This review aims to introduce the essential components and variants of structural equation modeling (SEM) and discuss common issues in its applications, particularly in ecological studies. The authors searched the Web of Science for publications on SEM in ecology from 1999 to 2016 and identified 146 relevant studies. They found that five SEM variants—latent growth curve model, Bayesian SEM, partial least square SEM, hierarchical SEM, and variable/model selection—had not been widely applied in ecology. The review also identified ten common issues in SEM applications, including the strength of causal assumptions, specification of feedback loops, selection of models and variables, identification of models, methods of estimation, explanation of latent variables, selection of fit indices, reporting of results, estimation of sample size, and model fit. The authors conclude that while SEM has great potential in ecological research, there are significant challenges in its application, such as the lack of reporting on necessary procedures, parameters, and indices. They suggest that future research should focus on improving data preparation protocols and combining hypothesis-based models with data-driven approaches to address these challenges.This review aims to introduce the essential components and variants of structural equation modeling (SEM) and discuss common issues in its applications, particularly in ecological studies. The authors searched the Web of Science for publications on SEM in ecology from 1999 to 2016 and identified 146 relevant studies. They found that five SEM variants—latent growth curve model, Bayesian SEM, partial least square SEM, hierarchical SEM, and variable/model selection—had not been widely applied in ecology. The review also identified ten common issues in SEM applications, including the strength of causal assumptions, specification of feedback loops, selection of models and variables, identification of models, methods of estimation, explanation of latent variables, selection of fit indices, reporting of results, estimation of sample size, and model fit. The authors conclude that while SEM has great potential in ecological research, there are significant challenges in its application, such as the lack of reporting on necessary procedures, parameters, and indices. They suggest that future research should focus on improving data preparation protocols and combining hypothesis-based models with data-driven approaches to address these challenges.