title: Power and potential bias in field studies of natural selection.
abstract: The advent of multiple regression analyses of natural selection has facilitated estimates of both the direct and indirect effects of selection on many traits in numerous organisms. However, low power in selection studies has possibly led to a bias in our assessment of the levels of selection shaping natural populations. Using calculations and simulations based on the statistical properties of selection coefficients, we find that power to detect total selection (the selection differential) depends on sample size and the strength of selection relative to the opportunity of selection. The power of detecting direct selection (selection gradients) is more complicated and depends on the relationship between the correlation of each trait and fitness and the pattern of correlation among traits. In a review of 298 previously published selection differentials, we find that most studies have had insufficient power to detect reported levels of selection acting on traits and that, in general, the power of detecting weak levels of selection is low given current study designs. We also find that potential publication bias could explain the trend that reported levels of direct selection tend to decrease as study sizes increase, suggesting that current views of the strength of selection may be inaccurate and biased upward. We suggest that studies should be designed so that selection is analyzed on at least several hundred individuals, the total opportunity of selection be considered along with the pattern of selection on individual traits, and nonsignificant results be actively reported combined with an estimate of power.
authors.affiliation: Center for Ecology and Evolutionary Biology, University of Oregon, Eugene, Oregon 97405-5289, USA. ehersch@uoregon.edu
authors.forename: Erika I
authors.initials: EI
authors.lastname: Hersch
authors.forename: Patrick C
authors.initials: PC
authors.lastname: Phillips
mesh_terms: D005828:Genetics, Population; D008954:Models, Biological; D012044:Regression Analysis; D012107:Research Design; D018401:Sample Size; D012641:Selection, Genetic