Enrichment of different taxa of the enigmatic candidate phyla radiation bacteria using a novel picolitre droplet technique.

The candidate phyla radiation (CPR) represents a distinct monophyletic clade and constitutes a major portion of the tree of life. Extensive efforts have focused on deciphering the functional diversity of its members, primarily using sequencing-based techniques. However, cultivation success remains scarce, presenting a significant challenge, particularly in CPR-dominated groundwater microbiomes characterized by low biomass. Here, we employ an advanced high-throughput droplet microfluidics technique to enrich CPR taxa from groundwater. Utilizing a low-volume filtration approach, we successfully harvested a microbiome resembling the original groundwater microbial community. We assessed CPR enrichment in droplet and aqueous bulk cultivation for 30 days using a novel CPR-specific primer to rapidly track the CPR fraction through the cultivation attempts. The combination of soil extract and microbial-derived necromass provided the most supportive conditions for CPR enrichment. Employing these supplemented conditions, droplet cultivation proved superior to bulk cultivation, resulting in up to a 13-fold CPR enrichment compared to a 1- to 2-fold increase in bulk cultivation. Amplicon sequencing revealed 10 significantly enriched CPR orders. The highest enrichment in CPRs was observed for some unknown members of the Parcubacteria order, Cand. Jorgensenbacteria, and unclassified UBA9983. Furthermore, we identified co-enriched putative host taxa, which may guide more targeted CPR isolation approaches in subsequent investigations.

Exploring freshwater stream bacterial communities as indicators of land use intensity.

BACKGROUND: Stream ecosystems comprise complex interactions among biological communities and their physicochemical surroundings, contributing to their overall ecological health. Despite this, many monitoring programs ignore changes in the bacterial communities that are the base of food webs in streams, often focusing on stream physicochemical assessments or macroinvertebrate community diversity instead. We used 16S rRNA gene sequencing to assess bacterial community compositions within 600 New Zealand stream biofilm samples from 204 sites within a 6-week period (February-March 2010). Sites were either dominated by indigenous forests, exotic plantation forests, horticulture, or pastoral grasslands in the upstream catchment. We sought to predict each site's catchment land use and environmental conditions based on the composition of the stream bacterial communities. RESULTS: Random forest modelling allowed us to use bacterial community composition to predict upstream catchment land use with 65% accuracy; urban sites were correctly assigned 90% of the time. Despite the variation inherent when sampling across a ~ 1000-km distance, bacterial community data could correctly differentiate undisturbed sites, grouped by their dominant environmental properties, with 75% accuracy. The positive correlations between actual values and those predicted by the models built using the stream biofilm bacterial data ranged from weak (average log N concentration in the stream water, R2 = 0.02) to strong (annual mean air temperature, R2 = 0.69). CONCLUSIONS: Freshwater bacterial community data provide useful insights into land use impacts on stream ecosystems; they may be used as an additional measure to screen stream catchment attributes.

Soil conditions are a more important determinant of microbial community composition and functional potential than neighboring plant diversity.

Replanting is an important tool for ecological recovery. Management strategies, such as planting areas with monocultures or species mixtures, have implications for restoration success. We used 16S and ITS rRNA gene amplicon sequencing and shotgun metagenomics to assess how the diversity of neighboring tree species impacted soil bacterial and fungal communities, and their functional potential, within the root zone of manuka (Leptospermum scoparium) trees. We compared data from monoculture and mixed tree species plots and confirmed that soil microbial taxonomic and functional community profiles significantly differed (p < 0.001). Compared to the diversity of neighboring tree species within the plot, soil environmental conditions and geographic distance was more important for structuring the microbial communities. The bacterial communities appeared more impacted by soil conditions, while the fungal communities displayed stronger spatial structuring, possibly due to wider bacterial dispersal. The different mechanisms structuring bacterial and fungal communities could have implications for ecological restoration outcomes.

The soil microbiome: An essential, but neglected, component of regenerative agroecosystems.

