IDEAS home Printed from https://ideas.repec.org/a/spr/comaot/v15y2009i4d10.1007_s10588-008-9045-z.html
   My bibliography  Save this article

Social networks in African elephants

Author

Listed:
  • Eric A. Vance

    (Virginia Tech)

  • Elizabeth A. Archie

    (Fordham University)

  • Cynthia J. Moss

    (Amboseli Elephant Research Project)

Abstract

Some of the most highly social animals-including elephants, and some primates, cetaceans, and social carnivores-live in “fission-fusion” societies where social groups divide and re-form over the course of hours, days, or weeks. These societies are thought to respond adaptively to changes in the physical and social environment, and are thus ideal for testing hypotheses about the evolutionary forces that shape sociality. However, few models have been developed to measure and explain fission-fusion dynamics. Here we isolate several key components of the social behavior of wild African elephants (Loxodonta africana) using a bilinear mixed effects model, proposed by Peter Hoff (J. Am. Stat. Assoc. 100(469):286–295, 2005). The model enables inference on environmental effects, such as rainfall and seasonality, and is flexible enough to include predictors of pairwise affiliation, such as kinship, which allows large-mammal ecologists to test assumptions about elephant social structure and to develop new theories of why and how elephants interact. In addition, this model includes an unobserved latent social space to represent the interactions between elephants not incorporated by the measured covariates.

Suggested Citation

  • Eric A. Vance & Elizabeth A. Archie & Cynthia J. Moss, 2009. "Social networks in African elephants," Computational and Mathematical Organization Theory, Springer, vol. 15(4), pages 273-293, December.
    • Handle: RePEc:spr:comaot:v:15:y:2009:i:4:d:10.1007_s10588-008-9045-z
    DOI: 10.1007/s10588-008-9045-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10588-008-9045-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10588-008-9045-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hoff P.D. & Raftery A.E. & Handcock M.S., 2002. "Latent Space Approaches to Social Network Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1090-1098, December.
    2. Peter D. Hoff, 2005. "Bilinear Mixed-Effects Models for Dyadic Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 286-295, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yu Zhang & Yu Wu, 2012. "How behaviors spread in dynamic social networks," Computational and Mathematical Organization Theory, Springer, vol. 18(4), pages 419-444, December.
    2. Jordán, Ferenc, 2022. "The network perspective: Vertical connections linking organizational levels," Ecological Modelling, Elsevier, vol. 473(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    2. Xiao-Li Meng, 2016. "Discussion: Should a Working Model Actually Work?," International Statistical Review, International Statistical Institute, vol. 84(3), pages 362-367, December.
    3. Áureo de Paula, 2015. "Econometrics of network models," CeMMAP working papers CWP52/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Chen, Mingli & Fernández-Val, Iván & Weidner, Martin, 2021. "Nonlinear factor models for network and panel data," Journal of Econometrics, Elsevier, vol. 220(2), pages 296-324.
    5. Koen Jochmans, 2018. "Semiparametric Analysis of Network Formation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 705-713, October.
    6. Marchette, David J. & Priebe, Carey E., 2008. "Predicting unobserved links in incompletely observed networks," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1373-1386, January.
    7. repec:spo:wpecon:info:hdl:2441/dpido2upv86tqc7td18fd2mna is not listed on IDEAS
    8. repec:hal:wpspec:info:hdl:2441/dpido2upv86tqc7td18fd2mna is not listed on IDEAS
    9. Mark S. Handcock, 2017. "Comment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1537-1539, October.
    10. Montes-Rojas Gabriel, 2022. "Subgraph Network Random Effects Error Components Models: Specification and Testing," Journal of Econometric Methods, De Gruyter, vol. 11(1), pages 17-34, January.
    11. Adrian E. Raftery, 2017. "Comment: Extending the Latent Position Model for Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1531-1534, October.
    12. Yingda Lu & Kinshuk Jerath & Param Vir Singh, 2013. "The Emergence of Opinion Leaders in a Networked Online Community: A Dyadic Model with Time Dynamics and a Heuristic for Fast Estimation," Management Science, INFORMS, vol. 59(8), pages 1783-1799, August.
    13. Sosa, Juan & Betancourt, Brenda, 2022. "A latent space model for multilayer network data," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    14. Michael Braun & André Bonfrer, 2011. "Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes," Marketing Science, INFORMS, vol. 30(3), pages 513-531, 05-06.
    15. Tyler H. McCormick & Tian Zheng, 2015. "Latent Surface Models for Networks Using Aggregated Relational Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1684-1695, December.
    16. Patacchini, Eleonora & Hsieh, Chih-Sheng & Lin, Xu, 2019. "Social Interaction Methods," CEPR Discussion Papers 14141, C.E.P.R. Discussion Papers.
    17. Amanda M. Y. Chu & Thomas W. C. Chan & Mike K. P. So & Wing-Keung Wong, 2021. "Dynamic Network Analysis of COVID-19 with a Latent Pandemic Space Model," IJERPH, MDPI, vol. 18(6), pages 1-22, March.
    18. Peter D. Hoff, 2009. "Multiplicative latent factor models for description and prediction of social networks," Computational and Mathematical Organization Theory, Springer, vol. 15(4), pages 261-272, December.
    19. Dass, Mayukh & Fox, Gavin L., 2011. "A holistic network model for supply chain analysis," International Journal of Production Economics, Elsevier, vol. 131(2), pages 587-594, June.
    20. Daniele Durante & David B. Dunson & Joshua T. Vogelstein, 2017. "Nonparametric Bayes Modeling of Populations of Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1516-1530, October.
    21. Finn, J.T. & Brownscombe, J.W. & Haak, C.R. & Cooke, S.J. & Cormier, R. & Gagne, T. & Danylchuk, A.J., 2014. "Applying network methods to acoustic telemetry data: Modeling the movements of tropical marine fishes," Ecological Modelling, Elsevier, vol. 293(C), pages 139-149.
    22. ter Braak, Cajo J.F. & Kourmpetis, Yiannis & Kiers, Henk A.L. & Bink, Marco C.A.M., 2009. "Approximating a similarity matrix by a latent class model: A reappraisal of additive fuzzy clustering," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3183-3193, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:comaot:v:15:y:2009:i:4:d:10.1007_s10588-008-9045-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.