Quantifying Evolutionary Genetic Constraints in the Ivyleaf Morning Glory,Ipomoea hederacea

Abstract

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 (b), 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 b 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.

Quantitative genetic variance and multivariate clines in the Ivyleaf morning glory, Ipomoea hederacea

“…The likely explanation for this trend is that northern populations experience shorter growing seasons, producing an advantage for lines that are capable of growing, reproducing and maturing fruit before a killing frost ends the season. In previous experiments-one well north of the current range (44.038 N; [41]) and one towards the centre of the latitudinal range sampled here (39.0648 N; [50])-we have documented strong natural selection favouring earlier flowering individuals and genotypes, and that frost damage to maturing fruits significantly decreases seed viability [41].…”

“…Because we used inbred lines, our estimates of genetic variance are estimates of the total genetic variance, rather than the additive genetic variance. However, given the high selfing rate of I. hederacea, it is likely that the total genetic variance is more relevant for responses to selection in this species [41]. In addition, the crosses necessary to estimate additive genetic variances would create individuals and families of unusually high heterozygosity that would be of questionable relevance to natural populations [41].…”

“…For flowering time, which is on an interval scale [42], this transformation does not alter conclusions about the relative timing differences between genotypes or individuals, although the exact values obtained depend on the inferred origin of the trait (e.g. days since germination versus days since snow-melt; [41,42]). …”

Biomechanical trade-offs bias rates of evolution in the feeding apparatus of fishes

“…Even in the presence of selection, genetic variance is necessary for traits to evolve, and so serves as a proxy for the immediate evolutionary potential of a trait. In many cases, traits evolve along genetic lines of least resistance, or as a response to other genetic constraints [40][41][42][43]. According to this mechanism, traits vary in their genetic potential to respond to selection, and constraints can bias the rates and directions of trait evolution.…”

Response to joint selection on germination and flowering phenology depends on the direction of selection

“…The strength and orientation of genetic correlations, in conjunction with the pattern of selection, can enhance or constrain evolutionary response (Agrawal & Stinchcombe, 2009; Conner et al., 2011; Lande, 1979; Simonsen & Stinchcombe, 2010; Teplitsky et al., 2014; Walling et al., 2014). For instance, if selection is in the same direction for two traits, a negative correlation between them will slow the adaptive response, while a positive correlation would accelerate it (Conner, 2012).…”