DOI: 10.5061/dryad.534f0
title: Data from: Fisher's geometric model with a moving optimum

description: Fisher's geometric model has been widely used to study the effects of
 pleiotropy and organismic complexity on phenotypic adaptation. Here, we
 study a version of Fisher's model in which a population adapts to a
 gradually moving optimum. Key parameters are the rate of environmental
 change, the dimensionality of phenotype space, and the patterns of
 mutational and selectional correlations. We focus on the distribution of
 adaptive substitutions, that is, the multivariate distribution of the
 phenotypic effects of fixed beneficial mutations. Our main results are
 based on an “adaptive-walk approximation”, which is checked against
 individual-based simulations. We find that (i) the distribution of
 adaptive substitutions is strongly affected by the ecological dynamics and
 largely depends on a single composite parameter γ, which scales the rate
 of environmental change by the “adaptive potential” of the population;
 (ii) the distribution of adaptive substitution reflects the shape of the
 fitness landscape if the environment changes slowly, whereas it mirrors
 the distribution of new mutations if the environment changes fast; (iii)
 in contrast to classical models of adaptation assuming a constant optimum,
 with a moving optimum, more complex organisms evolve via larger adaptive
 steps.
description: SimulateMovingOptimumC++ code used for "adaptive-walk" simulations of the multidimensional moving-optimum model. In these simulations, the population is assumed to be monomorphic at all times, and new mutations are immediately lost or fixed. As a result, adaptation proceeds as a series of discrete "steps". Adaptive-walk simulations are much faster than individual-based simulations, and are more closely related to the analytical results in the paper. Similar assumptions have been used in other models of the genetics of adaptation (Gillespie, Orr...), and have been justified by the so-called strong-selection-weak-mutation assumption.MovingOptimum_IndBasedC++ code used for individual-based simulations of the multidimensional moving-optimum model. These simulations implement all model assumptions (i.e., they represent the "full" model). In particular, they keep track of the action of selection, recombination and mutation on multilocus genotypes in a finite population. Individual-based simulations are, thus, realistic (but also more time-consuming) than "adaptive-walk simulations" (see SimulateMovingOptimum.cpp).IndividualBased_SimulationDataSimulation data from the individual-based simulation procedure described in the 'Model and Methods' section (paragraph 'Individual-based simulations'). Corresponding simulation program: MovingOptimum_IndBased.cpp. ReadMe files are contained within the .zip archive.IndividualBasedDATA.zipMonteCarlo_SimulationDataSimulation data from the Monte-Carlo simulation procedure described in the 'Model and Methods' section (paragraph 'The adaptive-walk approximation'). Corresponding simulation program: SimulateMovingOptimum.cpp. ReadMe files are contained within the .zip archive.MonteCarloDATA.zipMathematica_SimulationDataSimulation data from the analytical results (using Mathematica) as described in the 'Supporting Information 4'. ReadMe files are contained within the .zip archive.