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The Journal of animal ecology v.86 no.2, 2017년, pp.202 - 212   SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Analysing animal social network dynamics: the potential of stochastic actor‐oriented models

Fisher, David N. (Centre for Ecology and Conservation, University of Exeter, Penryn, Cornwall, TR10 9FE, UK ) ; Ilany, Amiyaal (The Mina and Everard Goodman Faculty of Life Sciences, Bar‐Ilan University, Ramat‐Gan, 5290002, Israel ) ; Silk, Matthew J. (Environment and Sustainability Institute, University of Exeter, Penryn, Cornwall, TR10 9FE, UK ) ; Tregenza, Tom (Centre for Ecology and Conservation, University of Exeter, Penryn, Cornwall, TR10 9FE, UK ) ; Sheldon, Ben ;
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    Summary Animals are embedded in dynamically changing networks of relationships with conspecifics. These dynamic networks are fundamental aspects of their environment, creating selection on behaviours and other traits. However, most social network‐based approaches in ecology are constrained to considering networks as static, despite several calls for such analyses to become more dynamic. There are a number of statistical analyses developed in the social sciences that are increasingly being applied to animal networks, of which stochastic actor‐oriented models (SAOMs) are a principal example. SAOMs are a class of individual‐based models designed to model transitions in networks between discrete time points, as influenced by network structure and covariates. It is not clear, however, how useful such techniques are to ecologists, and whether they are suited to animal social networks. We review the recent applications of SAOMs to animal networks, outlining findings and assessing the strengths and weaknesses of SAOMs when applied to animal rather than human networks. We go on to highlight the types of ecological and evolutionary processes that SAOMs can be used to study. SAOMs can include effects and covariates for individuals, dyads and populations, which can be constant or variable. This allows for the examination of a wide range of questions of interest to ecologists. However, high‐resolution data are required, meaning SAOMs will not be useable in all study systems. It remains unclear how robust SAOMs are to missing data and uncertainty around social relationships. Ultimately, we encourage the careful application of SAOMs in appropriate systems, with dynamic network analyses likely to prove highly informative. Researchers can then extend the basic method to tackle a range of existing questions in ecology and explore novel lines of questioning.


  • 주제어

    animal communities .   dynamics .   individual‐based models .   network‐based diffusion analysis .   social networks .   transmission.  

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