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      Constructing, conducting and interpreting animal social network analysis


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          1. Animal social networks are descriptions of social structure which, aside from their intrinsic interest for understanding sociality, can have significant bearing across many fields of biology.

          2. Network analysis provides a flexible toolbox for testing a broad range of hypotheses, and for describing the social system of species or populations in a quantitative and comparable manner. However, it requires careful consideration of underlying assumptions, in particular differentiating real from observed networks and controlling for inherent biases that are common in social data.

          3. We provide a practical guide for using this framework to analyse animal social systems and test hypotheses. First, we discuss key considerations when defining nodes and edges, and when designing methods for collecting data. We discuss different approaches for inferring social networks from these data and displaying them. We then provide an overview of methods for quantifying properties of nodes and networks, as well as for testing hypotheses concerning network structure and network processes. Finally, we provide information about assessing the power and accuracy of an observed network.

          4. Alongside this manuscript, we provide appendices containing background information on common programming routines and worked examples of how to perform network analysis using the r programming language.

          5. We conclude by discussing some of the major current challenges in social network analysis and interesting future directions. In particular, we highlight the under‐exploited potential of experimental manipulations on social networks to address research questions.

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          Most cited references52

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          Observational study of behavior: sampling methods.

          J Altmann (1974)
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            Ecological networks and their fragility.

            Darwin used the metaphor of a 'tangled bank' to describe the complex interactions between species. Those interactions are varied: they can be antagonistic ones involving predation, herbivory and parasitism, or mutualistic ones, such as those involving the pollination of flowers by insects. Moreover, the metaphor hints that the interactions may be complex to the point of being impossible to understand. All interactions can be visualized as ecological networks, in which species are linked together, either directly or indirectly through intermediate species. Ecological networks, although complex, have well defined patterns that both illuminate the ecological mechanisms underlying them and promise a better understanding of the relationship between complexity and ecological stability.
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              The architecture of complex weighted networks

              Networked structures arise in a wide array of different contexts such as technological and transportation infrastructures, social phenomena, and biological systems. These highly interconnected systems have recently been the focus of a great deal of attention that has uncovered and characterized their topological complexity. Along with a complex topological structure, real networks display a large heterogeneity in the capacity and intensity of the connections. These features, however, have mainly not been considered in past studies where links are usually represented as binary states, i.e. either present or absent. Here, we study the scientific collaboration network and the world-wide air-transportation network, which are representative examples of social and large infrastructure systems, respectively. In both cases it is possible to assign to each edge of the graph a weight proportional to the intensity or capacity of the connections among the various elements of the network. We define new appropriate metrics combining weighted and topological observables that enable us to characterize the complex statistical properties and heterogeneity of the actual strength of edges and vertices. This information allows us to investigate for the first time the correlations among weighted quantities and the underlying topological structure of the network. These results provide a better description of the hierarchies and organizational principles at the basis of the architecture of weighted networks.

                Author and article information

                J Anim Ecol
                J Anim Ecol
                The Journal of Animal Ecology
                John Wiley and Sons Inc. (Hoboken )
                September 2015
                11 August 2015
                : 84
                : 5 ( doiID: 10.1111/jane.2015.84.issue-5 )
                : 1144-1163
                [ 1 ] Department of Zoology Edward Grey Institute of Field OrnithologyUniversity of Oxford South Parks Road Oxford OX1 3PSUK
                [ 2 ] Department of Anthropology (Evolutionary)University of California Davis 1 Shields Avenue Davis CA 95616USA
                [ 3 ]Smithsonian Tropical Research Institute AnconPanama
                [ 4 ] Department of BiologyDalhousie University 1355 Oxford St Halifax NSCanada B3H 4J1
                Author notes
                [*] [* ]Correspondence author. E‐mail: damien.farine@ 123456zoo.ox.ac.uk
                © 2015 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of the British Ecological Society.

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                : 05 January 2015
                : 25 June 2015
                Page count
                Pages: 20
                Funded by: BBSRC
                Award ID: BB/L006081/1
                Funded by: ERC
                Award ID: AdG 250164
                Funded by: NSF
                Award ID: NSF‐IOS 1250895
                ‘How to…’ Paper
                How To…
                Custom metadata
                September 2015
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.9.4 mode:remove_FC converted:04.08.2016

                fission‐fusion dynamics,group living,methods,social behaviour,social dynamics,social network analysis,social organisation


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