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Inferring animal social networks with imperfect detection

Author

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  • Gimenez, Olivier
  • Mansilla, Lorena
  • Klaich, M. Javier
  • Coscarella, Mariano A.
  • Pedraza, Susana N.
  • Crespo, Enrique A.

Abstract

Social network analysis provides a powerful tool for understanding social organisation of animals. However, in free-ranging populations, it is almost impossible to monitor exhaustively the individuals of a population and to track their associations. Ignoring the issue of imperfect and possibly heterogeneous individual detection can lead to substantial bias in standard network measures. Here, we develop capture-recapture models to analyse network data while accounting for imperfect and heterogeneous detection. We carry out a simulation study to validate our approach. In addition, we show how the visualisation of networks and the calculation of standard metrics can account for detection probabilities. The method is illustrated with data from a population of Commerson’s dolphin (Cephalorhynchus commersonii) in Patagonia Argentina. Our approach provides a step towards a general statistical framework for the analysis of social networks of wild animal populations.

Suggested Citation

  • Gimenez, Olivier & Mansilla, Lorena & Klaich, M. Javier & Coscarella, Mariano A. & Pedraza, Susana N. & Crespo, Enrique A., 2019. "Inferring animal social networks with imperfect detection," Ecological Modelling, Elsevier, vol. 401(C), pages 69-74.
    • Handle: RePEc:eee:ecomod:v:401:y:2019:i:c:p:69-74
    DOI: 10.1016/j.ecolmodel.2019.04.001
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    References listed on IDEAS

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    1. Nina Luisa Santostasi & Silvia Bonizzoni & Giovanni Bearzi & Lavinia Eddy & Olivier Gimenez, 2016. "A Robust Design Capture-Recapture Analysis of Abundance, Survival and Temporary Emigration of Three Odontocete Species in the Gulf of Corinth, Greece," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-21, December.
    2. Roger Pradel, 2005. "Multievent: An Extension of Multistate Capture–Recapture Models to Uncertain States," Biometrics, The International Biometric Society, vol. 61(2), pages 442-447, June.
    3. Klaich, M. Javier & Kinas, Paul G. & Pedraza, Susana N. & Coscarella, Mariano A. & Crespo, Enrique A., 2011. "Estimating dyad association probability under imperfect and heterogeneous detection," Ecological Modelling, Elsevier, vol. 222(15), pages 2642-2650.
    4. Gimenez, Olivier & Lebreton, Jean-Dominique & Gaillard, Jean-Michel & Choquet, Rémi & Pradel, Roger, 2012. "Estimating demographic parameters using hidden process dynamic models," Theoretical Population Biology, Elsevier, vol. 82(4), pages 307-316.
    5. Gimenez, Olivier & Rossi, Vivien & Choquet, Rémi & Dehais, Camille & Doris, Blaise & Varella, Hubert & Vila, Jean-Pierre & Pradel, Roger, 2007. "State-space modelling of data on marked individuals," Ecological Modelling, Elsevier, vol. 206(3), pages 431-438.
    6. Butts, Carter T., 2008. "Social Network Analysis with sna," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i06).
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