Dataset Information

Predicting the Responses of Soil Nitrite-Oxidizers to Multi-Factorial Global Change: A Trait-Based Approach.

ABSTRACT: Soil microbial diversity is huge and a few grams of soil contain more bacterial taxa than there are bird species on Earth. This high diversity often makes predicting the responses of soil bacteria to environmental change intractable and restricts our capacity to predict the responses of soil functions to global change. Here, using a long-term field experiment in a California grassland, we studied the main and interactive effects of three global change factors (increased atmospheric CO2 concentration, precipitation and nitrogen addition, and all their factorial combinations, based on global change scenarios for central California) on the potential activity, abundance and dominant taxa of soil nitrite-oxidizing bacteria (NOB). Using a trait-based model, we then tested whether categorizing NOB into a few functional groups unified by physiological traits enables understanding and predicting how soil NOB respond to global environmental change. Contrasted responses to global change treatments were observed between three main NOB functional types. In particular, putatively mixotrophic Nitrobacter, rare under most treatments, became dominant under the 'High CO2+Nitrogen+Precipitation' treatment. The mechanistic trait-based model, which simulated ecological niches of NOB types consistent with previous ecophysiological reports, helped predicting the observed effects of global change on NOB and elucidating the underlying biotic and abiotic controls. Our results are a starting point for representing the overwhelming diversity of soil bacteria by a few functional types that can be incorporated into models of terrestrial ecosystems and biogeochemical processes.

SUBMITTER: Le Roux X

PROVIDER: S-EPMC4868854 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Predicting the Responses of Soil Nitrite-Oxidizers to Multi-Factorial Global Change: A Trait-Based Approach.

Le Roux Xavier X Bouskill Nicholas J NJ Niboyet Audrey A Barthes Laure L Dijkstra Paul P Field Chris B CB Hungate Bruce A BA Lerondelle Catherine C Pommier Thomas T Tang Jinyun J Terada Akihiko A Tourna Maria M Poly Franck F

Frontiers in microbiology 20160517

Soil microbial diversity is huge and a few grams of soil contain more bacterial taxa than there are bird species on Earth. This high diversity often makes predicting the responses of soil bacteria to environmental change intractable and restricts our capacity to predict the responses of soil functions to global change. Here, using a long-term field experiment in a California grassland, we studied the main and interactive effects of three global change factors (increased atmospheric CO2 concentra ...[more]

PMID: 27242680

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Project description:Rice paddy fields are characterized by regular flooding and nitrogen fertilization, but the functional importance of aerobic ammonia oxidizers and nitrite oxidizers under unique agricultural management is poorly understood. In this study, we report the differential contributions of ammonia-oxidizing archaea (AOA), bacteria (AOB) and nitrite-oxidizing bacteria (NOB) to nitrification in four paddy soils from different geographic regions (Zi-Yang (ZY), Jiang-Du (JD), Lei-Zhou (LZ) and Jia-Xing (JX)) that are representative of the rice ecosystems in China. In urea-amended microcosms, nitrification activity varied greatly with 11.9, 9.46, 3.03 and 1.43 μg NO3(-)-N g(-1) dry weight of soil per day in the ZY, JD, LZ and JX soils, respectively, over the course of a 56-day incubation period. Real-time quantitative PCR of amoA genes and pyrosequencing of 16S rRNA genes revealed significant increases in the AOA population to various extents, suggesting that their relative contributions to ammonia oxidation activity decreased from ZY to JD to LZ. The opposite trend was observed for AOB, and the JX soil stimulated only the AOB populations. DNA-based stable-isotope probing further demonstrated that active AOA numerically outcompeted their bacterial counterparts by 37.0-, 10.5- and 1.91-fold in (13)C-DNA from ZY, JD and LZ soils, respectively, whereas AOB, but not AOA, were labeled in the JX soil during active nitrification. NOB were labeled to a much greater extent than AOA and AOB, and the addition of acetylene completely abolished the assimilation of (13)CO2 by nitrifying populations. Phylogenetic analysis suggested that archaeal ammonia oxidation was predominantly catalyzed by soil fosmid 29i4-related AOA within the soil group 1.1b lineage. Nitrosospira cluster 3-like AOB performed most bacterial ammonia oxidation in the ZY, LZ and JX soils, whereas the majority of the (13)C-AOB in the JD soil was affiliated with the Nitrosomona communis lineage. The (13)C-NOB was overwhelmingly dominated by Nitrospira rather than Nitrobacter. A significant correlation was observed between the active AOA/AOB ratio and the soil oxidation capacity, implying a greater advantage of AOA over AOB under microaerophilic conditions. These results suggest the important roles of soil physiochemical properties in determining the activities of ammonia oxidizers and nitrite oxidizers.

Project description:While short-term plant responses to global change are driven by physiological mechanisms, which are represented relatively well by models, long-term ecosystem responses to global change may be determined by shifts in plant community structure resulting from other ecological phenomena such as interspecific interactions, which are represented poorly by models. In single-factor scenarios, plant communities often adjust to increase ecosystem response to that factor. For instance, some early global change experiments showed that elevated CO2 favours plants that respond strongly to elevated CO2, generally amplifying the response of ecosystem productivity to elevated CO2, a positive community feedback. However, most ecosystems are subject to multiple drivers of change, which can complicate the community feedback effect in ways that are more difficult to generalize. Recent studies have shown that (i) shifts in plant community structure cannot be reliably predicted from short-term plant physiological response to global change and (ii) that the ecosystem response to multi-factored change is commonly less than the sum of its parts. Here, we survey results from long-term field manipulations to examine the role community shifts may play in explaining these common findings. We use a simple model to examine the potential importance of community shifts in governing ecosystem response. Empirical evidence and the model demonstrate that with multi-factored change, the ecosystem response depends on community feedbacks, and that the magnitude of ecosystem response will depend on the relationship between plant response to one factor and plant response to another factor. Tradeoffs in the ability of plants to respond positively to, or to tolerate, different global change drivers may underlie generalizable patterns of covariance in responses to different drivers of change across plant taxa. Mechanistic understanding of these patterns will help predict the community feedbacks that determine long-term ecosystem responses.

