IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v23y2018i4d10.1007_s13253-018-0335-8.html
   My bibliography  Save this article

Analysis of Population Change and Movement Using Robust Design Removal Data

Author

Listed:
  • William A. Link

    (U.S. Geological Survey)

  • Sarah J. Converse

    (University of Washington)

  • Amy A. Yackel Adams

    (Fort Collins Science Center)

  • Nathan J. Hostetter

    (U.S. Geological Survey)

Abstract

In capture-mark-reencounter studies, Pollock’s robust design combines methods for open populations with methods for closed populations. Open population features of the robust design allow for estimation of rates of death or permanent emigration, and closed population features enhance estimation of population sizes. We describe a similar design, but for use with removal data. Data collection occurs on secondary sampling occasions clustered within primary sampling periods. Primary sampling periods are intervals of brief enough duration that it can be safely assumed that the population is unchanged by births, deaths, immigration or emigration during them; all population change and movement occurs between primary sampling periods. Our model provides a basis for inference about population size, changes in population size, and movement rates among sample locations between primary sampling periods. Movement rates are modeled as functions of distance and time. Capture probabilities are modeled as a function of effort. We apply the model to data obtained in attempting to eradicate an introduced population of veiled chameleons (Chamaeleo calyptratus) on the island of Maui in Hawaii. Supplementary materials accompanying this paper appear online.

Suggested Citation

  • William A. Link & Sarah J. Converse & Amy A. Yackel Adams & Nathan J. Hostetter, 2018. "Analysis of Population Change and Movement Using Robust Design Removal Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(4), pages 463-477, December.
    • Handle: RePEc:spr:jagbes:v:23:y:2018:i:4:d:10.1007_s13253-018-0335-8
    DOI: 10.1007/s13253-018-0335-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-018-0335-8
    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/s13253-018-0335-8?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. Robert M. Dorazio & Bhramar Mukherjee & Li Zhang & Malay Ghosh & Howard L. Jelks & Frank Jordan, 2008. "Modeling Unobserved Sources of Heterogeneity in Animal Abundance Using a Dirichlet Process Prior," Biometrics, The International Biometric Society, vol. 64(2), pages 635-644, June.
    2. Robert M. Dorazio & Howard L. Jelks & Frank Jordan, 2005. "Improving Removal-Based Estimates of Abundance by Sampling a Population of Spatially Distinct Subpopulations," Biometrics, The International Biometric Society, vol. 61(4), pages 1093-1101, 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. Antonio Canale & Igor Prünster, 2017. "Robustifying Bayesian nonparametric mixtures for count data," Biometrics, The International Biometric Society, vol. 73(1), pages 174-184, March.
    2. Zhang, Hongmei & Ghosh, Kaushik & Ghosh, Pulak, 2012. "Sampling designs via a multivariate hypergeometric-Dirichlet process model for a multi-species assemblage with unknown heterogeneity," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2562-2573.
    3. Robert M. Dorazio & Bhramar Mukherjee & Li Zhang & Malay Ghosh & Howard L. Jelks & Frank Jordan, 2008. "Modeling Unobserved Sources of Heterogeneity in Animal Abundance Using a Dirichlet Process Prior," Biometrics, The International Biometric Society, vol. 64(2), pages 635-644, June.
    4. Tenan, S. & Maffioletti, C. & Caccianiga, M. & Compostella, C. & Seppi, R. & Gobbi, M., 2016. "Hierarchical models for describing space-for-time variations in insect population size and sex-ratio along a primary succession," Ecological Modelling, Elsevier, vol. 329(C), pages 18-28.
    5. Luis G. León-Novelo & Peter Müller & Wadih Arap & Mikhail Kolonin & Jessica Sun & Renata Pasqualini & Kim-Anh Do, 2013. "Semiparametric Bayesian Inference for Phage Display Data," Biometrics, The International Biometric Society, vol. 69(1), pages 174-183, March.
    6. Carota, Cinzia & Nava, Consuelo Rubina & Soldani, Irene & Ghiglione, Claudio & Schiapparelli, Stefano, 2015. "Statistical models for species richness in the Ross Sea," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201531, University of Turin.
    7. Kyung, Minjung & Gill, Jeff & Casella, George, 2009. "Characterizing the variance improvement in linear Dirichlet random effects models," Statistics & Probability Letters, Elsevier, vol. 79(22), pages 2343-2350, November.
    8. Brun, Mélanie & Abraham, Christophe & Jarry, Marc & Dumas, Jacques & Lange, Frédéric & Prévost, Etienne, 2011. "Estimating an homogeneous series of a population abundance indicator despite changes in data collection procedure: A hierarchical Bayesian modelling approach," Ecological Modelling, Elsevier, vol. 222(5), pages 1069-1079.
    9. Ming Zhou & Rachel S. McCrea & Eleni Matechou & Diana J. Cole & Richard A. Griffiths, 2019. "Removal models accounting for temporary emigration," Biometrics, The International Biometric Society, vol. 75(1), pages 24-35, March.
    10. Antonio Lijoi & Bernardo Nipoti & Igor Prünster, 2013. "Dependent mixture models: clustering and borrowing information," DEM Working Papers Series 046, University of Pavia, Department of Economics and Management.
    11. repec:jss:jstsof:43:i10 is not listed on IDEAS
    12. Lijoi, Antonio & Nipoti, Bernardo & Prünster, Igor, 2014. "Dependent mixture models: Clustering and borrowing information," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 417-433.
    13. Adam Martin-Schwarze & Jarad Niemi & Philip Dixon, 2017. "Assessing the Impacts of Time-to-Detection Distribution Assumptions on Detection Probability Estimation," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 465-480, December.
    14. Linda M. Haines, 2020. "Multinomial N‐mixture models for removal sampling," Biometrics, The International Biometric Society, vol. 76(2), pages 540-548, June.

    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:23:y:2018:i:4:d:10.1007_s13253-018-0335-8. 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.