THE DIMENSIONALITY OF GENETIC VARIATION FOR WING SHAPE IN DROSOPHILA MELANOGASTER

The phenotypic and genetic covariance structure of drosphilid wings

Evolutionary constraint results from the interaction between the distribution of available genetic variation and the position of selective optima. The availability of genetic variance in multitrait systems, as described by the additive genetic variance-covariance matrix (G), has been the subject of recent attempts to assess the prevalence of genetic constraints. However, evolutionary constraints have not yet been considered from the perspective of the phenotypes available to multivariate selection, and whether genetic variance is present in all phenotypes potentially under selection. Determining the rank of the phenotypic variance-covariance matrix (P) to characterize the phenotypes available to selection, and contrasting it with the rank of G, may provide a general approach to determining the prevalence of genetic constraints. In a study of a laboratory population of Drosophila bunnanda from northern Australia we applied factor-analytic modeling to repeated measures of individual wing phenotypes to determine the dimensionality of the phenotypic space described by P. The phenotypic space spanned by the 10 wing traits had 10 statistically supported dimensions. In contrast, factor-analytic modeling of G estimated for the same 10 traits from a paternal half-sibling breeding design suggested G had fewer dimensions than traits. Statistical support was found for only five and two genetic dimensions, describing a total of 99% and 72% of genetic variance in wing morphology in females and males, respectively. The observed mismatch in dimensionality between P and G suggests that although selection might act to shift the intragenerational population mean toward any trait combination, evolution may be restricted to fewer dimensions.

The genetic architecture of a niche: variation and covariation in host use traits in the Colorado potato beetle

The genetic basis of host plant use by phytophagous insects can provide insight into the evolution of ecological niches, especially phenomena such as specialization and phylogenetic conservatism. We carried out a quantitative genetic analysis of multiple host use traits, estimated on five species of host plants, in the Colorado potato beetle, Leptinotarsa decemlineata (Coleoptera: Chrysomelidae). Mean values of all characters varied among host plants, providing evidence that adaptation to plants may require evolution of both behavioral (preference) and post-ingestive physiological (performance) characteristics. Significant additive genetic variation was detected for several characters on several hosts, but not in the capacity to use the two major hosts, a pattern that might be caused by directional selection. No negative genetic correlations across hosts were detected for any 'performance' traits, i.e. we found no evidence of trade-offs in fitness on different plants. Larval consumption was positively genetically correlated across host plants, suggesting that diet generalization might evolve as a distinct trait, rather than by independent evolution of feeding responses to each plant species, but several other traits did not show this pattern. We explored genetic correlations among traits expressed on a given plant species, in a first effort to shed light on the number of independent traits that may evolve in response to selection for host-plant utilization. Most traits were not correlated with each other, implying that adaptation to a novel potential host could be a complex, multidimensional 'character' that might constrain adaptation and contribute to the pronounced ecological specialization and the phylogenetic niche conservatism that characterize many clades of phytophagous insects.

A tale of two matrices: multivariate approaches in evolutionary biology

Two symmetric matrices underlie our understanding of microevolutionary change. The first is the matrix of nonlinear selection gradients (gamma) which describes the individual fitness surface. The second is the genetic variance-covariance matrix (G) that influences the multivariate response to selection. A common approach to the empirical analysis of these matrices is the element-by-element testing of significance, and subsequent biological interpretation of pattern based on these univariate and bivariate parameters. Here, I show why this approach is likely to misrepresent the genetic basis of quantitative traits, and the selection acting on them in many cases. Diagonalization of square matrices is a fundamental aspect of many of the multivariate statistical techniques used by biologists. Applying this, and other related approaches, to the analysis of the structure of gamma and G matrices, gives greater insight into the form and strength of nonlinear selection, and the availability of genetic variance for multiple traits.