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      Ecological Drivers of the Soil Microbial Diversity and Composition in Primary Old-Growth Forest and Secondary Woodland in a Subtropical Evergreen Broad-Leaved Forest Biome in the Ailao Mountains, China

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          Abstract

          Replacement of primary old-growth forests by secondary woodlands in threatened subtropical biomes drives important changes at the level of the overstory, understory and forest floor, but the impact on belowground microbial biodiversity is yet poorly documented. In the present study, we surveyed by metabarcoding sequencing, the diversity and composition of soil bacteria and fungi in the old-growth forest, dominated by stone oaks ( Lithocarpus spp.) and in the secondary Yunnan pine woodland of an iconic site for biodiversity research, the Ailaoshan National Nature Reserve (Ailao Mountains, Yunnan province, China). We assessed the effect of forest replacement and other environmental factors, including soil horizons, soil physicochemical characteristics and seasonality (monsoon vs. dry seasons). We showed that tree composition and variation in soil properties were major drivers for both bacterial and fungal communities, with a significant influence from seasonality. Ectomycorrhizal Operational Taxonomic Units (OTUs) dominated the functional fungal guilds. Species richness and diversity of the bacterial and fungal communities were higher in the pine woodland compared to the primary Lithocarpus forest, although prominent OTUs were different. The slightly lower complexity of the microbiome in the primary forest stands likely resulted from environmental filtering under relatively stable conditions over centuries, when compared to the secondary pine woodlands. In the old-growth forest, we found a higher number of species, but that communities were homogeneously distributed, whereas in the pine woodlands, there is a slightly lower number of species present but the communities are heterogeneously distributed. The present surveys of the bacterial and fungal diversity will serve as references in future studies aiming to assess the impact of the climate change on soil microbial diversity in both old-growth forests and secondary woodlands in Ailaoshan.

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          Most cited references63

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          edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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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.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                13 June 2022
                2022
                : 13
                : 908257
                Affiliations
                [1] 1Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University , Beijing, China
                [2] 2School of Ecology and Nature Conservation, Beijing Forestry University , Beijing, China
                [3] 3INRAE, UMR Interactions Arbres/Microorganismes, Centre INRAE-GrandEst-Nancy, Université de Lorraine , Champenoux, France
                [4] 4Chinese Academy of Sciences Key Laboratory for Plant Diversity and Biogeography of East Asia, Kunming Institute of Botany , Kunming, China
                [5] 5Yunnan Key Laboratory for Fungal Diversity and Green Development , Kunming, China
                Author notes

                Edited by: Jonathan M. Adams, Nanjing University, China

                Reviewed by: Gwen-Aelle Grelet, Manaaki Whenua – Landcare Research, New Zealand; Weijun Shen, Guangxi University, China

                *Correspondence: Francis M. Martin, francis.martin@ 123456inrae.fr

                These authors have contributed equally to this work

                This article was submitted to Terrestrial Microbiology, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2022.908257
                9234548
                35770159
                fa83419c-61b2-40d4-a192-985e00ad1ba1
                Copyright © 2022 Zeng, Lebreton, Man, Jia, Wang, Gong, Buée, Wu, Dai, Yang and Martin.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 March 2022
                : 20 May 2022
                Page count
                Figures: 8, Tables: 0, Equations: 0, References: 63, Pages: 16, Words: 10475
                Funding
                Funded by: Postdoctoral Research Foundation of China, doi 10.13039/501100010031;
                Award ID: 2019M660508
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                Award ID: 31970015
                Award ID: U1802231
                Funded by: Recherches Avancées sur la Biologie de l’Arbre et les Ecosystèmes Forestiers, doi 10.13039/100015818;
                Award ID: ANR-11-LABX-0002-01
                Funded by: University of Chinese Academy of Sciences, doi 10.13039/501100011332;
                Award ID: XDB31000000
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                community structure,evergreen sclerophyllous broad-leaved forest,forest replacement,pine forest,subtropical biome,soil microbiome,yunnan

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