Publications by authors named "Sanne J P van den Berg"

Fluoxetine is one of the worlds most prescribed antidepressant, and frequently detected in surface waters. Once present in the aquatic environment, fluoxetine has been shown to disrupt the swimming behaviour of fish and invertebrates. However, swimming behaviour is also known to be highly variable according to experimental conditions, potentially concealing relevant effects.

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In recent years, considerable computational advancements have been made allowing automated analysis of behavioural endpoints using video cameras. However, the results of such analyses are often confounded by a large variation among individuals, making it problematic to derive endpoints that allow distinguishing treatment effects in behavioural studies. In this study, we quantitatively analysed the effects of light conditions on the swimming behaviour of the freshwater amphipod Gammarus pulex by high-throughput tracking, and attempted to unravel among individual variation using size and sex.

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Monitoring of chemicals in the aquatic environment by chemical analysis alone cannot completely assess and predict the effects of chemicals on aquatic species and ecosystems. This is primarily because of the increasing number of (unknown) chemical stressors and mixture effects present in the environment. In addition, the ability of ecological indices to identify underlying stressors causing negative ecological effects is limited.

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Ecosystems are usually populated by many species. Each of these species carries the potential to show a different sensitivity towards all of the numerous chemical compounds that can be present in their environment. Since experimentally testing all possible species-chemical combinations is impossible, the ecological risk assessment of chemicals largely depends on cross-species extrapolation approaches.

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Current chemical risk assessment approaches rely on a standard suite of test species to assess toxicity to environmental species. Assessment factors are used to extrapolate from single species to communities and ecosystem effects. This approach is pragmatic, but lacks resolution in biological and environmental parameters.

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In this study, a trait-based macroinvertebrate sensitivity modeling tool is presented that provides two main outcomes: (1) it constructs a macroinvertebrate sensitivity ranking and, subsequently, a predictive trait model for each one of a diverse set of predefined Modes of Action (MOAs) and (2) it reveals data gaps and restrictions, helping with the direction of future research. Besides revealing taxonomic patterns of species sensitivity, we find that there was not one genus, family, or class which was most sensitive to all MOAs and that common test taxa were often not the most sensitive at all. Traits like life cycle duration and feeding mode were identified as important in explaining species sensitivity.

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