Data from: Fisher's geometrical model of fitness landscape and variance in fitness within a changing environment
Cite this dataset
Zhang, Xu-Sheng (2012). Data from: Fisher's geometrical model of fitness landscape and variance in fitness within a changing environment [Dataset]. Dryad. https://doi.org/10.5061/dryad.6r138b0h
The fitness of an individual can be simply defined as the number of its offspring in the next generation. However, it is not well understood how selection on the phenotype determines fitness. In accordance with Fisher’s fundamental theorem, fitness should have no or very little genetic variance, whereas empirical data suggest that is not the case. To bridge these knowledge gaps, we follow Fisher’s geometrical model and assume that fitness is determined by multivariate stabilizing selection towards an optimum that may vary among generations. We assume random mating, free recombination, additive genes, and uncorrelated stabilizing selection and mutational effects on traits. In a constant environment, we find that genetic variance in fitness under mutation-selection balance is a U-shaped function of the number of traits (i.e. of the so-called “organismal complexity”). Because the variance can be high if the organism is of either low or high complexity, this suggests that complexity has little direct costs. Under a temporally varying optimum, genetic variance increases relative to a constant optimum and increasingly so when the mutation rate is small. Therefore mutation and changing environment together can maintain high genetic variance. These results therefore lend support to Fisher’s geometric model of a fitness landscape.