| Here, z is the phenotype of an individual, theta_t is the optimum at time t, and the "variance" sigma^2 measures the width of the fitness function (i.e., selection is strong sigma^2 is small). Here and later on, our notation mainly follows ... (Bürger and Lynch 1995?). Frequently, it is assumed that, at time 0, z = theta_0, that is, the population is perfectly adapted before the optimum stops moving. Movement of the optimum is typically assumed to the linear, potentially accompanied by stochastic white noise: | Here, z is the phenotype of an individual, theta_t is the optimum at time t, and the "variance" sigma^2 measures the width of the fitness function (i.e., selection is strong sigma^2 is small). Here and later on, our notation mainly follows ... (Bürger and Lynch 1995?). Frequently, it is assumed that, at time 0, z = theta_0, that is, the population is perfectly adapted before the optimum starts moving. Movement of the optimum is typically assumed to be linear, potentially accompanied by stochastic white noise: |