Bryant David, Francis Andrew, Steel Mike
Computational Modeling, Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand.
Centre for Research in Mathematics, School of Computing, Engineering and Mathematics, Western Sydney University, Sydney, Australia.
Syst Biol. 2017 Jul 1;66(4):611-619. doi: 10.1093/sysbio/syx030.
Consensus methods are widely used for combining phylogenetic trees into a single estimate of the evolutionary tree for a group of species. As more taxa are added, the new source trees may begin to tell a different evolutionary story when restricted to the original set of taxa. However, if the new trees, restricted to the original set of taxa, were to agree exactly with the earlier trees, then we might hope that their consensus would either agree with or resolve the original consensus tree. In this article, we ask under what conditions consensus methods exist that are "future proof" in this sense. While we show that some methods (e.g., Adams consensus) have this property for specific types of input, we also establish a rather surprising "no-go" theorem: there is no "reasonable" consensus method that satisfies the future-proofing property in general. We then investigate a second notion of "future proofing" for consensus methods, in which trees (rather than taxa) are added, and establish some positive and negative results. We end with some questions for future work.
共识方法被广泛用于将系统发育树合并为一组物种进化树的单一估计。随着更多分类单元的加入,当新的源树仅限于原始分类单元集时,可能会开始讲述不同的进化故事。然而,如果仅限于原始分类单元集的新树与早期的树完全一致,那么我们可能希望它们的共识要么与原始共识树一致,要么解决原始共识树。在本文中,我们探讨在何种条件下存在从这个意义上来说“面向未来”的共识方法。虽然我们表明某些方法(例如,亚当斯共识)对于特定类型的输入具有此属性,但我们也建立了一个相当令人惊讶的“不可行”定理:一般来说,不存在满足面向未来属性的“合理”共识方法。然后,我们研究了共识方法的另一种“面向未来”的概念,即在其中添加树(而不是分类单元),并建立了一些正面和负面的结果。最后我们提出了一些未来工作的问题。