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 r...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 simula- tion Hedges＇ g, a widely used parametric estimate of effect size. Our analyses indicate that g is 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, ＇Aft＇, which is caiculated 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. Ap combines many desirable features of other distance metrics into a single metric; namely, compared to other metrics, p is relatively insensitive to unequal variances and sample sizes among the populations sam- pied. Furthermore, a key feature of Ap--and our main motivation for developing it--is that it easily accommodates simultaneous comparisons of any number of traits across any number of populations. To exemplify its utility, we employ Ap to address a ques- tion 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 [Current Zoology 58 （3）： 426-439 2012].展开更多
基金Acknowlegements We thank Matthew Arnegard, Carlos Botero, Tamra Mendelson, Rafael Rodriqu6z and Sander van Doom for excellent discussions about the need for a new phenotypic distance metric and Maria Servedio for the invitation and encouragement to formalize our ideas. This research was supported as part of the Sexual Selection and Speciation working group by the National Evolutionary Synthesis Center （NESCent）, NSF #EF-0905606. RJS and SMF were supported by the University of Colorado and National Science Founda- tion grant IOS-0717421to RJS. MK was supported by a grant from the Vienna Science and Technology Fund （WWTF） to the Mathematics and Biosciences Group at the University of Vienna. EAH thanks Mitch Bern for use of his Master＇s thesis data and was supported by the National Science Foundation grant lOS - 0643179. DEI and DPLT were supported by the Natural Sciences and Engineering Research Council of Can- ada （Discovery Grants 311931-2005 and 311931-2010 to DEI, CGS-D to DPLT）. NS and JAT were supported by the Royal Society, British Ecological Society and John Fell Fund （Ox- ford University）. ES supported by NSF-DDIG the American Ornithologists Union, the University of Chicago, and the American Philosophical Society Lewis and Clark award. JACU was funded by National Science Foundation grant lOS 0306175.
文摘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 simula- tion Hedges＇ g, a widely used parametric estimate of effect size. Our analyses indicate that g is 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, ＇Aft＇, which is caiculated 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. Ap combines many desirable features of other distance metrics into a single metric; namely, compared to other metrics, p is relatively insensitive to unequal variances and sample sizes among the populations sam- pied. Furthermore, a key feature of Ap--and our main motivation for developing it--is that it easily accommodates simultaneous comparisons of any number of traits across any number of populations. To exemplify its utility, we employ Ap to address a ques- tion 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 [Current Zoology 58 （3）： 426-439 2012].
