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Behavioral Ecology and Sociobiology

, Volume 69, Issue 12, pp 2059–2069 | Cite as

Social network dynamics: the importance of distinguishing between heterogeneous and homogeneous changes

  • Mathias FranzEmail author
  • Susan C. Alberts
Methods

Abstract

Social network analysis is increasingly applied to understand the evolution of animal sociality. Identifying ecological and evolutionary drivers of complex social structures requires inferring how social networks change over time. In most observational studies, sampling errors may affect the apparent network structures. Here, we argue that existing approaches tend not to control sufficiently for some types of sampling errors when social networks change over time. Specifically, we argue that two different types of changes may occur in social networks, heterogeneous and homogeneous changes, and that understanding network dynamics requires distinguishing between these two different types of changes, which are not mutually exclusive. Heterogeneous changes occur if relationships change differentially, e.g., if some relationships are terminated but others remain intact. Homogeneous changes occur if all relationships are proportionally affected in the same way, e.g., if grooming rates decline similarly across all dyads. Homogeneous declines in the strength of relationships can strongly reduce the probability of observing weak relationships, producing the appearance of heterogeneous network changes. Using simulations, we confirm that failing to differentiate homogeneous and heterogeneous changes can potentially lead to false conclusions about network dynamics. We also show that bootstrap tests fail to distinguish between homogeneous and heterogeneous changes. As a solution to this problem, we show that an appropriate randomization test can infer whether heterogeneous changes occurred. Finally, we illustrate the utility of using the randomization test by performing an example analysis using an empirical data set on wild baboons.

Keywords

Social networks Social network analysis Social network dynamics Sampling errors 

Notes

Acknowledgments

We thank two anonymous reviewers, Damien Farine, Daniel van der Post, and Emily McLean, for helpful suggestions and discussion. We thank the Kenya Wildlife Services, Institute of Primate Research, National Museums of Kenya, National Council for Science and Technology, members of the Amboseli-Longido pastoralist communities, Tortillis Camp, Ker & Downey Safaris, Air Kenya, and Safarilink for their cooperation and assistance in Kenya. Thanks also to R.S. Mututua, S. Sayialel, J.K. Warutere, V. Somen, and T. Wango in Kenya, and to J. Altmann, K. Pinc, N. Learn, L. Maryott, and J. Gordon in the US. This research was approved by the IACUC at Princeton University and at Duke University and adhered to all the laws and guidelines of Kenya.

Compliance with Ethical Standards

Funding

The National Science Foundation (most recently BCS 0323553, DEB 0846286, and IOS 0919200) and the National Institute on Aging (R01AG034513 and P01AG031719) for the majority of the data presented here. M. Franz was supported by the German Research Foundation (DFG) and by Duke University.

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. All procedures performed in studies involving animals were in accordance with the ethical standards of the institution or practice at which the studies were conducted.

Supplementary material

265_2015_2030_MOESM1_ESM.pdf (444 kb)
ESM 1 (PDF 443 kb)
265_2015_2030_MOESM2_ESM.r (3 kb)
ESM 2 (R 3 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of BiologyDuke UniversityDurhamUSA

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