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Continuous maize (Zea maysL.) (CM) cropping has been proposed to increase soil organic carbon stocks through greater residue return to soils. These residues, however, can contribute to a yield decrease known as the continuous maize yield penalty (CMYP). The objectives of this research were to (1) evaluate the efficacy of residue management practices to mitigate the CMYP when compared to a maize–soybean [Glycine max(L.) Merr.] (MS) rotation and (2) determine effects of long‐term rotation or residue sizing on soil microbial communities and carbon‐cycling enzyme activities. Two long‐term sites (17 and 19 years) with replicated blocks of CM and MS rotation were used. The yield response to management was similar at both sites, with an average CMYP of 2570 kg ha−1with no residue management, while chopping the residue along with broadcast ammonium sulfate (AMS) reduced the CMYP by 728 kg ha−1(28.3%). The CMYP was further reduced when a microbial blend was applied with AMS to the sized residues, reducing the CMYP by a total of 1303 kg ha−1(50.7%). Soil bacterial communities differed by site but were unchanged by crop rotation or residue sizing. Soil fungal assemblage, particularly arbuscular mycorrhizal fungi, was influenced by rotation and site. Sizing of residues resulted in higher soil cellobiohydrolase activity for CM, but not for MS. These findings indicate that the CMYP was not directly associated with the soil bacterial community, and that management to mitigate the CMYP may be aided by elevated levels of fungal diversity under CM. The yield penalty of continuous maize can be mitigated by agronomic practices without need for residue removal.Residue management practices, when combined, reduced the yield penalty up to 50%.Long‐term crop rotation effects on microbial communities were site‐specific.Soil bacterial communities were relatively similar after 17 or 19 years of continuous maize.Soil fungal assemblage was influenced by crop rotation and site.

Variations in microbial communities promoted by alterations in environmental conditions are reflected in similarities/differences both at taxonomic and functional levels. Here we used a natural gradient within mangroves from seashore to upland, to contrast the natural variability in bacteria, cyanobacteria and diazotroph assemblages in a pristine area compared to an oil polluted area along a timespan of three years, based on ARISA (bacteria and cyanobacteria) and nifH T-RFLP (diazotrophs) fingerprinting. The data presented herein indicated that changes in all the communities evaluated were mainly driven by the temporal effect in the contaminated area, while local effects were dominant on the pristine mangrove. A positive correlation of community structure between diazotrophs and cyanobacteria was observed, suggesting the functional importance of this phylum as nitrogen fixers in mangroves soils. Different ecological patterns explained the microbial behavior in the pristine and polluted mangroves. Stochastic models in the pristine mangrove indicate that there is not a specific environmental factor that determines the bacterial distribution, while cyanobacteria and diazotrophs better fitted in deterministic model in the same area. For the contaminated mangrove site, deterministic models better represented the variations in the communities, suggesting that the presence of oil might change the microbial ecological structures over time. Mangroves represent a unique environment threatened by global change, and this study contributed to the knowledge of the microbial distribution in such areas and its response on persistent contamination historic events.

Linking microbial community composition with the corresponding ecosystem functions remains challenging. Because microbial communities can differ in their functional responses, this knowledge gap limits ecosystem assessment, design and management. To develop models that explicitly incorporate microbial populations and guide efforts to characterize their functional differences, we propose a novel approach derived from reliability engineering. This reliability modeling approach is illustrated here using a microbial ecology dataset from denitrifying bioreactors. Reliability modeling is well-suited for analyzing the stability of complex networks composed of many microbial populations. It could also be applied to evaluate the redundancy within a particular biochemical pathway in a microbial community. Reliability modeling allows characterization of the system’s resilience and identification of failure-prone functional groups or biochemical steps, which can then be targeted for monitoring or enhancement. The reliability engineering approach provides a new perspective for unraveling the interactions between microbial community diversity, functional redundancy and ecosystem services, as well as practical tools for the design and management of engineered ecosystems.

Linking microbial community composition with the corresponding ecosystem functions remains challenging. Because microbial communities can differ in their functional responses, this knowledge gap limits ecosystem assessment, design and management. To develop models that explicitly incorporate microbial populations and guide efforts to characterize their functional differences, we propose a novel approach derived from reliability engineering. This reliability modeling approach is illustrated here using a microbial ecology dataset from denitrifying bioreactors. Reliability modeling is well-suited for analyzing the stability of complex networks composed of many microbial populations. It could also be applied to evaluate the redundancy within a particular biochemical pathway in a microbial community. Reliability modeling allows characterization of the system’s resilience and identification of failure-prone functional groups or biochemical steps, which can then be targeted for monitoring or enhancement. The reliability engineering approach provides a new perspective for unraveling the interactions between microbial community diversity, functional redundancy and ecosystem services, as well as practical tools for the design and management of engineered ecosystems.