This review summarizes the applications of structural equation modeling (SEM) in ecological studies, highlighting its potential and challenges. SEM is a multivariate statistical technique used to test and evaluate complex causal relationships. It combines confirmatory factor analysis and path analysis, allowing researchers to model latent variables and test hypotheses about causal relationships. The review identifies five SEM variants that are underutilized in ecological research: latent growth curve model, Bayesian SEM, partial least squares SEM, hierarchical SEM, and variable/model selection. Common issues in SEM applications include the strength of causal assumptions, specification of feedback loops, model identification, and the interpretation of latent variables. The review also discusses the importance of model fit indices, such as chi-square, RMSEA, CFI, and AIC/BIC, in evaluating model performance. It emphasizes the need for proper model specification, estimation methods, and validation to ensure accurate results. The review highlights the growing use of SEM in ecological studies, but also points out the need for improved data preparation, model validation, and the integration of hypothesis-based and data-driven approaches. The future of SEM in ecological research lies in its ability to handle complex data and provide insights into ecological processes, while also addressing the challenges of model validation and data interpretation. The review concludes that SEM has significant potential in ecological research, but its application requires careful consideration of model specification, validation, and interpretation.This review summarizes the applications of structural equation modeling (SEM) in ecological studies, highlighting its potential and challenges. SEM is a multivariate statistical technique used to test and evaluate complex causal relationships. It combines confirmatory factor analysis and path analysis, allowing researchers to model latent variables and test hypotheses about causal relationships. The review identifies five SEM variants that are underutilized in ecological research: latent growth curve model, Bayesian SEM, partial least squares SEM, hierarchical SEM, and variable/model selection. Common issues in SEM applications include the strength of causal assumptions, specification of feedback loops, model identification, and the interpretation of latent variables. The review also discusses the importance of model fit indices, such as chi-square, RMSEA, CFI, and AIC/BIC, in evaluating model performance. It emphasizes the need for proper model specification, estimation methods, and validation to ensure accurate results. The review highlights the growing use of SEM in ecological studies, but also points out the need for improved data preparation, model validation, and the integration of hypothesis-based and data-driven approaches. The future of SEM in ecological research lies in its ability to handle complex data and provide insights into ecological processes, while also addressing the challenges of model validation and data interpretation. The review concludes that SEM has significant potential in ecological research, but its application requires careful consideration of model specification, validation, and interpretation.