Three perspectives on the prediction of chemical effects in ecosystems.

The increasing production, use and emission of synthetic chemicals into the environment represents a major driver of global change. The large number of synthetic chemicals, limited knowledge on exposure patterns and effects in organisms and their interaction with other global change drivers hamper the prediction of effects in ecosystems. However, recent advances in biomolecular and computational methods are promising to improve our capacity for prediction. We delineate three idealised perspectives for the prediction of chemical effects: the suborganismal, organismal and ecological perspective, which are currently largely separated. Each of the outlined perspectives includes essential and complementary theories and tools for prediction but captures only part of the phenomenon of chemical effects. Links between the perspectives may foster predictive modelling of chemical effects in ecosystems and extrapolation between species. A major challenge for the linkage is the lack of data sets simultaneously covering different levels of biological organisation (here referred to as biological levels) as well as varying temporal and spatial scales. Synthesising the three perspectives, some central aspects and associated types of data seem particularly necessary to improve prediction. First, suborganism- and organism-level responses to chemicals need to be recorded and tested for relationships with chemical groups and organism traits. Second, metrics that are measurable at many biological levels, such as energy, need to be scrutinised for their potential to integrate across levels. Third, experimental data on the simultaneous response over multiple biological levels and spatiotemporal scales are required. These could be collected in nested and interconnected micro- and mesocosm experiments. Lastly, prioritisation of processes involved in the prediction framework needs to find a balance between simplification and capturing the essential complexity of a system. For example, in some cases, eco-evolutionary dynamics and interactions may need stronger consideration. Prediction needs to move from a static to a real-world eco-evolutionary view.

Cascading impacts of changes in subsidy quality on recipient ecosystem functioning.

Resource quantity and quality can differ between adjacent ecosystems, and these differences can impact subsidies exchanged between ecosystems. The quantity and quality of subsidies are rapidly changing in response to stressors associated with global environmental change, but while we have models to predict the effects of changes in subsidy quantity, we currently lack models to predict the effects of changes in subsidy quality on recipient ecosystem functioning. We developed a novel model to predict the effects of subsidy quality on recipient ecosystem biomass distribution, recycling, production, and efficiency. We parameterized the model for a case study of a riparian ecosystem subsidized by pulsed emergent aquatic insects. In this case study we focused on a common measure of subsidy quality that differs between riparian and aquatic ecosystems: the higher content of long-chain polyunsaturated fatty acids (PUFAs) in aquatic ecosystems. We analyzed how changes in the PUFA concentration of aquatic subsidies affect the dynamics in biomass stocks and functions of the riparian ecosystem. We also conducted a global sensitivity analysis to identify key drivers of subsidy impacts. Our analysis showed that subsidy quality increased the functioning of the recipient ecosystem. Recycling increased more strongly than production per unit subsidy quality increase, meaning there was a threshold where an increase in subsidy quality led to stronger effects of subsidies on recycling relative to the production of the recipient ecosystem. Our predictions were most sensitive to basal nutrient input, highlighting the relevance of recipient ecosystem nutrient levels to understanding the effects of ecosystem connections. We argue that recipient ecosystems that rely on high-quality subsidies, such as aquatic-terrestrial ecotones, are highly sensitive to changes in subsidy-recipient ecosystem connections. Our novel model unifies the subsidy hypothesis and food quality hypothesis and provides testable predictions to understand the effects of ecosystem connections on ecosystem functioning under global changes.

Chemical Mixtures and Multiple Stressors: Same but Different?

Ecosystems are strongly influenced by multiple anthropogenic stressors, including a wide range of chemicals and their mixtures. Studies on the effects of multiple stressors have largely focussed on nonchemical stressors, whereas studies on chemical mixtures have largely ignored other stressors. However, both research areas face similar challenges and require similar tools and methods to predict the joint effects of chemicals or nonchemical stressors, and frameworks to integrate multiple chemical and nonchemical stressors are missing. We provide an overview of the research paradigms, tools, and methods commonly used in multiple stressor and chemical mixture research and discuss potential domains of cross-fertilization and joint challenges. First, we compare the general paradigms of ecotoxicology and (applied) ecology to explain the historical divide. Subsequently, we compare methods and approaches for the identification of interactions, stressor characterization, and designing experiments. We suggest that both multiple stressor and chemical mixture research are too focused on interactions and would benefit from integration regarding null model selection. Stressor characterization is typically more costly for chemical mixtures. While for chemical mixtures comprehensive classification systems at suborganismal level have been developed, recent classification systems for multiple stressors account for environmental context. Both research areas suffer from rather simplified experimental designs that focus on only a limited number of stressors, chemicals, and treatments. We discuss concepts that can guide more realistic designs capturing spatiotemporal stressor dynamics. We suggest that process-based and data-driven models are particularly promising to tackle the challenge of prediction of effects of chemical mixtures and nonchemical stressors on (meta-)communities and (meta-)food webs. We propose a framework to integrate the assessment of effects for multiple stressors and chemical mixtures. Environ Toxicol Chem 2023;42:1915-1936. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.