{"title":"简单模型下分类特征的相关演化。","authors":"Michael C Grundler","doi":"10.1093/evolut/qpae166","DOIUrl":null,"url":null,"abstract":"<p><p>I describe a simple model for quantifying the strength of association between two categorical characters evolving on a phylogenetic tree. The model can be used to estimate a correlation statistic that asks whether or not the two characters tend to change at the same time (positive correlation) or at different times (no correlation). This is different than asking if changes in one character are associated with a particular state in another character, which has been the focus of most prior tests for phylogenetic correlation in categorical characters. Analyses of simulated data indicate that positive correlations can be accurately estimated over a range of different tree sizes and phylogenetic signals.</p>","PeriodicalId":12082,"journal":{"name":"Evolution","volume":" ","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Correlated evolution of categorical characters under a simple model.\",\"authors\":\"Michael C Grundler\",\"doi\":\"10.1093/evolut/qpae166\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>I describe a simple model for quantifying the strength of association between two categorical characters evolving on a phylogenetic tree. The model can be used to estimate a correlation statistic that asks whether or not the two characters tend to change at the same time (positive correlation) or at different times (no correlation). This is different than asking if changes in one character are associated with a particular state in another character, which has been the focus of most prior tests for phylogenetic correlation in categorical characters. Analyses of simulated data indicate that positive correlations can be accurately estimated over a range of different tree sizes and phylogenetic signals.</p>\",\"PeriodicalId\":12082,\"journal\":{\"name\":\"Evolution\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Evolution\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1093/evolut/qpae166\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Evolution","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1093/evolut/qpae166","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECOLOGY","Score":null,"Total":0}
Correlated evolution of categorical characters under a simple model.
I describe a simple model for quantifying the strength of association between two categorical characters evolving on a phylogenetic tree. The model can be used to estimate a correlation statistic that asks whether or not the two characters tend to change at the same time (positive correlation) or at different times (no correlation). This is different than asking if changes in one character are associated with a particular state in another character, which has been the focus of most prior tests for phylogenetic correlation in categorical characters. Analyses of simulated data indicate that positive correlations can be accurately estimated over a range of different tree sizes and phylogenetic signals.
期刊介绍:
Evolution, published for the Society for the Study of Evolution, is the premier publication devoted to the study of organic evolution and the integration of the various fields of science concerned with evolution. The journal presents significant and original results that extend our understanding of evolutionary phenomena and processes.