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A robust new metric of phenotypic distance to estimate and compare multiple trait differences among populations

Current Zoology 2012年第3期58卷 426-439页

作者： Rebecca SAFRAN Samuel FLAXMAN Michael KOPP Darren E. IRWIN Derek BRIGGS Matthew R. EVANS W. Chris FUNK David A. GRAY Eileen A. HEBETS nathalie seddon Elizabeth SCORDATO Laurel B. SYMES Joseph A. TOBIAS David P. L. TOEWS J. Albert C. UY Department of Ecology and Evolutionary Biology University of Colorado Boulder CO 80309 U.S.A. Mathematics and B iosciences Group Faculty of Mathematics University of Vienna A-1090 Vienna Austria Current address Evolutionary Biology and Modeling Group Faculty of Sciences Aix-Marseille University 13331 Marseille France. Department of Zoology University of British Columbia 6270 University Boulevard Vancouver BC Canada V6T 1Z4. Research ＆ Evaluation Methodology School of Education University of Colorado Boulder CO 80309 U.S.A. School of Biological and Chemical Sciences Queen Mary University of London London E1 4NS Department of Biology Colorado State University Fort Collins CO 80523 U.S.A. Department of Biology California State University Northridge CA 91330 U.S.A. School of Biological Sciences University of Nebraska Lincoln NE 68588 U.S.A. Edward Grey Institute Department of Zoology University of Oxford Oxford OX1 3PS UK Committee on Evolutionary Biology The University of Chicago Chicago IL 60637U.S.A. Department of Biology Dartmouth College Hanover NH 03755 U.S.A. Department of Biology University of Miami Coral Gables FL 33146 U.S.A.

Whereas a rich literature exists for estimating population genetic divergence, metrics of phenotypic trait divergence are lacking, particularly for comparing multiple traits among three or more populations. Here, we review and analyze via simulation Hedges’g, a widely used parametric estimate of effect size. Our analyses indicate thatgis sensitive to a combination of unequal trait variances and unequal sample sizes among populations and to changes in the scale of measurement. We then go on to derive and explain a new, non-parametric distance measure, “Δp”, which is calculated based upon a joint cumulative distribution function (CDF) from all populations under study. More precisely,distances are measured in terms of the percentiles in this CDF at which each population’s median lies. Δpcombines many desirable features of other distance metrics into a single metric; namely, compared to other metrics,pis relatively insensitive to unequal variances and sample sizes among the populations sampled. Furthermore, a key feature of Δp—and our main motivation for developing it—is that it easily accommodatessimultaneous comparisons of any number of traits across any number of populations. To exemplify its utility, we employ Δpto address a question related to the role of sexual selection in speciation: are sexual signals more divergent than ecological traits in closely related taxa? Using traits of known function in closely related populations, we show that traits predictive of reproductive performance are, indeed, more divergent and more sexually dimorphic than traits related to ecological adaptation.

关键词：距离估计距离度量表型性状多性状群体累积分布函数样本大小生态适应性

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