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THE POPULATION GENETICS OF ADAPTATION ON CORRELATED FITNESS LANDSCAPES: THE BLOCK MODEL
Author(s) -
Orr H. Allen
Publication year - 2006
Publication title -
evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.84
H-Index - 199
eISSN - 1558-5646
pISSN - 0014-3820
DOI - 10.1111/j.0014-3820.2006.tb01191.x
Subject(s) - fitness landscape , adaptation (eye) , biology , genetic fitness , local adaptation , block (permutation group theory) , evolutionary biology , population , population genetics , neutral network , ecology , genetics , computer science , biological evolution , artificial intelligence , mathematics , demography , geometry , neuroscience , sociology , artificial neural network
Several recent theoretical studies of the genetics of adaptation have focused on the mutational landscape model, which considers evolution on rugged fitness landscapes (i.e., ones having many local optima). Adaptation in this model is characterized by several simple results. Here I ask whether these results also hold on correlated fitness landscapes, which are smoother than those considered in the mutational landscape model. In particular, I study the genetics of adaptation in the block model, a tunably rugged model of fitness landscapes. Considering the scenario in which adaptation begins from a high fitness wild‐type DNA sequence, I use extreme value theory and computer simulations to study both single adaptive steps and entire adaptive walks. I show that all previous results characterizing single steps in adaptation in the mutational landscape model hold at least approximately on correlated landscapes in the block model; many entire‐walk results, however, do not.