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. IMPORTANCEThe 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.

Advances in the sequencing of DNA extracted from media such as soil and water offer huge opportunities for biodiversity monitoring and assessment, particularly where the collection or identification of whole organisms is impractical. However, there are myriad methods for the extraction, storage, amplification and sequencing of DNA from environmental samples. To help overcome potential biases that may impede the effective comparison of biodiversity data collected by different researchers, we propose a standardised set of procedures for use on different taxa and sample media, largely based on recent trends in their use. Our recommendations describe important steps for sample pre-processing and include the use of (a) Qiagen DNeasy PowerSoil ® and PowerMax ® kits for extraction of DNA from soil, sediment, faeces and leaf litter; (b) DNeasy PowerSoil ® for extraction of DNA from plant tissue; (c) DNeasy Blood and Tissue kits for extraction of DNA from animal tissue; (d) DNeasy Blood and Tissue kits for extraction of DNA from macroorganisms in water and ice; and (e) DNeasy PowerWater ® kits for extraction of DNA from microorganisms in water and ice. Based on key parameters, including the specificity and inclusivity of the primers for the target sequence, we recommend the use of the following primer pairs to amplify DNA for analysis by Illumina MiSeq DNA sequencing: (a) 515f and 806RB to target bacterial 16S rRNA genes (including regions V3 and V4); (b) #3 and #5RC to target eukaryote 18S rRNA genes (including regions V7 and V8); (c) #3 and #5RC are also recommended for the routine analysis of protist community DNA; (d) ITS6F and ITS7R to target the chromistan ITS1 internal transcribed spacer region; (e) S2F and S3R to target the ITS2 internal transcribed spacer in terrestrial plants; (f) fITS7 or gITS7, and ITS4 to target the fungal ITS2 region; (g) NS31 and AML2 to target glomeromycota 18S rRNA genes; and (h) mICOIintF and jgHCO2198 to target cytochrome c oxidase subunit I (COI) genes in animals. More research is currently required to confirm primers suitable for the selective amplification of DNA from specific vertebrate taxa such as fish. Combined, these recommendations represent a framework for efficient, comprehensive and robust DNA-based investigations of biodiversity, applicable to most taxa and ecosystems. The adoption of standardised protocols for biodiversity assessment and monitoring using DNA extracted from environmental samples will enable more informative comparisons among datasets, generating significant benefits for ecological science and biosecurity applications.

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.

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.

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