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      Multiplex social ecological network analysis reveals how social changes affect community robustness more than resource depletion

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          Significance

          Social capital ties are ubiquitous in modern life. For societies with people and landscapes tightly connected, in variable or marginal ecosystems, and with unreliable market sectors, social relations are critical. Each relation is a potential source of food, information, cash, labor, or expertise. Here, we present an analysis of multiplex, directed, and weighted networks representing actual flows of subsistence-related goods and services among households in three remote indigenous Alaska communities exposed to both extreme climate change and industrial development. We find that the principal challenge to the robustness of such communities is the loss of key households and the erosion of cultural ties linked to sharing and cooperative social relations rather than resource depletion.

          Abstract

          Network analysis provides a powerful tool to analyze complex influences of social and ecological structures on community and household dynamics. Most network studies of social–ecological systems use simple, undirected, unweighted networks. We analyze multiplex, directed, and weighted networks of subsistence food flows collected in three small indigenous communities in Arctic Alaska potentially facing substantial economic and ecological changes. Our analysis of plausible future scenarios suggests that changes to social relations and key households have greater effects on community robustness than changes to specific wild food resources.

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          Most cited references12

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          Network analysis in the social sciences.

          Over the past decade, there has been an explosion of interest in network research across the physical and social sciences. For social scientists, the theory of networks has been a gold mine, yielding explanations for social phenomena in a wide variety of disciplines from psychology to economics. Here, we review the kinds of things that social scientists have tried to explain using social network analysis and provide a nutshell description of the basic assumptions, goals, and explanatory mechanisms prevalent in the field. We hope to contribute to a dialogue among researchers from across the physical and social sciences who share a common interest in understanding the antecedents and consequences of network phenomena.
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            Is Open Access

            Community Structure in Time-Dependent, Multiscale, and Multiplex Networks

            Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly-connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one network slice to itself in other slices. This framework allows one to study community structure in a very general setting encompassing networks that evolve over time, have multiple types of links (multiplexity), and have multiple scales.
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              • Record: found
              • Abstract: found
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              Is Open Access

              Multilayer Networks

              In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time, and include other types of complications. Such systems include multiple subsystems and layers of connectivity, and it is important to take such "multilayer" features into account to try to improve our understanding of complex systems. Consequently, it is necessary to generalize "traditional" network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion. The origins of such efforts date back several decades and arose in multiple disciplines, and now the study of multilayer networks has become one of the most important directions in network science. In this paper, we discuss the history of multilayer networks (and related concepts) and review the exploding body of work on such networks. To unify the disparate terminology in the large body of recent work, we discuss a general framework for multilayer networks, construct a dictionary of terminology to relate the numerous existing concepts to each other, and provide a thorough discussion that compares, contrasts, and translates between related notions such as multilayer networks, multiplex networks, interdependent networks, networks of networks, and many others. We also survey and discuss existing data sets that can be represented as multilayer networks. We review attempts to generalize single-layer-network diagnostics to multilayer networks. We also discuss the rapidly expanding research on multilayer-network models and notions like community structure, connected components, tensor decompositions, and various types of dynamical processes on multilayer networks. We conclude with a summary and an outlook.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                29 November 2016
                16 November 2016
                16 November 2016
                : 113
                : 48
                : 13708-13713
                Affiliations
                [1] aDepartment of Environment and Society, Utah State University Logan , UT 84322;
                [2] bSchool of Human Evolution and Social Change, Arizona State University Phoenix , AZ 85287;
                [3] cDepartament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili , 43007 Tarragona, Spain;
                [4] dSchool of Natural Resources and Extension, University of Alaska Fairbanks , Fairbanks, AK 99775;
                [5] eInstitute of Arctic Biology, University of Alaska Fairbanks , Fairbanks, AK 99775
                Author notes
                1To whom correspondence should be addressed. Email: manlio.dedomenico@ 123456urv.cat .

                Edited by William C. Clark, Harvard University, Cambridge, MA, and approved October 7, 2016 (received for review March 16, 2016)

                Author contributions: S.B.B., J.S.M., and G.P.K. designed the empirical research; J.A.B., S.B.B., A.A., and M.D.D. contributed new tools and methods; S.B.B., J.S.M., and G.P.K. collected the data; J.A.B. and M.D.D. designed and performed the analysis; and J.A.B., S.B.B., A.A., J.S.M., G.P.K., and M.D.D. wrote the paper.

                Author information
                http://orcid.org/0000-0002-9616-4143
                http://orcid.org/0000-0001-5158-8594
                Article
                PMC5137762 PMC5137762 5137762 201604401
                10.1073/pnas.1604401113
                5137762
                27856752
                8408fe74-a150-4871-aa4f-92af0d7a2b9b

                Freely available online through the PNAS open access option.

                History
                Page count
                Pages: 6
                Funding
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: GEO 1115054
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: ACI-1639529
                Funded by: U.S. Department of the Interior (DOI) 100000201
                Award ID: 1010-0184
                Funded by: European Commission (EC) 501100000780
                Award ID: 317614
                Funded by: Ministerio de Economía y Competitividad (Ministry of Economy and Competitiveness) 501100003329
                Award ID: FIS2012-38266
                Funded by: Ministerio de Economía y Competitividad (Ministry of Economy and Competitiveness) 501100003329
                Award ID: IJCI-2014-20225
                Categories
                9
                Social Sciences
                Sustainability Science

                social–ecological systems,multiplex networks,food sharing,mixed subsistence–cash economies,climate change

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