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      A guide to null models for animal social network analysis

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          Summary

          1. Null models are an important component of the social network analysis toolbox. However, their use in hypothesis testing is still not widespread. Furthermore, several different approaches for constructing null models exist, each with their relative strengths and weaknesses, and often testing different hypotheses.

          2. In this study, I highlight why null models are important for robust hypothesis testing in studies of animal social networks. Using simulated data containing a known observation bias, I test how different statistical tests and null models perform if such a bias was unknown.

          3. I show that permutations of the raw observational (or ‘pre‐network’) data consistently account for underlying structure in the generated social network, and thus can reduce both type I and type II error rates. However, permutations of pre‐network data remain relatively uncommon in animal social network analysis because they are challenging to implement for certain data types, particularly those from focal follows and GPS tracking.

          4. I explain simple routines that can easily be implemented across different types of data, and supply R code that applies each type of null model to the same simulated dataset. The R code can easily be modified to test hypotheses with empirical data. Widespread use of pre‐network data permutation methods will benefit researchers by facilitating robust hypothesis testing.

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          Most cited references38

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          Observational study of behavior: sampling methods.

          J Altmann (1974)
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            Constructing, conducting and interpreting animal social network analysis

            Summary 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. 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. 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. 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. 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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              NEW SPECIFICATIONS FOR EXPONENTIAL RANDOM GRAPH MODELS

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                Author and article information

                Contributors
                dfarine@orn.mpg.de
                Journal
                Methods Ecol Evol
                Methods Ecol Evol
                10.1111/(ISSN)2041-210X
                MEE3
                Methods in Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                2041-210X
                12 April 2017
                October 2017
                : 8
                : 10 ( doiID: 10.1111/mee3.2017.8.issue-10 )
                : 1309-1320
                Affiliations
                [ 1 ] Department of Collective Behaviour Max Planck Institute for Ornithology 78457 Konstanz Germany
                [ 2 ] Chair of Biodiversity and Collective Behaviour Department of Biology University of Konstanz 78457 Konstanz Germany
                [ 3 ] Department of Zoology Edward Grey Institute of Field Ornithology Department of Zoology University of Oxford Oxford OX1 3PS UK
                Author notes
                [*] [* ]Correspondence author. E‐mail: dfarine@ 123456orn.mpg.de
                Author information
                http://orcid.org/0000-0003-2208-7613
                Article
                MEE312772
                10.1111/2041-210X.12772
                5656331
                29104749
                2fa3107f-155e-4ade-ad7a-4849b51fef07
                © 2017 The Author. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of 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.

                History
                : 08 November 2016
                : 13 March 2017
                Page count
                Figures: 9, Tables: 0, Pages: 12, Words: 8085
                Funding
                Funded by: BBSRC
                Award ID: BB/L006081/1
                Categories
                Research Article
                Networks
                Custom metadata
                2.0
                mee312772
                October 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.2.1 mode:remove_FC converted:25.10.2017

                group living,null model,permutation test,social network analysis,sociality

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