Spatially explicit structural equation modeling

• Published in: Ecology (Durham) Vol. 95; no. 9; pp. 2434 - 2442
• Main Authors: Lamb, Eric G, Mengersen, Kerrie L, Stewart, Katherine J, Attanayake, Udayanga, Siciliano, Steven D
• Format: Journal Article
• Language: English
• Published: Brooklyn Ecological Society of America 01-09-2014
• Subjects: Bryophytes; community structure; computer software; covariance; data collection; Datasets; Ecological modeling; environmental factors; lag distance; Landscape ecology; Lichens; Lowlands; Mathematical models; Modeling; nitrogen fixation; plant communities; plant competition; Plant ecology; Software packages; Soil ecology; Soil water; spatial correlation; spatial ecological analysis; spatial environment-ecological response relationships; Spatial models; Statistical methods; structural equation modeling; Variables; variance-covariance matrices
• ISSN: 0012-9658; 1939-9170
• DOI: 10.1890/13-1997.1

Structural equation modeling (SEM) is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with intercorrelated dependent and independent variables. SEM is commonly applied in ecology, but the spatial information commonly found in ecological data remains difficult to model in a SEM framework. Here we propose a simple method for spatially explicit SEM (SE-SEM) based on the analysis of variance/covariance matrices calculated across a range of lag distances. This method provides readily interpretable plots of the change in path coefficients across scale and can be implemented using any standard SEM software package. We demonstrate the application of this method using three studies examining the relationships between environmental factors, plant community structure, nitrogen fixation, and plant competition. By design, these data sets had a spatial component, but were previously analyzed using standard SEM models. Using these data sets, we demonstrate the application of SE-SEM to regularly spaced, irregularly spaced, and ad hoc spatial sampling designs and discuss the increased inferential capability of this approach compared with standard SEM. We provide an R package, sesem, to easily implement spatial structural equation modeling.