Soil Microbial Data Research Articles

Abiotic factors are more important than land management and biotic interactions in shaping vascular plant and soil fungal communities

Human-induced disturbances to ecosystems cause a direct loss of biodiversity, and also alter the inherent processes that shape ecosystems even after the main disturbance has ceased. Therefore, is it important to understand the ongoing consequences of past and present land use practices on both above- and belowground components of agroecosystems. Our study takes a detailed investigation of what shapes communities in semi-natural grasslands under long-term management as either pastures or meadows. We investigated the relative importance of land use (land management (LM) and land use intensity (LUI)) and abiotic conditions (soil, topographic, climatic) across five grassland sites in central Germany in explaining species occurrence patterns of vascular plants and soil fungi. Analyses included a hierarchical joint-species distribution modeling approach to uncover the role of possible drivers shaping the local communities. Our results show that abiotic factors are of particularly high importance compared to LM and LUI for both vascular plant and soil fungal communities. In general, the relative importance of explanatory variables was similar across both taxon groups, however, for plant communities, local climate conditions were more important, while for fungal communities the soil variables (e.g., soil acidity and fertility including soil carbon and potassium concentrations) played a more prominent role. Species-specific responses to the respective variables showed some similarity across the taxon groups, however less so than expected given the long legacy of past LM. Here we found that approximately 50% of the plant and fungi species had clear LM preferences and responses to LUI. More plant species were positively related to pasture than meadow management, while the opposite was found for fungal species. Our findings advance the understanding of how abiotic conditions and human land use impact local species communities in managed semi-natural grasslands, aiding further research and policy development for conserving multitrophic diversity within these biodiversity rich habitats. Our results highlight the importance of controlling for soil and climate in studying the impact of land use, and of considering the environmental context at both small and larger spatial grains when making land management and biodiversity conservation decisions. In so doing, the complexity of ecological processes within managed systems are accounted for and prioritized, promoting both conservation and ecological functioning of the agroecosystem. Plant data is archived on PANGAEA (Kautzner, Auge, Roscher, Baessler & Slabbert, 2020; https://doi.org10.1594/PANGA EA.919343) and the soil microbial data on the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under the accession PRJNA563995 (Kautzner, Baessler, Auge, Roscher & Wubet, 2020)

Climatic and edaphic factors affecting soil bacterial community biodiversity in different forests of China

The dominant factors influencing forest soil bacterial richness and diversity are still obscure. In this study, a meta-analysis method was employed to clarify the different patterns of forest soil bacterial community biodiversity in China and to reveal how climate and soil factors shape these patterns. In total, 105 groups of soil microbial data were collected from 47 study sites, covering four types of forests: deciduous broad-leaved forest (DBF), coniferous forest (CF), coniferous and broad-leaved mixed forest (CBF), and evergreen broad-leaved forest (EBF). Natural forests (NFs) covered approximately 47% of the sites, and the others were planted forests (PFs). The soil bacterial community biodiversity showed some differences among the different forest types, and the Chao1 index of the soil bacterial community was highest in CBF. The richness and diversity of soil bacteria in PFs were significantly higher than those in NFs. However, the MAT and MAP showed closer relationships to the bacterial community in NFs than in PFs. In addition, the structural equation model (SEM) indicated that bacterial richness (i.e., the Chao1 index) was positively correlated with soil pH and total phosphorus (TP) and negatively correlated with soil organic carbon (SOC). The soil bacterial diversity (Shannon and Simpson indices) was positively correlated with SOC, TP and soil organic matter (SOM). Overall, soil pH, TP and soil carbon served as the most important edaphic factors affecting forest soil bacterial community biodiversity.

Examining assumptions of soil microbial ecology in the monitoring of ecological restoration

Abstract1. Global interest in building healthy soils combined with new DNA sequencing technologies has led to the generation of a vast amount of soil microbial community (SMC) data.2. SMC analysis is being adopted widely for monitoring ecological restoration trajectories. However, despite the large and growing quantity of soil microbial data, it remains unclear how these data inform and best guide restoration practice.3. Here, we examine assumptions around SMC as a tool for guiding ecosystem restoration and evaluate the effectiveness of using species inventories of SMC as a benchmark for restoration success.4. We investigate other approaches of assessing soil health, and conclude that we can significantly enhance the utility of species inventory data for ecological restoration by complementing it with the use of non‐molecular approaches.

