Linking watershed-scale stream health and socioeconomic indicators with spatial clustering and structural equation modeling. (August 2015)

• Record Type: Journal Article
• Authors: Sanchez, Georgina M.; Nejadhashemi, A. Pouyan; Zhang, Zhen; Marquart-Pyatt, Sandra; Habron, Geoffrey; Shortridge, Ashton
• Is Part Of: Environmental modelling & software. Volume 70(2015:Aug.)
• Page Start: 113
• Page End: 127
• Publication Date: 2015-08
• Subjects: Spatial cluster -- Conditional autoregressive models -- Saginaw River Watershed -- Stream health indicator -- Socioeconomic -- Natural-human system; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.04.012
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: In this study, spatial clustering techniques were used in combination with Structural Equation Modeling (SEM) to characterize the relationships between in-stream health indicators and socioeconomic measures of communities. The study area is the Saginaw River Watershed in Michigan. Four measures of stream health were considered: the Index of Biological Integrity, Hilsenhoff Biotic Index, Family Index of Biological Integrity, and number of Ephemeroptera, Plecoptera, and Trichoptera taxa. The stream health indicators were predicted using nine socioeconomic variables that capture vulnerability in population. The results of spatial clustering showed that incorporating clustering configuration improves the model prediction. A total of 510 Confirmatory Factor Analysis (CFAs) and 85 multivariate regression models were developed for each spatial cluster within the watershed and compared with the model performance without spatial clustering (at the watershed level). In general, watershed level CFAs outperformed cluster level CFAs, while the reverse was true for the regression models. Highlights: Relationships between stream health and socioeconomic measures were studied. Spatial clustering techniques and Structural Equation Modeling were integrated. Spatial clustering improved regression model prediction. Confirmatory Factor Analysis performed well at the watershed level.