Machine learning algorithms improve the power of phytolith analysis: A case study of the tribe Oryzeae (Poaceae)

doi:10.1080/17538947.2016.1227380

	Machine learning algorithms improve the power of phytolith analysis: A case study of the tribe Oryzeae (Poaceae)
	Cai, Zhe 1; Ge, Song1
	2017
发表期刊	JOURNAL OF SYSTEMATICS AND EVOLUTION
ISSN	1674-4918
卷号	55 期号:4 页码:377-384
摘要	Phytoliths, as one of the important sources of microfossils, have been widely used in paleobotany-related studies, especially in the grass family (Poaceae) where abundant phytoliths are found. Despite great efforts, several challenges remain when phytoliths are used in various studies, including the accurate description of phytolith morphology and the effective utilization of phytolith traits in taxon identification or discrimination. In this study, we analyzed over 1000 phytolith samples from 18 taxa representing seven main genera in the tribe Oryzeae (subfamily Ehrhartoideae) and five taxa in the subfamilies Bambusoideae and Pooideae. By focusing on Oryzeae, which has been extensively investigated in terms of taxonomy and phylogeny, we were able to evaluate the discrimination power of phytoliths at lower taxonomic levels in grasses. With the help of morphometric analysis and by introducing several machine learning algorithms, we found that 87.7% of the phytolith samples could be classified correctly at the genus level. In spite of slightly different performances, all four machine learning algorithms significantly increased the resolving power of phytolith evidence in taxon identification and discrimination compared with the traditional phytolith analysis. Therefore, we propose a pipeline of phytolith analyses based on machine learning algorithms, including data collection, morphometric analysis, model building, and taxon discrimination. The methodology and pipeline presented here should be applied to various studies across different groups of plants. This study provides new insights into the utilization of phytoliths in evolutionary and ecology studies involving grasses and plants in general.
关键词	machine learning morphological character phytolith Poaceae taxon discrimination
学科领域	Physical Geography ; Remote Sensing
DOI	10.1080/17538947.2016.1227380
收录类别	SCI
语种	英语
WOS关键词	CLIMATE-CHANGE ; GRASSES ; RICE ; PHYLOGENY ; SHAPE ; CLASSIFICATION ; MORPHOMETRICS ; DIRECTIONS ; RADIATION ; SEQUENCES
WOS记录号	WOS:000395038600005
出版者	WILEY
Special Issue	SI
文献子类	Article; Proceedings Paper
出版地	HOBOKEN
EISSN	1759-6831
资助机构	National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [91231201, 30990240] ; CAS/SAFEA International Partnership Program for Creative Research TeamsChinese Academy of Sciences
作者邮箱	gesong@ibcas.ac.cn
引用统计	被引频次：28[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ibcas.ac.cn/handle/2S10CLM1/22141
专题	系统与进化植物学国家重点实验室
作者单位	1.Chinese Acad Sci, Inst Bot, State Key Lab Systemat & Evolutionary Bot, Beijing 100093, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Cai, Zhe,Ge, Song. Machine learning algorithms improve the power of phytolith analysis: A case study of the tribe Oryzeae (Poaceae)[J]. JOURNAL OF SYSTEMATICS AND EVOLUTION,2017,55(4):377-384.
APA	Cai, Zhe,&Ge, Song.(2017).Machine learning algorithms improve the power of phytolith analysis: A case study of the tribe Oryzeae (Poaceae).JOURNAL OF SYSTEMATICS AND EVOLUTION,55(4),377-384.
MLA	Cai, Zhe,et al."Machine learning algorithms improve the power of phytolith analysis: A case study of the tribe Oryzeae (Poaceae)".JOURNAL OF SYSTEMATICS AND EVOLUTION 55.4(2017):377-384.

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