The Paradox of Predictability Provides a Bridge Between Micro- and Macroevolution

The relationship between the evolutionary dynamics observed in contemporary populations (microevolution) and evolution on timescales of millions of years (macroevolution) has been a topic of considerable debate. Historically, this debate centers on inconsistencies between microevolutionary processes and macroevolutionary patterns. Here, we characterize a striking exception: emerging evidence indicates that standing variation in contemporary populations and macroevolutionary rates of phenotypic divergence are often positively correlated. This apparent consistency between micro- and macroevolution is paradoxical because it contradicts our previous understanding of phenotypic evolution and is so far unexplained. Here, we explore the prospects for bridging evolutionary timescales through an examination of this "paradox of predictability." We begin by explaining why the divergence-variance correlation is a paradox, followed by data analysis to show that the correlation is a general phenomenon across a broad range of temporal scales, from a few generations to tens of millions of years. Then we review complementary approaches from quantitative-genetics, comparative morphology, evo-devo, and paleontology to argue that they can help to address the paradox from the shared vantage point of recent work on evolvability. In conclusion, we recommend a methodological orientation that combines different kinds of short-term and long-term data using multiple analytical frameworks in an interdisciplinary research program. Such a program will increase our general understanding about how evolution works within and across timescales

Epicuticular compounds of Drosophila subquinaria and D. recens: identification, quantification, and their role in female mate choice

The epicuticle of various Drosophila species consists of long-chain cuticular hydrocarbons (CHCs) and their derivatives that play a role in waterproofing and a dynamic means of chemical communication. Here, via gas chromatography and mass spectrometry, we identified and quantified the epicuticular composition of D. recens and D. subquinaria, two closely related species that show a pattern of reproductive character displacement in nature. Twenty-four compounds were identified with the most abundant, 11-cis-Vaccenyl acetate, present only in males of each species. Also exclusive to males were five tri-acylglycerides. The 18 remaining compounds were CHCs, all shared between the sexes and species. These CHCs were composed of odd carbon numbers (C, C, C and C), with an increase in structural isomers in the C and C groups. Saturated hydrocarbons comprise only methyl-branched alkanes and were found only in the C and C groups. Alkenes were the least prevalent, with alkadienes dominating the chromatographic landscape in the longer chain lengths. Sexual dimorphism was extensive with 6/8 of the logcontrast CHCs differing significantly in relative concentration between males and females in D. recens and D. subquinaria, respectively. Males of the two species also differed significantly in relative concentration of six CHCs, while females differed in none. Female-choice mating trials revealed directional sexual selection on male CHCs in a population of each species, consistent with female mate preferences for these traits. The sexual selection vectors differed significantly in multivariate trait space, suggesting that different pheromone blends determine male attractiveness in each species

Comparing G: multivariate analysis of genetic variation in multiple populations

The additive genetic variance-covariance matrix (G) summarizes the multivariate genetic relationships among a set of traits. The geometry of G describes the distribution of multivariate genetic variance, and generates genetic constraints that bias the direction of evolution. Determining if and how the multivariate genetic variance evolves has been limited by a number of analytical challenges in comparing G-matrices. Current methods for the comparison of G typically share several drawbacks: metrics that lack a direct relationship to evolutionary theory, the inability to be applied in conjunction with complex experimental designs, difficulties with determining statistical confidence in inferred differences and an inherently pair-wise focus. Here, we present a cohesive and general analytical framework for the comparative analysis of G that addresses these issues, and that incorporates and extends current methods with a strong geometrical basis. We describe the application of random skewers, common subspace analysis, the 4th-order genetic covariance tensor and the decomposition of the multivariate breeders equation, all within a Bayesian framework. We illustrate these methods using data from an artificial selection experiment on eight traits in Drosophila serrata, where a multi-generational pedigree was available to estimate G in each of six populations. One method, the tensor, elegantly captures all of the variation in genetic variance among populations, and allows the identification of the trait combinations that differ most in genetic variance. The tensor approach is likely to be the most generally applicable method to the comparison of G-matrices from any sampling or experimental design