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FISHER'S GEOMETRIC MODEL WITH A MOVING OPTIMUM

机译:具有最优运动的费希尔几何模型

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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 (1) 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; (2) 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; (3) in contrast to classical models of adaptation assuming a constant optimum, with a moving optimum, more complex organisms evolve via larger adaptive steps.
机译:Fisher的几何模型已被广泛用于研究多效性和生物复杂性对表型适应的影响。在这里,我们研究了费舍尔模型的一种版本,其中种群适应了逐渐移动的最优值。关键参数是环境变化率,表型空间的维数以及突变和选择相关性的模式。我们专注于自适应取代的分布,即固定有益突变的表型效应的多元分布。我们的主要结果基于“自适应行走近似”,并与基于个体的模拟进行了比较。我们发现(1)适应性替代的分布受到生态动力学的强烈影响,并且很大程度上取决于单个综合参数γ,该参数通过人口的“适应潜力”来缩放环境变化的速率; (2)如果环境变化缓慢,适应性替代的分布反映了健身景观的形状,而如果环境变化较快,则反映了新突变的分布; (3)与经典的适应模型相反,它假设一个恒定的最优值,随着运动的最优值,更复杂的生物体通过更大的适应步长进化。

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