Annals of Applied Statistics

Continuous-time discrete-space models for animal movement

Ephraim M. Hanks, Mevin B. Hooten, and Mat W. Alldredge

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The processes influencing animal movement and resource selection are complex and varied. Past efforts to model behavioral changes over time used Bayesian statistical models with variable parameter space, such as reversible-jump Markov chain Monte Carlo approaches, which are computationally demanding and inaccessible to many practitioners. We present a continuous-time discrete-space (CTDS) model of animal movement that can be fit using standard generalized linear modeling (GLM) methods. This CTDS approach allows for the joint modeling of location-based as well as directional drivers of movement. Changing behavior over time is modeled using a varying-coefficient framework which maintains the computational simplicity of a GLM approach, and variable selection is accomplished using a group lasso penalty. We apply our approach to a study of two mountain lions (Puma concolor) in Colorado, USA.

Article information

Ann. Appl. Stat., Volume 9, Number 1 (2015), 145-165.

First available in Project Euclid: 28 April 2015

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Zentralblatt MATH identifier

Animal movement multiple imputation varying-coefficient model Markov chain


Hanks, Ephraim M.; Hooten, Mevin B.; Alldredge, Mat W. Continuous-time discrete-space models for animal movement. Ann. Appl. Stat. 9 (2015), no. 1, 145--165. doi:10.1214/14-AOAS803.

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Supplemental materials

  • Alternate path imputation distribution.: This supplement contains details of a Brownian bridge path imputation distribution and its use with our CTDS approach to modeling animal movement.