Regenerative agriculture (RA) is gaining traction globally as an approach for meeting growing food demands while avoiding, or even remediating, the detrimental environmental consequences associated with conventional farming. Momentum is building for science to provide evidence for, or against, the putative ecosystem benefits of RA practices relative to conventional farming. In this perspective article, we advance the argument that consideration of the soil microbiome in RA research is crucial for disentangling the varied and complex relationships RA practices have with the biotic and abiotic environment, outline the expected changes in soil microbiomes under RA, and make recommendations for designing research that will answer the outstanding questions on the soil microbiome under RA. Ultimately, deeper insights into the role of microbial communities in RA soils will allow the development of biologically relevant monitoring tools which will support land managers in addressing the key environmental issues associated with agriculture.

Phospholipid fatty acid (PLFA) analysis as a tool to estimate absolute abundances from compositional 16S rRNA bacterial metabarcoding data.

Microbial biodiversity monitoring through the analysis of DNA extracted from environmental samples is increasingly popular because it is perceived as being rapid, cost-effective, and flexible concerning the sample types studied. DNA can be extracted from diverse media before high-throughput sequencing of the prokaryotic 16S rRNA gene is used to characterize the taxonomic diversity and composition of the sample (known as metabarcoding). While sources of bias in metabarcoding methodologies are widely acknowledged, previous studies have focused mainly on the effects of these biases within a single substrate type, and relatively little is known of how these vary across substrates. We investigated the effect of substrate type (water, microbial mats, lake sediments, stream sediments, soil and a mock microbial community) on the relative performance of DNA metabarcoding in parallel with phospholipid fatty acid (PLFA) analysis. Quantitative estimates of the biomass of different taxonomic groups in samples were made through the analysis of PLFAs, and these were compared to the relative abundances of microbial taxa estimated from metabarcoding. Furthermore, we used the PLFA-based quantitative estimates of the biomass to adjust relative abundances of microbial groups determined by metabarcoding to provide insight into how the biomass of microbial taxa from PLFA analysis can improve understanding of microbial communities from environmental DNA samples. We used two sets of PLFA biomarkers that differed in their number of PLFAs to evaluate how PLFA biomarker selection influences biomass estimates. Metabarcoding and PLFA analysis provided significantly different views of bacterial composition, and these differences varied among substrates. We observed the most notable differences for the Gram-negative bacteria, which were overrepresented by metabarcoding in comparison to PLFA analysis. In contrast, the relative biomass and relative sequence abundances aligned reasonably well for Cyanobacteria across the tested freshwater substrates. Adjusting relative abundances of microbial taxa estimated by metabarcoding with PLFA-based quantification estimates of the microbial biomass led to significant changes in the microbial community compositions in all substrates. We recommend including independent estimates of the biomass of microbial groups to increase comparability among metabarcoding libraries from environmental samples, especially when comparing communities associated with different substrates.

Groundwater bacterial communities evolve over time in response to recharge.

Time series analyses are a crucial tool for uncovering the patterns and processes shaping microbial communities and their functions, especially in aquatic ecosystems. Subsurface aquatic environments are perceived to be more stable than surface oceans and lakes, due to the lack of sunlight, the absence of photosysnthetically-driven primary production, low temperature variations, and oligotrophic conditions. However, periodic groundwater recharge should affect the structure and succession of groundwater microbiomes. To disentangle the long-term temporal changes in bacterial communities of shallow fractured bedrock groundwater, and identify the drivers of the observed patterns, we analysed bacterial 16S rRNA gene sequencing data for samples collected monthly from three groundwater wells over a six-year period (n = 230) along a hillslope recharge area. We showed that the bacterial communities in the groundwater of limestone-mudstone alternations were not stable over time and exhibited non-linear dissimilarity patterns which corresponded to periods of groundwater recharge. Further, we observed an increase in dissimilarity over time (generalized additive model P < 0.001) indicating that the successive recharge events result in communities that are increasingly more dissimilar to the initial reference time point. The sampling period was able to explain up to 29.5% of the variability in bacterial community composition and the impact of recharge events on the groundwater microbiome was linked to the strength of the recharge and local environmental selection. Many groundwater bacteria originated from the recharge-related sources (mean = 66.5%, SD = 15.1%) and specific bacterial taxa were identified as being either enriched or repressed during recharge events. Overall, similar to surface aquatic environments, the microbiomes in shallow fractured-rock groundwater vary through time, though we revealed groundwater recharges as unique driving factors for these patterns. The high temporal resolution employed here highlights the dynamics of bacterial communities in groundwater, which is an essential resource for the provision of clean drinking water; understanding the biological complexities of these systems is therefore crucial.

