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.
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.
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.
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 (R
value = 0.35) to strong (R
value = 0.79). These predictions were accurate enough to allow bacterial communities to assign the correct soil quality scores with 50-95% accuracy.
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.
•Groundwater microbiomes are less stable through time than previously thought.•Over six years, the communities showed temporal patterns and gradual succession.•Temporal patterns in groundwater ...microbiomes are driven by recharge events.•Over half of the groundwater taxa originated from recharge-related sources.
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.
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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.
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.
Summary
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.
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.
ABSTRACT
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.
By sampling soil bacterial communities repeatedly over a period of 5 years, we showed that, overall, temporal variation was minimal, can be confounded by spatial variation and varies according to land use.
•eDNA sampling reveals differences in arthropod communities between forest types.•finer-scale differences not detected using eDNA, due to methodological limitations.•replicate DNA extractions can ...improve diversity detected and improve reliability.•Amplification biases must be addressed, and taxonomic databases need to be expanded.•Advancements needed but eDNA could improve invertebrate-based indicators.
Arthropods have long been appreciated as useful and important ecological indicators of beneficial or detrimental changes occurring in their environment, especially those driven by anthropogenic activity. However, morphological identification, especially of key groups such as soil arthropods, is laborious and requires high-level expertise, limiting the spatiotemporal scales of such monitoring. Molecular methods based on environmental DNA (eDNA) sampling have potential for scaling up sampling of arthropod biodiversity for environmental monitoring, due to the high-throughput capabilities of such methods. However, we still do not have a clear understanding of how well molecular methods detect variation in arthropod biodiversity over space and time, particularly at fine grains. We therefore conducted a study employing a standard eDNA metabarcoding approach to monitor the composition of subterranean arthropod communities in a homogeneous forest (pine plantation) and a heterogeneous forest (regenerating native woody vegetation). We sampled the two habitats at a range of spatial scales (cm to m) and with temporal replicates over ten months to capture seasonal variation. Our analysis of almost 800 samples showed that arthropod community composition differed significantly between the two forest types and habitat explained 16.7% of the variation observed overall. However, we did not observe strong temporal or seasonal change in either richness or composition. We attribute the perceived lack of fine-grained spatiotemporal pattern to current limitations of eDNA metabarcoding, which captured inadequate biodiversity to detect changes at these fine grains. We show that increased sampling and replicate DNA extractions result in more biodiversity being captured and reveal amplification biases and a lack of taxonomic assignments. Identifying these weaknesses highlights where efforts to improve eDNA metabarcoding should be focused. The many benefits that eDNA metabarcoding can bring to biodiversity based ecological monitoring mean the efforts still required to improve the reliability and reproducibility of these methods are undoubtedly worthwhile priorities.
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.
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