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

Abstract

Aims: 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. Methods: 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.Results: 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. Conclusions: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.

Seasonal Controls of CO2 and CH4 Dynamics in a Temporarily Flooded Subtropical Wetland

“…Path analysis is a structural equation model widely used in ecology to investigate complex ecological networks of relationships (Eisenhauer et al, ; Fan et al, ; Grace et al, ). As suggested previously (Grace, ) structural equation models exceed the descriptive nature of statistical methods such as simple and multiple regressions in that it quantifies the relationships among multiple variables and multivariate hypotheses (i.e., it can analyze models that are more complex than a multiple regression), partitions hypothesized direct and indirect effects, and makes distinction of the multiple pathways by which one entity can influence another.…”

Grazing enhances soil nutrient effects: Trade‐offs between aboveground and belowground biomass in alpine grasslands of the Tibetan Plateau

“…Structural equation modeling (SEM) is a common multivariate technique that has been increasingly used in scientific investigations to analyze and evaluate multivariate causal relationships. SEM differs from other modeling approaches as it tests both the direct and indirect effects on preassumed causal relationships (Fan et al, ). The initial step in a SEM application is to select the appropriate variables.…”

Glomalin contributed more to carbon, nutrients in deeper soils, and differently associated with climates and soil properties in vertical profiles

“…Structural equation modeling (SEM) is a powerful, multivariate technique found increasingly in scientific investigations to test and evaluate multivariate causal relationships 25 , and powerful in soil ecology from patterns to causalunderstanding 26 . Pearson correlation and stepwise regression are common-used methods for finding complex associations and possible casual relations 27–30 .…”