Dynamic Models of Animal Movement with Spatial Point Process Interactions

  • James C. Russell
  • Ephraim M. Hanks
  • Murali Haran


When analyzing animal movement, it is important to account for interactions between individuals. However, statistical models for incorporating interaction behavior in movement models are limited. We propose an approach that models dependent movement by augmenting a dynamic marginal movement model with a spatial point process interaction function within a weighted distribution framework. The approach is flexible, as marginal movement behavior and interaction behavior can be modeled independently. Inference for model parameters is complicated by intractable normalizing constants. We develop a double Metropolis–Hastings algorithm to perform Bayesian inference. We illustrate our approach through the analysis of movement tracks of guppies (Poecilia reticulata).


Auxiliary variable MCMC algorithm Collective motion Biased correlated random walk Group navigation Poecilia reticulata State-space model 



We would like to acknowledge the insightful and constructive comments provided by two anonymous reviewers and the associate editor which have clarified and improved the manuscript. This material is based upon work supported by the National Science Foundation under Grant No. 1414296 (Russell and Hanks).


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Copyright information

© International Biometric Society 2015

Authors and Affiliations

  • James C. Russell
    • 1
  • Ephraim M. Hanks
    • 1
  • Murali Haran
    • 1
  1. 1.Pennsylvania State UniversityUniversity ParkUSA

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