Adding Dynamical Sufficiency to Fisher's Fundamental Theorem of Natural Selection

Fisher's "fundamental theorem of natural selection" is famously robust and versatile but, frustratingly, it is dynamically insufficient: it can only predict how fitness will evolve over the course of a single generation. Consequently, his theorem has not been used for predicting the course of evolution but instead for detecting natural selection. His theorem employs only one facet of the fitness distribution, namely, the variance, and thus does not take advantage of all the available information. The theory we have developed shares some common ground with Fisher's theorem, but takes full advantage of the available information by employing the higher moments of the fitness distribution in addition to the variance. The theory developed here suggests: 1) a way to non-parametrically infer the distribution of mutational effects that feeds ongoing evolution, and 2) a framework for predicting fitness (and fitness-related) evolution.