结构方程模型在植物功能性状研究中的应用

IF 8.3 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Yihang Zhu, Cong Liu, Changhui Peng, Xiaolu Zhou, Binggeng Xie, Tong Li, Peng Li, Ziying Zou, Jiayi Tang, Zelin Liu
{"title":"结构方程模型在植物功能性状研究中的应用","authors":"Yihang Zhu, Cong Liu, Changhui Peng, Xiaolu Zhou, Binggeng Xie, Tong Li, Peng Li, Ziying Zou, Jiayi Tang, Zelin Liu","doi":"10.1139/er-2023-0128","DOIUrl":null,"url":null,"abstract":"1.Plant functional traits, which encompass morphological, physiological, and ecological characteristics, are key to plant adaptation, growth, and development. In recent years, the structural equation model (SEM) has gained widespread use as a powerful statistical tool for studying plant functional traits and conducting research in this field. Its ability to distinguish between direct and indirect effects makes the SEM a robust method for investigating the complex relationships among environment components, traits and ecosystem functions. 2.Here, we review and discuss four commonly used SEMs: (1) the covariance-based structural equation model, (2) the piecewise structural equation model, (3) the Bayesian structural equation model, and (4) the partial least squares structural equation model. We also explore their applications in three typical ecosystems—forest, grassland, and wetland ecosystems—and investigate these forms of SEM in the context of their use in trait-ecosystem function research. 3.Our specific objectives were to: (i) compare the advantages and disadvantages of these four types of SEMs; (ii) analyze the current state of research on SEM applications in plant functional traits across diverse ecosystems; and (iii) highlight new approaches and potential research areas for the future application of SEM in plant functional traits. 4.In this paper, several key findings were obtained: (i) the selection of SEM type is influenced by the different spatial scales of the study. (ii) latent and composite variables were less commonly utilized in recent SEM studies. (iii) while SEMs have proven effective in distinguishing between direct and indirect effects to unravel the complex relationships among multiple variables, indirect effects deserve more attention in general studies. We propose that future applications of SEMs in plant functional traits should incorporate a broader spectrum of traits as well as the trade-offs between them. Larger and more diverse databases of plant functional traits would help make SEM analyses more accurate across different scales.","PeriodicalId":5,"journal":{"name":"ACS Applied Materials & Interfaces","volume":"119 43","pages":""},"PeriodicalIF":8.3000,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Applications of structural equation modeling in plant functional trait research\",\"authors\":\"Yihang Zhu, Cong Liu, Changhui Peng, Xiaolu Zhou, Binggeng Xie, Tong Li, Peng Li, Ziying Zou, Jiayi Tang, Zelin Liu\",\"doi\":\"10.1139/er-2023-0128\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"1.Plant functional traits, which encompass morphological, physiological, and ecological characteristics, are key to plant adaptation, growth, and development. In recent years, the structural equation model (SEM) has gained widespread use as a powerful statistical tool for studying plant functional traits and conducting research in this field. Its ability to distinguish between direct and indirect effects makes the SEM a robust method for investigating the complex relationships among environment components, traits and ecosystem functions. 2.Here, we review and discuss four commonly used SEMs: (1) the covariance-based structural equation model, (2) the piecewise structural equation model, (3) the Bayesian structural equation model, and (4) the partial least squares structural equation model. We also explore their applications in three typical ecosystems—forest, grassland, and wetland ecosystems—and investigate these forms of SEM in the context of their use in trait-ecosystem function research. 3.Our specific objectives were to: (i) compare the advantages and disadvantages of these four types of SEMs; (ii) analyze the current state of research on SEM applications in plant functional traits across diverse ecosystems; and (iii) highlight new approaches and potential research areas for the future application of SEM in plant functional traits. 