You must choose, but choose wisely: Model-based approaches for microbial community analysis

Soil microbial community data produced by next-generation sequencing platforms has introduced a new era in microbial ecology studies but poses a challenge for data analysis: huge tables with highly sparse data combined with methodological limitations leading to biased analyses. Methodological studies have attempted to improve data interpretation via data transformation and/or rarefaction but usually neglect the assumptions required for an appropriate analysis. Advances in both mathematics and computation are now making model-based approaches feasible, especially latent variable modeling (LVM). LVM is a cornerstone of modern unsupervised learning that permits the evaluation of evolutionary, temporal, and count structure in a unified approach that directly incorporates the data distribution. Despite these advantages, LVM is rarely applied in data analyses of the soil microbiome. Here, we review available methods to handle the characteristics of soil microbial data obtained from next-generation sequencing and advocate for model-based approaches. We focus on the importance of assumption checking for guiding the selection of the most appropriate method of data analysis. We also provide future directions by advocating for the consideration of the dataset produced by sequencing as a representation of microbial detections instead of abundances and for the adoption of hierarchical models to convert these detections into estimated abundances prior to evaluating the microbial community. In summary, we show that model assessment is important for qualifying interpretations and can further guide refinements in subsequent analyses. We have only begun to understand the factors regulating soil microbial communities and the impacts of this microbiota on the environment/ecosystem. Understanding the assumptions of new methods is essential to fully harness their power to test hypotheses using high-throughput sequencing data.

MODELING MICROBIAL ABUNDANCES AND DYSBIOSIS WITH BETA-BINOMIAL REGRESSION.

Using a sample from a population to estimate the proportion of the population with a certain category label is a broadly important problem. In the context of microbiome studies, this problem arises when researchers wish to use a sample from a population of microbes to estimate the population proportion of a particular taxon, known as the taxon's relative abundance. In this paper, we propose a beta-binomial model for this task. Like existing models, our model allows for a taxon's relative abundance to be associated with covariates of interest. However, unlike existing models, our proposal also allows for the overdispersion in the taxon's counts to be associated with covariates of interest. We exploit this model in order to propose tests not only for differential relative abundance, but also for differential variability. The latter is particularly valuable in light of speculation that dysbiosis, the perturbation from a normal microbiome that can occur in certain disease conditions, may manifest as a loss of stability, or increase in variability, of the counts associated with each taxon. We demonstrate the performance of our proposed model using a simulation study and an application to soil microbial data.

Mechanisms of soil bacterial and fungal community assembly differ among and within islands.

The study of islands has made substantial contributions to the development of evolutionary and ecological theory. However, we know little about microbial community assembly on islands. Using soil microbial data collected from 29 lake islands and nearby mainland, we examined the assembly mechanisms of soil bacterial and fungal communities among and within islands. We found that deterministic processes, especially homogeneous selection, tended to be more important in shaping the assembly of soil bacterial communities among islands, while stochastic processes tended to be more important within islands. Moreover, increasing island area increased the importance of homogeneous selection, but reduced the importance of variable selection, for soil bacterial community assembly within islands. By contrast, stochastic processes tended to dominate soil fungal community assembly both among and within islands, with dispersal limitation playing a more important role within than among islands. Our results highlight the scale- and taxon-dependence of insular soil microbial community assembly, suggesting that spatial scale should be explicitly considered when evaluating the influences of habitat fragmentation on soil microbial communities.

