Despite glyphosate's wide use for weed control in agriculture, questions remain about the herbicide's effect on soil microbial communities. The existing scientific literature contains conflicting ...results, from no observable effect of glyphosate to the enrichment of agricultural pathogens such as
spp. We conducted a comprehensive field-based study to compare the microbial communities on the roots of plants that received a foliar application of glyphosate to adjacent plants that did not. The 2-year study was conducted in Beltsville, MD, and Stoneville, MS, with corn and soybean crops grown in a variety of organic and conventional farming systems. By sequencing environmental metabarcode amplicons, the prokaryotic and fungal communities were described, along with chemical and physical properties of the soil. Sections of corn and soybean roots were plated to screen for the presence of plant pathogens. Geography, farming system, and season were significant factors determining the composition of fungal and prokaryotic communities. Plots treated with glyphosate did not differ from untreated plots in overall microbial community composition after controlling for other factors. We did not detect an effect of glyphosate treatment on the relative abundance of organisms such as
spp.
Increasing the efficiency of food production systems while reducing negative environmental effects remains a key societal challenge to successfully meet the needs of a growing global population. The herbicide glyphosate has become a nearly ubiquitous component of agricultural production across the globe, enabling an increasing adoption of no-till agriculture. Despite this widespread use, there remains considerable debate on the consequences of glyphosate exposure. In this paper, we examine the effect of glyphosate on soil microbial communities associated with the roots of glyphosate-resistant crops. Using metabarcoding techniques, we evaluated prokaryotic and fungal communities from agricultural soil samples (
= 768). No effects of glyphosate were found on soil microbial communities associated with glyphosate-resistant corn and soybean varieties across diverse farming systems.
Agriculture, by its intentional design, manipulates the ecological functions of soils. It does so by altering carbon and nutrient inputs; by controlling the plant community; and in the case of ...tillage, by physically disrupting and redistributing the soil within the soil profile. While there are many studies that contrast soil microbiomes across farming systems, few studies have examined the effect of farming system on the vertical organization of taxa in the soil profile. We hypothesized that large effects of farming systems on edaphic factors would lead to large impacts on the microbial community that would reflect the underlying life history strategy of the microbes. For example, that tillage would increase the proportion of unicellular fungi. Our study compared farming systems that had been in place for 13 years in the mid-Atlantic region of the United States. Each system is a 3-year rotation of corn (Zea mays), soybean (Glycine max), and wheat (Triticum aestivum), managed with either conventional no-till, conventional chisel-till, or using organic methods. We determined the relative effect of long-term farming system management on edaphic factors and the soil microbial community with depth structured sampling (0–5, 5–10, 10-Ap, Ap-30 cm). We found relatively small effects of farming system but substantial effects of depth on edaphic factors and microbial communities. For example, differences in management resulted in subtle differences in %C above the Ap horizon. Fungal gene abundance increased in the organic system relative to the no-till system, although neither fungal nor bacterial richness differed across farming systems. Farming system effects on microbial community composition were greatest in the top 10 cm but did not affect the abundance of unicellular fungi. Several groups including Pseudomonas and Mortierellomycota appear to be sensitive to redistribution by tillage in the organic system, but it is not clear to what extent this effect is due to legacy DNA from tillage.
Thirteen-year management of organic and conventional grain farming systems results in modest differences to microbial communities along soil depth profiles1)Organic treatment results in a increase in soil %C and differences in %C depth distribution2)No difference in richness and evenness among farming systems3)A 30 cm depth profile represents a much greater environmental gradient for microbial abundance and diversity than differences among long-term row-crop farming systems that differ in tillage, fertility and pest management Display omitted
•Depth had larger effect than farming system on microbial community.•Farming system effects were concentrated in top 10 cm.•Farming systems altered the distribution of some taxa across depth increments.•No effect of farming system on abundance of unicellular fungi.
