Contributors:Rose Danya, Kim Peter, Hawkes Kristen
Archive of Matlab code developed for our paper on the use of the adult sex ratio (ASR) as an index for male mating strategy, including plots and Matlab data file.
Of particular note, twelve PNG formatted image files (each containing six sub-plots) indicating contours of constant ASR and strategy outcome for various parameter choices (including variation of initial conditions). Images 5-8 constitute figures 4-7 in our paper.
Contributors:Pearce, Michael T., Fisher, Daniel S.
Genetic exchange in microbes and other facultative sexuals can be rare enough that evolution is almost entirely asexual and populations almost clonal. But the benefits of genetic exchange depend crucially on the diversity of genotypes in a population. How very rare recombination together with the accumulation of new mutations shapes the diversity of large populations and gives rise to faster adaptation is still poorly understood. This paper analyzes a particularly simple model: organisms with two asexual chromosomes that can reassort during rare matings that occur at a rate r. The speed of adaptation for large population sizes, N, is found to depend on the ratio ∼ log(Nr)/log(N). For larger populations, the r needed to yield the same speed deceases as a power of N. Remarkably, the population undergoes spontaneous oscillations alternating between phases when the fittest individuals are created by mutation and when they are created by reassortment, which—in contrast to conventional regimes—decreases the diversity. Between the two phases, the mean fitness jumps rapidly. The oscillatory dynamics and the strong fluctuations this induces have implications for the diversity and coalescent statistics. The results are potentially applicable to large microbial populations, especially viruses that have a small number of chromosomes. Some of the key features may be more broadly applicable for large populations with other types of rare genetic exchange.,Simulations of two chromosome evolutionary dynamicsMatlab files for simulations for the rapid adaptation of individuals with two chromosomes and occasional reassortment. Both stochastic and deterministic simulations are included. Also included are files that were used to visualize the dynamics and make several plots for the associated paper.two_chromosomes_simulations.zip,
In finite populations, mutation limitation and genetic drift can hinder evolutionary diversification. We consider the evolution of a quantitative trait in an asexual population whose size can vary and depends explicitly on the trait. Previous work showed that evolutionary branching is certain (“deterministic branching”) above a threshold population size, but uncertain (“stochastic branching”) below it. Using the stationary distribution of the population’s trait variance, we identify three qualitatively different sub-domains of “stochastic branching” and illustrate our results using a model of social evolution. We find that in very small populations, branching will almost never be observed; in intermediate populations, branching is intermittent, arising and disappearing over time; in larger populations, finally, branching is expected to occur and persist for substantial periods of time. Our study provides a clearer picture of the ecological conditions that facilitate the appearance and persistence of novel evolutionary lineages in the face of genetic drift.,Analysis and simulation codeZip file includes three directories. (1) Mathematica code to derive the main results. (2) R code to generate the figures. (3) C code to run the simulations.codes.zip,
Contributors:Boettiger, Carl, Dushoff, Jonathan, Weitz, Joshua S.
We derive an expression for the variation between parallel trajectories in phenotypic evolution, extending the well known result that predicts the mean evolutionary path in adaptive dynamics or quantitative genetics. We show how this expression gives rise to the notion of fluctuation domains–parts of the fitness landscape where the rate of evolution is very predictable (due to fluctuation dissipation) and parts where it is highly variable (due to fluctuation enhancement). These fluctuation domains are determined by the curvature of the fitness landscape. Regions of the fitness landscape with positive curvature, such as adaptive valleys or branching points, experience enhancement. Regions with negative curvature, such as adaptive peaks, experience dissipation. We explore these dynamics in the ecological scenarios of implicit and explicit competition for a limiting resource.,fluctuationDomainsR package containing the software used to simulate, analyze, and visualize the data considered in this publication.,