IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v75y2019i4p1345-1355.html
   My bibliography  Save this article

Open population maximum likelihood spatial capture‐recapture

Author

Listed:
  • Richard Glennie
  • David L. Borchers
  • Matthew Murchie
  • Bart J. Harmsen
  • Rebecca J. Foster

Abstract

Open population capture‐recapture models are widely used to estimate population demographics and abundance over time. Bayesian methods exist to incorporate open population modeling with spatial capture‐recapture (SCR), allowing for estimation of the effective area sampled and population density. Here, open population SCR is formulated as a hidden Markov model (HMM), allowing inference by maximum likelihood for both Cormack‐Jolly‐Seber and Jolly‐Seber models, with and without activity center movement. The method is applied to a 12‐year survey of male jaguars (Panthera onca) in the Cockscomb Basin Wildlife Sanctuary, Belize, to estimate survival probability and population abundance over time. For this application, inference is shown to be biased when assuming activity centers are fixed over time, while including a model for activity center movement provides negligible bias and nominal confidence interval coverage, as demonstrated by a simulation study. The HMM approach is compared with Bayesian data augmentation and closed population models for this application. The method is substantially more computationally efficient than the Bayesian approach and provides a lower root‐mean‐square error in predicting population density compared to closed population models.

Suggested Citation

  • Richard Glennie & David L. Borchers & Matthew Murchie & Bart J. Harmsen & Rebecca J. Foster, 2019. "Open population maximum likelihood spatial capture‐recapture," Biometrics, The International Biometric Society, vol. 75(4), pages 1345-1355, December.
  • Handle: RePEc:bla:biomet:v:75:y:2019:i:4:p:1345-1355
    DOI: 10.1111/biom.13078
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13078
    Download Restriction: no

    File URL: https://libkey.io/10.1111/biom.13078?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
    ---><---

    Citations

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


    Cited by:

    1. Murray G. Efford & Matthew R. Schofield, 2020. "A spatial open‐population capture‐recapture model," Biometrics, The International Biometric Society, vol. 76(2), pages 392-402, June.
    2. Nathan J Hostetter & Nicholas J Lunn & Evan S Richardson & Eric V Regehr & Sarah J Converse, 2021. "Age-structured Jolly-Seber model expands inference and improves parameter estimation from capture-recapture data," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-19, June.
    3. Ben C. Stevenson & Rachel M. Fewster & Koustubh Sharma, 2022. "Spatial correlation structures for detections of individuals in spatial capture–recapture models," Biometrics, The International Biometric Society, vol. 78(3), pages 963-973, September.
    4. M. G. Efford, 2022. "Efficient Discretization of Movement Kernels for Spatiotemporal Capture–Recapture," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 641-651, December.
    5. Nathan J Crum & Lisa C Neyman & Timothy A Gowan, 2021. "Abundance estimation for line transect sampling: A comparison of distance sampling and spatial capture-recapture models," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-17, May.
    6. Paul McLaughlin & Haim Bar, 2021. "A spatial capture–recapture model with attractions between individuals," Environmetrics, John Wiley & Sons, Ltd., vol. 32(1), February.

    More about this item

    Statistics

    Access and download statistics

    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:bla:biomet:v:75:y:2019:i:4:p:1345-1355. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.