Estimation of bird distribution based on ring re-encounters: precision and bias of the division coefficient and its relation to multi-state models

Fränzi Korner-Nievergelt, Michael Schaub, Kasper Thorup, Michael Vock, Wojciech Kania

    14 Citations (Scopus)

    Abstract

    Capsule: The division coefficient is an estimate of the proportion of ringed birds migrating to different destination areas taking into account area-specific re-encounter probabilities. Aims: To explore precision and bias of the division coefficient method by a simulation study and to compare the approach with multi-state models. Methods: In a simulation study true and estimated division coefficients were compared. The division coefficient method was mathematically compared with the multi-state model. Results: The estimated division coefficients seemed to be unbiased if the assumptions were met. The precision decreased if the bird distribution became similar in both bird groups and when difference between area-specific re-encounter probabilities increased. A bootstrap method to assess precision is presented. The estimates from the division coefficient method equal the maximum likelihood estimates in a multi-state model including only one time interval. Conclusion: Before applying the division coefficient method or a multi-state model to real data a simulation study should be conducted in order to explore the behaviour of parameter estimation. The division coefficient method with the bootstrap confidence intervals is an easy alternative to a multi-state model with one time interval when the bird distribution between destination areas (e.g. migratory connectivity) alone is of interest.

    Original languageEnglish
    JournalBird Study
    Volume57
    Issue number1
    Pages (from-to)56-68
    Number of pages13
    ISSN0006-3657
    DOIs
    Publication statusPublished - Feb 2010

    Fingerprint

    Dive into the research topics of 'Estimation of bird distribution based on ring re-encounters: precision and bias of the division coefficient and its relation to multi-state models'. Together they form a unique fingerprint.

    Cite this