Potential propagation of agricultural pesticide exposure and effects to upstream sections in a biosphere reserve

Microbial community history and leaf species shape bottom-up effects in a freshwater shredding amphipod

Arable land use and the associated application of agrochemicals can affect local freshwater communities with consequences for the entire ecosystem. For instance, the structure and function of leaf-associated microbial communities can be affected by pesticides, such as fungicides. Additionally, the leaf species on which these microbial communities grow reflects another environmental filter for community structure. These factors and their interaction may jointly modify leaves' nutritional quality for higher trophic levels. To test this assumption, we studied the structure of leaf-associated microbial communities with distinct exposure histories (pristine [P] vs vineyard run off [V]) colonising two leaf species (black alder, European beech, and a mixture thereof). By offering these differently colonised leaves as food to males and females of the leaf-shredding amphipod Gammarus fossarum (Crustacea; Amphipoda) we assessed for potential bottom-up effects. The growth rate, feeding rate, faeces production and neutral lipid fatty acid profile of the amphipod served as response variable in a 2 × 3 × 2-factorial test design over 21d. A clear separation of community history (P vs V), leaf species and an interaction between the two factors was observed for the leaf-associated aquatic hyphomycete (i.e., fungal) community. Sensitive fungal species were reduced by up to 70 % in the V- compared to P-community. Gammarus' growth rate, feeding rate and faeces production were affected by the factor leaf species. Growth was negatively affected when Gammarus were fed with beech leaves only, whereas the impact of alder and the mixture of both leaf species was sex-specific. Overall, this study highlights that leaf species identity had a more substantial impact on gammarids relative to the microbial community itself. Furthermore, the sex-specificity of the observed effects (excluding fatty acid profile, which was only measured for male) questions the procedure of earlier studies, that is using either only one sex or not being able to differentiate between males and females. However, these results need additional verification to support a reliable extrapolation.

Increased bat hunting at polluted streams suggests chemical exposure rather than prey shortage

Streams and their riparian areas are important habitats and foraging sites for bats feeding on emergent aquatic insects. Chemical pollutants entering freshwater streams from agricultural and wastewater sources have been shown to alter aquatic insect emergence, yet little is known about how this impacts insectivorous bats in riparian areas. In this study, we investigate the relationships between the presence of wastewater effluent, in-stream pesticide toxicity, the number of emergent and flying aquatic insects, and the activity and hunting behaviour of bats at 14 streams in southwestern Germany. Stream sites were located in riparian forests, sheltered from direct exposure to pollutants from agricultural and urban areas. We focused on three bat species associated with riparian areas: Myotis daubentonii, M. cf. brandtii, and Pipistrellus pipistrellus. We found that streams with higher pesticide toxicity and more frequent detection of wastewater also tended to be warmer and have higher nutrient and lower oxygen concentrations. We did not observe a reduction of insect emergence, bat activity or hunting rates in association with pesticide toxicity and wastewater detections. Instead, the activity and hunting rates of Myotis spp. were higher at more polluted sites. The observed increase in bat hunting at more polluted streams suggests that instead of reduced prey availability, chemical pollution at the levels measured in the present study could expose bats to pollutants transported from the stream by emergent aquatic insects.

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.