High Dietary Niche Overlap Between Non-native and Native Ant Species in Natural Ecosystems.

Ants represent a highly diverse and ecologically important group of insects found in almost all terrestrial ecosystems. A subset of ant species have been widely transported around the globe and invade many natural ecosystems, often out-competing native counterparts and causing varying impacts on recipient ecosystems. Decisions to control non-native ant populations require an understanding of their interactions and related impacts on native communities. We employed stable isotope analysis and metabarcoding techniques to identify potential dietary niche overlap and identify gut contents of 10 ant species found in natural ecosystems in Aotearoa New Zealand. Additionally, we looked at co-occurrence to identify potential competitive interactions among native and non-native ant species. Ants fed mainly across two trophic levels, with high dietary overlap. Relative to other ant species sampled, two non-native ant species, Linepithema humile and Technomyrmex jocosus, were found to feed at the lowest trophic level. The largest isotopic niche overlap was observed between the native Monomorium antarcticum and the invasive Ochetellus glaber, with analyses revealing a negative co-occurrence pattern. Sequence data of ant gut content identified 51 molecular operational taxonomic units, representing 22 orders and 34 families, and primarily consisting of arthropod DNA. Although we generally found high dietary overlap among species, negative occurrence between a dominant, non-native species and a ubiquitous native species indicates that species-specific interactions could be negatively impacting native ecosystems. Our research progresses and informs the currently limited knowledge around establishing protocols for metabarcoding to investigate ant diet and interactions between native and non-native ant species.

Temporal variation in soil bacterial communities can be confounded with spatial variation.

Investigating temporal variation in soil bacterial communities advances our fundamental understanding of the causal processes driving biological variation, and how the composition of these important ecosystem members may change into the future. Despite this, temporal variation in soil bacteria remains understudied, and the effects of spatial heterogeneity in bacterial communities on the detection of temporal changes is largely unknown. Using 16S rRNA gene amplicon sequencing, we evaluated temporal patterns in soil bacterial communities from indigenous forest and human-impacted sites sampled repeatedly over a 5-year period. Temporal variation appeared to be greater when fewer spatial samples per site were analysed, as well as in human-impacted compared to indigenous sites (P < 0.01 for both). The biggest portion of variation in bacterial community richness and composition was explained by soil physicochemical variables (13-24%) rather than spatial distance or sampling time (<1%). These results highlight the importance of adequate spatiotemporal replication when sampling soil communities for environmental monitoring, and the importance of conducting temporal research across a wide variety of land uses. This will ensure we have a true understanding of how bacterial communities change over space and time; the work presented here provides important considerations for how such research should be designed.

Using soil bacterial communities to predict physico-chemical variables and soil quality.

BACKGROUND: Soil ecosystems consist of complex interactions between biological communities and physico-chemical variables, all of which contribute to the overall quality of soils. Despite this, changes in bacterial communities are ignored by most soil monitoring programs, which are crucial to ensure the sustainability of land management practices. We applied 16S rRNA gene sequencing to determine the bacterial community composition of over 3000 soil samples from 606 sites in New Zealand. Sites were classified as indigenous forests, exotic forest plantations, horticulture, or pastoral grasslands; soil physico-chemical variables related to soil quality were also collected. The composition of soil bacterial communities was then used to predict the land use and soil physico-chemical variables of each site. RESULTS: Soil bacterial community composition was strongly linked to land use, to the extent where it could correctly determine the type of land use with 85% accuracy. Despite the inherent variation introduced by sampling across ~ 1300 km distance gradient, the bacterial communities could also be used to differentiate sites grouped by key physico-chemical properties with up to 83% accuracy. Further, individual soil variables such as soil pH, nutrient concentrations and bulk density could be predicted; the correlations between predicted and true values ranged from weak (R2 value = 0.35) to strong (R2 value = 0.79). These predictions were accurate enough to allow bacterial communities to assign the correct soil quality scores with 50-95% accuracy. CONCLUSIONS: The inclusion of biological information when monitoring soil quality is crucial if we wish to gain a better, more accurate understanding of how land management impacts the soil ecosystem. We have shown that soil bacterial communities can provide biologically relevant insights on the impacts of land use on soil ecosystems. Furthermore, their ability to indicate changes in individual soil parameters shows that analysing bacterial DNA data can be used to screen soil quality. Video Abstract.

