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Journal Article•DOI•

An ecologist's guide to the animal model

Alastair J. Wilson1, Denis Réale2, Michelle N. Clements1, Michael M. Morrissey1, Erik Postma3, Craig A. Walling1, Loeske E. B. Kruuk1, Daniel H. Nussey1 •
University of Edinburgh1, Université du Québec à Montréal2, University of Zurich3
01 Jan 2010-Journal of Animal Ecology (Wiley/Blackwell (10.1111))-Vol. 79, Iss: 1, pp 13-26
TL;DR: A practical guide for ecologists interested in exploring the potential to apply this quantitative genetic method in their research, by outlining key concepts in quantitative genetics and how an animal model estimates relevant quantitative genetic parameters, such as heritabilities or genetic correlations.
Abstract: 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.
Citations
More filters
Journal Article•
Human biochemical genetics

[...]

Grüneberg H
01 Jul 1960-The Eugenics Review
TL;DR: For the next few weeks the course is going to be exploring a field that’s actually older than classical population genetics, although the approach it’ll be taking to it involves the use of population genetic machinery.
Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

9,847 citations

Journal Article•DOI•
Genetics and analysis of quantitative traits

[...]

Warren J. Ewens1•
University of Pennsylvania1
01 Nov 1999-American Journal of Human Biology

3,213 citations

Journal Article•DOI•
Morphoj: an integrated software package for geometric morphometrics

[...]

Christian Peter Klingenberg1•
University of Manchester1
01 Mar 2011-Molecular Ecology Resources
TL;DR: The MorphoJ software offers an integrated and user‐friendly environment for standard multivariate analyses such as principal components, discriminant analysis and multivariate regression as well as specialized applications including phylogenetics, quantitative genetics and analyses of modularity in shape data.
Abstract: Increasingly, data on shape are analysed in combination with molecular genetic or ecological information, so that tools for geometric morphometric analysis are required. Morphometric studies most often use the arrangements of morphological landmarks as the data source and extract shape information from them by Procrustes superimposition. The MorphoJ software combines this approach with a wide range of methods for shape analysis in different biological contexts. The program offers an integrated and user-friendly environment for standard multivariate analyses such as principal components, discriminant analysis and multivariate regression as well as specialized applications including phylogenetics, quantitative genetics and analyses of modularity in shape data. MorphoJ is written in Java and versions for the Windows, Macintosh and Unix/Linux platforms are freely available from http://www.flywings.org.uk/MorphoJ_page.htm.

3,122 citations

Cites methods from "An ecologist's guide to the animal ..."

  • ...…feasible, even in natural populations when pedigree information is available from behavioural observations or molecular markers (McGuigan 2006; Wilson et al. 2010), and the methods for such studies have been extended to shape analyses (Klingenberg & Leamy 2001; Klingenberg & Monteiro 2005;…...

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  • ...Quantitative genetic studies are increasingly feasible, even in natural populations when pedigree information is available from behavioural observations or molecular markers (McGuigan 2006; Wilson et al. 2010), and the methods for such studies have been extended to shape analyses (Klingenberg & Leamy 2001; Klingenberg & Monteiro 2005; Myers et al....

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Journal Article•DOI•
A brief introduction to mixed effects modelling and multi-model inference in ecology.

[...]

Xavier A. Harrison1, Lynda Donaldson2, Lynda Donaldson3, Maria Eugenia Correa-Cano3, Julian C. Evans3, Julian C. Evans4, David N. Fisher5, David N. Fisher3, Cecily E. D. Goodwin3, Beth S. Robinson3, David J. Hodgson3, Richard Inger3 •
Zoological Society of London1, Wildfowl & Wetlands Trust2, University of Exeter3, University of Ottawa4, University of Guelph5
23 May 2018-PeerJ
TL;DR: This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.
Abstract: The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.

1,210 citations

Cites background or methods from "An ecologist's guide to the animal ..."

  • ...In particular, quantitative genetic studies rely on variance component analysis for estimating the heritability of traits such as body mass or size of secondary sexual characteristics (Wilson et al., 2010)....

    [...]

  • ...We recommend the tutorials in Wilson et al. (2010) and Houslay & Wilson (2017) for a deeper understanding of the power and flexibility of variance component analysis....

    [...]

