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Sewall Wright originally conceived of his Adaptive Landscape as a visual device to capture the consequences of non-linear (epistatic) interactions between genes. A useful way to visualise a multivariate non-linear function is through a ‘landscape’, an important factor to consider when applying Adaptive Landscape models to questions about the evolution of development. This chapter examines how a phenotype landscape (also known as phenotypic landscape or developmental landscape) can explicitly map genetic and developmental traits to the phenotypic traits upon which selection acts. After outlining the basic properties of phenotype landscapes, it considers how they are used in concert with an Adaptive Landscape to study the evolution of development. It then describes the formal theory for evolution on phenotype landscapes and how it generalises the quantitative genetic approaches that are often applied to Adaptive Landscapes. The chapter concludes by illustrating how phenotype landscape theory can be used to study the evolution of genetic covariance, heritability, and novelty.

Most phenotypic traits are the product of many genes as well as environmental effects, and the resulting phenotypic variation is quantitative rather than qualitative. The extent to which traits are under genetic control is termed heritability, and can be estimated by analyzing the phenotypic similarity of related individuals. Quantitative genetic approaches can be used to estimate population differentiation. Selection on quantitative traits produces changes in phenotypes as a function of the heritability, the intensity of selection, and the amount of phenotypic variation within a population. Human activities, such as size-limited harvesting and habitat degradation, can impose selection on natural populations and result in changes in phenotypes, and genetic drift in small populations can erode quantitative genetic variation. Genome-wide association studies can identify genes and markers associated with quantitative trait variation that can then be used to predict phenotypes from polygenic scores.

This chapter examines rates of biological evolution via two complementary approaches. The dominant approach is through analyzing naturally occurring biotic and abiotic samples. The less common approach that is progressively gaining more attention is the experimental evolution approach. The chapter describes the three broad methods (mutation accumulation, directional selection, and adaptation) used in experimental evolution studies and provides representative examples to highlight the utilities of these methods in addressing a variety of biological questions. The studies specifically focus on the rapid rates of change and divergence among replicate laboratory populations for a variety of phenotypic traits across the major life forms, from viruses to bacteria, fungi, plants, and animals. With recent developments in high throughput data generation platforms and novel analyses technologies, experimental evolutionary studies promise to bring more exciting discoveries in the coming years and decades.

This chapter reviews evidence for the existence of dispersal syndromes in common lizards. Many previous studies have suggested a connection between phenotypic traits and dispersal decisions, and one has indicated variation with context. A clear example of context dependency of dispersal syndrome is the way dispersers and residents experience social interactions. An excess of social interactions has long been known to induce departure decision in some species, while intermediate to high densities of conspecifics are thought to attract individuals in some other species. The focus in this chapter is on behavioural specializations of dispersers, illustrating those specializations with studies on common lizards. Condition-dependent dispersal in this species is also reviewed since behavioural specializations are often linked to individual condition, competitive ability, and/or health status.

This chapter discusses the objective of this volume which is to provide a comprehensive representation of maternal effects research in different mammalian groups, with a balanced emphasis between theory and data, genetic and environmental effects, evolutionary approaches and studies of mechanisms, field and laboratory approaches, and analyses at the populational, organismal, and molecular level. It explains that maternal effects have been the focus of intense scrutiny in recent evolutionary biology research because knowledge of maternal effects is required to fully understand the evolution of traits by natural selection. In addition, maternal effects may also provide a mechanism by which maladaptive phenotypic traits are transmitted across generations.

This chapter talks about how in the event of understanding and discussing how personal genetic traits relate to phenotypic traits, then it is necessary to also explore and understand the consequences of certain interactions between the gene and its environment (G x E). The chapter explores several challenges — many of them unsolved — that have been encountered when exploring and understanding G x E interactions. Its application to personal genomics is still in its early phases, however, and the foremost difficulty is in measuring the dynamic environmental elements and agents — like radiation, pollutants, physical and psychological stressors — that individuals have been exposed to over and over. Measuring these elements over the course of a single day is already overly complex and difficult, and although there are academic discussions and active research programs investigating and developing high-throughput environmental exposure profiling technologies, a more personal measurement is far from being generally available.