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Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in closed form. Markov chain Monte Carlo methods solve this problem by sampling from a series of simpler conditional distributions that can be evaluated. The R package MCM-Cglmm,(More)
Inferring the parentage of a sample of individuals is often a prerequisite for many types of analysis in molecular ecology, evolutionary biology and quantitative genetics. In all but a few cases, the method of parentage assignment is divorced from the methods used to estimate the parameters of primary interest, such as mate choice or heritability. Here we(More)
Related individuals often have similar phenotypes, but this similarity may be due to the effects of shared environments as much as to the effects of shared genes. We consider here alternative approaches to separating the relative contributions of these two sources to phenotypic covariances, comparing experimental approaches such as cross-fostering,(More)
Although many of the statistical techniques used in comparative biology were originally developed in quantitative genetics, subsequent development of comparative techniques has progressed in relative isolation. Consequently, many of the new and planned developments in comparative analysis already have well-tested solutions in quantitative genetics. In this(More)
Some individuals die before a trait is measured or expressed (the invisible fraction), and some relevant traits are not measured in any individual (missing traits). This paper discusses how these concepts can be cast in terms of missing data problems from statistics. Using missing data theory, I show formally the conditions under which a valid evolutionary(More)
Best linear unbiased prediction (BLUP) is a method for obtaining point estimates of a random effect in a mixed effect model. Over the past decade it has been used extensively in ecology and evolutionary biology to predict individual breeding values and reaction norms. These predictions have been used to infer natural selection, evolutionary change,(More)
Introduction These are (incomplete) course notes about generalised linear mixed models (GLMM). Special emphasis is placed on understanding the underlying structure of a GLMM in order to show that slight modifications of this structure can produce a wide range of models. These include familiar models like regression and ANOVA, but also models with(More)
Theory predicts that biogeographic factors should play a central role in promoting population divergence and speciation. Previous empirical studies into biogeography and diversification have been relatively restricted in terms of the geographical area, phylogenetic scope, and the range of biogeographic factors considered. Here we present a global analysis(More)
Phenotypic differences between populations often correlate with climate variables, resulting from a combination of environment-induced plasticity and local adaptation. Species comprising populations that are genetically adapted to local climatic conditions should be more vulnerable to climate change than those comprising phenotypically plastic populations.(More)
Temporal variation in selection is a fundamental determinant of evolutionary outcomes. A recent paper presented a synthetic analysis of temporal variation in selection in natural populations. The authors concluded that there is substantial variation in the strength and direction of selection over time, but acknowledged that sampling error would result in(More)