Via Sara, Lande Russell
Department of Biology, The University of Chicago, Chicago, IL, 60637.
Evolution. 1985 May;39(3):505-522. doi: 10.1111/j.1558-5646.1985.tb00391.x.
Studies of spatial variation in the environment have primarily focused on how genetic variation can be maintained. Many one-locus genetic models have addressed this issue, but, for several reasons, these models are not directly applicable to quantitative (polygenic) traits. One reason is that for continuously varying characters, the evolution of the mean phenotype expressed in different environments (the norm of reaction) is also of interest. Our quantitative genetic models describe the evolution of phenotypic response to the environment, also known as phenotypic plasticity (Gause, 1947), and illustrate how the norm of reaction (Schmalhausen, 1949) can be shaped by selection. These models utilize the statistical relationship which exists between genotype-environment interaction and genetic correlation to describe evolution of the mean phenotype under soft and hard selection in coarse-grained environments. Just as genetic correlations among characters within a single environment can constrain the response to simultaneous selection, so can a genetic correlation between states of a character which are expressed in two environments. Unless the genetic correlation across environments is ± 1, polygenic variation is exhausted, or there is a cost to plasticity, panmictic populations under a bivariate fitness function will eventually attain the optimum mean phenotype for a given character in each environment. However, very high positive or negative correlations can substantially slow the rate of evolution and may produce temporary maladaptation in one environment before the optimum joint phenotype is finally attained. Evolutionary trajectories under hard and soft selection can differ: in hard selection, the environments with the highest initial mean fitness contribute most individuals to the mating pool. In both hard and soft selection, evolution toward the optimum in a rare environment is much slower than it is in a common one. A subdivided population model reveals that migration restriction can facilitate local adaptation. However, unless there is no migration or one of the special cases discussed for panmictic populations holds, no geographical variation in the norm of reaction will be maintained at equilibrium. Implications of these results for the interpretation of spatial patterns of phenotypic variation in natural populations are discussed.
对环境空间变异的研究主要集中在如何维持遗传变异上。许多单基因座遗传模型都探讨了这个问题，但由于多种原因，这些模型并不直接适用于数量（多基因）性状。一个原因是，对于连续变化的性状，在不同环境中表达的平均表型（反应规范）的进化也很重要。我们的数量遗传模型描述了对环境的表型反应的进化，也称为表型可塑性（高斯，1947），并说明了反应规范（施马尔豪森，1949）是如何通过选择形成的。这些模型利用基因型 - 环境相互作用与遗传相关性之间存在的统计关系，来描述在粗粒度环境中软选择和硬选择下平均表型的进化。正如单一环境中性状之间的遗传相关性会限制对同时选择的反应一样，在两种环境中表达的同一性状状态之间的遗传相关性也会如此。除非跨环境的遗传相关性为±1、多基因变异耗尽，或者可塑性存在代价，否则在双变量适合度函数下的随机交配种群最终将在每个环境中达到给定性状的最佳平均表型。然而，非常高的正相关或负相关会大大减缓进化速度，并可能在最终达到最佳联合表型之前在一个环境中产生暂时的适应不良。硬选择和软选择下的进化轨迹可能不同：在硬选择中，初始平均适合度最高的环境为交配库贡献的个体最多。在硬选择和软选择中，在稀有环境中向最优状态的进化都比在常见环境中慢得多。一个细分种群模型表明，迁移限制可以促进局部适应。然而，除非没有迁移或随机交配种群所讨论的特殊情况之一成立，否则在平衡状态下反应规范不会维持地理变异。本文讨论了这些结果对解释自然种群中表型变异空间模式的意义。
