Applications of structural equation modeling (SEM) in ecological studies: an updated review

TLDR

The growing availability of ecological data gives SEM immense potential for future research. This review introduces SEM components and variants, synthesizes common application issues, and outlines future ecological uses. We searched Web of Science for SEM studies in ecology from 1999 to 2016, summarized their potential, analyzed common methodological problems, and proposed directions for future work. We identified 146 relevant papers, found five rarely used SEM variants, highlighted ten common issues, and noted that latent variable measurement, parameter explanation, and key statistics reporting were often omitted while advanced applications were largely ignored.

Abstract

This review was developed to introduce the essential components and variants of structural equation modeling (SEM), synthesize the common issues in SEM applications, and share our views on SEM's future in ecological research. We searched the Web of Science on SEM applications in ecological studies from 1999 through 2016 and summarized the potential of SEMs, with a special focus on unexplored uses in ecology. We also analyzed and discussed the common issues with SEM applications in previous publications and presented our view for its future applications. We searched and found 146 relevant publications on SEM applications in ecological studies. We found that five SEM variants had not commenly been applied in ecology, including the latent growth curve model, Bayesian SEM, partial least square SEM, hierarchical SEM, and variable/model selection. We identified ten common issues in SEM applications including strength of causal assumption, specification of feedback loops, selection of models and variables, identification of models, methods of estimation, explanation of latent variables, selection of fit indices, report of results, estimation of sample size, and the fit of model. In previous ecological studies, measurements of latent variables, explanations of model parameters, and reports of key statistics were commonly overlooked, while several advanced uses of SEM had been ignored overall. With the increasing availability of data, the use of SEM holds immense potential for ecologists in the future.