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A spatial open‐population capture‐recapture model

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  • Murray G. Efford
  • Matthew R. Schofield

Abstract

A spatial open‐population capture‐recapture model is described that extends both the non‐spatial open‐population model of Schwarz and Arnason and the spatially explicit closed‐population model of Borchers and Efford. The superpopulation of animals available for detection at some time during a study is conceived as a two‐dimensional Poisson point process. Individual probabilities of birth and death follow the conventional open‐population model. Movement between sampling times may be modeled with a dispersal kernel using a recursive Markovian algorithm. Observations arise from distance‐dependent sampling at an array of detectors. As in the closed‐population spatial model, the observed data likelihood relies on integration over the unknown animal locations; maximization of this likelihood yields estimates of the birth, death, movement, and detection parameters. The models were fitted to data from a live‐trapping study of brushtail possums (Trichosurus vulpecula) in New Zealand. Simulations confirmed that spatial modeling can greatly reduce the bias of capture‐recapture survival estimates and that there is a degree of robustness to misspecification of the dispersal kernel. An R package is available that includes various extensions.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:biomet:v:76:y:2020:i:2:p:392-402
    DOI: 10.1111/biom.13150
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    References listed on IDEAS

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    1. Peter Guttorp & Walter W. Piegorsch & B. J. Reich & B. Gardner, 2014. "A spatial capture‐recapture model for territorial species," Environmetrics, John Wiley & Sons, Ltd., vol. 25(8), pages 630-637, December.
    2. Jesse Whittington & Michael A Sawaya, 2015. "A Comparison of Grizzly Bear Demographic Parameters Estimated from Non-Spatial and Spatial Open Population Capture-Recapture Models," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-17, July.
    3. D. L. Borchers & M. G. Efford, 2008. "Spatially Explicit Maximum Likelihood Methods for Capture–Recapture Studies," Biometrics, The International Biometric Society, vol. 64(2), pages 377-385, June.
    4. 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.
    5. Slone, D.H., 2011. "Increasing accuracy of dispersal kernels in grid-based population models," Ecological Modelling, Elsevier, vol. 222(3), pages 573-579.
    6. William A. Link & Richard J. Barker, 2005. "Modeling Association among Demographic Parameters in Analysis of Open Population Capture–Recapture Data," Biometrics, The International Biometric Society, vol. 61(1), pages 46-54, March.
    7. William L. Kendall & Rhema Bjorkland, 2001. "Using Open Robust Design Models to Estimate Temporary Emigration from Capture—Recapture Data," Biometrics, The International Biometric Society, vol. 57(4), pages 1113-1122, December.
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    Cited by:

    1. 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.

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