Building genetic networks using relatedness information: a novel approach for the estimation of dispersal and characterization of group structure in social animals

Authors

  • LEE ANN ROLLINS,

    1. Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia
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  • LUCY E. BROWNING,

    1. University of New South Wales, Arid Zone Research Station, via Broken Hill, NSW 2880, Australia
    2. Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
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  • CLARE E. HOLLELEY,

    1. Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia
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    • Present address: Institute for Applied Ecology, University of Canberra, Canberra, ACT, Australia

  • JAMES L. SAVAGE,

    1. Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
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  • ANDREW F. RUSSELL,

    1. Centre for Ecology and Conservation, College of Life and Environmental Sciences, University of Exeter, Penryn, Cornwall TR10 9EZ, UK
    2. Station d’Ecologie Expérimentale du CNRS USR 2936, Moulis 09200, France
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  • SIMON C. GRIFFITH

    1. Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia
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Lee Ann Rollins, Fax: +61 2 9850 9231; E-mail: lee.rollins@mq.edu.au

Abstract

Natal dispersal is an important life history trait driving variation in individual fitness, and therefore, a proper understanding of the factors underlying dispersal behaviour is critical to many fields including population dynamics, behavioural ecology and conservation biology. However, individual dispersal patterns remain difficult to quantify despite many years of research using direct and indirect methods. Here, we quantify dispersal in a single intensively studied population of the cooperatively breeding chestnut-crowned babbler (Pomatostomus ruficeps) using genetic networks created from the combination of pairwise relatedness data and social networking methods and compare this to dispersal estimates from re-sighting data. This novel approach not only identifies movements between social groups within our study sites but also provides an estimation of immigration rates of individuals originating outside the study site. Both genetic and re-sighting data indicated that dispersal was strongly female biased, but the magnitude of dispersal estimates was much greater using genetic data. This suggests that many previous studies relying on mark–recapture data may have significantly underestimated dispersal. An analysis of spatial genetic structure within the sampled population also supports the idea that females are more dispersive, with females having no structure beyond the bounds of their own social group, while male genetic structure expands for 750 m from their social group. Although the genetic network approach we have used is an excellent tool for visualizing the social and genetic microstructure of social animals and identifying dispersers, our results also indicate the importance of applying them in parallel with behavioural and life history data.

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