IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v22y2017i3d10.1007_s13253-017-0291-8.html
   My bibliography  Save this article

Reflected Stochastic Differential Equation Models for Constrained Animal Movement

Author

Listed:
  • Ephraim M. Hanks

    (The Pennsylvania State University)

  • Devin S. Johnson

    (NOAA Fisheries)

  • Mevin B. Hooten

    (U.S. Geological Survey
    Colorado State University)

Abstract

Movement for many animal species is constrained in space by barriers such as rivers, shorelines, or impassable cliffs. We develop an approach for modeling animal movement constrained in space by considering a class of constrained stochastic processes, reflected stochastic differential equations. Our approach generalizes existing methods for modeling unconstrained animal movement. We present methods for simulation and inference based on augmenting the constrained movement path with a latent unconstrained path and illustrate this augmentation with a simulation example and an analysis of telemetry data from a Steller sea lion (Eumatopias jubatus) in southeast Alaska.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:jagbes:v:22:y:2017:i:3:d:10.1007_s13253-017-0291-8
    DOI: 10.1007/s13253-017-0291-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-017-0291-8
    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/s13253-017-0291-8?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. Pierre Del Moral & Arnaud Doucet & Ajay Jasra, 2006. "Sequential Monte Carlo samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 411-436, June.
    2. 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.
    3. Mevin B. Hooten & Devin S. Johnson, 2017. "Basis Function Models for Animal Movement," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 578-589, April.
    4. Rex Dalton, 2005. "Is this any way to save a species?," Nature, Nature, vol. 436(7047), pages 14-16, July.
    5. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
    6. Ephraim M Hanks & Mevin B Hooten & Devin S Johnson & Jeremy T Sterling, 2011. "Velocity-Based Movement Modeling for Individual and Population Level Inference," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-17, August.
    7. Ephraim M. Hanks & Mevin B. Hooten, 2013. "Circuit Theory and Model-Based Inference for Landscape Connectivity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 22-33, March.
    8. Mevin B. Hooten & Christopher K. Wikle & Robert M. Dorazio & J. Andrew Royle, 2007. "Hierarchical Spatiotemporal Matrix Models for Characterizing Invasions," Biometrics, The International Biometric Society, vol. 63(2), pages 558-567, June.
    9. Amanda R. Cangelosi & Mevin B. Hooten, 2009. "Models for Bounded Systems with Continuous Dynamics," Biometrics, The International Biometric Society, vol. 65(3), pages 850-856, September.
    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. Dhanushi A Wijeyakulasuriya & Elizabeth W Eisenhauer & Benjamin A Shaby & Ephraim M Hanks, 2020. "Machine learning for modeling animal movement," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-30, July.
    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. Elizabeth Eisenhauer & Ephraim Hanks, 2020. "A lattice and random intermediate point sampling design for animal movement," Environmetrics, John Wiley & Sons, Ltd., vol. 31(6), September.
    4. 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.

    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. Arnaud Dufays, 2016. "Evolutionary Sequential Monte Carlo Samplers for Change-Point Models," Econometrics, MDPI, vol. 4(1), pages 1-33, March.
    2. Brignone, Riccardo & Gonzato, Luca & Lütkebohmert, Eva, 2023. "Efficient Quasi-Bayesian Estimation of Affine Option Pricing Models Using Risk-Neutral Cumulants," Journal of Banking & Finance, Elsevier, vol. 148(C).
    3. Naoki Awaya & Yasuhiro Omori, 2021. "Particle Rolling MCMC with Double-Block Sampling ," CIRJE F-Series CIRJE-F-1175, CIRJE, Faculty of Economics, University of Tokyo.
    4. Nicolas Chopin & Mathieu Gerber, 2017. "Sequential quasi-Monte Carlo: Introduction for Non-Experts, Dimension Reduction, Application to Partly Observed Diffusion Processes," Working Papers 2017-35, Center for Research in Economics and Statistics.
    5. Fulop, Andras & Heng, Jeremy & Li, Junye & Liu, Hening, 2022. "Bayesian estimation of long-run risk models using sequential Monte Carlo," Journal of Econometrics, Elsevier, vol. 228(1), pages 62-84.
    6. Owen Jamie & Wilkinson Darren J. & Gillespie Colin S., 2015. "Likelihood free inference for Markov processes: a comparison," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(2), pages 189-209, April.
    7. Herbst, Edward & Schorfheide, Frank, 2019. "Tempered particle filtering," Journal of Econometrics, Elsevier, vol. 210(1), pages 26-44.
    8. Cozzini, Alberto & Jasra, Ajay & Montana, Giovanni & Persing, Adam, 2014. "A Bayesian mixture of lasso regressions with t-errors," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 84-97.
    9. Lau, F. Din-Houn & Gandy, Axel, 2014. "RMCMC: A system for updating Bayesian models," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 99-110.
    10. Ramis Khabibullin & Sergei Seleznev, 2022. "Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference," Papers 2210.07154, arXiv.org.
    11. Andras Fulop & Jeremy Heng & Junye Li, 2022. "Efficient Likelihood-based Estimation via Annealing for Dynamic Structural Macrofinance Models," Papers 2201.01094, arXiv.org.
    12. Pierre Del Moral & Ajay Jasra & Yan Zhou, 2017. "Biased Online Parameter Inference for State-Space Models," Methodology and Computing in Applied Probability, Springer, vol. 19(3), pages 727-749, September.
    13. Christopher Wikle & Mevin Hooten, 2010. "A general science-based framework for dynamical spatio-temporal models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(3), pages 417-451, November.
    14. Crucinio, Francesca R. & Johansen, Adam M., 2023. "Properties of marginal sequential Monte Carlo methods," Statistics & Probability Letters, Elsevier, vol. 203(C).
    15. Naoki Awaya & Yasuhiro Omori, 2019. "Particle rolling MCMC," CIRJE F-Series CIRJE-F-1110, CIRJE, Faculty of Economics, University of Tokyo.
    16. Gareth W. Peters & Rodrigo S. Targino & Mario V. Wüthrich, 2017. "Bayesian Modelling, Monte Carlo Sampling and Capital Allocation of Insurance Risks," Risks, MDPI, vol. 5(4), pages 1-51, September.
    17. Garbuno-Inigo, A. & DiazDelaO, F.A. & Zuev, K.M., 2016. "Gaussian process hyper-parameter estimation using Parallel Asymptotically Independent Markov Sampling," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 367-383.
    18. Duan, Jin-Chuan & Fulop, Andras & Hsieh, Yu-Wei, 2020. "Data-cloning SMC2: A global optimizer for maximum likelihood estimation of latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    19. Zhao, Yunfei & Gao, Wei & Smidts, Carol, 2021. "Sequential Bayesian inference of transition rates in the hidden Markov model for multi-state system degradation," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    20. Fulop, Andras & Li, Junye, 2019. "Bayesian estimation of dynamic asset pricing models with informative observations," Journal of Econometrics, Elsevier, vol. 209(1), pages 114-138.

    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:22:y:2017:i:3:d:10.1007_s13253-017-0291-8. 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.