IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v21y2016i1d10.1007_s13253-015-0219-0.html
   My bibliography  Save this article

Dynamic Models of Animal Movement with Spatial Point Process Interactions

Author

Listed:
  • James C. Russell

    (Pennsylvania State University)

  • Ephraim M. Hanks

    (Pennsylvania State University)

  • Murali Haran

    (Pennsylvania State University)

Abstract

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).

Suggested Citation

  • James C. Russell & Ephraim M. Hanks & Murali Haran, 2016. "Dynamic Models of Animal Movement with Spatial Point Process Interactions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(1), pages 22-40, March.
    • Handle: RePEc:spr:jagbes:v:21:y:2016:i:1:d:10.1007_s13253-015-0219-0
    DOI: 10.1007/s13253-015-0219-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-015-0219-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13253-015-0219-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Devin S. Johnson & Dana L. Thomas & Jay M. Ver Hoef & Aaron Christ, 2008. "A General Framework for the Analysis of Animal Resource Selection from Telemetry Data," Biometrics, The International Biometric Society, vol. 64(3), pages 968-976, September.
    2. Jones, Galin L. & Haran, Murali & Caffo, Brian S. & Neath, Ronald, 2006. "Fixed-Width Output Analysis for Markov Chain Monte Carlo," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1537-1547, December.
    3. Joshua Goldstein & Murali Haran & Ivan Simeonov & John Fricks & Francesca Chiaromonte, 2015. "An attraction–repulsion point process model for respiratory syncytial virus infections," Biometrics, The International Biometric Society, vol. 71(2), pages 376-385, June.
    4. Richard P Mann, 2011. "Bayesian Inference for Identifying Interaction Rules in Moving Animal Groups," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-10, August.
    5. J. Møller & A. N. Pettitt & R. Reeves & K. K. Berthelsen, 2006. "An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants," Biometrika, Biometrika Trust, vol. 93(2), pages 451-458, June.
    6. Harris, Keith J. & Blackwell, Paul G., 2013. "Flexible continuous-time modelling for heterogeneous animal movement," Ecological Modelling, Elsevier, vol. 255(C), pages 29-37.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Patrick L. McDermott & Christopher K. Wikle & Joshua Millspaugh, 2017. "Hierarchical Nonlinear Spatio-temporal Agent-Based Models for Collective Animal Movement," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 294-312, September.
    2. 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.
    3. 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.
    4. Matthias Eckardt & Mehdi Moradi, 2024. "Marked Spatial Point Processes: Current State and Extensions to Point Processes on Linear Networks," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(2), pages 346-378, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Svetlana V. Tishkovskaya & Paul G. Blackwell, 2021. "Bayesian estimation of heterogeneous environments from animal movement data," Environmetrics, John Wiley & Sons, Ltd., vol. 32(6), September.
    2. Park, Jaewoo & Jin, Ick Hoon & Schweinberger, Michael, 2022. "Bayesian model selection for high-dimensional Ising models, with applications to educational data," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
    3. 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.
    4. Ninna Vihrs & Jesper Møller & Alan E. Gelfand, 2022. "Approximate Bayesian inference for a spatial point process model exhibiting regularity and random aggregation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 185-210, March.
    5. Rajala, T. & Penttinen, A., 2014. "Bayesian analysis of a Gibbs hard-core point pattern model with varying repulsion range," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 530-541.
    6. Laleh Tafakori & Armin Pourkhanali & Riccardo Rastelli, 2022. "Measuring systemic risk and contagion in the European financial network," Empirical Economics, Springer, vol. 63(1), pages 345-389, July.
    7. Del Negro, Marco & Schorfheide, Frank, 2008. "Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)," Journal of Monetary Economics, Elsevier, vol. 55(7), pages 1191-1208, October.
    8. Joshua Chan & Arnaud Doucet & Roberto León-González & Rodney W. Strachan, 2018. "Multivariate Stochastic Volatility with Co-Heteroscedasticity," Working Paper series 18-38, Rimini Centre for Economic Analysis.
    9. Johnson, Alicia A. & Jones, Galin L., 2015. "Geometric ergodicity of random scan Gibbs samplers for hierarchical one-way random effects models," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 325-342.
    10. Zhengyi Zhou & David S. Matteson & Dawn B. Woodard & Shane G. Henderson & Athanasios C. Micheas, 2015. "A Spatio-Temporal Point Process Model for Ambulance Demand," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 6-15, March.
    11. Vitoratou, Silia & Ntzoufras, Ioannis & Moustaki, Irini, 2016. "Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions," LSE Research Online Documents on Economics 57685, London School of Economics and Political Science, LSE Library.
    12. Chen Ray-Bing & Chen Yi-Chi & Chu Chi-Hsiang & Lee Kuo-Jung, 2017. "On the determinants of the 2008 financial crisis: a Bayesian approach to the selection of groups and variables," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(5), pages 1-17, December.
    13. Bee, Marco & Benedetti, Roberto & Espa, Giuseppe, 2017. "Approximate maximum likelihood estimation of the Bingham distribution," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 84-96.
    14. Luigi Spezia, 2019. "Modelling covariance matrices by the trigonometric separation strategy with application to hidden Markov models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 399-422, June.
    15. Sierra Pugh & Matthew J. Heaton & Jeff Svedin & Neil Hansen, 2019. "Spatiotemporal Lagged Models for Variable Rate Irrigation in Agriculture," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 634-650, December.
    16. Takaaki Koike & Mihoko Minami, 2017. "Estimation of Risk Contributions with MCMC," Papers 1702.03098, arXiv.org, revised Jan 2019.
    17. Patrick L. McDermott & Christopher K. Wikle & Joshua Millspaugh, 2017. "Hierarchical Nonlinear Spatio-temporal Agent-Based Models for Collective Animal Movement," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 294-312, September.
    18. Solaiman Afroughi & Soghrat Faghihzadeh & Majid Jafari Khaledi & Mehdi Ghandehari Motlagh & Ebrahim Hajizadeh, 2011. "Analysis of clustered spatially correlated binary data using autologistic model and Bayesian method with an application to dental caries of 3--5-year-old children," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(12), pages 2763-2774, February.
    19. Masahiro Kuroda & Hiroki Hashiguchi & Shigekazu Nakagawa & Zhi Geng, 2013. "MCMC using Markov bases for computing $$p$$ -values in decomposable log-linear models," Computational Statistics, Springer, vol. 28(2), pages 831-850, April.
    20. Thomas M Newsome & Guy-Anthony Ballard & Christopher R Dickman & Peter J S Fleming & Chris Howden, 2013. "Anthropogenic Resource Subsidies Determine Space Use by Australian Arid Zone Dingoes: An Improved Resource Selection Modelling Approach," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jagbes:v:21:y:2016:i:1:d:10.1007_s13253-015-0219-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.