IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v393y2019icp20-24.html
   My bibliography  Save this article

Dynamic N-mixture models with temporal variability in detection probability

Author

Listed:
  • Zhao, Qing
  • Royle, J. Andrew

Abstract

In theory parameters of dynamic N-mixture models can be estimated with multiple years of data without the robust design under the assumption of constant detection probability. However, such an assumption can rarely be met in long-term studies, and the consequences of violating this assumption in the inferences of dynamic N-mixture models have not been assessed. In this study we used simulation studies to evaluate inferences of the original dynamic N-mixture model and two of its spatial extensions in the face of temporal variability in detection probability. We first evaluated the dynamic N-mixture models when detection probability that varied temporally was wrongly treated as a constant. We then evaluated if the robust design was necessary for dynamic N-mixture models to provide valid parameter estimates when detection probability was correctly assumed to vary temporally. Our results showed that, when detection probability that varied temporally was wrongly treated as a constant, biases were introduced in the parameter estimates of dynamic N-mixture models. When detection probability was correctly assumed to vary temporally, the models could provide valid parameter estimates with the robust design. The model could also provide valid parameter estimates when detection probability was a random effect, even without the robust design. Based on our results, we strongly recommended considering temporal variability in detection probability when using dynamic N-mixture models to analyze long-term data and adopting the robust design in long-term surveys. Our work here is not only useful for data analysis but also important for research design, and thus are relevant to a wide range of studies.

Suggested Citation

  • Zhao, Qing & Royle, J. Andrew, 2019. "Dynamic N-mixture models with temporal variability in detection probability," Ecological Modelling, Elsevier, vol. 393(C), pages 20-24.
  • Handle: RePEc:eee:ecomod:v:393:y:2019:i:c:p:20-24
    DOI: 10.1016/j.ecolmodel.2018.12.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380018304186
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2018.12.007?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. D. Dail & L. Madsen, 2011. "Models for Estimating Abundance from Repeated Counts of an Open Metapopulation," Biometrics, The International Biometric Society, vol. 67(2), pages 577-587, June.
    2. Péter Sólymos & Subhash Lele & Erin Bayne, 2012. "Conditional likelihood approach for analyzing single visit abundance survey data in the presence of zero inflation and detection error," Environmetrics, John Wiley & Sons, Ltd., vol. 23(2), pages 197-205, March.
    3. Ben Brintz & Claudio Fuentes & Lisa Madsen, 2018. "An asymptotic approximation to the N‐mixture model for the estimation of disease prevalence," Biometrics, The International Biometric Society, vol. 74(4), pages 1512-1518, December.
    Full references (including those not matched with items on IDEAS)

    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. Perry J. Williams & Cody Schroeder & Pat Jackson, 2020. "Estimating Reproduction and Survival of Unmarked Juveniles Using Aerial Images and Marked Adults," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(2), pages 133-147, June.
    2. Wen‐Han Hwang & Richard Huggins & Jakub Stoklosa, 2022. "A model for analyzing clustered occurrence data," Biometrics, The International Biometric Society, vol. 78(2), pages 598-611, June.
    3. Vasilios Liordos & Jukka Jokimäki & Marja-Liisa Kaisanlahti-Jokimäki & Evangelos Valsamidis & Vasileios J. Kontsiotis, 2021. "Niche Analysis and Conservation of Bird Species Using Urban Core Areas," Sustainability, MDPI, vol. 13(11), pages 1-15, June.
    4. D. Dail & L. Madsen, 2013. "Estimating Open Population Site Occupancy from Presence–Absence Data Lacking the Robust Design," Biometrics, The International Biometric Society, vol. 69(1), pages 146-156, March.
    5. Hefley, Trevor J. & Tyre, Andrew J. & Blankenship, Erin E., 2013. "Fitting population growth models in the presence of measurement and detection error," Ecological Modelling, Elsevier, vol. 263(C), pages 244-250.
    6. Matt Higham & Jay Ver Hoef & Lisa Madsen & Andy Aderman, 2021. "Adjusting a finite population block kriging estimator for imperfect detection," Environmetrics, John Wiley & Sons, Ltd., vol. 32(1), February.
    7. Whitlock, Steven L. & Womble, Jamie N. & Peterson, James T., 2020. "Modelling pinniped abundance and distribution by combining counts at terrestrial sites and in-water sightings," Ecological Modelling, Elsevier, vol. 420(C).
    8. Xiaoli Fan & Miguel I. Gómez & Shady S. Atallah & Jon M. Conrad, 2020. "A Bayesian State‐Space Approach for Invasive Species Management: The Case of Spotted Wing Drosophila," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1227-1244, August.
    9. Emily B. Dennis & Byron J.T. Morgan & Martin S. Ridout, 2015. "Computational aspects of N-mixture models," Biometrics, The International Biometric Society, vol. 71(1), pages 237-246, March.
    10. repec:jss:jstsof:43:i10 is not listed on IDEAS
    11. Hefley, Trevor J. & Tyre, Andrew J. & Blankenship, Erin E., 2017. "Reprint of: Fitting population growth models in the presence of measurement and detection error," Ecological Modelling, Elsevier, vol. 359(C), pages 461-467.
    12. Duarte, Adam & Adams, Michael J. & Peterson, James T., 2018. "Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches," Ecological Modelling, Elsevier, vol. 374(C), pages 51-59.
    13. Matthew R. P. Parker & Laura L. E. Cowen & Jiguo Cao & Lloyd T. Elliott, 2023. "Computational Efficiency and Precision for Replicated-Count and Batch-Marked Hidden Population Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(1), pages 43-58, March.
    14. Laura L. E. Cowen & Panagiotis Besbeas & Byron J. T. Morgan & Carl J. Schwarz, 2017. "Hidden Markov models for extended batch data," Biometrics, The International Biometric Society, vol. 73(4), pages 1321-1331, December.
    15. Rafael A. Moral & John Hinde & Clarice G. B. Demétrio & Carolina Reigada & Wesley A. C. Godoy, 2018. "Models for Jointly Estimating Abundances of Two Unmarked Site-Associated Species Subject to Imperfect Detection," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(1), pages 20-38, March.

    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:eee:ecomod:v:393:y:2019:i:c:p:20-24. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    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.