AI Article Synopsis

  • Studies on spatial variation in the environment mainly explore how genetic diversity is maintained, but traditional one-locus models don't apply well to complex traits that vary continuously.
  • The text discusses how quantitative genetic models help understand phenotypic plasticity—the ability of organisms to change their phenotype in response to environmental conditions—and how selection can affect the norm of reaction, which describes mean phenotypic traits across different environments.
  • It highlights the importance of genetic correlations for the evolution of traits over time, noting that under certain conditions, these correlations can hinder or delay reaching optimal trait adaptations in varying environments, emphasizing the differences between hard and soft selection in evolutionary processes.

Article Abstract

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.

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Source
http://dx.doi.org/10.1111/j.1558-5646.1985.tb00391.xDOI Listing

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