20
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Protist species richness and soil microbiome complexity increase towards climax vegetation in the Brazilian Cerrado

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Biodiversity underlies ecosystem functioning. While aboveground biodiversity is often well studied, the belowground microbiome, in particular protists, remains largely unknown. Indeed, holistic insights into soil microbiome structures in natural soils, especially in hyperdiverse biomes such as the Brazilian Cerrado, remain unexplored. Here, we study the soil microbiome across four major vegetation zones of the Cerrado, ranging from grass-dominated to tree-dominated vegetation with a focus on protists. We show that protist taxon richness increases towards the tree-dominated climax vegetation. Early successional habitats consisting of primary grass vegetation host most potential plant pathogens and least animal parasites. Using network analyses combining protist with prokaryotic and fungal sequences, we show that microbiome complexity increases towards climax vegetation. Together, this suggests that protists are key microbiome components and that vegetation succession towards climax vegetation is stimulated by higher loads of animal and plant pathogens. At the same time, an increase in microbiome complexity towards climax vegetation might enhance system stability.

          Abstract

          Araujo et al. investigate the soil microbiome across four major vegetation zones of the Brazilian Cerrado and find that protist taxon richness increases towards the tree-dominated climax vegetation. Their findings suggest that increased microbiome complexity might enhance system stability towards climax vegetation.

          Related collections

          Most cited references43

          • Record: found
          • Abstract: not found
          • Article: not found

          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            The structure and function of complex networks

            M. Newman (2003)
            Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Modularity and community structure in networks

              M. Newman (2006)
              Many networks of interest in the sciences, including a variety of social and biological networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure has attracted considerable recent attention. One of the most sensitive detection methods is optimization of the quality function known as "modularity" over the possible divisions of a network, but direct application of this method using, for instance, simulated annealing is computationally costly. Here we show that the modularity can be reformulated in terms of the eigenvectors of a new characteristic matrix for the network, which we call the modularity matrix, and that this reformulation leads to a spectral algorithm for community detection that returns results of better quality than competing methods in noticeably shorter running times. We demonstrate the algorithm with applications to several network data sets.
                Bookmark

                Author and article information

                Contributors
                asfaruaj@yahoo.com.br
                s.geisen@nioo.knaw.nl
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                6 September 2018
                6 September 2018
                2018
                : 1
                : 135
                Affiliations
                [1 ]ISNI 0000 0001 2176 3398, GRID grid.412380.c, Agricultural Science Center, , Federal University of Piauí, ; 64049-550 Teresina, PI Brazil
                [2 ]ISNI 0000 0004 1937 0722, GRID grid.11899.38, Cell and Molecular Biology Laboratory, , Center for Nuclear Energy in Agriculture CENA, University of Sao Paulo USP, ; 13416-000 Piracicaba, SP Brazil
                [3 ]Genome Laboratory Agronomic Institute of Pernambuco, 50761-000 Recife, PE Brazil
                [4 ]ISNI 0000 0001 2160 0329, GRID grid.8395.7, Laboratory of Microbial Ecology and Biotechnology, , Lembiotech, Federal University of Ceara, ; 60020-181 Fortaleza, CE Brazil
                [5 ]ISNI 0000 0000 9007 5698, GRID grid.412294.8, Universidade do Oeste Paulista, ; 19050-920 Presidente Prudente, SP Brazil
                [6 ]ISNI 0000 0001 1013 0288, GRID grid.418375.c, Department of Terrestrial Ecology, , Netherlands Institute of Ecology NIOO-KNAW, ; 6708 PB Wageningen, The Netherlands
                Author information
                http://orcid.org/0000-0002-3212-3852
                http://orcid.org/0000-0003-0980-7006
                http://orcid.org/0000-0002-0898-568X
                http://orcid.org/0000-0001-7122-2506
                http://orcid.org/0000-0002-3302-8857
                http://orcid.org/0000-0003-0734-727X
                Article
                129
                10.1038/s42003-018-0129-0
                6127325
                30272014
                8fc46f0f-8de0-4bde-bce9-6de4ca970139
                © The Author(s) 2018

                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
                : 21 March 2018
                : 6 August 2018
                Categories
                Article
                Custom metadata
                © The Author(s) 2018

                Comments

                Comment on this article