Agriculture versus wastewater pollution as drivers of macroinvertebrate community structure in streams.

Abstract

Water pollution is ubiquitous globally, yet how the effects of pollutants propagate through natural ecosystems remains poorly understood. This is because the interactive effects of multiple stressors are generally hard to predict. Agriculture and municipal wastewater treatment plants (WWTPs) are often major sources of contaminants for streams, but their relative importance and the role of different pollutants (e.g. nutrients or pesticides) are largely unknown. Using a real world experiment with sampling locations up- and downstream of WWTPs, we studied how effluent discharges affected water quality and macroinvertebrate communities in 23 Swiss streams across a broad land-use gradient. Variation partitioning of community composition revealed that overall water quality explained approximately 30% of community variability, whereby nutrients and pesticides each independently explained 10% and 2%, respectively. Excluding oligochaetes (which were highly abundant downstream of the WWTPs) from the analyses, resulted in a relatively stronger influence (3%) of pesticides on the macroinvertebrate community composition, whereas nutrients had no influence. Generally, the macroinvertebrate community composition downstream of the WWTPs strongly reflected the upstream conditions, likely due to a combination of efficient treatment processes, environmental filtering and organismal dispersal. Wastewater impacts were most prominently by the Saprobic index, whereas the SPEAR index (a trait-based macroinvertebrate metrics reflecting sensitivity to pesticides) revealed a strong impact of arable cropping but only a weak impact of wastewater. Overall, our results indicate that agriculture can have a stronger impact on headwater stream macroinvertebrate communities than discharges from WWTP. Yet, effects of wastewater-born micropollutants were clearly quantifiable among all other influence factors. Improving our ability to further quantify the impacts of micropollutants requires highly-resolved water quality and taxonomic data with adequate spatial and temporal sampling. These improvements would help to better account for the underlying causal pathways that drive observed biological responses, such as episodic contaminant peaks and dispersal-related processes.