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      Ecological memory of recurrent drought modifies soil processes via changes in soil microbial community

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          Abstract

          Climate change is altering the frequency and severity of drought events. Recent evidence indicates that drought may produce legacy effects on soil microbial communities. However, it is unclear whether precedent drought events lead to ecological memory formation, i.e., the capacity of past events to influence current ecosystem response trajectories. Here, we utilize a long-term field experiment in a mountain grassland in central Austria with an experimental layout comparing 10 years of recurrent drought events to a single drought event and ambient conditions. We show that recurrent droughts increase the dissimilarity of microbial communities compared to control and single drought events, and enhance soil multifunctionality during drought (calculated via measurements of potential enzymatic activities, soil nutrients, microbial biomass stoichiometry and belowground net primary productivity). Our results indicate that soil microbial community composition changes in concert with its functioning, with consequences for soil processes. The formation of ecological memory in soil under recurrent drought may enhance the resilience of ecosystem functioning against future drought events.

          Abstract

          Legacies of past ecological disturbances are expected but challenging to demonstrate. Here the authors report a 10-year field experiment in a mountain grassland that shows ecological memory of soil microbial community and functioning in response to recurrent drought.

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          Most cited references103

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            DADA2: High resolution sample inference from Illumina amplicon data

            We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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              The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

              SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.

                Author and article information

                Contributors
                alberto.canarini@hotmail.it
                andreas.richter@univie.ac.at
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                6 September 2021
                6 September 2021
                2021
                : 12
                : 5308
                Affiliations
                [1 ]GRID grid.10420.37, ISNI 0000 0001 2286 1424, Centre for Microbiology and Environmental Systems Science, , University of Vienna, ; Vienna, Austria
                [2 ]GRID grid.5771.4, ISNI 0000 0001 2151 8122, Department of Ecology, , University of Innsbruck, ; Innsbruck, Austria
                Author information
                http://orcid.org/0000-0003-2516-5955
                http://orcid.org/0000-0003-4288-4257
                http://orcid.org/0000-0001-7482-9776
                http://orcid.org/0000-0003-3282-4808
                Article
                25675
                10.1038/s41467-021-25675-4
                8421443
                34489463
                c72b7ded-dc79-4f70-b345-c1917e43bd66
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 3 February 2021
                : 6 August 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100002428, Austrian Science Fund (Fonds zur Förderung der Wissenschaftlichen Forschung);
                Award ID: I 1056
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001822, Österreichischen Akademie der Wissenschaften (Austrian Academy of Sciences);
                Award ID: ESS-programme, project ClimLUC
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                microbial ecology,carbon cycle,climate-change ecology
                Uncategorized
                microbial ecology, carbon cycle, climate-change ecology

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