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Implementing the trinomial mark–recapture–recovery model in program mark
Author(s) -
Bonner Simon J.
Publication year - 2013
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/j.2041-210x.2012.00265.x
Subject(s) - trinomial , covariate , computer science , code (set theory) , model selection , set (abstract data type) , r package , mark and recapture , software , selection (genetic algorithm) , software package , statistics , programming language , econometrics , mathematics , machine learning , discrete mathematics , population , demography , sociology
Summary 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. C atchpole et al. [ J ournal of the R oyal S tatistical S ociety: S eries B ( S tatistical M ethodology) , 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 B urnham ( M arked I ndividuals in the S tudy of B ird P opulation , 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.