The soil microbiome at the Gi-FACE experiment responds to a moisture gradient but not to CO2 enrichment

Menezes, Alexandre B. de; Müller, Christoph; Clipson, Nicholas; Doyle, Evelyn

doi:10.1099/mic.0.000341

Cited by 15 publications

(13 citation statements)

References 72 publications

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“…Nonetheless, trac's taxonomic groupings provide meaningful information about the taxonomic structure of soil microbiota along moisture gradients. For example, the model supports the positive association between Proteobacteria and moisture, as previously observed in a study along a vegetation gradient on the Loess Plateau in China (Zeng et al, 2016), and the negative effect of moisture on the phylum Verrucomicrobia and the positive effect on Deltaproteobacteria in the Giessen free-air CO2 enrichment (Gi-FACE) experiment (de Menezes et al, 2016). The Gi-FACE study, however, also reported several relationships between the microbiome and the soil moisture that are incongruent with our model, including the role of Acidobacteria.…”

Section: Predicting Central Park Soil Properties From Microbial Commusupporting

confidence: 84%

Tree-Aggregated Predictive Modeling of Microbiome Data

Bien

Yan

Simpson

et al. 2020

Preprint

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Modern high-throughput sequencing technologies provide low-cost microbiome survey data across all habitats of life at unprecedented scale. At the most granular level, the primary data consist of sparse counts of amplicon sequence variants or operational taxonomic units that are associated with taxonomic and phylogenetic group information. In this contribution, we leverage the hierarchical structure of amplicon data and propose a data-driven, parameter-free, and scalable tree-guided aggregation framework to associate microbial subcompositions with response variables of interest. The excess number of zero or low count measurements at the read level forces traditional microbiome data analysis workflows to remove rare sequencing variants or group them by a fixed taxonomic rank, such as genus or phylum, or by phylogenetic similarity. By contrast, our framework, which we call trac (tree-aggregation of compositional data), learns data-adaptive taxon aggregation levels for predictive modeling making user-defined aggregation obsolete while simultaneously integrating seamlessly into the compositional data analysis framework. We illustrate the versatility of our framework in the context of large-scale regression problems in human-gut, soil, and marine microbial ecosystems. We posit that the inferred aggregation levels provide highly interpretable taxon groupings that can help microbial ecologists gain insights into the structure and functioning of the underlying ecosystem of interest.

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Section: Predicting Central Park Soil Properties From Microbial Commusupporting

confidence: 84%

Tree-Aggregated Predictive Modeling of Microbiome Data

Bien

Yan

Simpson

et al. 2020

Preprint

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“…A lack of response of microbial communities against long-term exposure to e CO 2 was reported previously (Haase et al, 2008 ; Nelson et al, 2010 ; Marhan et al, 2011 ; Regan et al, 2011 ; Pujol Pereira et al, 2013 ; Dunbar et al, 2014 ). In addition, de Menezes et al ( 2016 ) showed for GiFACE that e CO 2 exerted no substantial effects on the composition of the overall soil bacterial community. Likewise, microbial communities involved in denitrification, ammonia oxidation, and DNRA remained unaffected by e CO 2 in other studies (Deiglmayr et al, 2004 ; Haase et al, 2008 ; Marhan et al, 2011 ; Pujol Pereira et al, 2013 ).…”

Section: Discussionmentioning

confidence: 99%

“…A previous study found differences in the water level in the deeper soil layers at GiFACE in the order: E1/A1 < E3/A3 < E2/A2 (Lenhart, 2008 ). de Menezes et al ( 2016 ) attributed differences in bacterial community composition at GiFACE to the soil moisture gradient. Long-term differences in the water level of the deeper soil layers probably determined the differences in pH and the concentration of C- and N-compounds observed in the top soil layer (0–7.5 cm) of the sets.…”

Section: Discussionmentioning

confidence: 99%

“…The composition of microbial communities containing nirK / nirS, nosZ , archaeal and bacterial amoA, nifH, nrfA , and archaeal 16S rRNA genes was explored by terminal restriction length polymorphism (T-RFLP) and barcode labeled 454 pyrosequencing analyses of PCR amplified gene fragments. Overall bacterial community composition based on bacterial 16S rRNA genes was assessed by T-RFLP, pyrosequencing data are available from de Menezes et al ( 2016 ). Primers and PCR conditions used are given in Table S2 .…”

