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尖端的进化:萝藦科系统基因组学与叶片表面的新视角。

Evolution at the tips: Asclepias phylogenomics and new perspectives on leaf surfaces.

机构信息

Department of Plant Biology, Ecology & Evolution, Oklahoma State University, Stillwater, OK, 74078, USA.

Department of Biology, Hobart and William Smith Colleges, Geneva, NY, 14456, USA.

出版信息

Am J Bot. 2018 Mar;105(3):514-524. doi: 10.1002/ajb2.1062. Epub 2018 Apr 25.

DOI:10.1002/ajb2.1062
PMID:29693728
Abstract

PREMISE OF THE STUDY

Leaf surface traits, such as trichome density and wax production, mediate important ecological processes such as anti-herbivory defense and water-use efficiency. We present a phylogenetic analysis of Asclepias plastomes as a framework for analyzing the evolution of trichome density and presence of epicuticular waxes.

METHODS

We produced a maximum-likelihood phylogeny using plastomes of 103 species of Asclepias. We reconstructed ancestral states and used model comparisons in a likelihood framework to analyze character evolution across Asclepias.

KEY RESULTS

We resolved the backbone of Asclepias, placing the Sonoran Desert clade and Incarnatae clade as successive sisters to the remaining species. We present novel findings about leaf surface evolution of Asclepias-the ancestor is reconstructed as waxless and sparsely hairy, a macroevolutionary optimal trichome density is supported, and the rate of evolution of trichome density has accelerated.

CONCLUSIONS

Increased sampling and selection of best-fitting models of evolution provide more resolved and robust estimates of phylogeny and character evolution than obtained in previous studies. Evolutionary inferences are more sensitive to character coding than model selection.

摘要

研究前提

叶片表面特征,如毛密度和蜡质产生,介导着重要的生态过程,如抗食草防御和水利用效率。我们提出了一个Asclepias 质体的系统发育分析,作为分析毛密度和表皮蜡质存在进化的框架。

方法

我们使用 103 种 Asclepias 的质体产生了最大似然系统发育。我们重建了祖先状态,并在似然框架中使用模型比较来分析 Asclepias 中的特征进化。

主要结果

我们解决了 Asclepias 的骨干问题,将索诺兰沙漠群和 incarnatae 群作为剩余物种的连续姐妹群。我们提出了关于 Asclepias 叶片表面进化的新发现——祖先被重建为无蜡质和稀疏的毛,支持宏观进化的最佳毛密度,并且毛密度的进化速度已经加快。

结论

增加采样和选择最佳进化模型比以前的研究提供了更具分辨率和稳健性的系统发育和特征进化估计。进化推断比模型选择对特征编码更敏感。

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