Development of a protocol for environmental impact studies using causal modelling.

1- A major challenge when assessing the impacts of human activities on the globe's natural resources is to account for the impacts of land use versus natural spatiotemporal variation in system dynamics. Current freshwater assessments are unable to resolve spatiotemporal confounding through study designs or statistical analyses. Observational studies typically fail to consider the period-by-location interaction in the absence of an impact. 2- Here, I address the problem of spatiotemporal confounding using causal modelling based on spatiotemporal data to infer causal effects of wastewater on biotic ecosystems. A combination of statistical analysis and causation theory was used to address confounding bias. Benthic macroinvertebrate and environmental variable data were collected from locations upstream and downstream of a wastewater treatment plant discharge point in south-eastern Australia over 1.5 years. The composite hypotheses based on the theoretical relationships among these variables were summarised in a causal diagram. Model building and testing was conducted using Structural Equation Modelling (SEM). Distance-based redundancy analysis (dbRDA) was used for model building and hypothesis testing. 3- The results indicated that the causal effects of effluent on macroinvertebrate communities could be inferred using causal modelling. The macroinvertebrate communities responded to water quality degradation with a clear shift in community composition after the discharge point and this change varied seasonally. Chlorophyll a, total organic carbon, zinc, conductivity, temperature, and its interaction with conductivity, were important determinants of the macroinvertebrate community composition. Causal models also explained the spatiotemporal variations in environmental variables. The consistency of data with the structure of the causal diagram was confirmed with global Fisher's C-test. 4- Causal modelling has been shown to be a useful tool in environmental impact studies. In this study, the usefulness of causal modelling was attributed to its proficiency in combining all of the elements necessary for an efficient risk assessment through dealing with spatial and temporal confounding, facilitating communication between scientists and resource managers, and supporting management decision making.