Applications of structural equation modeling in plant functional trait research

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.