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Bayesian network (BN) models are increasingly used as tools to support probabilistic environmental risk assessments (ERAs), because they can better account for uncertainty compared with the simpler approaches commonly used in traditional ERA. We used BNs as metamodels 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 the Albufera Natural Park (Valencia, Spain), and considered three selected pesticides: acetamiprid (an insecticide), 2-methyl-4-chlorophenoxyacetic acid (MCPA; a herbicide), and azoxystrobin (a fungicide). The developed BN linked the inputs and outputs of two pesticide models: a process-based exposure model (Rice Water Quality [RICEWQ]), and a probabilistic effects model (Predicts the Ecological Risk of Pesticides [PERPEST]) using case-based reasoning with data from microcosm and mesocosm experiments. The model characterized 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 characterized 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 the 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%) resulted from risk to both plants (35%) and invertebrates (38%); the latter might represent (

This dataset details the growth of Gammarus pulex at 11°C and 15°C when exposed to imidacloprid concentrations of 0, 0.01, 0.1, and 0.3 µg/L for periods of 3 months and 6 months. The organisms were housed individually in 100 mL bottles with an adequate supply of food, and the medium was refreshed monthly. In the 6-month experiment, the ages of the test organisms were known, and their initial size was approximately 2 mm. Five specimens were sampled destructively each month to record body size, head width, and dry weight. For the 3-month experiment, organisms with an initial size of about 4 mm were used, and growth was recorded every month using non-destructive methods. At the end of the 3-month experiment, the sex of the organisms was also determined.

An understanding of the combined effects of climate change (CC) and other anthropogenic stressors, such as chemical exposures, is essential for improving ecological risk assessments of vulnerable ecosystems. In the Great Barrier Reef, coral reefs are under increasingly severe duress from increasing ocean temperatures, acidification, and cyclone intensities associated with CC. In addition to these stressors, inshore reef systems, such as the Mackay–Whitsunday coastal zone, are being impacted by other anthropogenic stressors, including chemical, nutrient, and sediment exposures related to more intense rainfall events that increase the catchment runoff of contaminated waters. To illustrate an approach for incorporating CC into ecological risk assessment frameworks, we developed an adverse outcome pathway network to conceptually delineate the effects of climate variables and photosystem II herbicide (diuron) exposures on scleractinian corals. This informed the development of a Bayesian network (BN) to quantitatively compare the effects of historical (1975–2005) and future projected climate on inshore hard coral bleaching, mortality, and cover. This BN demonstrated how risk may be predicted for multiple physical and biological stressors, including temperature, ocean acidification, cyclones, sediments, macroalgae competition, and crown of thorns starfish predation, as well as chemical stressors such as nitrogen and herbicides. Climate scenarios included an ensemble of 16 downscaled models encompassing current and future conditions based on multiple emission scenarios for two 30-year periods. It was found that both climate-related and catchment-related stressors pose a risk to these inshore reef systems, with projected increases in coral bleaching and coral mortality under all future climate scenarios. This modeling exercise can support the identification of risk drivers for the prioritization of management interventions to build future resilient reefs. Integr Environ Asse

The Society of Environmental Toxicology and Chemistry (SETAC) convened a Pellston workshop in 2022 to examine how information on climate change could be better incorporated into the ecological risk assessment (ERA) process for chemicals as well as other environmental stressors. A major impetus for this workshop is that climate change can affect components of ecological risks in multiple direct and indirect ways, including the use patterns and environmental exposure pathways of chemical stressors such as pesticides, the toxicity of chemicals in receiving environments, and the vulnerability of species of concern related to habitat quality and use. This article explores a modeling approach for integrating climate model projections into the assessment of near- and long-term ecological risks, developed in collaboration with climate scientists. State-of-the-art global climate modeling and downscaling techniques may enable climate projections at scales appropriate for the study area. It is, however, also important to realize the limitations of individual global climate models and make use of climate model ensembles represented by statistical properties. Here, we present a probabilistic modeling approach aiming to combine projected climatic variables as well as the associated uncertainties from climate model ensembles in conjunction with ERA pathways. We draw upon three examples of ERA that utilized Bayesian networks for this purpose and that also represent methodological advancements for better prediction of future risks to ecosystems. We envision that the modeling approach developed from this international collaboration will contribute to better assessment and management of risks from chemical stressors in a changing climate. Integr Environ Assess Manag 2024;00:1–17.