Urbanization results in the systemic conversion of land-use, driving habitat and biodiversity loss. The “urban convergence hypothesis” posits that urbanization represents a merging of habitat ...characteristics, in turn driving physiological and functional responses within the biotic community. To test this hypothesis, we sampled five cities (Baltimore, MD, United States; Helsinki and Lahti, Finland; Budapest, Hungary; Potchefstroom, South Africa) across four different biomes. Within each city, we sampled four land-use categories that represented a gradient of increasing disturbance and management (from least intervention to highest disturbance: reference, remnant, turf/lawn, and ruderal). Previously, we used amplicon sequencing that targeted bacteria/archaea (16S rRNA) and fungi (ITS) and reported convergence in the archaeal community. Here, we applied shotgun metagenomic sequencing and QPCR of functional genes to the same soil DNA extracts to test convergence in microbial function. Our results suggest that urban land-use drives changes in gene abundance related to both the soil N and C metabolism. Our updated analysis found taxonomic convergence in both the archaeal and bacterial community (16S amplicon data). Convergence of the archaea was driven by increased abundance of ammonia oxidizing archaea and genes for ammonia oxidation (QPCR and shotgun metagenomics). The proliferation of ammonia-oxidizers under turf and ruderal land-use likely also contributes to the previously documented convergence of soil mineral N pools. We also found a higher relative abundance of methanogens (amplicon sequencing), a higher relative abundance of gene sequences putatively identified as Ni-Fe hydrogenase and nickel uptake (shotgun metagenomics) under urban land-use; and a convergence of gene sequences putatively identified as contributing to the nickel transport function under urban turf sites. High levels of disturbance lead to a higher relative abundance of gene sequences putatively identified as multiple antibiotic resistance protein marA and multidrug efflux pump mexD, but did not lead to an overall convergence in antibiotic resistance gene sequences.
In urban landscapes, humans are the most significant factor determining belowground diversity, including earthworms. Within the framework of the Global Urban Soil Ecology and Education Network ...(GLUSEEN), a multi-city comparison was carried out to assess the effects of soil disturbance on earthworms. In each of five cities (Baltimore, USA; Budapest, Hungary; Helsinki and Lahti, Finland; Potchefstroom, South Africa), covering four climatic and biogeographical regions, four habitat types (ruderal, turf/lawn, remnant and reference) were sampled. The survey resulted in 19 species belonging to 9 genera and 4 families. The highest total species richness was recorded in Baltimore (16), while Budapest and the Finnish cities had relatively low (5–6) species numbers. Remnant forests and lawns supported the highest earthworm biomass. Soil properties (i.e. pH and organic matter content) explained neither earthworm community composition nor abundance. Evaluating all cities together, earthworm communities were significantly structured by habitat type. Communities in the two adjacent cities, Helsinki and Lahti were very similar, but Budapest clearly separated from the Finnish cities. Earthworm community structure in Baltimore overlapped with that of the other cities. Despite differences in climate, soils and biogeography among the cities, earthworm communities were highly similar within the urban habitat types. This indicates that human-mediated dispersal is an important factor shaping the urban fauna, both at local and regional scales.
•Earthworm communities varied within and among urban habitat types.•Remnant forests and urban lawns were the most favorable habitats for earthworms.•Different mechanisms lead to high similarity of urban earthworm assemblages.
Abstract
The global recognition of modern agricultural practices’ impact on the environment has fuelled policy responses to ameliorate environmental degradation in agricultural landscapes. In the US ...and the EU, agri-environmental subsidies (AES) promote widespread adoption of sustainable practices by compensating farmers who voluntarily implement them on working farmland. Previous studies, however, have suggested limitations of their spatial targeting, with funds not allocated towards areas of the greatest environmental need. We analysed AES in the US and EU—specifically through the Environmental Quality Incentives Program (EQIP) and selected measures of the European Agricultural Fund for Rural Development (EAFRD)—to identify if AES are going where they are most needed to achieve environmental goals, using a set of environmental need indicators, socio-economic variables moderating allocation patterns, and contextual variables describing agricultural systems. Using linear mixed models and linear models we explored the associations among AES allocation and these predictors at different scales. We found that higher AES spending was associated with areas of low soil organic carbon and high greenhouse gas emissions both in the US and EU, and nitrogen surplus in the EU. More so than successes, however, clear mismatches of funding and environmental need emerged—AES allocation did not successfully target areas of highest water stress, biodiversity loss, soil erosion, and nutrient runoff. Socio-economic and agricultural context variables may explain some of these mismatches; we show that AES were allocated to areas with higher proportions of female producers in the EU but not in the US, where funds were directed towards areas with less tenant farmers. Moreover, we suggest that the potential for AES to remediate environmental issues may be curtailed by limited participation in intensive agricultural landscapes. These findings can help inform refinements to EQIP and EAFRD allocation mechanisms and identify opportunities for improving future targeting of AES spending.
