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Using path analysis to measure natural selection
Author(s) -
Samuel M. Scheiner,
Randall J. Mitchell,
Hilary S. Callahan
Publication year - 2000
Publication title -
journal of evolutionary biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.289
H-Index - 128
eISSN - 1420-9101
pISSN - 1010-061X
DOI - 10.1046/j.1420-9101.2000.00191.x
Subject(s) - selection (genetic algorithm) , directional selection , biology , inflorescence , natural selection , path analysis (statistics) , path (computing) , path coefficient , population , nonlinear system , statistics , evolutionary biology , mathematics , computer science , ecology , machine learning , physics , demography , sociology , programming language , quantum mechanics
We expand current methods for calculating selection coefficients using path analysis and demonstrate how to analyse nonlinear selection. While this incorporation is a straightforward extension of current procedures, the rules for combining these traits to calculate selection coefficients can be complex. We demonstrate our method with an analysis of selection in an experimental population of Arabidopsis thaliana consisting of 289 individuals. Multiple regression analyses found positive directional selection and positive nonlinear selection only for inflorescence height. In contrast, the path analyses also revealed positive directional selection for number of rosette leaves and positive nonlinear selection for leaf number and time of inflorescence initiation. These changes in conclusions came about because indirect selection was converted into direct selection with the change in causal structure. Path analysis has great promise for improving our understanding of natural selection but must be used with caution since coefficient estimates depend on the assumed causal structure.