Detection of adaptive shifts on phylogenies by using shifted stochastic processes on a tree

• 成果类型: Article
• 署名作者: Bastide, Paul; Mariadassou, Mahendra; Robin, Stephane
• 署名单位: AgroParisTech; Universite Paris Saclay; INRAE; INRAE; Universite Paris Saclay
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12206
• 发表日期: 2017
• 页码: 1067-1093
• 关键词: stabilizing selection; EVOLUTION; MODEL; regression

摘要

Comparative and evolutive ecologists are interested in the distribution of quantitative traits between related species. The classical framework for these distributions consists of a random process running along the branches of a phylogenetic tree relating the species. We consider shifts in the process parameters, which reveal fast adaptation to changes of ecological niches. We show that models with shifts are not identifiable in general. Constraining the models to be parsimonious in the number of shifts partially alleviates the problem but several evolutionary scenarios can still provide the same joint distribution for the extant species. We provide a recursive algorithm to enumerate all the equivalent scenarios and to count the number of effectively different scenarios. We introduce an incomplete-data framework and develop a maximum likelihood estimation procedure based on the expectation-maximization algorithm. Finally, we propose a model selection procedure, based on the cardinal of effective scenarios, to estimate the number of shifts and for which we prove an oracle inequality.