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      Microbial legacies alter decomposition in response to simulated global change

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          Abstract

          Terrestrial ecosystem models assume that microbial communities respond instantaneously, or are immediately resilient, to environmental change. Here we tested this assumption by quantifying the resilience of a leaf litter community to changes in precipitation or nitrogen availability. By manipulating composition within a global change experiment, we decoupled the legacies of abiotic parameters versus that of the microbial community itself. After one rainy season, more variation in fungal composition could be explained by the original microbial inoculum than the litterbag environment (18% versus 5.5% of total variation). This compositional legacy persisted for 3 years, when 6% of the variability in fungal composition was still explained by the microbial origin. In contrast, bacterial composition was generally more resilient than fungal composition. Microbial functioning (measured as decomposition rate) was not immediately resilient to the global change manipulations; decomposition depended on both the contemporary environment and rainfall the year prior. Finally, using metagenomic sequencing, we showed that changes in precipitation, but not nitrogen availability, altered the potential for bacterial carbohydrate degradation, suggesting why the functional consequences of the two experiments may have differed. Predictions of how terrestrial ecosystem processes respond to environmental change may thus be improved by considering the legacies of microbial communities.

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          Fundamentals of Microbial Community Resistance and Resilience

          Microbial communities are at the heart of all ecosystems, and yet microbial community behavior in disturbed environments remains difficult to measure and predict. Understanding the drivers of microbial community stability, including resistance (insensitivity to disturbance) and resilience (the rate of recovery after disturbance) is important for predicting community response to disturbance. Here, we provide an overview of the concepts of stability that are relevant for microbial communities. First, we highlight insights from ecology that are useful for defining and measuring stability. To determine whether general disturbance responses exist for microbial communities, we next examine representative studies from the literature that investigated community responses to press (long-term) and pulse (short-term) disturbances in a variety of habitats. Then we discuss the biological features of individual microorganisms, of microbial populations, and of microbial communities that may govern overall community stability. We conclude with thoughts about the unique insights that systems perspectives – informed by meta-omics data – may provide about microbial community stability.
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            Generalized eta and omega squared statistics: measures of effect size for some common research designs.

            The editorial policies of several prominent educational and psychological journals require that researchers report some measure of effect size along with tests for statistical significance. In analysis of variance contexts, this requirement might be met by using eta squared or omega squared statistics. Current procedures for computing these measures of effect often do not consider the effect that design features of the study have on the size of these statistics. Because research-design features can have a large effect on the estimated proportion of explained variance, the use of partial eta or omega squared can be misleading. The present article provides formulas for computing generalized eta and omega squared statistics, which provide estimates of effect size that are comparable across a variety of research designs.
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              Rapid denoising of pyrosequencing amplicon data: exploiting the rank-abundance distribution

              We developed a fast method for denoising pyrosequencing for community 16S rRNA analysis. We observe a 2–4 fold reduction in the number of observed OTUs (operational taxonomic units) comparing denoised with non-denoised data. ~50,000 sequences can be denoised on a laptop within an hour, two orders of magnitude faster than published techniques. We demonstrate the effects of denoising on alpha and beta diversity of large 16S rRNA datasets.
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                Author and article information

                Journal
                ISME J
                ISME J
                The ISME Journal
                Nature Publishing Group
                1751-7362
                1751-7370
                February 2017
                14 October 2016
                1 February 2017
                : 11
                : 2
                : 490-499
                Affiliations
                [1 ]Department of Ecology and Evolutionary Biology, University of California , Irvine, CA, USA
                [2 ]Department of Earth System Science, University of California , Irvine, CA, USA
                [3 ]Department of Biology, California State University , Long Beach, CA, USA
                [4 ]Ecology Department, Earth and Environmental Sciences, Lawrence Berkeley National Laboratory , Berkeley, CA, USA
                [5 ]Department of Environmental Science, Policy and Management, University of California , Berkeley, CA, USA
                Author notes
                [* ]Department of Ecology and Evolutionary Biology, University of California , 321 Steinhaus Hall, Irvine, CA, 92697 USA. E-mail: jmartiny@ 123456uci.edu
                Article
                ismej2016122
                10.1038/ismej.2016.122
                5270563
                27740610
                48185143-2885-46cd-9665-419a7fe79374
                Copyright © 2017 International Society for Microbial Ecology

                This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/

                History
                : 16 May 2016
                : 11 July 2016
                : 05 August 2016
                Categories
                Original Article

                Microbiology & Virology
                Microbiology & Virology

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