Section: Methodsmentioning

confidence: 99%

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Soil Conditions Rather Than Long-Term Exposure to Elevated CO2 Affect Soil Microbial Communities Associated with N-Cycling

et al. 2017

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Continuously rising atmospheric CO2 concentrations may lead to an increased transfer of organic C from plants to the soil through rhizodeposition and may affect the interaction between the C- and N-cycle. For instance, fumigation of soils with elevated CO2 (eCO2) concentrations (20% higher compared to current atmospheric concentrations) at the Giessen Free-Air Carbon Dioxide Enrichment (GiFACE) sites resulted in a more than 2-fold increase of long-term N2O emissions and an increase in dissimilatory reduction of nitrate compared to ambient CO2 (aCO2). We hypothesized that the observed differences in soil functioning were based on differences in the abundance and composition of microbial communities in general and especially of those which are responsible for N-transformations in soil. We also expected eCO2 effects on soil parameters, such as on nitrate as previously reported. To explore the impact of long-term eCO2 on soil microbial communities, we applied a molecular approach (qPCR, T-RFLP, and 454 pyrosequencing). Microbial groups were analyzed in soil of three sets of two FACE plots (three replicate samples from each plot), which were fumigated with eCO2 and aCO2, respectively. N-fixers, denitrifiers, archaeal and bacterial ammonia oxidizers, and dissimilatory nitrate reducers producing ammonia were targeted by analysis of functional marker genes, and the overall archaeal community by 16S rRNA genes. Remarkably, soil parameters as well as the abundance and composition of microbial communities in the top soil under eCO2 differed only slightly from soil under aCO2. Wherever differences in microbial community abundance and composition were detected, they were not linked to CO2 level but rather determined by differences in soil parameters (e.g., soil moisture content) due to the localization of the GiFACE sets in the experimental field. We concluded that +20% eCO2 had little to no effect on the overall microbial community involved in N-cycling in the soil but that spatial heterogeneity over extended periods had shaped microbial communities at particular sites in the field. Hence, microbial community composition and abundance alone cannot explain the functional differences leading to higher N2O emissions under eCO2 and future studies should aim at exploring the active members of the soil microbial community.

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“…However, it should be noted that elevated CO 2 level in most of the FACE study sites was >500 ppm (Drissner et al, ; Ebersberger et al, ; Hagedorn et al, ; Xu et al, ), the highest CO 2 concentration applied in this study. A recent study at Giessen free‐air CO 2 enrichment (Gi‐FACE) facility in Germany, which examined grassland communities exposed to ambient (400 ppm) and moderate (480 ppm) CO 2 levels (de Menezes, Müller, Clipson, & Doyle, ), also demonstrated negligible effect of CO 2 on bacterial communities. In agreement with our findings, they showed more pronounced effects of soil moisture than CO 2 enrichment on bacterial community structure and diversity.…”

Section: Discussionmentioning

confidence: 99%

Bacterial community response to a preindustrial‐to‐future CO₂ gradient is limited and soil specific in Texas Prairie grassland

Raut

Polley

Fay

et al. 2018

Global Change Biology

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Rising atmospheric CO concentration directly stimulates plant productivity and affects nutrient dynamics in the soil. However, the influence of CO enrichment on soil bacterial communities remains elusive, likely due to their complex interactions with a wide range of plant and soil properties. Here, we investigated the bacterial community response to a decade long preindustrial-to-future CO gradient (250-500 ppm) among three contrasting soil types using 16S rRNA gene amplicon sequencing. In addition, we examined the effect of seasonal variation and plant species composition on bacterial communities. We found that Shannon index (H') and Faith's phylogenetic diversity (PD) did not change in response to the CO gradient (R = 0.01, p > 0.05). CO gradient and season also had a negligible effect on overall community structure, although silty clay soil communities were better structured on a CO gradient (p < 0.001) among three soils. Similarly, CO gradient had no significant effect on the relative abundance of different phyla. However, we observed soil-specific variation of CO effects in a few individual families. For example, the abundance of Pirellulaceae family decreased linearly with CO gradient, but only in sandy loam soils. Conversely, the abundance of Micromonosporaceae and Gaillaceae families increased with CO gradient in clay soils. Soil water content (SWC) and nutrient properties were the key environmental constraints shaping bacterial community structure, one manifestation of which was a decline in bacterial diversity with increasing SWC. Furthermore, the impact of plant species composition on community structure was secondary to the strong influence of soil properties. Taken together, our findings indicate that bacterial communities may be largely unresponsive to indirect effects of CO enrichment through plants. Instead, bacterial communities are strongly regulated by edaphic conditions, presumably because soil differences create distinct environmental niches for bacteria.

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The soil microbiome at the Gi-FACE experiment responds to a moisture gradient but not to CO2 enrichment

Cited by 15 publications

References 72 publications

Tree-Aggregated Predictive Modeling of Microbiome Data

Tree-Aggregated Predictive Modeling of Microbiome Data

Soil Conditions Rather Than Long-Term Exposure to Elevated CO2 Affect Soil Microbial Communities Associated with N-Cycling

Bacterial community response to a preindustrial‐to‐future CO₂ gradient is limited and soil specific in Texas Prairie grassland

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The soil microbiome at the Gi-FACE experiment responds to a moisture gradient but not to CO2 enrichment

Cited by 15 publications

References 72 publications

Tree-Aggregated Predictive Modeling of Microbiome Data

Tree-Aggregated Predictive Modeling of Microbiome Data

Soil Conditions Rather Than Long-Term Exposure to Elevated CO2 Affect Soil Microbial Communities Associated with N-Cycling

Bacterial community response to a preindustrial‐to‐future CO2 gradient is limited and soil specific in Texas Prairie grassland

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Bacterial community response to a preindustrial‐to‐future CO₂ gradient is limited and soil specific in Texas Prairie grassland