This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in Web of Science Alert me to new issues of the journal Add to Saved Citations Download to citation manager Add to My Marked Citations Citing Articles Citing Articles via Web of Science (2) Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Browne, W. J Articles by Pettifor, R. A Search for Related Content Social Bookmarking                         What's this?

Using cross-classified multivariate mixed response models with application to life history traits in great tits (Parus major)

William J Browne, Department of Clinical Veterinary Science, University of Bristol, Langford House, Langford, Bristol BS40 5DU, UK. E-mail: william.browne{at}bristol.ac.uk

Robin H McCleery

Edward Grey Institute, University of Oxford, UK

Ben C Sheldon

Edward Grey Institute, University of Oxford, UK

Richard A Pettifor

Institute of Zoology, UK

Longitudinal observations on known individuals are an important source of data with which to test evolutionary theory within natural populations, in particular, the evolution and maintenance of life-history traits. In this paper, we concentrate on the reproductive behaviour and survival of a small passerine bird, the great tit (Parus major). The dataset we consider is taken from the long-term study of great tits in Wytham Woods in Oxfordshire. The models we consider are designed to relate variation in several phenotypic response variables that are linked to evolutionary fitness, alongside the correlations between them, to both general environmental and individual-specific factors. We fit multivariate cross-classified random effects models using a Markov chain Monte Carlo (MCMC) estimation algorithm described in the paper. Our results show for which traits variability is influenced by environmental factors and for which traits individual bird factors are more important. The partitioning of correlations is particularly illuminating, producing some pairs of ‘antagonistic’ correlations which are biologically meaningful.

Key Words: complex data structures • cross-classified models • Markov chain Monte Carlo (MCMC) • mixed responses • multilevel modelling

Statistical Modelling, Vol. 7, No. 3, 217-238 (2007)
DOI: 10.1177/1471082X0700700301


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?