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Exploiting natural history variation: looking to fishes for quantitative genetic models of natural populations
Author(s) -
Morrissey Michael B.
Publication year - 2011
Publication title -
ecology of freshwater fish
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.667
H-Index - 55
eISSN - 1600-0633
pISSN - 0906-6691
DOI - 10.1111/j.1600-0633.2010.00445.x
Subject(s) - microevolution , biology , quantitative genetics , genetic variation , phenotypic plasticity , evolutionary biology , adaptation (eye) , variation (astronomy) , natural selection , ecology , selection (genetic algorithm) , population , gene , genetics , computer science , machine learning , demography , physics , neuroscience , sociology , astrophysics
Abstract – Quantitative genetic techniques are contributing greatly to our understanding of evolutionary processes in the wild. However, the vast majority of applications of quantitative genetic methodologies to natural populations have been conducted in very narrow ranges of the ‘life history space’ expressed by organisms in nature. Recent and ongoing technological and analytical advances are making expansion of the taxonomic scope of such studies both possible and highly desirable. I review a number of ways in which fishes can be exploited to better understand the phenotypic expression of genetic variation in the wild. In particular, I argue that the culturability of fishes can be exploited in the execution of study designs where pedigrees can be manipulated, while maintaining the ecological relevance of phenotypic and genetic variation as expressed in the wild. In general, the model organisms which are currently most intensively studied in a quantitative genetic framework in the wild are not easily culturable. Thus, at least among vertebrates, the proposed approaches are uniquely suited to fishes. I highlight how fishes may provide particularly good models with which to study the genetic basis of variation in phenotypic plasticity, the genetic basis of the environmental dependence of genetic parameters and to detect adaptive phenotypic microevolution.