Unification of regression-based methods for the analysis of natural selection

Michael Blair Morrissey, Krzysztof Sakrejda

Research output: Contribution to journalArticlepeer-review

73 Citations (Scopus)


Regression analyses are central to characterization of the form and strength of natural selection in nature. Two common analyses that are currently used to characterize selection are (1) least squares–based approximation of the individual relative fitness surface for the purpose of obtaining quantitatively useful selection gradients, and (2) spline-based estimation of (absolute) fitness functions to obtain flexible inference of the shape of functions by which fitness and phenotype are related. These two sets of methodologies are often implemented in parallel to provide complementary inferences of the form of natural selection. We unify these two analyses, providing a method whereby selection gradients can be obtained for a given observed distribution of phenotype and characterization of a function relating phenotype to fitness. The method allows quantitatively useful selection gradients to be obtained from analyses of selection that adequately model nonnormal distributions of fitness, and provides unification of the two previously separate regression-based fitness analyses. We demonstrate the method by calculating directional and quadratic selection gradients associated with a smooth regression-based generalized additive model of the relationship between neonatal survival and the phenotypic traits of gestation length and birth mass in humans.
Original languageEnglish
Pages (from-to)2094-2100
Issue number7
Early online date21 Mar 2013
Publication statusPublished - Jul 2013


  • Birth mass;fitness;generalized additive models;gestation length;microevolution;natural selection;selection gradients;sexual selection


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