Implementing the trinomial mark–recapture–recovery model in program mark
Article first published online: 13 DEC 2012
© 2012 The Author. Methods in Ecology and Evolution © 2012 British Ecological Society
Methods in Ecology and Evolution
Volume 4, Issue 1, pages 95–98, January 2013
How to Cite
Bonner, S. J. (2013), Implementing the trinomial mark–recapture–recovery model in program mark. Methods in Ecology and Evolution, 4: 95–98. doi: 10.1111/j.2041-210X.2012.00265.x
- Issue published online: 24 JAN 2013
- Article first published online: 13 DEC 2012
- Manuscript Accepted: 16 SEP 2012
- Manuscript Received: 13 JUL 2012
- missing data;
- program mark ;
- rmark ;
- time varying individual covariates;
- trinomial model
- Time-varying individual covariates present a challenge in modelling data from mark–recapture–recovery (MRR) experiments of wild animals. Many values of the covariate will be unknown because they can be observed only when an individual is captured, and the missing values cannot be ignored.
- Catchpole et al. [Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70, 445–460, 2008] presents one solution to this problem by constructing a conditional likelihood depending only on the observed covariate information – the so-called trinomial model.
- This paper describes the link between the trinomial model and the mark–recapture–recovery model of Burnham (Marked Individuals in the Study of Bird Population, 199–213, 1993) and shows how the trinomial model can be implemented in the software package program mark. This provides the user with access to all of the features of program mark including the facilities for model building and model selection without having to write custom code.
- I provide details on the analysis of a simulated data set and discuss an r package developed to help users format their data and to implement the model through the existing rmark package.