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

作　　者：Zhe Cai Song Ge 

机构地区：[1]State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China [2]University of Chinese Academy of Sciences, Beijing 100049, China

出　　处：《Journal of Systematics and Evolution》2017年第4期377-384,共8页植物分类学报（英文版）

基　　金：We thank Yu-Fei Wang, Wen-Li Chen, and members of the Ge laboratory for their valuable discussions and suggestions. We also thank the International Rice Research Institute （Los Banos, Philippines） for providing seed samples of some Oryzeae species. This work was supported by the National Natural Science Foundation of China （Grant Nos. 91231201 and 3o990240） and the CAS/SAFEA International Partnership Program for Creative Research Teams.

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 lOOO 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.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 lOOO 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. 

分 类 号：Q949.714.2[生物学—植物学] TP181[自动化与计算机技术—控制理论与控制工程]