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ON THE ASSIGNMENT OF FITNESS VALUES IN STATISTICAL ANALYSES OF SELECTION
Author(s) -
Brodie Edmund D.,
Janzen Fredric J.
Publication year - 1996
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.1558-5646.1996.tb04505.x
Subject(s) - biology , selection (genetic algorithm) , evolutionary biology , computational biology , statistics , machine learning , mathematics , computer science
tained directional change in the optimum phenotype, a small amount of additive genetic variance always greatly reduces the total genetic load on the population. In contrast, with a cyclically fluctuating optimum, additive genetic variance significantly enhances population mean fitness only when the period of the oscillation is long and the amplitude is large. The situation is similar for positively autocorrelated fluctuations in the optimum: additive genetic variance is most effective in reducing the total load when the fluctuations exhibit a long autocorrelation time and high variance. If one or more types of predictable environmental change occur simultaneously, additive genetic variance is more likely to diminish the total genetic load. Therefore, unless a population can remain in an environment to which it is preadapted by shifting its geographic distribution, for example, through habitat selection behavior (Pease et al. 1989), additive genetic variance and adaptive evolution can be critical for long-term population survival. With increasing habitat fragmentation and geographic isolation of populations caused by human activities, the maintenance of normal levels of additive genetic variance will become increasingly important as a mechanism of adaptation and population persistence in a changing environment.