  • ...…component analysis is a powerful tool for partitioning variation in a focal trait among biologically interesting groups, and several more complex examples exist Harrison et al. (2018), PeerJ, DOI 10.7717/peerj.4794 6/32 (see Nakagawa & Schielzeth, 2010; Wilson et al., 2010; Houslay & Wilson, 2017)....

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Journal Article•DOI•
Quantifying individual variation in behaviour: mixed-effect modelling approaches

[...]

Niels Jeroen Dingemanse1, Niels Jeroen Dingemanse2, Ned A. Dochtermann3•
Max Planck Society1, Ludwig Maximilian University of Munich2, North Dakota State University3
01 Jan 2013-Journal of Animal Ecology
TL;DR: An overview of how mixed-effect models can be used to partition variation in, and correlations among, phenotypic attributes into between- and within-individual variance components is provided.
Abstract: Growing interest in proximate and ultimate causes and consequences of between- and within-individual variation in labile components of the phenotype - such as behaviour or physiology - characterizes current research in evolutionary ecology. The study of individual variation requires tools for quantification and decomposition of phenotypic variation into between- and within-individual components. This is essential as variance components differ in their ecological and evolutionary implications. We provide an overview of how mixed-effect models can be used to partition variation in, and correlations among, phenotypic attributes into between- and within-individual variance components. Optimal sampling schemes to accurately estimate (with sufficient power) a wide range of repeatabilities and key (co)variance components, such as between- and within-individual correlations, are detailed. Mixed-effect models enable the usage of unambiguous terminology for patterns of biological variation that currently lack a formal statistical definition (e.g. 'animal personality' or 'behavioural syndromes'), and facilitate cross-fertilisation between disciplines such as behavioural ecology, ecological physiology and quantitative genetics.

854 citations

Cites background from "An ecologist's guide to the animal ..."

  • ...…so are available in the statistical (Henderson 1982; Goldstein 1995; Snijders & Bosker 1999; McCulloch & Searle 2000; Pinheiro & Bates 2000; Gelman & Hill 2007; Zuur et al. 2009; Hadfield 2010; Hox 2010) and quantitative genetics literature (Lynch & Walsh 1998; Schaeffer 2004; Wilson et al. 2010)....

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  • ...2009; Hadfield 2010; Hox 2010) and quantitative genetics literature (Lynch & Walsh 1998; Schaeffer 2004; Wilson et al. 2010)....

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  • ...Wilson et al. (2010) outline approaches for further partitioning into genetic (vs. nongenetic) components for pedigreed data sets....

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  • ...Importantly, ‘permanent’ refers here to environmental variation causing between-individual differences over the time span within which the repeated measures were taken (Wilson et al. 2010); it does not imply that such environmental factors have effects that are permanent....

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  • ...Importantly, ‘permanent’ refers here to environmental variation causing between-individual differences over the time span within which the repeated measures were taken (Wilson et al. 2010); it does not imply that such environmental factors have effects that are permanent....

    [...]

References
More filters
Book•
Introduction to quantitative genetics

[...]

D. S. Falconer
01 Jan 1981
TL;DR: The genetic constitution of a population: Hardy-Weinberg equilibrium and changes in gene frequency: migration mutation, changes of variance, and heritability are studied.
Abstract: Part 1 Genetic constitution of a population: Hardy-Weinberg equilibrium. Part 2 Changes in gene frequency: migration mutation. Part 3 Small populations - changes in gene frequency under simplified conditions. Part 4 Small populations - less simplified conditions. Part 5 Small populations - pedigreed populations and close inbreeding. Part 6 Continuous variation. Part 7 Values and means. Part 8 Variance. Part 9 Resemblance between relatives. Part 10 Heritability. Part 11 Selection - the response and its prediction. Part 12 Selection - the results of experiments. Part 13 Selection - information from relatives. Part 14 Inbreeding and crossbreeding - changes of mean value. Part 15 Inbreeding and crossbreeding - changes of variance. Part 16 Inbreeding and crossbreeding - applications. Part 17 Scale. Part 18 Threshold characters. Part 19 Correlated characters. Part 20 Metric characters under natural selection.

20,288 citations

Book•
Mixed-Effects Models in S and S-PLUS

[...]

