Reverse evolution of fitness in Drosophila melanogaster

Abstract The evolution of fitness is central to evolutionary theory, yet few experimental systems allow us to track its evolution in genetically and environmentally relevant contexts. Reverse evolution experiments allow the study of the evolutionary return to ancestral phenotypic states, including fitness. This in turn permits well‐defined tests for the dependence of adaptation on evolutionary history and environmental conditions. In the experiments described here, 20 populations of heterogeneous evolutionary histories were returned to their common ancestral environment for 50 generations, and were then compared with both their immediate differentiated ancestors and populations which had remained in the ancestral environment. One measure of fitness returned to ancestral levels to a greater extent than other characters did. The phenotypic effects of reverse evolution were also contingent on previous selective history. Moreover, convergence to the ancestral state was highly sensitive to environmental conditions. The phenotypic plasticity of fecundity, a character directly selected for, evolved during the experimental time frame. Reverse evolution appears to force multiple, diverged populations to converge on a common fitness state through different life‐history and genetic changes.     

REVIEW: Optimality models in the age of experimental evolution and genomics

Optimality models have been used to predict evolution of many properties of organisms. They typically neglect genetic details, whether by necessity or design. This omission is a common source of criticism, and although this limitation of optimality is widely acknowledged, it has mostly been defended rather than evaluated for its impact. Experimental adaptation of model organisms provides a new arena for testing optimality models and for simultaneously integrating genetics. First, an experimental context with a well‐researched organism allows dissection of the evolutionary process to identify causes of model failure – whether the model is wrong about genetics or selection. Second, optimality models provide a meaningful context for the process and mechanics of evolution, and thus may be used to elicit realistic genetic bases of adaptation – an especially useful augmentation to well‐researched genetic systems. A few studies of microbes have begun to pioneer this new direction. Incompatibility between the assumed and actual genetics has been demonstrated to be the cause of model failure in some cases. More interestingly, evolution at the phenotypic level has sometimes matched prediction even though the adaptive mutations defy mechanisms established by decades of classic genetic studies. Integration of experimental evolutionary tests with genetics heralds a new wave for optimality models and their extensions that does not merely emphasize the forces driving evolution.     

Adaptation in a spider mite population after long-term evolution on a single host plant

Evolution in a single environment is expected to erode genetic variability, thereby precluding adaptation to novel environments. To test this, a large population of spider mites kept on cucumber for approximately 300 generations was used to establish populations on novel host plants (tomato or pepper), and changes in traits associated to adaptation were measured after 15 generations. Using a half-sib design, we investigated whether trait changes were related to genetic variation in the base population. Juvenile survival and fecundity exhibited genetic variation and increased in experimental populations on novel hosts. Conversely, no variation was detected for host choice and developmental time and these traits did not evolve. Longevity remained unchanged on novel hosts despite the presence of genetic variation, suggesting weak selection for this trait. Hence, patterns of evolutionary changes generally matched those of genetic variation, and changes in some traits were not hindered by long-term evolution in a constant environment.     

The trade‐off between rate and yield in the design of microbial metabolism

Extra energy devoted to resource acquisition speeds metabolic rate, but reduces the net yield of energy. In direct competition, microbial strains with high rates of resource acquisition often outcompete strains with slower resource acquisition but higher yield, reducing the net output of the group. Here, I use mathematical models to analyse the genetic and demographic factors that tip the balance toward either rate or yield. My models clarify the widely discussed roles of kin selection and the spatial structure of populations. I also emphasize the strong effect of two previously ignored factors: demographic aspects of colony survival and reproduction strongly shape the design of metabolic rate and efficiency, and competitive mutants within long‐lived colonies favour rate over yield, degrading the efficiency of the population.