de Visser, JAGM; Elena, SF; Fragata, I; Matuszewski, S
Introduction: The prospect that we may be able to predict the outcome of future evolutionary processes has motivated recent investigations of the factors that determine this predictability (Lässig et al. 2017). At first sight, predicting evolution may seem an unsurmountable goal, and perhaps even naïve, given the many stochastic factors involved, prominently among them environmental change and the origin of new heritable variants. Yet, the situation is not entirely hopeless, as we can predict general features of evolution, such as the dynamics of adaptation resulting from available genetic variation (Fisher 1930), and the rate of genome evolution in the absence of selection (Kimura 1968). A particularly complicating factor, realized long ago by Sewall Wright (Wright 1932), is that the fitness consequences of mutations may vary in an unpredictable manner across genetic backgrounds due to pervasive epistasis. To understand evolutionary processes in the face of epistasis, Wright introduced the concept of the fitness landscape. In the visually appealing 3-dimensional version of the fitness landscape, epistasis introduces mountain ranges with multiple peaks, each representing an alternative adaptive solution for a genotype in a particular condition, separated by lower-fitness regions. In one way, such rugged landscapes prevent precise evolutionary predictions, since it is impossible to know towards which of the many peaks evolution will head off or which mutational pathways may be more likely than others. However, Weinreich et al. (2006) emphasized that epistasis also reduces the number of mutational pathways natural selection will promote, thus enhancing predictability once evolution has committed to a particular peak (Palmer et al. 2015). In other words, one may predict the outcome, but not the specific pathway, when epistasis is weak. When epistasis is strong (i.e., the landscape highly rugged) it may be more difficult to predict the outcome, but perhaps the evolutionary pathway can be predicted once the approximate direction of evolution becomes clearer (Szendro et al. 2013; Bank et al. 2016). This realization motivated many recent efforts to analyze fitness landscapes empirically, and to study how their topography directs evolution (de Visser and Krug 2014).