From pine to pasture: land use history has long-term impacts on soil bacterial community composition and functional potential.

Bacterial communities are crucial to soil ecosystems and are known to be sensitive to environmental changes. However, our understanding of how present-day soil bacterial communities remain impacted by historic land uses is limited; implications for their functional potential are especially understudied. Through 16S rRNA gene amplicon and shotgun metagenomic sequencing, we characterized the structure and functional potential of soil bacterial communities after land use conversion. Sites converted from pine plantations to dairy pasture were sampled five- and eight-years post conversion. The bacterial community composition and functional potential at these sites were compared to long-term dairy pastures and pine forest reference sites. Bacterial community composition and functional potential at the converted sites differed significantly from those at reference sites (P = 0.001). On average, they were more similar to those in the long-term dairy sites and showed gradual convergence (P = 0.001). Differences in composition and functional potential were most strongly related to nutrients such as nitrogen, Olsen P and the carbon to nitrogen ratio. Genes related to the cycling of nitrogen, especially denitrification, were underrepresented in converted sites compared to long-term pasture soils. Together, our study highlights the long-lasting impacts land use conversion can have on microbial communities, and the implications for future soil health and functioning.

Connecting through space and time: catchment-scale distributions of bacteria in soil, stream water and sediment.

Terrestrial and aquatic environments are linked through hydrological networks that transport abiotic components from upslope environments into aquatic ecosystems. However, our understanding of how bacteria are transported through these same networks is limited. Here, we applied 16S rRNA gene sequencing to over 500 soil, stream water and stream sediment samples collected within a native forest catchment to determine the extent to which bacterial communities in these habitats are connected. We provide evidence that while the bacterial communities in each habitat were significantly distinct from one another (PERMANOVA pairwise P < 0.001), the bacterial communities in soil and stream samples were weakly connected to each other when stream sediment sample locations were downhill of surface runoff flow paths. This pattern decreased with increasing distance between the soil and sediment samples. The connectivity between soil and stream water samples was less apparent and extremely transient; the greatest similarity between bacterial communities in soil and stream water overall was when comparing stream samples collected 1 week post soil sampling. This study shows how bacterial communities in soil, stream water and stream sediments are connected at small spatial scales and provides rare insights into the temporal dynamics of terrestrial and aquatic bacterial community connectivity.

Perspectives on the Impact of Sampling Design and Intensity on Soil Microbial Diversity Estimates.

Soil bacterial communities have long been recognized as important ecosystem components, and have been the focus of many local and regional studies. However, there is a lack of data at large spatial scales, on the biodiversity of soil microorganisms; national or more extensive studies to date have typically consisted of low replication of haphazardly collected samples. This has led to large spatial gaps in soil microbial biodiversity data. Using a pre-existing dataset of bacterial community composition across a 16-km regular sampling grid in France, we show that the number of detected OTUs changes little under different sampling designs (grid, random, or representative), but increases with the number of samples collected. All common OTUs present in the full dataset were detected when analyzing just 4% of the samples, yet the number of rare OTUs increased exponentially with sampling effort. We show that far more intensive sampling, across all global biomes, is required to detect the biodiversity of soil microorganisms. We propose avenues such as citizen science to ensure these large sample datasets can be more realistically achieved. Furthermore, we argue that taking advantage of pre-existing resources and programs, utilizing current technologies efficiently and considering the potential of future technologies will ensure better outcomes from large and extensive sample surveys. Overall, decreasing the spatial gaps in global soil microbial diversity data will increase our understanding on what governs the distribution of soil taxa, and how these distributions, and therefore their ecosystem contributions, will continue to change into the future.

Microbial assemblages and bioindicators as proxies for ecosystem health status: potential and limitations.

