A practical method to control spatiotemporal confounding in environmental impact studies.

Area of Science:

• Environmental Science
• Ecology
• Statistical Modeling

Background:

• Distinguishing human impacts from natural spatiotemporal variation is crucial for environmental studies.
• Existing statistical methods often struggle to isolate causal effects in complex ecosystems.

Purpose of the Study:

• To develop and apply a causal modeling framework for inferring the effects of wastewater on biotic ecosystems.
• To integrate structural equation modeling (SEM) and Bayesian Networks (BNs) for robust causal inference.
• To control for spatiotemporal confounding in environmental impact assessments.

Main Methods:

• Utilized causal modeling techniques: structural equation modeling (SEM) for deterministic models and Bayesian Networks (BNs) for hypothesis testing.
• Integrated SEM with statistical analyses (multivariate redundancy analysis, multiple regression, ordination).
• Incorporated spatiotemporal data, adjusting for 'time' and 'distance' to control confounding.

Main Results:

• The developed causal models successfully separated natural variation from human-induced impacts.
• The models demonstrated improved external validity, enabling predictions of intervention effects.
• Inclusion of time-varying variables, like discharge effluent quantity, enhanced predictive capabilities.

Conclusions:

• Causal modeling using SEM and BNs provides a powerful tool for environmental impact assessment.
• The approach allows for reliable prediction of intervention outcomes, such as changes in wastewater discharge loads.
• This methodology enhances the understanding of cause-effect relationships in biotic ecosystems under anthropogenic influence.