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Guest Editor’s Introduction to the Special Issue on “Animal Movement Modeling”

Author

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  • Mevin B. Hooten

    (Colorado State University
    Colorado State University)

  • Ruth King

    (University of Edinburgh)

  • Roland Langrock

    (Bielefeld University)

Abstract

In this introduction, we provide a brief overview to statistical models for animal trajectories and then summarize the set of invited articles that comprise the issue.

Suggested Citation

  • Mevin B. Hooten & Ruth King & Roland Langrock, 2017. "Guest Editor’s Introduction to the Special Issue on “Animal Movement Modeling”," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 224-231, September.
    • Handle: RePEc:spr:jagbes:v:22:y:2017:i:3:d:10.1007_s13253-017-0299-0
    DOI: 10.1007/s13253-017-0299-0
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    References listed on IDEAS

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    1. Roland Langrock & Thomas Kneib & Alexander Sohn & Stacy L. DeRuiter, 2015. "Nonparametric inference in hidden Markov models using P-splines," Biometrics, The International Biometric Society, vol. 71(2), pages 520-528, June.
    2. Juan Manuel Morales & Agustina Virgilio & María Delgado & Otso Ovaskainen, 2017. "A General Approach to Model Movement in (Highly) Fragmented Patch Networks," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 393-412, September.
    3. Mevin B. Hooten & Frances E. Buderman & Brian M. Brost & Ephraim M. Hanks & Jacob S. Ivan, 2016. "Hierarchical animal movement models for population‐level inference," Environmetrics, John Wiley & Sons, Ltd., vol. 27(6), pages 322-333, September.
    4. James C. Russell & Ephraim M. Hanks & Andreas P. Modlmeier & David P. Hughes, 2017. "Modeling Collective Animal Movement Through Interactions in Behavioral States," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 313-334, September.
    5. Jennifer Pohle & Roland Langrock & Floris M. Beest & Niels Martin Schmidt, 2017. "Selecting the Number of States in Hidden Markov Models: Pragmatic Solutions Illustrated Using Animal Movement," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 270-293, September.
    6. Toby A. Patterson & Alison Parton & Roland Langrock & Paul G. Blackwell & Len Thomas & Ruth King, 2017. "Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 399-438, October.
    7. Vianey Leos-Barajas & Eric J. Gangloff & Timo Adam & Roland Langrock & Floris M. Beest & Jacob Nabe-Nielsen & Juan M. Morales, 2017. "Multi-scale Modeling of Animal Movement and General Behavior Data Using Hidden Markov Models with Hierarchical Structures," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 232-248, September.
    8. Ephraim M. Hanks & Devin S. Johnson & Mevin B. Hooten, 2017. "Reflected Stochastic Differential Equation Models for Constrained Animal Movement," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 353-372, September.
    9. Hooten, Mevin B. & Wikle, Christopher K., 2010. "Statistical Agent-Based Models for Discrete Spatio-Temporal Systems," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 236-248.
    10. Brett T. McClintock, 2017. "Incorporating Telemetry Error into Hidden Markov Models of Animal Movement Using Multiple Imputation," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 249-269, September.
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    Cited by:

    1. Ethan Lawler & Kim Whoriskey & William H. Aeberhard & Chris Field & Joanna Mills Flemming, 2019. "The Conditionally Autoregressive Hidden Markov Model (CarHMM): Inferring Behavioural States from Animal Tracking Data Exhibiting Conditional Autocorrelation," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 651-668, December.

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