Using a Bayesian Network Model to Predict Risk of Pesticides on Aquatic Community Endpoints in a Rice Field—A Southern European Case Study

Abstract

Bayesian network (BN) models are increasingly used as tools to support probabilistic environmental risk assessments (ERA), as they can better account for uncertainty compared to the simpler approaches commonly used in traditional ERA. We used BNs as meta‐models to link various sources of information in a probabilistic framework, to predict the risk of pesticides to aquatic communities under given scenarios. The research focused on rice fields surrounding a Spanish Natural Park Albufera, considering three selected pesticides: acetamiprid (insecticide), MCPA (herbicide), and azoxystrobin (fungicide). The developed BN linked the inputs and outputs of two pesticide models: a process‐based exposure model (RICEWQ), and probabilistic effects model (PERPEST) using case‐based reasoning with data from microcosm and mesocosm experiments. The model characterised risk at three levels in a hierarchy: biological endpoints (e.g., molluscs, zooplankton, insects, etc.), endpoint groups (plants, invertebrates, vertebrates, and community processes), and community. The pesticide risk to a biological endpoint was characterised as the probability of an effect for a given pesticide concentration interval. The risk to an endpoint group was calculated as the joint probability of effect on any of the endpoints in the group. Likewise, community‐level risk was calculated as the joint probability of any of the endpoint groups being affected. This approach enabled comparison of risk to endpoint groups across different pesticide types. For example, in a scenario for year 2050, the predicted risk of the insecticide to the community (40% probability of effect) was dominated by the risk to invertebrates (36% risk). In contrast, herbicide‐related risk to the community (63%) was resulting from risk to both plants (35%) and invertebrates (38%); the latter might here represent indirect effects of toxicity through the food chain. This novel approach combines the quantification of spatial variability of exposure with probabilistic risk prediction for different components of aquatic ecosystems.