The Phylogenetic Mixed Model

Housworth, Elizabeth A.; Martins, Emı́lia P.; Lynch, Michael

doi:10.1086/380570

Cited by 274 publications

(370 citation statements)

References 37 publications

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“…The model on which PGLS is based assumes that evolution can be described as a continuous multivariate random walk and that all variation is phylogenetic, that is, it is a function of the properties of the ⌺ n matrix. The ⌺ n matrix can include variation within a species (by extending the lengths of the ter- The phylogenetic mixed model (PMM) developed by Lynch (1991) and extended by Housworth et al (2004), distinguishes between heritable (phylogenetic) and residual (environmental) components of variation. Felsenstein (2004) outlines another approach to incorporating the effects of sampling error.…”

Section: Discussionmentioning

confidence: 99%

“…To avoid this, one might just add closely related species but that is not very efficient statistically since the observations are not independent. Housworth et al (2004) report that the PMM also yields unbiased estimates so that much of the discussion given above about the statistical properties of the PGLS method should also apply to the PMM method. Paradis and Claude (2002) proposed a different generalization using generalized estimating equations.…”

Section: Discussionmentioning

confidence: 99%

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A Comment on Phylogenetic Correction

Rohle¹

2006

Evolution

125

155

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Abstract. Statistical methods are now commonly used to take into account the expected lack of independence of observations across different species (due to their phylogenetic relatedness) when computing correlations or regressions among traits. The methods are often interpreted as removing that part of the regression or correlation that is an artifact due to phylogeny and there is an expectation that the corrected regression or correlation coefficients will usually be closer to zero. It is shown here that this is not an accurate way to interpret these methods. The effect of taking phylogeny into account is to reduce the variance of the estimated regression or correlation coefficients. Their means are not because since estimates of regression coefficients are unbiased whether or not the correct phylogeny is taken into account. Estimates of correlations are only slightly biased (and in the opposite direction that many expect).

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Comment on Phylogenetic Correction

Rohle¹

2006

Evolution

125

155

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“…Four general approaches (discussed in detail below) have been developed to address phylogenetic non-independence: analysis of phylogenetically independent contrasts (PICs, [34]), generalized least squares (GLS, [35 -37]), phylogenetic autoregression [38,39] and phylogenetic mixed models [33,40,41]. The approaches differ in attempting either to incorporate phylogenetic and other effects simultaneously (PIC, GLS, phylogenetic mixed models), or to remove phylogenetic effects and examine patterns in residual trait variation (phylogenetic autoregression).…”

Section: Coping With Phylogenetic Non-independence In Cross-species Cmentioning

confidence: 99%

Controlling for non-independence in comparative analysis of patterns across populations within species

Stone¹,

Nee²,

Felsenstein

2011

Phil. Trans. R. Soc. B

136

127

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How do we quantify patterns (such as responses to local selection) sampled across multiple populations within a single species? Key to this question is the extent to which populations within species represent statistically independent data points in our analysis. Comparative analyses across species and higher taxa have long recognized the need to control for the non-independence of species data that arises through patterns of shared common ancestry among them (phylogenetic non-independence), as have quantitative genetic studies of individuals linked by a pedigree. Analyses across populations lacking pedigree information fall in the middle, and not only have to deal with shared common ancestry, but also the impact of exchange of migrants between populations (gene flow). As a result, phenotypes measured in one population are influenced by processes acting on others, and may not be a good guide to either the strength or direction of local selection. Although many studies examine patterns across populations within species, few consider such non-independence. Here, we discuss the sources of non-independence in comparative analysis, and show why the phylogeny-based approaches widely used in cross-species analyses are unlikely to be useful in analyses across populations within species. We outline the approaches (intraspecific contrasts, generalized least squares, generalized linear mixed models and autoregression) that have been used in this context, and explain their specific assumptions. We highlight the power of 'mixed models' in many contexts where problems of non-independence arise, and show that these allow incorporation of both shared common ancestry and gene flow. We suggest what can be done when ideal solutions are inaccessible, highlight the need for incorporation of a wider range of population models in intraspecific comparative methods and call for simulation studies of the error rates associated with alternative approaches.

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“…Polytomies in the phylogeny were taken into account by reducing the number of degrees of freedom of the statistical tests (Purvis and Garland 1993). Both raw data analyses and independent contrasts can occasionally give wrong answers under E167 E168 The American Naturalist some evolutionary scenarios, and more recent methods (e.g., PMM; Housworth et al 2004) may be a better choice when more is known about the underlying evolutionary processes. Instead of applying these other methods, here we follow the suggestion of Martins et al (2002) of placing confidence only in conclusions that hold true for both independent contrasts and raw data analyses.…”

Section: Phylogenetically Controlled Comparative Analysesmentioning

confidence: 99%

Comparative Analysis of Worker Reproduction and Policing in Eusocial Hymenoptera Supports Relatedness Theory

Wenseleers¹,

Ratnieks²

2006

The American Naturalist

206

279

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In many bees, wasps, and ants, workers police each other in order to prevent individual workers from selfishly producing their own male offspring. Although several factors can selectively favor worker policing, genetic relatedness is considered to be of special importance. In particular, kin selection theory predicts that worker policing should be more common in species where workers are more related to the queen's sons than to other workers' sons. Here we provide strong novel support for this theory based on a comparative analysis of policing and male parentage in 109 species of ants, bees, and wasps. First, an analysis of behavioral data confirms that worker policing occurs more frequently in species where workers are more related to the queen's sons than to other workers' sons. Second, an analysis of male parentage shows that a significantly higher percentage of the males are workers' sons in species where the workers are more related to other workers' sons. Both conclusions also hold if data are analyzed using phylogenetically independent contrasts. Although our analysis provides strong overall support for the theory that relatedness affects kin conflict over male parentage, there is also significant residual variation. Several factors that may explain this variation are discussed.Keywords: worker policing, queen policing, worker reproduction, social Hymenoptera, inclusive fitness, kin selection.A major challenge in evolutionary biology is to explain how social groups can reduce or prevent costly conflicts

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The Phylogenetic Mixed Model

Cited by 274 publications

References 37 publications

A Comment on Phylogenetic Correction

A Comment on Phylogenetic Correction

Controlling for non-independence in comparative analysis of patterns across populations within species

Comparative Analysis of Worker Reproduction and Policing in Eusocial Hymenoptera Supports Relatedness Theory

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