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moult: An R Package to Analyze Moult in Birds

Author

Listed:
  • Erni, Birgit
  • Bonnevie, Bo T.
  • Oschadleus, Hans-Dieter
  • Altwegg, Res
  • Underhill, Les G.

Abstract

Moult is the process by which birds replace their feathers. It is a costly process in terms of energy and reduced flight ability but necessary for the maintenance of the plumage and its functions. Because birds generally avoid to moult while engaged with other energy demanding activities such as breeding and migration, the analysis of moult data gives insight into how birds fit this life stage into the annual cycle, on time constraints in the annual cycle, and on the effects of environmental variables on the timing of moult. The analysis of moult data requires non-standard statistical techniques. More than 20~years ago Underhill and Zucchini developed a likelihood approach for estimating duration, mean start date and variation in start date of a population of moulting birds. However, use of these models has been limited, mainly due to the lack of user-friendly software. The moult package for R implements the Underhill-Zucchini models, allowing the user to specify moult models in a regression type formula. In addition the functions allow the moult parameters (duration, and mean and variation in start date) to depend on explanatory variables. We here describe the package, give a brief summary of the theory and illustrate the models on two datasets included in the package.

Suggested Citation

  • Erni, Birgit & Bonnevie, Bo T. & Oschadleus, Hans-Dieter & Altwegg, Res & Underhill, Les G., 2013. "moult: An R Package to Analyze Moult in Birds," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i08).
  • Handle: RePEc:jss:jstsof:v:052:i08
    DOI: http://hdl.handle.net/10.18637/jss.v052.i08
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    References listed on IDEAS

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    1. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    2. Burbea, Jacob & del Castillo, Joan, 1992. "Geodesic submanifolds of statistical models with location parameters," Computational Statistics & Data Analysis, Elsevier, vol. 14(3), pages 301-313, October.
    3. Zeileis, Achim & Croissant, Yves, 2010. "Extended Model Formulas in R: Multiple Parts and Multiple Responses," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i01).
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