How do we quantify patterns (such as responses to local selection) sampledacross multiple populations within a single species? Key to this question is theextent to which populations within species represent statistically independentdata points in our analysis. Comparative analyses across species and higher taxahave long recognized the need to control for the non-independence of speciesdata that arises through patterns of shared common ancestry among them(phylogenetic non-independence), as have quantitative genetic studies ofindividuals linked by a pedigree. Analyses across populations lacking pedigreeinformation fall in the middle, and not only have to deal with shared commonancestry, but also the impact of exchange of migrants between populations(gene flow). As a result, phenotypes measured in one population are influencedby processes acting on others, and may not be a good guide to either thestrength or direction of local selection. Although many studies examine patternsacross populations within species, few consider such non-independence. Here,we discuss the sources of non-independence in comparative analysis, and showwhy the phylogeny-based approaches widely used in cross-species analyses areunlikely to be useful in analyses across populations within species. We outlinethe approaches (intraspecific contrasts, generalized least squares, generalizedlinear 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 thatthese allow incorporation of both shared common ancestry and gene flow. Wesuggest what can be done when ideal solutions are inaccessible, highlight theneed for incorporation of a wider range of population models in intraspecificcomparative methods and call for simulation studies of the error rates associatedwith alternative approaches.
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