Mitchell Jonathan S
Committee on Evolutionary Biology, University of Chicago, 5034 S. Woodlawn, Chicago, Illinois, 60615.
Ecology and Evolutionary Biology Department, The University of Michigan, 2019 Kraus Nat. Sci. Bldg, 830 N. University, Ann Arbor, Michigan, 48109.
Evolution. 2015 Sep;69(9):2414-24. doi: 10.1111/evo.12738. Epub 2015 Aug 24.
Most extant species are in clades with poor fossil records, and recent studies of comparative methods show they have low power to infer even highly simplified models of trait evolution without fossil data. Birds are a well-studied radiation, yet their early evolutionary patterns are still contentious. The fossil record suggests that birds underwent a rapid ecological radiation after the end-Cretaceous mass extinction, and several smaller, subsequent radiations. This hypothesized series of repeated radiations from fossil data is difficult to test using extant data alone. By uniting morphological and phylogenetic data on 604 extant genera of birds with morphological data on 58 species of extinct birds from 50 million years ago, the "halfway point" of avian evolution, I have been able to test how well extant-only methods predict the diversity of fossil forms. All extant-only methods underestimate the disparity, although the ratio of within- to between-clade disparity does suggest high early rates. The failure of standard models to predict high early disparity suggests that recent radiations are obscuring deep time patterns in the evolution of birds. Metrics from different models can be used in conjunction to provide more valuable insights than simply finding the model with the highest relative fit.
大多数现存物种所在的进化枝化石记录匮乏,最近的比较方法研究表明,在没有化石数据的情况下,即使是推断高度简化的性状进化模型,这些方法的效力也很低。鸟类是一个经过充分研究的辐射类群,但其早期进化模式仍存在争议。化石记录表明,鸟类在白垩纪末大灭绝之后经历了一次快速的生态辐射,以及随后几次规模较小的辐射。仅依据现存数据,很难检验从化石数据中推测出的这一系列重复辐射假说。通过整合604个现存鸟类属的形态学和系统发育数据,以及来自5000万年前鸟类进化“中间点”的58种已灭绝鸟类的形态学数据,我得以检验仅基于现存数据的方法对化石形态多样性的预测能力。所有仅基于现存数据的方法都低估了差异,尽管分支内差异与分支间差异的比率确实表明早期速率很高。标准模型未能预测出早期的高差异,这表明近期的辐射正在掩盖鸟类进化中的深层时间模式。不同模型的指标可以结合使用,以提供比单纯找出拟合度最高的模型更有价值的见解。