QUANTIFYING EVOLUTIONARY GENETIC CONSTRAINTS IN THE IVYLEAF MORNING GLORY, IPOMOEA HEDERACEA

The ability of a population to respond to natural selection will be determined by the patterns of genetic variation and covariation in traits under selection. In the quantitative genetic framework, these patterns of genetic variation and covariation are described by the G matrix, which for a given pattern of selection will determine the size and direction of evolutionary responses. Several methods have been developed to evaluate the nature of evolutionary constraints imposed by G, although this multitude of methods has never been applied to a common data set to compare their strengths and weaknesses, or the similarity of evolutionary inferences they produce. Here we compare several multivariate methods that calculate genetic constraint using a quantitative genetic field study in the ivyleaf morning glory, Ipomoea hederacea. We focus on a tractable number of traits (size at flowering, final size, and flowering time), which allows us to pair multivariate quantitative methods with qualitative interpretations of both G and the pattern of natural selection. In methods that rely on either the geometry of G or the multivariate orientation of G and the pattern of natural selection (beta), we found high levels of inferred constraint. In contrast, when one considers how genetic covariances are likely to affect the rate of adaptation over very short timescales, we inferred relatively low levels of constraint. Two consistent results emerge from our analyses. First, the inferences about evolutionary genetic constraints from all of these metrics are very sensitive to whether traits are unstandardized, standardized by the standard deviation, or standardized by the mean. In general, weaker evolutionary genetic constraints are inferred for metrics utilizing a mean standardization. Second, the discordance between methods that consider the geometric orientation of G and beta and those that evaluate how covariances affect the short-term rate of adaptation suggests that alternative constraint metrics might be informative, depending on whether the goal is to evaluate adaptation in general or the evolution of particular traits.