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Inferring animal social networks with imperfect detection

Published on Jun 1, 2019in Ecological Modelling2.63
· DOI :10.1016/j.ecolmodel.2019.04.001
Olivier Gimenez39
Estimated H-index: 39
(University of Montpellier),
Olivier Gimenez10
Estimated H-index: 10
(University of Montpellier)
+ 3 AuthorsEnrique A. Crespo25
Estimated H-index: 25
(CONICET: National Scientific and Technical Research Council)
Abstract
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.
  • References (38)
  • Citations (1)
References38
Newest
#1Paméla Lagrange (Université de Sherbrooke)H-Index: 3
#2Roger Pradel (CNRS: Centre national de la recherche scientifique)H-Index: 45
Last.Olivier Gimenez (CNRS: Centre national de la recherche scientifique)H-Index: 39
view all 4 authors...
#1Antica Culina (University of Oxford)H-Index: 7
#2Shelly Lachish (University of Oxford)H-Index: 16
Last.Ben C. Sheldon (University of Oxford)H-Index: 78
view all 5 authors...
#1Rémi Choquet (CNRS: Centre national de la recherche scientifique)H-Index: 22
#2Ana Sanz-Aguilar (CSIC: Spanish National Research Council)H-Index: 13
Last.Olivier Gimenez (CNRS: Centre national de la recherche scientifique)H-Index: 39
view all 7 authors...
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