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      Environmental disturbance increases social connectivity in a passerine bird

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          Abstract

          Individual level response to natural and anthropogenic disturbance represents an increasingly important, but as yet little understood, component of animal behavior. Disturbance events often alter habitat, which in turn can modify behaviors of individuals in affected areas, including changes in habitat use and associated changes in social structure. To better understand these relationships, we investigated aspects of habitat selection and social connectivity of a small passerine bird, the red-backed fairywren ( Malurus melanocephalus), before vs. after naturally occurring fire disturbance in Northern Territory, Australia. We utilized a social network framework to evaluate changes in social dynamics pre- vs. post-fire. Our study covered the non-breeding season in two consecutive years in which fires occurred, and individuals whose habitat was affected and those that were not affected by fire. Individuals in habitat affected by fires had stronger social ties (i.e. higher weighted degree) after fires, while those that were in areas that were not affected by fire actually had lower weighted degree. We suggest that this change in social connections may be linked to habitat. Before fires, fairywrens used habitat that had similar grass cover to available habitat plots randomly generated within our study site. Fire caused a reduction in grass cover, and fairywrens responded by selecting habitat with higher grass cover relative to random plots. This study demonstrates how changes in habitat and/or resource availability caused by disturbance can lead to substantive changes in the social environment that individuals experience.

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          Most cited references48

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          Climate Change and Forest Disturbances

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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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              Experimentally induced innovations lead to persistent culture via conformity in wild birds

              In human societies, cultural norms arise when behaviours are transmitted with high-fidelity social learning through social networks 1 . However a paucity of experimental studies has meant that there is no comparable understanding of the process by which socially transmitted behaviours may spread and persist in animal populations 2,3 . Here, we introduce alternative novel foraging techniques into replicated wild sub-populations of great tits (Parus major), and employ automated tracking to map the diffusion, establishment and long-term persistence of seeded behaviours. We further use social network analysis to examine social factors influencing diffusion dynamics. From just two trained birds in each sub-population, information spread rapidly through social network ties to reach an average of 75% of individuals, with 508 knowledgeable individuals performing 58,975 solutions. Sub-populations were heavily biased towards the technique originally introduced, resulting in established local arbitrary traditions that were stable over two generations, despite high population turnover. Finally, we demonstrate a strong effect of social conformity, with individuals disproportionately adopting the most frequent local variant when first learning, but then also continuing to favour social over personal information by matching their technique to the majority variant. Cultural conformity is thought to be a key factor in the evolution of complex culture in humans 4-7 . In providing the first experimental demonstration of conformity in a wild non-primate, and of cultural norms in foraging techniques in any wild animal, our results suggest a much wider evolutionary occurrence of such apparently complex cultural behaviour.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                30 August 2017
                2017
                : 12
                : 8
                : e0183144
                Affiliations
                [001]Department of Ecology and Evolutionary Biology, Tulane University, New Orleans, Louisiana, United States of America
                University of Oxford, UNITED KINGDOM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Article
                PONE-D-16-46970
                10.1371/journal.pone.0183144
                5576644
                28854197
                2fefea0d-f8c9-43d8-a40c-2ec5f05157a7
                © 2017 Lantz, Karubian

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 27 November 2016
                : 31 July 2017
                Page count
                Figures: 4, Tables: 1, Pages: 15
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: 1131641
                Award Recipient : Jordan Karubian
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: 1460048
                Award Recipient : Jordan Karubian
                The research was supported by NSF IRES awards #1131641 and 1460048, and a dissertation semester fellowship from the Department of Ecology & Evolutionary Biology at Tulane University.
                Categories
                Research Article
                Ecology and Environmental Sciences
                Habitats
                Biology and Life Sciences
                Organisms
                Animals
                Vertebrates
                Amniotes
                Birds
                Biology and Life Sciences
                Organisms
                Plants
                Grasses
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Sciences
                Sociology
                Social Networks
                Engineering and Technology
                Fire Engineering
                Engineering and Technology
                Fire Engineering
                Fire Suppression Technology
                Biology and Life Sciences
                Behavior
                Animal Behavior
                Animal Sociality
                Biology and Life Sciences
                Zoology
                Animal Behavior
                Animal Sociality
                Physical Sciences
                Mathematics
                Discrete Mathematics
                Combinatorics
                Permutation
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

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