摘要翻译：
适应变化的环境是生物系统的一个特征。性状的多样性对适应是必要的，并可能影响面对新奇事物的种群的生存。在许多世代保持稳定的生境中，稳定的选择减少了种群内的性状差异，从而似乎消除了在新环境中遗传适应性反应所需的多样性。矛盾的是，野外研究记录了许多种群在长期的稳定选择和进化停滞下，在变化的环境条件下迅速进化。本文综述了隐秘遗传变异(CGV)是如何解决这一多样性悖论的，它允许处于稳定环境中的群体逐渐积累隐藏的遗传多样性，这种多样性在环境变化时表现为性状差异。而不是在冲突中，环境停滞支持CGV积累，因此似乎有助于快速适应新的环境，正如最近的CGV研究所表明的。同样，退化被发现支持遗传和非遗传适应在生物组织的许多层次上。退化，与多样性或冗余性相反，在某些环境背景下，整体在功能上是冗余的，但在其他环境背景下，整体在功能上是多样性的。CGV和退化模式的适应是综合在这篇综述中，揭示了一套共同的原则，支持适应在多个层次的生物组织。通过对模拟研究、基于分子的实验系统、群体遗传学原理和野外实验的讨论，我们论证了CGV和退化分别反映了自上而下和自下而上对同一基本现象的互补概念化，可以说捕捉到了生物适应过程的普遍特征。
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英文标题：
《The Diversity Paradox: How Nature Resolves an Evolutionary Dilemma》
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作者：
James M. Whitacre and Sergei P. Atamas
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最新提交年份：
2011
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分类信息：

一级分类：Physics        物理学
二级分类：Adaptation and Self-Organizing Systems        自适应和自组织系统
分类描述：Adaptation, self-organizing systems, statistical physics, fluctuating systems, stochastic processes, interacting particle systems, machine learning
自适应，自组织系统，统计物理，波动系统，随机过程，相互作用粒子系统，机器学习
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Quantitative Biology        数量生物学
二级分类：Populations and Evolution        种群与进化
分类描述：Population dynamics, spatio-temporal and epidemiological models, dynamic speciation, co-evolution, biodiversity, foodwebs, aging; molecular evolution and phylogeny; directed evolution; origin of life
种群动力学；时空和流行病学模型；动态物种形成；协同进化；生物多样性；食物网；老龄化；分子进化和系统发育；定向进化；生命起源
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英文摘要：
  Adaptation to changing environments is a hallmark of biological systems. Diversity in traits is necessary for adaptation and can influence the survival of a population faced with novelty. In habitats that remain stable over many generations, stabilizing selection reduces trait differences within populations, thereby appearing to remove the diversity needed for heritable adaptive responses in new environments. Paradoxically, field studies have documented numerous populations under long periods of stabilizing selection and evolutionary stasis that have rapidly evolved under changed environmental conditions. In this article, we review how cryptic genetic variation (CGV) resolves this diversity paradox by allowing populations in a stable environment to gradually accumulate hidden genetic diversity that is revealed as trait differences when environments change. Instead of being in conflict, environmental stasis supports CGV accumulation and thus appears to facilitate rapid adaptation in new environments as suggested by recent CGV studies. Similarly, degeneracy has been found to support both genetic and non-genetic adaptation at many levels of biological organization. Degenerate, as opposed to diverse or redundant, ensembles appear functionally redundant in certain environmental contexts but functionally diverse in others. CGV and degeneracy paradigms for adaptation are integrated in this review, revealing a common set of principles that support adaptation at multiple levels of biological organization. Though a discussion of simulation studies, molecular-based experimental systems, principles from population genetics, and field experiments, we demonstrate that CGV and degeneracy reflect complementary top-down and bottom-up, respectively, conceptualizations of the same basic phenomenon and arguably capture a universal feature of biological adaptive processes.
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PDF链接：
https://arxiv.org/pdf/1112.3115