IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v22y2017i3d10.1007_s13253-017-0285-6.html
   My bibliography  Save this article

Incorporating Telemetry Error into Hidden Markov Models of Animal Movement Using Multiple Imputation

Author

Listed:
  • Brett T. McClintock

    (NOAA-NMFS)

Abstract

When data streams are observed without error and at regular time intervals, discrete-time hidden Markov models (HMMs) have become immensely popular for the analysis of animal location and auxiliary biotelemetry data. However, measurement error and temporally irregular data are often pervasive in telemetry studies, particularly in marine systems. While relatively small amounts of missing data that are missing-completely-at-random are not typically problematic in HMMs, temporal irregularity can result in few (if any) observations aligning with the regular time steps required by HMMs. Fitting HMMs that explicitly account for uncertainty attributable to location measurement error, temporally irregular observations, or other forms of missing data typically requires computationally demanding techniques, such as Markov chain Monte Carlo (MCMC). Using simulation and a real-world bearded seal (Erignathus barbatus) example, I investigate a practical alternative to incorporating measurement error and temporally irregular observations into HMMs based on multiple imputation of the position process drawn from a single-state continuous-time movement model. This two-stage approach is relatively simple, performed with existing software using efficient maximum likelihood methods, and completely parallelizable. I generally found the approach to perform well across a broad range of simulated measurement error and irregular sampling rates, with latent states and locations reliably recovered in nearly all simulated scenarios. However, high measurement error coupled with low sampling rates often induced bias in both the estimated probability distributions of data streams derived from the imputed position process and the estimated effects of spatial covariates on state transition probabilities. Results from the two-stage analysis of the bearded seal data were similar to a more computationally intensive single-stage MCMC analysis, but the two-stage analysis required much less computation time and no custom model-fitting algorithms. I thus found the two-stage multiple-imputation approach to be promising in terms of its ease of implementation, computation time, and performance. Code for implementing the approach using the R package “momentuHMM” is provided. Supplementary materials accompanying this paper appear online.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:jagbes:v:22:y:2017:i:3:d:10.1007_s13253-017-0285-6
    DOI: 10.1007/s13253-017-0285-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-017-0285-6
    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/s13253-017-0285-6?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. Joanna M. Bagniewska & Tom Hart & Lauren A. Harrington & David W. Macdonald, 2013. "Hidden Markov analysis describes dive patterns in semiaquatic animals," Behavioral Ecology, International Society for Behavioral Ecology, vol. 24(3), pages 659-667.
    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. Toryn L. J. Schafer & Christopher K. Wikle & Jay A. VonBank & Bart M. Ballard & Mitch D. Weegman, 2020. "A Bayesian Markov Model with Pólya-Gamma Sampling for Estimating Individual Behavior Transition Probabilities from Accelerometer Classifications," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 365-382, September.
    2. 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.
    3. 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.
    4. 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.
    5. Henry Scharf & Mevin B. Hooten & Devin S. Johnson, 2017. "Imputation Approaches for Animal Movement Modeling," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 335-352, 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. Ann E. McKellar & Roland Langrock & Jeffrey R. Walters & Dylan C. Kesler, 2015. "Using mixed hidden Markov models to examine behavioral states in a cooperatively breeding bird," Behavioral Ecology, International Society for Behavioral Ecology, vol. 26(1), pages 148-157.

    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_s13253-017-0285-6. 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.