five

Character evolution and missing (morphological) data across Asteridae

收藏
NIAID Data Ecosystem2026-03-11 收录
下载链接:
http://datadryad.org/dataset/doi%253A10.5061%252Fdryad.7783j
下载链接
链接失效反馈
官方服务:
资源简介:
Premise of the study: Our current understanding of flowering plant phylogeny provides an excellent framework for exploring various aspects of character evolution through comparative analyses. However, attempts to synthesize this phylogenetic framework with extensive morphological datasets have been surprisingly rare. Here, we explore character evolution in Asteridae (asterids), a major angiosperm clade, using an extensive morphological data set and a well-resolved phylogeny. Methods: We scored 15 phenotypic characters (spanning chemistry, vegetative anatomy, and floral, fruit, and seed features) across 248 species for ancestral state reconstruction using a phylogenetic framework based on 73 plastid genes and the same 248 species. Key results: Iridoid production, unitegmic ovules, and cellular endosperm were all reconstructed as synapomorphic for Asteridae. Sympetaly, long associated with asterids, shows complex patterns of evolution, suggesting it arose several times independently within the clade. Stamens equal in number to the petals is likely a synapomorphy for Gentianidae, a major asterid subclade. Members of Lamianae, a major gentianid subclade, are potentially diagnosed by adnate stamens, unilacunar nodes, and simple perforation plates. Conclusions: The analyses presented here provide a greatly improved understanding of character evolution across Asteridae, highlighting multiple characters potentially synapomorphic for major clades. However, several important parts of the asterid tree are poorly known for several key phenotypic features (e.g., degree of petal fusion, integument number, nucellus type, endosperm type, iridoid production). Further morphological, anatomical, developmental, and chemical investigations of these poorly known asterids are critical for a more detailed understanding of early asterid evolution.
创建时间:
2019-03-21
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作