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ESTIMATING NONLINEAR SELECTION GRADIENTS USING QUADRATIC REGRESSION COEFFICIENTS: DOUBLE OR NOTHING?
Article first published online: 4 JUL 2008
DOI: 10.1111/j.1558-5646.2008.00449.x
© 2008 The Author(s). Journal compilation © 2008 The Society for the Study of Evolution
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How to Cite
Stinchcombe, J. R., Agrawal, A. F., Hohenlohe, P. A., Arnold, S. J. and Blows, M. W. (2008), ESTIMATING NONLINEAR SELECTION GRADIENTS USING QUADRATIC REGRESSION COEFFICIENTS: DOUBLE OR NOTHING?. Evolution, 62: 2435–2440. doi: 10.1111/j.1558-5646.2008.00449.x
Publication History
- Issue published online: 3 SEP 2008
- Article first published online: 4 JUL 2008
- Received February 29, 2008Accepted June 2, 2008
- Abstract
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Keywords:
- Adaptive landscape;
- canonical analysis;
- correlational selection;
- disruptive selection;
- fitness surface;
- nonlinear selection;
- stabilizing selection
The use of regression analysis has been instrumental in allowing evolutionary biologists to estimate the strength and mode of natural selection. Although directional and correlational selection gradients are equal to their corresponding regression coefficients, quadratic regression coefficients must be doubled to estimate stabilizing/disruptive selection gradients. Based on a sample of 33 papers published in Evolution between 2002 and 2007, at least 78% of papers have not doubled quadratic regression coefficients, leading to an appreciable underestimate of the strength of stabilizing and disruptive selection. Proper treatment of quadratic regression coefficients is necessary for estimation of fitness surfaces and contour plots, canonical analysis of the γ matrix, and modeling the evolution of populations on an adaptive landscape.