An ecologist's guide to the animal model

Alastair J. Wilson, Denis Reale, Michelle N. Clements, Michael M. Morrissey, Erik Postma, Craig A. Walling, Loeske E. B. Kruuk, Daniel H. Nussey

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

1. Efforts to understand the links between evolutionary and ecological dynamics hinge on our ability to measure and understand how genes influence phenotypes, fitness and population dynamics. Quantitative genetics provides a range of theoretical and empirical tools with which to achieve this when the relatedness between individuals within a population is known.

2. A number of recent studies have used a type of mixed-effects model, known as the animal model, to estimate the genetic component of phenotypic variation using data collected in the field. Here, we provide a practical guide for ecologists interested in exploring the potential to apply this quantitative genetic method in their research.

3. We begin by outlining, in simple terms, key concepts in quantitative genetics and how an animal model estimates relevant quantitative genetic parameters, such as heritabilities or genetic correlations.

4. We then provide three detailed example tutorials, for implementation in a variety of software packages, for some basic applications of the animal model. We discuss several important statistical issues relating to best practice when fitting different kinds of mixed models.

5. We conclude by briefly summarizing more complex applications of the animal model, and by highlighting key pitfalls and dangers for the researcher wanting to begin using quantitative genetic tools to address ecological and evolutionary questions.

Original languageEnglish
Pages (from-to)13-26
Number of pages14
JournalJournal of Animal Ecology
Issue number1
Publication statusPublished - Jan 2010

Keywords / Materials (for Non-textual outputs)

  • animal model
  • genetic correlation
  • heritability
  • mixed-effect model
  • natural selection
  • pedigree
  • quantitative genetics


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