4.In this paper, several key findings were obtained: (i) the selection of SEM type is influenced by the different spatial scales of the study. (ii) latent and composite variables were less commonly utilized in recent SEM studies. (iii) while SEMs have proven effective in distinguishing between direct and indirect effects to unravel the complex relationships among multiple variables, indirect effects deserve more attention in general studies. We propose that future applications of SEMs in plant functional traits should incorporate a broader spectrum of traits as well as the trade-offs between them. Larger and more diverse databases of plant functional traits would help make SEM analyses more accurate across different scales.\",\"PeriodicalId\":5,\"journal\":{\"name\":\"ACS Applied Materials & Interfaces\",\"volume\":\"119 43\",\"pages\":\"\"},\"PeriodicalIF\":8.3000,\"publicationDate\":\"2024-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Materials & Interfaces\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1139/er-2023-0128\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Materials & Interfaces","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1139/er-2023-0128","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

1.植物功能性状包括形态、生理和生态特征,是植物适应、生长和发育的关键。近年来,结构方程模型(SEM)作为研究植物功能性状和开展该领域研究的强大统计工具得到了广泛应用。结构方程模型能够区分直接效应和间接效应,是研究环境成分、性状和生态系统功能之间复杂关系的可靠方法。2.在此,我们回顾并讨论了四种常用的 SEM:(1) 基于协方差的结构方程模型;(2) 计件结构方程模型;(3) 贝叶斯结构方程模型;(4) 偏最小二乘结构方程模型。我们还探讨了它们在森林、草地和湿地这三种典型生态系统中的应用,并研究了这些形式的 SEM 在性状-生态系统功能研究中的应用。3.我们的具体目标是3.我们的具体目标是:(i) 比较这四种 SEM 的优缺点;(ii) 分析 SEM 在不同生态系统中应用于植物功能性状的研究现状;(iii) 强调 SEM 未来应用于植物功能性状的新方法和潜在研究领域。4.本文获得了几项重要发现:(i) SEM 类型的选择受不同研究空间尺度的影响。(ii) 在最近的 SEM 研究中,潜变量和复合变量较少使用。(iii) 虽然事实证明 SEM 能有效区分直接效应和间接效应,从而揭示多个变量之间的复杂关系,但在一般研究中,间接效应应得到更多关注。我们建议,今后在植物功能性状中应用 SEM 时,应纳入更广泛的性状以及它们之间的权衡。更大、更多样化的植物功能性状数据库将有助于使 SEM 分析在不同尺度上更加准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applications of structural equation modeling in plant functional trait research
1.Plant functional traits, which encompass morphological, physiological, and ecological characteristics, are key to plant adaptation, growth, and development. In recent years, the structural equation model (SEM) has gained widespread use as a powerful statistical tool for studying plant functional traits and conducting research in this field. Its ability to distinguish between direct and indirect effects makes the SEM a robust method for investigating the complex relationships among environment components, traits and ecosystem functions. 2.Here, we review and discuss four commonly used SEMs: (1) the covariance-based structural equation model, (2) the piecewise structural equation model, (3) the Bayesian structural equation model, and (4) the partial least squares structural equation model. We also explore their applications in three typical ecosystems—forest, grassland, and wetland ecosystems—and investigate these forms of SEM in the context of their use in trait-ecosystem function research. 3.Our specific objectives were to: (i) compare the advantages and disadvantages of these four types of SEMs; (ii) analyze the current state of research on SEM applications in plant functional traits across diverse ecosystems; and (iii) highlight new approaches and potential research areas for the future application of SEM in plant functional traits. 4.In this paper, several key findings were obtained: (i) the selection of SEM type is influenced by the different spatial scales of the study. (ii) latent and composite variables were less commonly utilized in recent SEM studies. (iii) while SEMs have proven effective in distinguishing between direct and indirect effects to unravel the complex relationships among multiple variables, indirect effects deserve more attention in general studies. We propose that future applications of SEMs in plant functional traits should incorporate a broader spectrum of traits as well as the trade-offs between them. Larger and more diverse databases of plant functional traits would help make SEM analyses more accurate across different scales.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
ACS Applied Materials & Interfaces
ACS Applied Materials & Interfaces 工程技术-材料科学:综合
CiteScore
16.00
自引率
6.30%
发文量
4978
审稿时长
1.8 months
期刊介绍: ACS Applied Materials & Interfaces is a leading interdisciplinary journal that brings together chemists, engineers, physicists, and biologists to explore the development and utilization of newly-discovered materials and interfacial processes for specific applications. Our journal has experienced remarkable growth since its establishment in 2009, both in terms of the number of articles published and the impact of the research showcased. We are proud to foster a truly global community, with the majority of published articles originating from outside the United States, reflecting the rapid growth of applied research worldwide.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信