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A spatial capture–recapture model with attractions between individuals

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  • Paul McLaughlin
  • Haim Bar

Abstract

Over the past two decades there have been many advancements in modeling capture–recapture (CR) data to account for emerging data collection technology and techniques. Spatial capture–recapture (SCR) models have been introduced to estimate population size and numerous other demographic parameters from spatially explicit CR data. Recently SCR models have also begun incorporating realistic animal movement to account for individual dispersion and attraction to resources. While some species of animals are known to exhibit attractive behavior, nearly all SCR models assume complete independence among individual's movement and capture probability. In this article, we introduce an SCR model which allows for attractions between individuals via their daily location. We demonstrate via a simulation study that accounting for the attractions specified by our model, when present, can improve population size estimation. In addition, we apply our model to an iconic SCR dataset to estimate the population size and attraction parameters of a Bengal tiger (Panthera tigris tigris) population.

Suggested Citation

  • Paul McLaughlin & Haim Bar, 2021. "A spatial capture–recapture model with attractions between individuals," Environmetrics, John Wiley & Sons, Ltd., vol. 32(1), February.
    • Handle: RePEc:wly:envmet:v:32:y:2021:i:1:n:e2653
    DOI: 10.1002/env.2653
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    References listed on IDEAS

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    1. Robert M Dorazio, 2013. "Bayes and Empirical Bayes Estimators of Abundance and Density from Spatial Capture-Recapture Data," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    2. D. L. Borchers & B. C. Stevenson & D. Kidney & L. Thomas & T. A. Marques, 2015. "A Unifying Model for Capture-Recapture and Distance Sampling Surveys of Wildlife Populations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 195-204, March.
    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. Zachary D. Weller & Jennifer A. Hoeting & Joseph C. von Fischer, 2018. "A calibration capture–recapture model for inferring natural gas leak population characteristics using data from Google Street View cars," Environmetrics, John Wiley & Sons, Ltd., vol. 29(7), November.
    6. 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.
    7. Matthew R. Schofield & Richard J. Barker & Nicholas Gelling, 2018. "Continuous†time capture–recapture in closed populations," Biometrics, The International Biometric Society, vol. 74(2), pages 626-635, June.
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    1. Simon J. Bonner & Wei Zhang & Jiaqi Mu, 2024. "On the identifiability of the trinomial model for mark‐recapture‐recovery studies," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.

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