Project description:Partial nitrification of ammonium to nitrite under oxic conditions (nitritation) is a critical process for the effective use of alternative nitrogen removal technologies from wastewater. Here we investigated the conditions which promote establishment of a suitable microbial community for performing nitritation when starting from regular sewage sludge. Reactors were operated in duplicate under different conditions (pH, temperature, and dilution rate) and were fed with 50 mM ammonium either as synthetic medium or as sludge digester supernatant. In all cases, stable nitritation could be achieved within 10 to 20 days after inoculation. Quantitative in situ hybridization analysis with group-specific fluorescent rRNA-targeted oligonucleotides (FISH) in the different reactors showed that nitrite-oxidizing bacteria of the genus Nitrospira were only active directly after inoculation with sewage sludge (up to 4 days and detectable up to 10 days). As demonstrated by quantitative FISH and restriction fragment length polymorphism (RFLP) analyses of the amoA gene (encoding the active-site subunit of the ammonium monooxygenase), the community of ammonia-oxidizing bacteria changed within the first 15 to 20 days from a more diverse set of populations consisting of members of the Nitrosomonas communis and Nitrosomonas oligotropha sublineages and the Nitrosomonas europaea-Nitrosomonas eutropha subgroup in the inoculated sludge to a smaller subset in the reactors. Reactors operated at 30 degrees C and pH 7.5 contained reproducibly homogeneous communities dominated by one amoA RFLP type from the N. europaea-N. eutropha group. Duplicate reactors at pH 7.0 developed into diverse communities and showed transient population changes even within the ammonia oxidizer community. Reactors at pH 7.5 and 25 degrees C formed communities that were indistinguishable by the applied FISH probes but differing in amoA RFLP types. Communities in reactors fed with sludge digester supernatant exhibited a higher diversity and were constantly reinoculated with ammonium oxidizers from the supernatant. Therefore, such systems could be maintained at a higher dilution rate (0.75 day(-1) compared to 0.2 day(-1) for the synthetic wastewater reactors). Despite similar reactor performance with respect to chemical parameters, the underlying community structures were different, which may have an influence on stability during perturbations.

Project description:Biochar is an important soil amendment that can enhance the biological properties of soil, as well as nitrogen (N) uptake and utilization in N-fertilized crops. However, few studies have characterized the effects of urea and biochar application on soil biochemical traits and its effect on paddy rice. Therefore, a field trial was conducted in the early and late seasons of 2020 in a randomized complete block design with two N levels (135 and 180 kg ha-1) and four levels of biochar (0, 10, 20, and 30 t ha-1). The treatment combinations were as follows: 135 kg N ha-1 + 0 t B ha-1 (T1), 135 kg N ha-1 + 10 t B ha-1 (T2), 135 kg N ha-1 + 20 t B ha-1 (T3), 135 kg N ha-1 + 30 t B ha-1 (T4), 180 kg N ha-1 + 0 t B ha-1 (T5), 180 kg N ha-1 + 10 t B ha-1 (T6), 180 kg N ha-1 + 20 t B ha-1 (T7) and 180 kg N ha-1 + 30 t B ha-1 (T8). The results showed that soil amended with biochar had higher soil pH, soil organic carbon content, total nitrogen content, and mineral nitrogen (NH4+-N and NO3--N) than soil that had not been amended with biochar. In both seasons, the 20 t ha-1 and 30 t ha-1 biochar treatments had the highest an average concentrations of NO3--N (10.54 mg kg-1 and 10.25 mg kg-1, respectively). In comparison to soil that had not been treated with biochar, the average activity of the enzymes urease, polyphenol oxidase, dehydrogenase, and chitinase was, respectively, 25.28%, 14.13%, 67.76%, and 22.26% greater; however, the activity of the enzyme catalase was 15.06% lower in both seasons. Application of biochar considerably increased the abundance of ammonia-oxidizing bacteria (AOB), which was 48% greater on average in biochar-amended soil than in unamended soil. However, there were no significant variations in the abundances of ammonia-oxidizing archaea (AOA) or nitrite-oxidizing bacteria (NOB) across treatments. In comparison to soil that had not been treated with biochar, the average N content was 24.46%, 20.47%, and 19.08% higher in the stem, leaves, and panicles, respectively. In general, adding biochar at a rate of 20 to 30 t ha-1 with low-dose urea (135 kg N ha-1) is a beneficial technique for improving the nutrient balance and biological processes of soil, as well as the N uptake and grain yield of rice plants.

Dataset Information

Predicting the Responses of Soil Nitrite-Oxidizers to Multi-Factorial Global Change: A Trait-Based Approach.

Publications

Predicting the Responses of Soil Nitrite-Oxidizers to Multi-Factorial Global Change: A Trait-Based Approach.

Similar Datasets

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