions in population size commonly experienced by threatened species influence microbiome diversity. Consequences of such reductions are normally interpreted in terms of a loss of genetic variation, increased inbreeding and associated inbreeding depression. However, fitness effects of population bottlenecks might also be mediated through microbiome diversity, such as through loss of functionally important microbes. Here we utilise 50 Drosophila melanogaster lines with different histories of population bottlenecks to explore these questions. The lines were phenotyped for egg-to-adult viability and their genomes sequenced to estimate genetic variation. The bacterial 16S rRNA gene was amplified in these lines to investigate microbial diversity. We found that 1) host population bottlenecks constrained microbiome richness and diversity, 2) core microbiomes of hosts with low genetic variation were constituted from subsets of microbiomes found in flies with higher genetic variation, 3) both microbiome diversity and host genetic variation contributed to host population fitness, 4) connectivity and robustness of bacterial networks was low in the inbred lines regardless of host genetic variation, 5) reduced microbial diversity was associated with weaker evolutionary responses of hosts in stressful environments, and 6) these effects were unrelated to Wolbachia density. These findings suggest that population bottlenecks reduce hologenomic variation (combined host and microbial genetic variation). Thus, while the current biodiversity crisis focuses on population sizes and genetic variation of eukaryotes, an additional focal point should be the microbial diversity carried by the eukaryotes, which in turn may influence host fitness and adaptability with consequences for the persistence of populations.

We studied the effect of many years of streptomycin use in apple orchards on the proportion of phyllosphere bacteria resistant to streptomycin and bacterial community structure. Leaf samples were collected during early July through early September from four orchards that had been sprayed with streptomycin during spring of most years for at least 10 years and four orchards that had not been sprayed. The percentage of cultured phyllosphere bacteria resistant to streptomycin at non-sprayed orchards (mean of 65%) was greater than at sprayed orchards (mean of 50%) (P = 0.0271). For each orchard, a 16S rRNA gene clone library was constructed from leaf samples. Proteobacteria dominated the bacterial communities at all orchards, accounting for 71 of 104 OTUs (determined at 97% sequence similarity) and 93% of all sequences. The genera Massilia, Methylobacterium, Pantoea, Pseudomonas, and Sphingomonas were shared across all sites. Shannon and Simpson's diversity indices and Pielou's evenness index were similar among orchards regardless of streptomycin use. Analysis of Similarity (ANOSIM) indicated that long-term streptomycin treatment did not account for the observed variability in community structure among orchards (R = -0.104, P = 0.655). Other variables, including time of summer, temperature and time at sampling, and relative distance of the orchards from each other, also had no significant effect on bacterial community structure. We conclude that factors other than streptomycin exposure drive both the proportion of streptomycin-resistant bacteria and phylogenetic makeup of bacterial communities in the apple phyllosphere in middle to late summer.

Methanogenic archaea are ubiquitous in peat soils; however, their diversity and distributions within and among peatland ecosystems are not well known. We used comprehensive clone libraries of 16S rRNA gene sequences to investigate spatial patterns in diversity (richness, evenness of taxa) and composition (taxonomic, phylogenetic) of the methanogenic community in six peatlands arrayed 775 km from eastern Ontario, Canada to West Virginia, USA. Five sites were Sphagnum (moss) and shrub dominated; one site was sedge dominated; and, potential rates of methane (CH4) production ranged from 15 to 450 nmol/g day. The gradient allowed us to examine influences of site conditions, site history, and climate on community composition. The region had representatives of methanogens from four taxonomic orders. We observed 29 operationally defined units (OTUs) based on >97% sequence identity. One OTU accounted for 43% of all clones, whereas 15 OTUs were rare with <1% of the total number of clones. The number of OTUs per site ranged from 4 to 21, and statistical analysis suggested diversity of 4–43 per site. Eighteen of the OTUs were endemic to one site; albeit, most endemics occurred in the sedge dominated site. One OTU was cosmopolitan, occurring in all six sites. We found a positive relationship between methanogen diversity and rates of CH4 production per site (Pearson r = 0.93). Turnover in community composition between sites was weakly related to geographic distance between sites, whereas variation in soil pH and annual temperature played larger roles. About 50% of the variation in community composition was unexplained by distance, pH, mean climate, and site age. We conclude that methanogen diversity in peatlands of the central Appalachian region is shaped by present-day environmental conditions, suggesting an influence of impending climatic and environmental changes.

A taste for acid Microbiologists have succeeded in culturing the most acid-loving methanogen ever discovered. The new species, a member of the Methanomicrobiales group, was found in McLean Bog in New York State. It grows at a preferred pH of around 5, beating the previous record-holder, Methanobacterium espanolae , which has an optimum pH of between 5.5 and 6.0. Although some other methanogens can survive a pH as low as 4.5, the new species is the first to show growth and optimal methanogenesis in such acidic conditions. Microbes living in acidic soils are important sources of atmospheric methane, which is linked to global warming. Acidic peatlands are among the largest natural sources of atmospheric methane and harbour a large diversity of methanogenic Archaea 1 . Despite the ubiquity of methanogens in these peatlands, indigenous methanogens capable of growth at acidic pH values have resisted culture and isolation 2 , 3 , 4 ; these recalcitrant methanogens include members of an uncultured family-level clade in the Methanomicrobiales prevalent in many acidic peat bogs in the Northern Hemisphere 1 , 5 , 6 . However, we recently succeeded in obtaining a mixed enrichment culture of a member of this clade 7 . Here we describe its isolation and initial characterization. We demonstrate that the optimum pH for methanogenesis by this organism is lower than that of any previously described methanogen.

