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Global climate change will significantly impact the biodiversity of freshwater ecosystems, both directly and indirectly via the exacerbation of impacts from other stressors. Pesticides form a prime example of chemical stressors that are expected to synergize with climate change. Aquatic exposures to pesticides might change in magnitude due to increased runoff from agricultural fields, and in composition, as application patterns will change due to changes in pest pressures and crop types. Any prospective chemical risk assessment that aims to capture the influence of climate change should properly and comprehensively account for the variabilities and uncertainties that are inherent to projections of future climate. This is only feasible if they probabilistically propagate extensive ensembles of climate model projections. However, current prospective risk assessments typically make use of process‐based models of chemical fate that do not typically allow for such high‐throughput applications. Here, we describe a Bayesian network model that does. It incorporates a two‐step univariate regression model based on a 30‐day antecedent precipitation index, circumventing the need for computationally laborious mechanistic models. We show its feasibility and application potential in a case study with two pesticides in a Norwegian stream: the fungicide trifloxystrobin and herbicide clopyralid. Our analysis showed that variations in pesticide application rates as well as precipitation intensity lead to variations in in‐stream exposures. When relating to aquatic risks, the influence of these processes is reduced and distributions of risk are dominated by effect‐related parameters. Predicted risks for clopyralid were negligible, but the probability of unacceptable future environmental risks due to exposure to trifloxystrobin (i.e., a risk quotient >1) was 8%–12%. This percentage further increased to 30%–35% when a more conservative precautionary factor of 100 instead of 30 was used. Integr Environ Assess Manag 2024;20:384–400. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Key Points: A Bayesian network (BN) model was presented that allows the high‐throughput incorporation of local climate projections into the environmental risk assessment of pesticides in aquatic ecosystems.Predictions for the fungicide trifloxystrobin and the herbicide clopyralid showed that variations in pesticide application rates and precipitation intensity lead to variations in in‐stream exposures.Predicted risks for clopyralid were negligible, but there was an 8%–12% probability of unacceptable future risks due to trifloxystrobin exposure (at a precautionary factor of 30), increasing to 30%–35% when a more precautionary factor of 100 was used.The BN model is set up to facilitate future inclusion of climate‐dependent pesticide applications and large ensembles of local climate projections, downscaled from global climate models. [ABSTRACT FROM AUTHOR]

Our knowledge about population-level effects of abiotic stressors is limited, largely due to lack of appropriate time-series data. To analyse interactions between an abiotic stressor and density-dependent processes, we used experimental time-series data for stage-structured populations (the blowfly Lucilia sericata) exposed to the toxicant cadmium through 20 generations. Resource limitation results in competition both in the larval and the adult stages. The toxicant has only negative effects at the organism level, but nevertheless, there were positive population-level effects. These are necessarily indirect, and indicate overcompensatory density-dependent responses. A non-parametric model (generalized additive model) was used to investigate the density-dependent structures of the demographic rates, without making assumptions about the functional forms. The estimated structures were used to develop a parametric model, with which we analysed effects of the toxicant on density-dependent and density-independent components of the stage-specific demographic rates. The parameter estimates identified both synergistic and antagonistic density-toxicant interactions. It is noteworthy that the synergistic interaction occurred together with a net positive effect of the toxicant. Hence, the effects of such interactions should be considered together with the capacity for compensatory responses. The combination of the two modelling approaches provided new insight into mechanisms for compensatory responses to abiotic stressors.