Microorganisms play fundamental roles in the diversity and functional stability of environments, including nutrient and energy cycling. However, microbial biodiversity loss and change because of global climate and land use change remain poorly understood. Many microbial taxa exhibit fast growth rates and are highly sensitive to environmental change. This suggests they have potential to be efficient biological indicators to assess and monitor the state of the habitats within which they occur. Here, we describe and illustrate a range of univariate and multivariate statistical approaches that can be used to identify effective microbial indicators of environmental perturbations and quantify changes in microbial communities. We show that the integration of multiple approaches, such as linear discriminant analysis effect size and indicator value analysis, is optimal for the quantification of the effects of perturbation on microbial communities. We demonstrate the most prevalent techniques using microbial community data derived from soils under different land uses. We discuss the limitations to the development and use of microbial bioindicators and identify future research directions, such as the creation of reliable, standardised reference databases to provide baseline metrics that are indicative of healthy microbial communities. If reliable and globally-relevant microbial indicators of environmental health can be developed, there is enormous potential for their use, both as a standalone monitoring tool and via their integration with existing physical, chemical and biological measures of environmental health.

Optimal extraction methods for the simultaneous analysis of DNA from diverse organisms and sample types.

Using environmental DNA (eDNA) to assess the distribution of micro- and macroorganisms is becoming increasingly popular. However, the comparability and reliability of these studies is not well understood as we lack evidence on how different DNA extraction methods affect the detection of different organisms, and how this varies among sample types. Our aim was to quantify biases associated with six DNA extraction methods and identify one which is optimal for eDNA research targeting multiple organisms and sample types. We assessed each methods' ability to simultaneously extract bacterial, fungal, plant, animal and fish DNA from soil, leaf litter, stream water, stream sediment, stream biofilm and kick-net samples, as well as from mock communities. Method choice affected alpha-diversity for several combinations of taxon and sample type, with the majority of the differences occurring in the bacterial communities. While a single method performed optimally for the extraction of DNA from bacterial, fungal and plant mock communities, different methods performed best for invertebrate and fish mock communities. The consistency of methods, as measured by the similarity of community compositions resulting from replicate extractions, varied and was lowest for the animal communities. Collectively, these data provide the first comprehensive assessment of the biases associated with DNA extraction for both different sample types and taxa types, allowing us to identify DNeasy PowerSoil as a universal DNA extraction method. The adoption of standardized approaches for eDNA extraction will ensure that results can be more reliably compared, and biases quantified, thereby advancing eDNA as an ecological research tool.

Bacteria as Emerging Indicators of Soil Condition.

Bacterial communities are important for the health and productivity of soil ecosystems and have great potential as novel indicators of environmental perturbations. To assess how they are affected by anthropogenic activity and to determine their ability to provide alternative metrics of environmental health, we sought to define which soil variables bacteria respond to across multiple soil types and land uses. We determined, through 16S rRNA gene amplicon sequencing, the composition of bacterial communities in soil samples from 110 natural or human-impacted sites, located up to 300 km apart. Overall, soil bacterial communities varied more in response to changing soil environments than in response to changes in climate or increasing geographic distance. We identified strong correlations between the relative abundances of members of Pirellulaceae and soil pH, members of Gaiellaceae and carbon-to-nitrogen ratios, members of Bradyrhizobium and the levels of Olsen P (a measure of plant available phosphorus), and members of Chitinophagaceae and aluminum concentrations. These relationships between specific soil attributes and individual soil taxa not only highlight ecological characteristics of these organisms but also demonstrate the ability of key bacterial taxonomic groups to reflect the impact of specific anthropogenic activities, even in comparisons of samples across large geographic areas and diverse soil types. Overall, we provide strong evidence that there is scope to use relative taxon abundances as biological indicators of soil condition. IMPORTANCE: The impact of land use change and management on soil microbial community composition remains poorly understood. Therefore, we explored the relationship between a wide range of soil factors and soil bacterial community composition. We included variables related to anthropogenic activity and collected samples across a large spatial scale to interrogate the complex relationships between various bacterial community attributes and soil condition. We provide evidence of strong relationships between individual taxa and specific soil attributes even across large spatial scales and soil and land use types. Collectively, we were able to demonstrate the largely untapped potential of microorganisms to indicate the condition of soil and thereby influence the way that we monitor the effects of anthropogenic activity on soil ecosystems into the future.