Wiens John J
Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York 11794-5245, USA.
Syst Biol. 2005 Oct;54(5):731-42. doi: 10.1080/10635150500234583.
Taxon sampling may be critically important for phylogenetic accuracy because adding taxa can help to subdivide misleading long branches. Although the idea that added taxa can break up long branches was exemplified by a study of "incomplete" fossil taxa, the issue of taxon completeness (i.e., proportion of missing data) has been largely ignored in most subsequent discussions of taxon sampling and long-branch attraction. In this article, I use simulations to test the ability of incomplete taxa to subdivide long branches and improve phylogenetic accuracy in situations of potential long-branch attraction. The results show that for most methods and conditions examined, adding taxa that are only 50% complete may provide similar benefits to adding the same number of complete taxa (suggesting that the advantages of increased taxon sampling may be obtained with less data than previously considered). For parsimony, taxa that are less complete (5% to 25% complete) may often have limited ability to rescue analyses from long-branch attraction. In contrast, highly incomplete taxa can be surprisingly beneficial when using model-based methods. The results also suggest the importance of model-based methods in phylogenetic analyses that combine molecular and fossil data.
分类群抽样对于系统发育准确性可能至关重要，因为增加分类群有助于细分误导性的长分支。尽管增加分类群可拆分长分支这一观点在一项关于“不完整”化石分类群的研究中得到了例证，但在随后大多数关于分类群抽样和长分支吸引的讨论中，分类群完整性问题（即缺失数据的比例）在很大程度上被忽视了。在本文中，我使用模拟来测试不完整分类群在潜在长分支吸引情况下细分长分支并提高系统发育准确性的能力。结果表明，对于所研究的大多数方法和条件，添加仅50%完整的分类群可能与添加相同数量的完整分类群具有相似的益处（这表明增加分类群抽样的优势可能用比之前认为的更少的数据就能获得）。对于简约法，完整性较低（5%至25%完整）的分类群通常在从长分支吸引中挽救分析方面能力有限。相比之下，当使用基于模型的方法时，高度不完整的分类群可能会带来惊人的益处。结果还表明了基于模型的方法在结合分子和化石数据的系统发育分析中的重要性。
