Borenstein E, Meilijson I, Ruppin E
School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel.
J Evol Biol. 2006 Sep;19(5):1555-70. doi: 10.1111/j.1420-9101.2006.01125.x.
When facing the challenge of developing an individual that best fits its environment, nature demonstrates an interesting combination of two fundamentally different adaptive mechanisms: genetic evolution and phenotypic plasticity. Following numerous computational models, it has become the accepted wisdom that lifetime acclimation (e.g. via learning) smooths the fitness landscape and consequently accelerates evolution. However, analytical studies, focusing on the effect of phenotypic plasticity on evolution in simple unimodal landscapes, have often found that learning hinders the evolutionary process rather than accelerating it. Here, we provide a general framework for studying the effect of plasticity on evolution in multipeaked landscapes and introduce a rigorous mathematical analysis of these dynamics. We show that the convergence rate of the evolutionary process in a given arbitrary one-dimensional fitness landscape is dominated by the largest descent (drawdown) in the landscape and provide numerical evidence to support an analogous dominance also in multidimensional landscapes. We consider several schemes of phenotypic plasticity and examine their effect on the landscape drawdown, identifying the conditions under which phenotypic plasticity is advantageous. The lack of such a drawdown in unimodal landscapes vs. its dominance in multipeaked landscapes accounts for the seemingly contradictory findings of previous studies.
在面对培育最能适应其环境的个体这一挑战时，自然界展现出两种根本不同的适应性机制的有趣组合：基因进化和表型可塑性。继众多计算模型之后，人们普遍认为终身适应（例如通过学习）使适应度景观变得平滑，从而加速进化。然而，专注于简单单峰景观中表型可塑性对进化影响的分析研究常常发现，学习会阻碍而非加速进化过程。在此，我们提供一个用于研究多峰景观中可塑性对进化影响的通用框架，并对这些动态过程进行严格的数学分析。我们表明，在给定的任意一维适应度景观中，进化过程的收敛速度由景观中的最大下降（回撤）主导，并提供数值证据以支持在多维景观中也存在类似的主导情况。我们考虑了几种表型可塑性方案，并研究它们对景观回撤的影响，确定表型可塑性具有优势的条件。单峰景观中不存在这种回撤与多峰景观中其占主导地位，解释了先前研究中看似矛盾的结果。