High-throughput, multiplexed-amplicon sequencing has become a core tool for understanding environmental microbiomes. As researchers have widely adopted sequencing, many open-source analysis pipelines ...have been developed to compare microbiomes using compositional analysis frameworks. However, there is increasing evidence that compositional analyses do not provide the information necessary to accurately interpret many community assembly processes. This is especially true when there are large gradients that drive distinct community assembly processes. Recently, sequencing has been combined with Q-PCR (among other sources of total quantitation) to generate “Quantitative Sequencing” (QSeq) data. QSeq more accurately estimates the true abundance of taxa, is a more reliable basis for inferring correlation, and, ultimately, can be more reliably related to environmental data to infer community assembly processes. In this paper, we use a combination of published data sets, synthesis, and empirical modeling to offer guidance for which contexts QSeq is advantageous. As little as 5% variation in total abundance among experimental groups resulted in more accurate inference by QSeq than compositional methods. Compositional methods for differential abundance and correlation unreliably detected patterns in abundance and covariance when there was greater than 20% variation in total abundance among experimental groups. Whether QSeq performs better for beta diversity analysis depends on the question being asked, and the analytic strategy (e.g., what distance metric is being used); for many questions and methods, QSeq and compositional analysis are equivalent for beta diversity analysis. QSeq is especially useful for taxon-specific analysis; QSeq transformation and analysis should be the default for answering taxon-specific questions of amplicon sequence data. Publicly available bioinformatics pipelines should incorporate support for QSeq transformation and analysis.
The advent of DNA sequencing revolutionized the field of microbiome research. Many organisms, by virtue of their codependence and/or growth rate, are either impossible or extremely challenging to get ...into pure culture. Sequencing allows important taxonomic and phylogenetic information to be obtained independent of culturing. Development of the sequencing technology itself has allowed for high throughput workflow that has allowed low cost and extensive sampling of microbiomes across environments. The co-development of reference datasets for taxonomy and functional assignments, along with open-source bioinformatics pipelines has further empowered scientists to explore microbiomes in many environments. However, there are limitations to sequence data that have constrained the ecological inferences in microbiome research. One such limitation, the compositional nature of sequence data, has impeded our ability to make accurate inferences about the environmental drivers of taxon abundance and covariance across conditions. In this dissertation I explore the use of quantitative PCR in combination with sequencing techniques to generate “Quantitative Sequencing” data (QSeq) that mitigates the limitations of compositionality on inferences relating to taxon abundance and covariance across environmental gradients. In chapter 1, I reviewed key characteristics of the soil environment and sequencing as a mechanism for sampling. In chapter 2, I leveraged modeling, synthesis, and literature review methods to establish the questions and data characteristics that demand QSeq methodology. I show that even small amounts of variation in total abundance make determining the effects of environment (biotic and abiotic factors) on any given taxon unreliable without QSeq. In Chapter 3, I extend the logic of quantitative sequencing to improve metagenome prediction from PICRUSt2. Using data synthesis methods, accounting for 16S gene abundance consistently improved the accuracy of predicted functional genes. This was confirmed by high correlations between predicted and measured gene abundance (QPCR). There was however a large variation in prediction accuracy, likely due in part to database biases and in part to decoupling of bacterial function from taxonomy. In Chapter 4, I applied QSeq in the context of an experimental, long-term farming system that has large gradients in total abundance with depth, and I used QSeq to identify taxa that changed in abundance due to different farming system management and soil depth. Finally in Chapter 5, I used QSeq to identify putative N-fixing taxa that responded to glyphosate in four experimental farming systems. I show that the abundance of these taxa were decoupled from other effects of glyphosate on N-fixation in soybean across farming systems.
Urbanization is associated with global biodiversity loss of macrophauna and flora through direct and indirect mechanisms, but to date few studies have examined urban soil microbes. Although there are ...numerous studies on the influence of agricultural management on soil microbial community composition, there has been no global-scale study of human control over urban soil microbial communities. This thesis extends the literature of urban ecology to include soil microbial communities by analyzing soils that are part of the Global Urban Soil Ecology and Education Network (GLUSEEN). Chapter 1 sets the context for urban ecology; Chapters 2 addresses patterns of community assembly, biodiversity loss, and the phylogenetic relationships among community members; Chapter 3 addresses the metabolic pathways that characterize microbial communities existing under different land-uses across varying geographic scales; and Chapter 4 relates Chapter 2 and 3 to one another and to evolutionary theory, tackling assumptions that are particular to microbial ecology.
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