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      Social structure modulates the evolutionary consequences of social plasticity: A social network perspective on interacting phenotypes

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          Abstract

          Organisms express phenotypic plasticity during social interactions. Interacting phenotype theory has explored the consequences of social plasticity for evolution, but it is unclear how this theory applies to complex social structures. We adapt interacting phenotype models to general social structures to explore how the number of social connections between individuals and preference for phenotypically similar social partners affect phenotypic variation and evolution. We derive an analytical model that ignores phenotypic feedback and use simulations to test the predictions of this model. We find that adapting previous models to more general social structures does not alter their general conclusions but generates insights into the effect of social plasticity and social structure on the maintenance of phenotypic variation and evolution. Contribution of indirect genetic effects to phenotypic variance is highest when interactions occur at intermediate densities and decrease at higher densities, when individuals approach interacting with all group members, homogenizing the social environment across individuals. However, evolutionary response to selection tends to increase at greater network densities as the effects of an individual's genes are amplified through increasing effects on other group members. Preferential associations among similar individuals (homophily) increase both phenotypic variance within groups and evolutionary response to selection. Our results represent a first step in relating social network structure to the expression of social plasticity and evolutionary responses to selection.

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          Behavioural reaction norms: animal personality meets individual plasticity

          Recent studies in the field of behavioural ecology have revealed intriguing variation in behaviour within single populations. Increasing evidence suggests that individual animals differ in their average level of behaviour displayed across a range of contexts (animal 'personality'), and in their responsiveness to environmental variation (plasticity), and that these phenomena can be considered complementary aspects of the individual phenotype. How should this complex variation be studied? Here, we outline how central ideas in behavioural ecology and quantitative genetics can be combined within a single framework based on the concept of 'behavioural reaction norms'. This integrative approach facilitates analysis of phenomena usually studied separately in terms of personality and plasticity, thereby enhancing understanding of their adaptive nature. Copyright 2009 Elsevier Ltd. All rights reserved.
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            Phenotypic Plasticity and the Origins of Diversity

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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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                Author and article information

                Contributors
                montiglio.pierre-olivier@uqam.ca
                dfarine@orn.mpg.de
                Journal
                Ecol Evol
                Ecol Evol
                10.1002/(ISSN)2045-7758
                ECE3
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                2045-7758
                27 December 2017
                February 2018
                : 8
                : 3 ( doiID: 10.1002/ece3.2018.8.issue-3 )
                : 1451-1464
                Affiliations
                [ 1 ] Department of Biology & Redpath Museum McGill University Montreal QC Canada
                [ 2 ] Department of Biological Sciences Virginia Tech Blacksburg VA USA
                [ 3 ] Department of Collective Behaviour Max Planck Institute for Ornithology Konstanz Germany
                [ 4 ] Department of Biology Chair of Biodiversity and Collective Behaviour University of Konstanz Konstanz Germany
                [ 5 ] Department of Zoology Edward Grey Institute University of Oxford Oxford UK
                Author notes
                [*] [* ] Correspondence

                Pierre‐Olivier Montiglio, Groupe de Recherche en Ecologie Comportementale et Animale (GRECA), Department of Biological Sciences University of Quebec at Montreal Montreal, Quebec, Canada.

                Email: montiglio.pierre-olivier@ 123456uqam.ca

                and

                Damien R. Farine, Department of Collective Behaviour, Max Planck Institute for Ornithology, Konstanz, Germany.

                Email: dfarine@ 123456orn.mpg.de

                Author information
                http://orcid.org/0000-0002-1313-9410
                http://orcid.org/0000-0003-3645-6264
                Article
                ECE33753
                10.1002/ece3.3753
                5792542
                29435224
                5f35ef5a-768c-4689-9403-40ee576eba8d
                © 2017 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

                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
                : 24 July 2017
                : 27 November 2017
                : 28 November 2017
                Page count
                Figures: 7, Tables: 0, Pages: 14, Words: 11782
                Funding
                Funded by: NSERC
                Funded by: BBSRC
                Award ID: BB/L006081/1
                Funded by: Max Planck Society
                Categories
                Original Research
                Original Research
                Custom metadata
                2.0
                ece33753
                February 2018
                Converter:WILEY_ML3GV2_TO_NLMPMC version:version=5.3.2 mode:remove_FC converted:31.01.2018

                Evolutionary Biology
                evolution,quantitative genetics,social interactions,social network,social plasticity

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