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Regression Analysis of Natural Selection: Statistical Inference and Biological Interpretation
- Source :
- Evolution. 41:1149
- Publication Year :
- 1987
- Publisher :
- Oxford University Press (OUP), 1987.
-
Abstract
- Recent theoretical work in quantitative genetics has fueled interest in measuring natural selection in the wild. We discuss statistical and biological issues that may arise in applications of Lande and Arnold's (1983) multiple-regression approach to measuring selection. We review assumptions involved in estimation and hypothesis testing in regression problems, and we note difficulties that frequently arise as a result of violation of these assumptions. In particular, multicollinearity (extreme intercorrelation of characters) and extrinsic, unmeasured factors affecting fitness may seriously complicate inference regarding selection. Further, violation of the assumption that residuals are normally distributed vitiates tests of significance. For this situation, we suggest applications of recently developed jackknife tests of significance. While fitness regression permits direct assessment of selection in a form suitable for predicting selection response, we suggest that the aim of inferring causal relationships about the effects of phenotypic characters on fitness is greatly facilitated by manipulative experiments. Finally, we discuss alternative definitions of stabilizing and disruptive selection.
- Subjects :
- 0106 biological sciences
0301 basic medicine
Natural selection
Disruptive selection
Inference
Biology
010603 evolutionary biology
01 natural sciences
03 medical and health sciences
Predictive inference
030104 developmental biology
Evolutionary biology
Frequentist inference
Econometrics
Statistical inference
Genetics
General Agricultural and Biological Sciences
Selection (genetic algorithm)
Ecology, Evolution, Behavior and Systematics
Statistical hypothesis testing
Subjects
Details
- ISSN :
- 00143820
- Volume :
- 41
- Database :
- OpenAIRE
- Journal :
- Evolution
- Accession number :
- edsair.doi.dedup.....c9f96d7e62a95787c073efbd511cc4bc