The rivers of KwaZulu-Natal, South Africa, are being impacted by various anthropogenic activities that threaten their sustainability. Our study demonstrated how Bayesian networks could be used to conduct an environmental risk assessment of macroinvertebrate biodiversity and their associated ecosystem to assess the overall effects of these anthropogenic stressors in the rivers. We examined the exposure pathways through various habitats in the study area using a conceptual model that linked the sources of stressors through cause-effect pathways. A Bayesian network was constructed to represent the observed complex interactions and overall risk from water quality, flow and habitat stressors. The model outputs and sensitivity analysis showed ecosystem threat and river health (represented by macroinvertebrate assessment index – MIRAI) could have high ecological risks on macroinvertebrate biodiversity and the ecosystem, respectively. The results of our study demonstrated that Bayesian networks can be used to calculate risk for multiple stressors and that they are a powerful tool for informing future strategies for achieving best management practices and policymaking. Apart from the current scenario, which was developed from field data, we also simulated three other scenarios to predict potential risks to our selected endpoints. We further simulated the low and high risks to the endpoints to demonstrate that the Bayesian network can be an effective adaptive management tool for decision making.
Effects of multiple stressors associated with agriculture on stream macroinvertebrate communities in a tropical catchment
