Data from: Probabilistic divergence time estimation without branch lengths: dating the origins of dinosaurs, avian flight and crown birds

Branch lengths—measured in character changes—are an essential requirement
of clock-based divergence estimation, regardless of whether the fossil
calibrations used represent nodes or tips. However, a separate set of
divergence time approaches are typically used to date palaeontological
trees, which may lack such branch lengths. Among these methods,
sophisticated probabilistic approaches have recently emerged, in contrast
with simpler algorithms relying on minimum node ages. Here, using a novel
phylogenetic hypothesis for Mesozoic dinosaurs, we apply two such
approaches to estimate divergence times for: (i) Dinosauria, (ii) Avialae
(the earliest birds) and (iii) Neornithes (crown birds). We find: (i) the
plausibility of a Permian origin for dinosaurs to be dependent on whether
Nyasasaurus is the oldest dinosaur, (ii) a Middle to Late Jurassic origin
of avian flight regardless of whether Archaeopteryx or Aurornis is
considered the first bird and (iii) a Late Cretaceous origin for
Neornithes that is broadly congruent with other node- and tip-dating
estimates. Demonstrating the feasibility of probabilistic time-scaling
further opens up divergence estimation to the rich histories of extinct
biodiversity in the fossil record, even in the absence of detailed
character data.

以性状变化量为计量标准的分支长度，是基于演化时钟的分化时间估算的必备前提，无论所使用的化石校准（fossil calibrations）对应的是系统发育节点还是末端类群。然而，古生物树（palaeontological trees）往往并不具备此类分支长度，因此学界通常会采用另一套分化时间估算方法对其进行定年。在这些方法中，相较于依赖最小节点年龄的简易算法，近年来涌现出了更为精密的概率化估算方法。本研究基于中生代恐龙（Mesozoic dinosaurs）的全新系统发育假说（phylogenetic hypothesis），采用上述两类方法对三大类群的分化时间进行估算：(1) 恐龙总目（Dinosauria）；(2) 鸟翼类（Avialae，即最早的鸟类）；(3) 今鸟亚纲（Neornithes，即冠群鸟类）。本研究结果显示：(1) 恐龙类群起源于二叠纪（Permian）的合理性，取决于尼雅萨龙（Nyasasaurus）是否为已知最古老的恐龙；(2) 鸟类飞行能力的起源时间不晚于中侏罗世至晚侏罗世，无论将始祖鸟（Archaeopteryx）还是奥龙鸟（Aurornis）认定为最早的鸟类；(3) 今鸟亚纲的起源时间为白垩纪晚期，这一结果与其他基于节点和末端类群校准的估算结果大体一致。本次研究证实了概率化时间校准的可行性，这一成果进一步拓展了分化时间估算的应用范围，使其可依托化石记录中丰富的已灭绝生物多样性历史展开，即便缺乏详尽的性状数据亦可实现。