Soil microbes play a key role in shaping terrestrial ecosystems. It is therefore essential to understand what drives their distribution. While multivariate analyses have been used to characterise microbial communities and drivers of their spatial patterns, few studies have focused on predicting the distribution of amplicon sequence variants (ASVs). Here, we evaluate the potential of species distribution models (SDMs) to predict the presence–absence and relative abundance distribution of bacteria, archaea, fungi, and protist ASVs in the western Swiss Alps. Advanced automated selection of abiotic covariates was used to circumvent the lack of knowledge on the ecology of each ASV. Presence–absence SDMs could be fitted for most ASVs, yielding better predictions than null models. Relative abundance SDMs performed less well, with low fit and predictive power overall, but displayed a good capacity to differentiate between sites with high and low relative abundance of the modelled ASV. SDMs for bacteria and archaea displayed better predictive power than for fungi and protists, suggesting a closer link of the former with the abiotic covariates used. Microorganism distributions were mostly related to edaphic covariates. In particular, pH was the most selected covariate across models. The study shows the potential of using SDM frameworks to predict the distribution of ASVs obtained from topsoil DNA. It also highlights the need for further development of precise edaphic mapping and scenario modelling to enhances prediction of microorganism distributions in the future.

Aim To investigate the potential of a large range of soil variables to improve topo‐climatic models of plant species distributions in a temperate mountain region encompassing complex relief. Location The western Swiss Alps. Methods Fitting topo‐climatic models for >60 plant species across >250 sites with and without added soil predictor variables (>30). Testing included the following: (a) which soil variables improve plant species distribution models; (b) whether an optimal subset of soil variables can improve models for the majority of species and habitat types and (c) how much variation in plant species distributions soil variables alone explain. Results Geochemical variables (i.e. CaO, pH and inorganic carbon) and a drainage indicator (i.e. bulk soil water content) improved the predictive abilities of the models across the large majority of alpine plant species. The improvement of the models after the addition of soil information varied strongly between plant species and habitat types, but a trade‐off was found between the number of soil variables and the associated gain in model performance. Finally, across all species, one specific combination of soil variables – bulk soil water content + total phosphorus +δ13C – outperformed the commonly used topo‐climatic variables. Main conclusions Several soil variables significantly increased the predictive power of plant species distribution models in the temperate mountain region. Geochemical and drainage variables proved most important.

ContextHuman-induced changes in landscape structure are among the main causes of biodiversity loss. Despite their important contribution to biodiversity and ecosystem functioning, microbes—and particularly protists—remain spatially understudied. Soil microbiota are most often driven by local soil properties, but the influence of the surrounding landscape is rarely assessed.ObjectivesWe assessed the effect of landscape structure on soil protist alpha and beta diversity in meadows in the western Swiss Alps.MethodsWe sampled 178 plots along an elevation gradient representing a broad range of environmental conditions and land-use. We measured landscape structure around each plot at 5 successive spatial scales (i.e. neighbourhood windows of increasing radius, ranging from 100 to 2000 m around a plot). We investigated the changes of protist alpha and beta diversity as a function of landscape structure, local environmental conditions and geographic distance.ResultsLandscape structures, especially percentage of meadows, forests, or open habitats, played a key role for protist alpha and beta diversity. The importance of landscape structure was comparable to that of environmental conditions and spatial variables, and increased with the size of the neighbourhood window considered.ConclusionsOur results suggest that dispersal from neighbouring habitats is a key driver of protist alpha and beta diversity which highlight the importance of landscape-scale assembly mechanisms for microbial diversity. Landscape structure emerges as a key driver of microbial communities which has profound implications for our understanding of the consequences of land-use change on soil microbial communities and their associated functions.

Aim - Trends in spatial patterns of diversity in macroscopic organisms can be well predicted from correlative models, using topo‐climatic variables for plants and animals allowing inference over large scales. By contrast, diversity in soil microorganisms is generally considered as mostly driven by edaphic variables and, therefore, difficult to extrapolate on a large spatial scale based on predictive models. Here, we compared the power of topo‐climatic versus edaphic variables for predicting the diversity of various soil protist groups at the regional scale. Location - Swiss western Alps. Taxa - Full protist community and nine clades belonging respectively to three functional groups: parasites (Apicomplexa, Peronosporomycetes and Phytomyxea), phagotrophs (Sarcomonadea, Tubulinea and Spirotrichea) and phototrophs (Chlorophyta, Trebouxiophyceae and Diatomeae). Methods - We extracted soil DNA from 178 sites along a wide range of elevations with a random‐stratified sampling design. We defined protist Operational Taxonomic Units assemblages by metabarcoding of the V4 region of the rRNA small subunit gene. We assessed and modelled the diversity (Shannon index) patterns of all above‐mentioned taxonomic groups based on topo‐climatic (topography, slope southness, slope steepness and average summer temperature) and edaphic (soil temperature, relative humidity, pH, electroconductivity, phosphorus percentage, carbon/nitrogen, loss on ignition and shale percentage) variables in Generalized Additive Models (GAM). Results - The respective significance of topo‐climatic and edaphic variables varied among taxonomic and—to a certain extent—functional groups: while many variables explained significantly the diversity of the three phototrophs this was less the case for the three parasites. Topo‐climatic variables had a better predictive power than edaphic variables, yet predictive power varied among taxonomic groups. Main conclusions - Topo‐climatic variables (particularly slope steepness and summer temperature if we consider their significance in the GAMs) were, on average, better predictors of protist diversity at the landscape scale than edaphic variables. However, the predictive power of these variables on diversity differed considerably among taxonomic groups; such relationships may be due to direct and/or indirect (e.g. biotic) influences (like with parasitic taxa, where low predictive power is most likely explained by the absence of information on the hosts’ distribution). Future prospects include using such spatial models to predict hotspots of diversity and disease outbreaks.