Literature DB >> 28561964

GENOTYPE-ENVIRONMENT INTERACTION AND THE EVOLUTION OF PHENOTYPIC PLASTICITY.

Sara Via1, Russell Lande1.   

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. © 1985 The Society for the Study of Evolution.

Year:  1985        PMID: 28561964     DOI: 10.1111/j.1558-5646.1985.tb00391.x

Source DB:  PubMed          Journal:  Evolution        ISSN: 0014-3820            Impact factor:   3.694


  207 in total

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Journal:  Oecologia       Date:  2004-12-15       Impact factor: 3.225

5.  Interaction between maternal effects and temperature affects diapause occurrence in the cricket Allonemobius socius.

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Journal:  Oecologia       Date:  2005-08-26       Impact factor: 3.225

6.  How does resource supply affect evolutionary diversification?

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Journal:  Proc Biol Sci       Date:  2007-01-07       Impact factor: 5.349

7.  Plastic responses to temporal variation in moisture availability: consequences for water use efficiency and plant performance.

Authors:  Joshua J Picotte; David M Rosenthal; Jennifer M Rhode; Mitchell B Cruzan
Journal:  Oecologia       Date:  2007-07-17       Impact factor: 3.225

8.  Unexpected patterns of plastic energy allocation in stochastic environments.

Authors:  Barbara Fischer; Barbara Taborsky; Ulf Dieckmann
Journal:  Am Nat       Date:  2009-03       Impact factor: 3.926

9.  Quantitative trait loci affecting phenotypic plasticity and the allometric relationship of ovariole number and thorax length in Drosophila melanogaster.

Authors:  Alan O Bergland; Anne Genissel; Sergey V Nuzhdin; Marc Tatar
Journal:  Genetics       Date:  2008-08-20       Impact factor: 4.562

10.  Potential constraints on evolution: sexual dimorphism and the problem of protandry in the butterfly Bicyclus anynana.

Authors:  Bas J Zwaan; Wilte G Zijlstra; Marieke Keller; Jeroen Pijpe; Paul M Brakefield
Journal:  J Genet       Date:  2008-12       Impact factor: 1.166

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