Chemical variables influencing microbial properties in composted tannery sludge-treated soil

Repeated applications of composted tannery sludge to arable soils have the potential to greatly alter soil chemistry and thus potentially influence the soil microbial community over time. This study performed multivariate analyses using the data of soil microbial biomass, respiration, and enzymes activities obtained during 5 years (2010–2014) in a long-term experiment with composted tannery sludge amendment. The correlation between the soil microbial and chemical properties, via the analysis of similarity matrices, revealed calcium as the main single factor influencing the microbial properties, in 2010 and 2011. Afterward, chromium was the most important chemical variables driving the microbial properties in 2012, 2013, and 2014. The non-metric multidimensional scaling demonstrated that the soil microbial properties changed with composted tannery sludge application from 2010 to 2014. Multivariate analysis from soil microbial data with composted tannery sludge amendment, during 5 years, showed calcium and chromium as being the most significant variables influencing the soil microbial properties in composted tannery sludge-treated soil.

Application of a Bayesian nonparametric model to derive toxicity estimates based on the response of Antarctic microbial communities to fuel-contaminated soil.

Ecotoxicology is primarily concerned with predicting the effects of toxic substances on the biological components of the ecosystem. In remote, high latitude environments such as Antarctica, where field work is logistically difficult and expensive, and where access to adequate numbers of soil invertebrates is limited and response times of biota are slow, appropriate modeling tools using microbial community responses can be valuable as an alternative to traditional single-species toxicity tests. In this study, we apply a Bayesian nonparametric model to a soil microbial data set acquired across a hydrocarbon contamination gradient at the site of a fuel spill in Antarctica. We model community change in terms of OTUs (operational taxonomic units) in response to a range of total petroleum hydrocarbon (TPH) concentrations. The Shannon diversity of the microbial community, clustering of OTUs into groups with similar behavior with respect to TPH, and effective concentration values at level x, which represent the TPH concentration that causes x% change in the community, are presented. This model is broadly applicable to other complex data sets with similar data structure and inferential requirements on the response of communities to environmental parameters and stressors.

Relating soil microbial properties to yields of no-till canola on the Canadian prairies

Soil microorganisms mediate many important biological processes for sustainable agriculture. However, correlations between soil microbial properties and crop productivity cannot always be demonstrated. We collected soil microbial data from a canola (Brassica napus L.) study that was conducted at seven sites on the Canadian prairies about agricultural practices focused on increasing canola yields. The treatments consisted of two canola seeding rates (75 or 150seedsm−2), two nitrogen rates (1× and 1.5× soil test recommendation) and three nitrogen form–fungicide (prothioconazole) combinations (uncoated urea, no fungicide; uncoated urea+fungicide; and 50% polymer-coated urea+fungicide) in a 2×2×3 factorial arrangement. Microbial biomass C (MBC), β-glucosidase enzyme activity and functional bacterial diversity (based on C substrate utilization patterns) were determined in canola rhizosphere and in bulk soil and related to canola yields. The effects of seeding rate, nitrogen (N) rate and N form on soil microbial biomass, enzyme activity or bacterial functional diversity were usually not statistically significant. In the few cases where significance occurred, doubling the seeding rate from 75 to 150seedsm−2 usually increased these microbial properties in canola rhizosphere or bulk soil. Increasing N rate to 1.5× the recommended rate had mostly positive effects in canola rhizosphere and negative effects in bulk soil. The effects of N form (including addition of fungicide) were inconsistent. Soil MBC and β-glucosidase enzyme activity correlated positively with canola grain yield at the five sites where yields were <4000kgha−1 (r=0.51** to 0.76**), but no or weak negative correlations were observed at the two sites with yields >4000kgha−1. The functional diversity of soil bacteria was not or was weakly negatively correlated with grain yields. Some of these relationships appeared to be influenced by canola root maggot damage because root damage was usually negatively correlated with the soil microbial characteristics, but the correlations were too weak to be relevant. These results suggest underground feedback interactions between crops and soil microbes, i.e., crop/soil management practices that enhance crop growth also enhance soil microbial communities and their activities, and vice versa.

