REDUCING ENVIRONMENTAL BIAS WHEN MEASURING NATURAL SELECTION
Crucial to understanding the process of natural selection is characterizing phenotypic selection. Measures of phenotypic selection can be biased by environmental variation among individuals that causes a spurious correlation between a trait and fitness. One solution is analyzing genotypic data, rath...
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Published in | Evolution Vol. 56; no. 11; pp. 2156 - 2167 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Society for the Study of Evolution
01.11.2002
Oxford University Press |
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Abstract | Crucial to understanding the process of natural selection is characterizing phenotypic selection. Measures of phenotypic selection can be biased by environmental variation among individuals that causes a spurious correlation between a trait and fitness. One solution is analyzing genotypic data, rather than phenotypic data. Genotypic data, however, are difficult to gather, can be gathered from few species, and typically have low statistical power. Environmental correlations may act through traits other than through fitness itself. A path analytic framework, which includes measures of such traits, may reduce environmental bias in estimates of selection coefficients. We tested the efficacy of path analysis to reduce bias by re-analyzing three experiments where both phenotypic and genotypic data were available. All three consisted of plant species (Impatiens capensis, Arabidopsis thaliana, and Raphanus sativus) grown in experimental plots or the greenhouse. We found that selection coefficients estimated by path analysis using phenotypic data were highly correlated with those based on genotypic data with little systematic bias in estimating the strength of selection. Although not a panacea, using path analysis can substantially reduce environmental biases in estimates of selection coefficients. Such confidence in phenotypic selection estimates is critical for progress in the study of natural selection. |
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AbstractList | Crucial to understanding the process of natural selection is characterizing phenotypic selection. Measures of phenotypic selection can be biased by environmental variation among individuals that causes a spurious correlation between a trait and fitness. One solution is analyzing genotypic data, rather than phenotypic data. Genotypic data, however, are difficult to gather, can be gathered from few species, and typically have low statistical power. Environmental correlations may act through traits other than through fitness itself. A path analytic framework, which includes measures of such traits, may reduce environmental bias in estimates of selection coefficients. We tested the efficacy of path analysis to reduce bias by re-analyzing three experiments where both phenotypic and genotypic data were available. All three consisted of plant species (Impatiens capensis, Arabidopsis thaliana, and Raphanus sativus) grown in experimental plots or the greenhouse. We found that selection coefficients estimated by path analysis using phenotypic data were highly correlated with those based on genotypic data with little systematic bias in estimating the strength of selection. Although not a panacea, using path analysis can substantially reduce environmental biases in estimates of selection coefficients. Such confidence in phenotypic selection estimates is critical for progress in the study of natural selection. |
Author | Mazer, Susan J Dorn, Lisa A Donohue, Kathleen Scheiner, Samuel M Wolfe, Lorne M |
Author_xml | – sequence: 1 givenname: Samuel M surname: Scheiner fullname: Scheiner, Samuel M email: sscheine@nsf.gov organization: Division of Environmental Biology, Room 635, National Science Foundation, 4201 Wilson Boulevard, Arlington, Virginia 22230 – sequence: 2 givenname: Kathleen surname: Donohue fullname: Donohue, Kathleen email: kdonohue@oeb.harvard.edu organization: Department of Ecology and Evolutionary Biology, Box G-W, Brown University, Providence, Rhode Island 02912 – sequence: 3 givenname: Lisa A surname: Dorn fullname: Dorn, Lisa A email: Lisa_Dorn@brown.edu organization: Department of Ecology and Evolutionary Biology, Box G-W, Brown University, Providence, Rhode Island 02912 – sequence: 4 givenname: Susan J surname: Mazer fullname: Mazer, Susan J email: mazer@lifesci.lscf.ucsb.edu organization: Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, California 93106 – sequence: 5 givenname: Lorne M surname: Wolfe fullname: Wolfe, Lorne M email: wolfe@gasou.edu organization: Department of Biology, Georgia Southern University, Statesboro, Georgia 30460 |
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SubjectTerms | Analysis Analytical estimating Arabidopsis - genetics Arabidopsis thaliana Correlations Ecological competition Environment Estimation bias Evolution Genes, Plant Genotype Impatiens - genetics Impatiens capensis Models, Genetic Multiple regression Natural selection path analysis Phenotype Phenotypic selection Phenotypic traits Pollen Raphanus - genetics Raphanus sativus regression analysis REGULAR ARTICLES selection gradient Selection, Genetic Statistics as Topic |
Title | REDUCING ENVIRONMENTAL BIAS WHEN MEASURING NATURAL SELECTION |
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