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将化石纳入树蕨目Cyatheales 的系统发育和生物地理学联合推断中。

Incorporating fossils into the joint inference of phylogeny and biogeography of the tree fern order Cyatheales.

机构信息

Departamento de Botánica, Instituto de Biología, Universidad Nacional Autónoma de México, Ciudad de México, México.

出版信息

Evolution. 2024 May 1;78(5):919-933. doi: 10.1093/evolut/qpae034.

DOI:10.1093/evolut/qpae034
PMID:38437579
Abstract

Present-day geographic and phylogenetic patterns often reflect the geological and climatic history of the planet. Neontological distribution data are often sufficient to unravel a lineage's biogeographic history, yet ancestral range inferences can be at odds with fossil evidence. Here, I use the fossilized birth-death process and the dispersal-extinction cladogenesis model to jointly infer the dated phylogeny and range evolution of the tree fern order Cyatheales. I use data for 101 fossil and 442 extant tree ferns to reconstruct the biogeographic history of the group over the last 220 million years. Fossil-aware reconstructions evince a prolonged occupancy of Laurasia over the Triassic-Cretaceous by Cyathealean tree ferns, which is evident in the fossil record but hidden from analyses relying on neontological data alone. Nonetheless, fossil-aware reconstructions are affected by uncertainty in fossils' phylogenetic placement, taphonomic biases, and specimen sampling and are sensitive to interpretation of paleodistributions and how these are scored. The present results highlight the need and challenges of incorporating fossils into joint inferences of phylogeny and biogeography to improve the reliability of ancestral geographic range estimation.

摘要

现今的地理和系统发育格局往往反映了地球的地质和气候历史。新生分布数据通常足以揭示谱系的生物地理历史,但祖先范围推断可能与化石证据存在矛盾。在这里,我使用了化石化的生死过程和扩散-灭绝 cladogenesis 模型,共同推断树蕨目 Cyatheales 的有时间分辨率的系统发育和范围演化。我使用了 101 个化石和 442 个现存树蕨的数据,来重建该组在过去 2.2 亿年中的生物地理历史。化石感知重建表明,在三叠纪-白垩纪期间,树蕨目 Cyatheales 在劳亚古陆的占据时间很长,这在化石记录中是明显的,但仅依靠新生数据的分析则无法发现。尽管如此,化石感知重建受到化石系统发育位置的不确定性、埋藏偏差以及标本采样的影响,并且对古分布的解释以及如何对其进行评分很敏感。目前的结果强调了在系统发育和生物地理学的联合推断中纳入化石的必要性和挑战,以提高祖先地理范围估计的可靠性。

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