Continuous-time discrete-space models for animal movement

Item Metadata

Dublin Core

Title

Continuous-time discrete-space models for animal movement

Description

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.

Bibliographic Citation

Hanks, E. M., M. B. Hooten, and M. W. Alldredge. 2015. Continuous-time discrete-space models for animal movement. The Annals of Applied Statistics 9:145-165. https://doi.org/10.1214/14-AOAS803

Creator

Hanks, Ephraim M.
Hooten, Mevin B.
Alldredge, Mathew W.

Subject

Animal movement
Multiple imputation
Varying-coefficient model
Markov chain

Extent

21 pages

Date Created

2015-03

Type

Article

Format

application/pdf

Language

English

Is Part Of

The Annals of Applied Statistics

Collection

Citation

Hanks, Ephraim M., Hooten, Mevin B., and Alldredge, Mathew W., “Continuous-time discrete-space models for animal movement,” CPW Digital Collections, accessed March 28, 2024, https://cpw.cvlcollections.org/items/show/72.