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Measuring density dependence in survival from mark-recapture data

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  • Richard Barker
  • David Fletcher
  • Paul Scofield

Abstract

We discuss the analysis of mark-recapture data when the aim is to quantify density dependence between survival rate and abundance. We describe an analysis for a random effects model that includes a linear relationship between abundance and survival using an errors-in-variables regression estimator with analytical adjustment for approximate bias. The analysis is illustrated using data from short-tailed shearwaters banded for 48 consecutive years at Fisher Island, Tasmania, and Hutton's shearwater banded at Kaikoura, New Zealand for nine consecutive years. The Fisher Island data provided no evidence of a density dependence relationship between abundance and survival, and confidence interval widths rule out anything but small density dependent effects. The Hutton's shearwater data were equivocal with the analysis unable to rule out anything but a very strong density dependent relationship between survival and abundance.

Suggested Citation

  • Richard Barker & David Fletcher & Paul Scofield, 2002. "Measuring density dependence in survival from mark-recapture data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(1-4), pages 305-313.
  • Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:305-313
    DOI: 10.1080/02664760120108782
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    Cited by:

    1. J. D. Lebreton & R. Choquet & O. Gimenez, 2012. "Simple Estimation and Test Procedures in Capture–Mark–Recapture Mixed Models," Biometrics, The International Biometric Society, vol. 68(2), pages 494-503, June.
    2. Stoklosa, Jakub & Dann, Peter & Huggins, Richard M. & Hwang, Wen-Han, 2016. "Estimation of survival and capture probabilities in open population capture–recapture models when covariates are subject to measurement error," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 74-86.
    3. Abadi, Fitsum & Gimenez, Olivier & Jakober, Hans & Stauber, Wolfgang & Arlettaz, Raphaël & Schaub, Michael, 2012. "Estimating the strength of density dependence in the presence of observation errors using integrated population models," Ecological Modelling, Elsevier, vol. 242(C), pages 1-9.

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