Josae C. Pinheiro, Douglas M. Bates
29 Mar 2013
TL;DR: Linear Mixed-Effects and Nonlinear Mixed-effects (NLME) models have been studied in the literature as mentioned in this paper, where the structure of grouped data has been used for fitting LME models.
Abstract: Linear Mixed-Effects * Theory and Computational Methods for LME Models * Structure of Grouped Data * Fitting LME Models * Extending the Basic LME Model * Nonlinear Mixed-Effects * Theory and Computational Methods for NLME Models * Fitting NLME Models

10,715 citations

Journal Article•
Human biochemical genetics

[...]

Grüneberg H
01 Jul 1960-The Eugenics Review
TL;DR: For the next few weeks the course is going to be exploring a field that’s actually older than classical population genetics, although the approach it’ll be taking to it involves the use of population genetic machinery.
Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

9,847 citations

"An ecologist's guide to the animal ..." refers background in this paper

  • ...However, VD and VI are extremely difficult to estimate in non-experimental settings and both animal breeders and field ecologists have tended to focus on measuring additive genetic variance by estimating the phenotypic similarity of relatives (Falconer & Mackay 1996; Kruuk 2004)....

    [...]

  • ...Probably the most familiar technique is parent–offspring regression in which, for example, a trait’s heritability can be estimated as the slope of the regression of offspring phenotype on midparent phenotype (Falconer & Mackay 1996)....

    [...]

  • ...…with continuous or discrete distribution quantitative geneticists assume that phenotypic differences observed among individuals are related to differences in a large number of genes, each of them having a minor effect (the so called infinitesimal model; Falconer & Mackay 1996; Lynch&Walsh 1998)....

    [...]

  • ...For a single trait, we can estimate the amount of phenotypic variance (VP) that is due to genetic differences among individuals (VG) (Falconer & Mackay 1996)....

    [...]

  • ...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....

    [...]

Journal Article•DOI•
Generalized linear mixed models: a practical guide for ecology and evolution

[...]

Benjamin M. Bolker1, Mollie Elizabeth Brooks1, Connie J. Clark1, Shane W. Geange2, John R. Poulsen1, M. Henry H. Stevens3, Jada-Simone S. White1 •
University of Florida1, Victoria University of Wellington2, Miami University3
01 Mar 2009-Trends in Ecology and Evolution
TL;DR: The use (and misuse) of GLMMs in ecology and evolution are reviewed, estimation and inference are discussed, and 'best-practice' data analysis procedures for scientists facing this challenge are summarized.
Abstract: How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.

7,207 citations

Book•
Genetics and Analysis of Quantitative Traits

[...]

Michael Lynch, Bruce Walsh
01 Jan 1996
TL;DR: This book discusses the genetic Basis of Quantitative Variation, Properties of Distributions, Covariance, Regression, and Correlation, and Properties of Single Loci, and Sources of Genetic Variation for Multilocus Traits.
Abstract: I. The Genetic Basis of Quantitative Variation - An Overview of Quantitative Genetics - Properties of Distributions - Covariance, Regression, and Correlation - Properties of Single Loci - Sources of Genetic Variation for Multilocus Traits - Sources of Environmental Variation - Resemblance Between Relatives - Introduction to Matrix Algebra and Linear Models - Analysis of Line Crosses - Inbreeding Depression - Matters of Scale - II. Quantitative-Trait Loci - Polygenes and Polygenic Mutation - Detecting Major Genes - Basic Concepts of Marker-Based Analysis - Mapping and Characterizing QTLs: Inbred-Line Crosses - Mapping and Characterizing QTLs: Outbred Populations - III. Estimation Procedures - Parent-Offspring Regression - Sib AnalysisTwins and Clones - Cross-Classified Designs - Correlations Between Characters - Genotype x Environment Interaction - Maternal Effects Sex Linkage and Sexual Dimorphism - Threshold Characters - Estimation of Breeding Values - Variance-Component Estimation with Complex Pedigrees - Appendices - Expectations, Variances and Covariances of Compound Variables - Path Analysis - Matrix Algebra and Linear Models - Maximum Likelihood Estimation and Likelihood-Ratio Tests - Estimation of Power of Statistical Tests -

6,530 citations

Related Papers (5)
MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package

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02 Feb 2010-Journal of Statistical Software
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Introduction to quantitative genetics

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01 Jan 1981
D. S. Falconer
Genetics and Analysis of Quantitative Traits

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01 Jan 1996
Michael Lynch, Bruce Walsh
The measurement of selection on correlated characters

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