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      Ocean sprawl facilitates dispersal and connectivity of protected species

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          Abstract

          Highly connected networks generally improve resilience in complex systems. We present a novel application of this paradigm and investigated the potential for anthropogenic structures in the ocean to enhance connectivity of a protected species threatened by human pressures and climate change. Biophysical dispersal models of a protected coral species simulated potential connectivity between oil and gas installations across the North Sea but also metapopulation outcomes for naturally occurring corals downstream. Network analyses illustrated how just a single generation of virtual larvae released from these installations could create a highly connected anthropogenic system, with larvae becoming competent to settle over a range of natural deep-sea, shelf and fjord coral ecosystems including a marine protected area. These results provide the first study showing that a system of anthropogenic structures can have international conservation significance by creating ecologically connected networks and by acting as stepping stones for cross-border interconnection to natural populations.

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          Fast unfolding of communities in large networks

          We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection method in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2.6 million customers and by analyzing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad-hoc modular networks. .
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            Exceptional twentieth-century slowdown in Atlantic Ocean overturning circulation

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              Universal resilience patterns in complex networks.

              Resilience, a system's ability to adjust its activity to retain its basic functionality when errors, failures and environmental changes occur, is a defining property of many complex systems. Despite widespread consequences for human health, the economy and the environment, events leading to loss of resilience--from cascading failures in technological systems to mass extinctions in ecological networks--are rarely predictable and are often irreversible. These limitations are rooted in a theoretical gap: the current analytical framework of resilience is designed to treat low-dimensional models with a few interacting components, and is unsuitable for multi-dimensional systems consisting of a large number of components that interact through a complex network. Here we bridge this theoretical gap by developing a set of analytical tools with which to identify the natural control and state parameters of a multi-dimensional complex system, helping us derive effective one-dimensional dynamics that accurately predict the system's resilience. The proposed analytical framework allows us systematically to separate the roles of the system's dynamics and topology, collapsing the behaviour of different networks onto a single universal resilience function. The analytical results unveil the network characteristics that can enhance or diminish resilience, offering ways to prevent the collapse of ecological, biological or economic systems, and guiding the design of technological systems resilient to both internal failures and environmental changes.
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                Author and article information

                Contributors
                l.henry@ed.ac.uk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                16 August 2018
                16 August 2018
                2018
                : 8
                : 11346
                Affiliations
                [1 ]ISNI 0000 0004 1936 7988, GRID grid.4305.2, School of GeoSciences, Grant Institute, James Hutton Road, King’s Buildings, , University of Edinburgh, ; Edinburgh, EH9 3FE United Kingdom
                [2 ]National Oceanography Centre, Joseph Proudman Building, 6 Brownlow Street, Liverpool, L3 5DA United Kingdom
                [3 ]BMT Cordah, Broadfold House, Broadfold Road, Bridge of Don, Aberdeen, AB23 8EE United Kingdom
                [4 ]ISNI 0000 0004 0427 3161, GRID grid.10917.3e, Institute of Marine Research, ; Bergen, 5005 Norway
                [5 ]ISNI 0000 0000 9813 0452, GRID grid.217197.b, Center for Marine Science, , University of North Carolina Wilmington, ; 601 S. College Road, Wilmington, North Carolina 28403-5928 United States of America
                [6 ]Present Address: ECAP Consultancy Group, Spean Bridge, Argyll, PH34 4EG United Kingdom
                [7 ]Present Address: KIMO, Aberdeenshire Council, Woodhill House, Westburn Road, Aberdeen, AB16 5HG United Kingdom
                [8 ]Present Address: Joint Nature Conservation Committee, Inverdee House, Baxter House, Aberdeen, AB11 9QA United Kingdom
                Author information
                http://orcid.org/0000-0001-5134-1102
                http://orcid.org/0000-0003-0131-5250
                Article
                29575
                10.1038/s41598-018-29575-4
                6095900
                30115932
                8a6c016b-80bd-4ad5-a6ce-cf0a12a4f2f6
                © 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
                : 26 January 2018
                : 10 July 2018
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100007601, EC | Horizon 2020 (European Union Framework Programme for Research and Innovation);
                Award ID: 678760
                Award Recipient :
                Funded by: INSITE Research Programme
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