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Estimating dyad association probability under imperfect and heterogeneous detection

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  • Klaich, M. Javier
  • Kinas, Paul G.
  • Pedraza, Susana N.
  • Coscarella, Mariano A.
  • Crespo, Enrique A.

Abstract

In animal behaviour studies, association indices estimate the proportion of time two individuals (i.e. a dyad) spend in association. In terms of dyads, all association indices can be interpreted as estimators of the probability that a dyad is associated. However, traditional indices rely on the assumptions that the probability to detect a particular individual (p) is either approximately one and/or homogeneous between associated and not associated individuals. Based on marked individuals we develop a likelihood based model to estimate the probability a dyad is associated (ψ) accounting for p<1 and possibly varying between associated and not associated individuals. The proposed likelihood based model allows for both individual and dyadic missing observations. In addition, the model can easily be extended to incorporate covariate information for modeling p and ψ. A simulation study showed that the likelihood based model approach yield reasonably unbiased estimates, even for low and heterogeneous individual detection probabilities, while, in contrast, traditional indices showed moderate to strong biases. The application of the proposed approach is illustrated using a real data set collected from a population of Commerson's dolphin (Cephalorhynchus commersonii) in Patagonia Argentina. Finally, we discuss possible extensions of the proposed model and its applicability in animal behaviour and ecological studies.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ecomod:v:222:y:2011:i:15:p:2642-2650
    DOI: 10.1016/j.ecolmodel.2011.03.027
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    1. László Zsolt Garamszegi & Sara Calhim & Ned Dochtermann & Gergely Hegyi & Peter L. Hurd & Christian Jørgensen & Nobuyuki Kutsukake & Marc J. Lajeunesse & Kimberly A. Pollard & Holger Schielzeth & Matt, 2009. "Changing philosophies and tools for statistical inferences in behavioral ecology," Behavioral Ecology, International Society for Behavioral Ecology, vol. 20(6), pages 1363-1375.
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    1. 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.

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