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      Multilayer networks reveal the spatial structure of seed-dispersal interactions across the Great Rift landscapes

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          Abstract

          Species interaction networks are traditionally explored as discrete entities with well-defined spatial borders, an oversimplification likely impairing their applicability. Using a multilayer network approach, explicitly accounting for inter-habitat connectivity, we investigate the spatial structure of seed–dispersal networks across the Gorongosa National Park, Mozambique. We show that the overall seed–dispersal network is composed by spatially explicit communities of dispersers spanning across habitats, functionally linking the landscape mosaic. Inter-habitat connectivity determines spatial structure, which cannot be accurately described with standard monolayer approaches either splitting or merging habitats. Multilayer modularity cannot be predicted by null models randomizing either interactions within each habitat or those linking habitats; however, as habitat connectivity increases, random processes become more important for overall structure. The importance of dispersers for the overall network structure is captured by multilayer versatility but not by standard metrics. Highly versatile species disperse many plant species across multiple habitats, being critical to landscape functional cohesion.

          Abstract

          Species interaction networks have been usually delimited by perceived habitat borders. Here, seed-dispersal is analyzed as a regional multilayer network of interconnected habitats, highlighting the key role of versatile dispersers for the functional cohesion of the whole Gorongosa landscape.

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          Metapopulation dynamics

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            Landscape Ecology: The Effect of Pattern on Process

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              Functional cartography of complex metabolic networks

              , (2005)
              High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodology that enables us to extract and display information contained in complex networks. Specifically, we demonstrate that one can (i) find functional modules in complex networks, and (ii) classify nodes into universal roles according to their pattern of intra- and inter-module connections. The method thus yields a ``cartographic representation'' of complex networks. Metabolic networks are among the most challenging biological networks and, arguably, the ones with more potential for immediate applicability. We use our method to analyze the metabolic networks of twelve organisms from three different super-kingdoms. We find that, typically, 80% of the nodes are only connected to other nodes within their respective modules, and that nodes with different roles are affected by different evolutionary constraints and pressures. Remarkably, we find that low-degree metabolites that connect different modules are more conserved than hubs whose links are mostly within a single module.
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                Author and article information

                Contributors
                stimoteo@gmail.com
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                10 January 2018
                10 January 2018
                2018
                : 9
                : 140
                Affiliations
                ISNI 0000 0000 9511 4342, GRID grid.8051.c, CFE – Centre for Functional Ecology, Department of Life Sciences, , University of Coimbra, ; Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
                Author information
                http://orcid.org/0000-0003-2417-3259
                http://orcid.org/0000-0002-2058-3229
                Article
                2658
                10.1038/s41467-017-02658-y
                5762785
                29321529
                730b87f1-7599-4381-a55e-d677804eea75
                © The Author(s) 2017

                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
                : 18 October 2016
                : 18 December 2017
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