Current progress and future prospects in phylofloristics

被引:12
|
作者
Li, Rong [1 ]
Qian, Lishen [1 ,2 ]
Sun, Hang [1 ]
机构
[1] Chinese Acad Sci, Key Lab Plant Div & Biogeog East Asia, Kunming Inst Bot, Kunming 650201, Yunnan, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
基金
中国国家自然科学基金;
关键词
Big data; Biogeography; Ecology; Evolution; Flora; Mega-phylogeny; PHYLOGENETIC BETA DIVERSITY; SPECIES RICHNESS; EVOLUTIONARY PROCESSES; ECOLOGICAL COMMUNITIES; SPATIAL PHYLOGENETICS; FUNCTIONAL DIVERSITY; BIODIVERSITY HOTSPOT; HENGDUAN MOUNTAINS; VASCULAR FLORA; CONSERVATION;
D O I
10.1016/j.pld.2018.07.003
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
The species composition of regional plant assemblages can in large part be explained by a long history of biogeographical and evolutionary events. Traditional attempts of floristic studies typically focus on the analyses of taxonomic composition, often ignoring the rich context that evolutionary history can provide. In 2014, Swenson and Umana introduced the term 'phylofloristics' to define a phylogenetically enabled analysis of the species composition of regional floras. Integrating phylogenetic information into traditional floristic analysis can provide a promising way to explore the ecological, biogeographic, and evolutionary processes that drive plant assemblies at multiple spatial scales. In this review, we summarize the current progress on the phylogenetic structure, spatial phylogenetic pattern, origin and diversification, phylogenetic regionalization of floristic assemblages, and application of phylogenetic information in biodiversity conservation. These summaries highlight the importance of incorporating phylogenetic information to improve our understanding of floristic assembly from an evolutionary perspective. The review ends with a brief outlook on future challenges for phylofloristic studies, including generating a highly resolved species-level phylogenetic tree, compiling detailed and refined information regarding the geographic distribution of all plant life, extracting trait information from publications and herbarium specimens, and developing technological and methodological approaches for big data analysis. Copyright (C) 2018 Kunming Institute of Botany, Chinese Academy of Sciences. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd.
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页码:141 / 146
页数:6
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