A Molecular Clone and Culture Inventory of the Root Fungal Community Associated with Eastern Hemlock in Great Smoky Mountains National Park

Below- and above-ground microbial communities are expected to dramatically change following the loss of healthy Tsuga canadensis (Eastern Hemlock) ecosystems due to the effects of Adelges tsugae (Hemlock Woolly Adelgid [HWA]) infestation. In 2006, few healthy Eastern Hemlock stands remained in Great Smoky Mountains National Park (GRSM), and we sought to obtain baseline soil microbial data from these undisturbed areas to better understand them. This study surveyed root fungal communities (e.g., mycorrhizae) using molecular cloning and cultural methods at 2 remaining healthy Eastern Hemlock stands in GRSM. We sampled roots from 40 trees representing 2 age classes (<75 and >150 years old) and 2 elevations (486 m and 1158 m) to obtain molecular clones and isolated cultures. Molecular clones from the ITS region indicated the presence of 124 taxa in 23 genera. The dominant genera were Piloderma (8.2%), Russula (6.8%), Tomentella (4.0%), Meliniomyces (3.4%), and Clavulina (2.9%). Ninety-seven of the taxa identified occurred at <1.0% relative abundance. The most frequent fungal species were Meliniomyces variabilis (22.6%), Russula granulata (16.1%), Phialocephala fortinii (9.7%), and Cladophialophora chaetospira (16.1%). Species richness, diversity, and evenness for total fungi, ectomycorrhizal species, and saprobic species were similar at different elevations and stand ages. Using ITS sequence data, we identified 9 species from 1800 fungal isolates from Eastern Hemlock root tissue, the most common of which were Trichoderma harzianum (24.4%), Trichoderma viride (24.4%), Trichoderma asperellum (17.1%), and Ilyonectria radicicola (12.2%). Species richness, diversity, and evenness were not significantly different across locations for the molecular data. Baseline data for the fungal taxa identified in the study may be important for understanding ecological changes that can occur post-HWA damage and HWA-caused stand losses. These data could also be used to support Eastern Hemlock reforestation efforts within current or former habitats in GRSM and surrounding ecosystems.

Non-parametric multivariate comparisons of soil fungal composition: Sensitivity to thresholds and indications of structural redundancy in T-RFLP data

Complex soil microbial data produced by molecular profiling techniques, such as terminal restriction fragment length polymorphism (T-RFLP), are often analysed using multivariate statistical methods. Despite this, there has been little evaluation of the sensitivity of multivariate methods to routine data manipulations such as the application of noise thresholds. We examine effects of three percentage area thresholds (0.1%, 0.5% and 1.0%; i.e. ranging from low to commonly applied) on non-metric multidimensional scaling (NMDS) ordinations of soil fungal T-RFLP profiles. We then interpret threshold effects by introducing the concept of structural redundancy in T-RFLP data. NMDS ordinations compared 20 soil samples (encompassing seven sites, two forest types, and three locations) for T-RFLP presence/absence data, which were produced using primers specific to the rDNA internal transcribed spacer (ITS) region, and three separate restriction enzymes (HinfI, AluI and TaqI). Increasing thresholds from 0.1% to 1.0% led to decreases in average number of detected reproducible fragments per site of 57% (HinfI), 75% (AluI), and 69% (TaqI). However, despite the removal of many fragments unique to site/forest type/location, the application of increasing thresholds did not significantly change NMDS patterns for any enzyme in both the complete data and in a more weakly structured sub-group (involving one forest type and two locations). Thus, our data indicate that application of area thresholds up to 1.0% would have minimal impact on NMDS-based comparisons of soil fungal composition. Robustness of NMDS patterns to considerable fragment loss with increasing threshold suggested a degree of ‘structural redundancy’ in our T-RFLP data – that is, multiple fragments per soil sample were interchangeable in the way they contributed to between-sample NMDS patterns. Indeed, for each restriction enzyme, we found 3–4 peels of data (i.e. mutually exclusive sub-sets) that were capable of reproducing overall patterns in presence/absence data at the 0.1% threshold. Despite potential in the T-RFLP method for production of more than one fragment per fungal species/phylotype, we present arguments to support at least some translation of structural redundancy in T-RFLP data to structural redundancy in fungal community comparisons (i.e. the between-soil similarity patterns can be explained by, or are imprinted in, multiple groups of fungal phylotypes). Our analyses highlight structural redundancy as an efficient method for identifying key phylotypes responsible for differences in community composition among soils, and as a promising starting point for improved understanding of many aspects of spatial soil ecology.